API reference

NumPy name index

Every public NumPy 2.5.3 name that numera tracks, with its JavaScript equivalent in v1.0.3. 1045 are implemented and 9 are not yet. Names marked documented have a full reference entry with an example. For the others, open signature to see the TypeScript declaration, and follow the NumPy link for the semantics, which numera mirrors.

The JavaScript name is the NumPy name in camelCase, and Python keyword arguments become an options object. The compatibility page lists every documented difference from NumPy.

only not implemented

numpy.random.Generator# 44 / 45

NumPy	numera	Summary
`beta`method	`rng.beta` documented signature `rng.beta(a: number, b: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a Beta distribution.
`binomial`method	`rng.binomial` documented signature `rng.binomial(n: number, p: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a binomial distribution.
`bit_generator`attribute	`rng.bit_generator` documented signature `rng.bit_generator: string`	Gets the bit generator instance used by the generator
`bytes`method	`rng.bytes` signature `rng.bytes(length: number): Uint8Array<ArrayBufferLike>`	Return random bytes.
`chisquare`method	`rng.chisquare` documented signature `rng.chisquare(df: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a chi-square distribution.
`choice`method	`rng.choice` documented signature `rng.choice(a: Population, size?: Size \| { size?: Size \| undefined; replace?: boolean \| undefined; p?: unknown; axis?: number \| undefined; shuffle?: boolean \| undefined; } \| null \| undefined, replace?: boolean \| undefined, p?: unknown, axis?: number \| undefined, shuffle?: boolean \| undefined): Out`	Generates a random sample from a given array
`dirichlet`method	`rng.dirichlet` documented signature `rng.dirichlet(alpha: readonly number[], size?: Size \| null \| undefined): NDArray`	Draw samples from the Dirichlet distribution.
`exponential`method	`rng.exponential` documented signature `rng.exponential(scale?: number \| undefined, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from an exponential distribution.
`f`method	`rng.f` documented signature `rng.f(dfnum: number, dfden: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from an F distribution.
`gamma`method	`rng.gamma` documented signature `rng.gamma(shape: number, scale?: number \| undefined, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a Gamma distribution.
`geometric`method	`rng.geometric` signature `rng.geometric(p: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from the geometric distribution.
`gumbel`method	`rng.gumbel` documented signature `rng.gumbel(loc?: number \| undefined, scale?: number \| undefined, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a Gumbel distribution.
`hypergeometric`method	`rng.hypergeometric` documented signature `rng.hypergeometric(ngood: number, nbad: number, nsample: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a Hypergeometric distribution.
`integers`method	`rng.integers` documented signature `rng.integers(low: number \| bigint \| { low: number \| bigint; high?: number \| bigint \| null \| undefined; size?: Size \| undefined; dtype?: DTypeLike \| undefined; endpoint?: boolean \| undefined; }, high?: number \| bigint \| null \| undefined, size?: Size \| undefined, dt?: DTypeLike \| undefined, endpoint?: boolean \| undefined): Out`	Return random integers from `low` (inclusive) to `high` (exclusive), or if endpoint=True, `low` (inclusive) to `high` (inclusive). Replaces `RandomState.randint` (with endpoint=False) and `RandomState.random_integers` (with endpoint=True)
`laplace`method	`rng.laplace` documented signature `rng.laplace(loc?: number \| undefined, scale?: number \| undefined, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from the Laplace or double exponential distribution with specified location (or mean) and scale (decay).
`logistic`method	`rng.logistic` documented signature `rng.logistic(loc?: number \| undefined, scale?: number \| undefined, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a logistic distribution.
`lognormal`method	`rng.lognormal` documented signature `rng.lognormal(mean?: number \| undefined, sigma?: number \| undefined, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a log-normal distribution.
`logseries`method	`rng.logseries` documented signature `rng.logseries(p: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a logarithmic series distribution.
`multinomial`method	`rng.multinomial` documented signature `rng.multinomial(n: number, pvals: readonly number[], size?: Size \| null \| undefined): NDArray`	Draw samples from a multinomial distribution.
`multivariate_hypergeometric`method	`rng.multivariateHypergeometric` documented signature `rng.multivariateHypergeometric(colors: readonly number[], nsample: number, size?: Size \| null \| undefined, method?: "count" \| "marginals" \| undefined): NDArray`	Generate variates from a multivariate hypergeometric distribution.
`multivariate_normal`method	not implemented	Draw random samples from a multivariate normal distribution.
`negative_binomial`method	`rng.negativeBinomial` documented signature `rng.negativeBinomial(n: number, p: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a negative binomial distribution.
`noncentral_chisquare`method	`rng.noncentralChisquare` documented signature `rng.noncentralChisquare(df: number, nonc: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a noncentral chi-square distribution.
`noncentral_f`method	`rng.noncentralF` documented signature `rng.noncentralF(dfnum: number, dfden: number, nonc: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from the noncentral F distribution.
`normal`method	`rng.normal` documented signature `rng.normal(loc?: number \| { loc?: number \| undefined; scale?: number \| undefined; size?: Size \| undefined; } \| undefined, scale?: number \| undefined, size?: Size \| undefined): Out`	Draw random samples from a normal (Gaussian) distribution.
`pareto`method	`rng.pareto` documented signature `rng.pareto(a: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a Pareto II (AKA Lomax) distribution with specified shape.
`permutation`method	`rng.permutation` documented signature `rng.permutation(x: NDArray \| NestedArray, axis?: number \| undefined): NDArray`	Randomly permute a sequence, or return a permuted range.
`permuted`method	`rng.permuted` documented signature `rng.permuted(x: NDArray, axis?: number \| undefined): NDArray`	Randomly permute `x` along axis `axis`.
`poisson`method	`rng.poisson` documented signature `rng.poisson(lam?: number \| undefined, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a Poisson distribution.
`power`method	`rng.power` documented signature `rng.power(a: number, size?: Size \| null \| undefined): number \| NDArray`	Draws samples in [0, 1] from a power distribution with positive exponent a - 1.
`random`method	`rng.random` signature `rng.random(size?: Size \| { size?: Size \| undefined; dtype?: DTypeLike \| undefined; } \| undefined, dt?: DTypeLike \| undefined): Out`	Return random floats in the half-open interval [0.0, 1.0).
`rayleigh`method	`rng.rayleigh` documented signature `rng.rayleigh(scale?: number \| undefined, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a Rayleigh distribution.
`shuffle`method	`rng.shuffle` documented signature `rng.shuffle(x: NDArray, axis?: number \| undefined): void`	Modify an array or sequence in-place by shuffling its contents.
`spawn`method	`rng.spawn` documented signature `rng.spawn(n: number): Generator[]`	Create new independent child generators.
`standard_cauchy`method	`rng.standardCauchy` documented signature `rng.standardCauchy(size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a standard Cauchy distribution with mode = 0.
`standard_exponential`method	`rng.standardExponential` documented signature `rng.standardExponential(size?: Size \| null \| undefined): number \| NDArray`	Draw samples from the standard exponential distribution.
`standard_gamma`method	`rng.standardGamma` documented signature `rng.standardGamma(shape: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a standard Gamma distribution.
`standard_normal`method	`rng.standardNormal` documented signature `rng.standardNormal(size?: Size \| { size?: Size \| undefined; dtype?: DTypeLike \| undefined; } \| undefined, dt?: DTypeLike \| undefined): Out`	Draw samples from a standard Normal distribution (mean=0, stdev=1).
`standard_t`method	`rng.standardT` documented signature `rng.standardT(df: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a standard Student's t distribution with `df` degrees of freedom.
`triangular`method	`rng.triangular` documented signature `rng.triangular(left: number, mode: number, right: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from the triangular distribution over the interval `[left, right]`.
`uniform`method	`rng.uniform` documented signature `rng.uniform(low?: number \| { low?: number \| undefined; high?: number \| undefined; size?: Size \| undefined; } \| undefined, high?: number \| undefined, size?: Size \| undefined): Out`	Draw samples from a uniform distribution.
`vonmises`method	`rng.vonmises` documented signature `rng.vonmises(mu: number, kappa: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a von Mises distribution.
`wald`method	`rng.wald` documented signature `rng.wald(mean: number, scale: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a Wald, or inverse Gaussian, distribution.
`weibull`method	`rng.weibull` documented signature `rng.weibull(a: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a Weibull distribution.
`zipf`method	`rng.zipf` documented signature `rng.zipf(a: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a Zipf distribution.

numpy.random.RandomState# 47 / 48

NumPy	numera	Summary
`beta`method	`np.random.RandomState#beta` signature `rs.beta(a: number, b: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a Beta distribution.
`binomial`method	`np.random.RandomState#binomial` signature `rs.binomial(n: number, p: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a binomial distribution.
`bytes`method	`np.random.RandomState#bytes` signature `rs.bytes(length: number): Uint8Array<ArrayBufferLike>`	Return random bytes.
`chisquare`method	`np.random.RandomState#chisquare` signature `rs.chisquare(df: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a chi-square distribution.
`choice`method	`np.random.RandomState#choice` signature `rs.choice(a: Population, size?: Size \| { size?: Size \| undefined; replace?: boolean \| undefined; p?: unknown; } \| null \| undefined, replace?: boolean \| undefined, p?: unknown): Out`	Generates a random sample from a given 1-D array
`dirichlet`method	`np.random.RandomState#dirichlet` signature `rs.dirichlet(alpha: readonly number[], size?: Size \| null \| undefined): NDArray`	Draw samples from the Dirichlet distribution.
`exponential`method	`np.random.RandomState#exponential` signature `rs.exponential(scale?: number \| undefined, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from an exponential distribution.
`f`method	`np.random.RandomState#f` signature `rs.f(dfnum: number, dfden: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from an F distribution.
`gamma`method	`np.random.RandomState#gamma` signature `rs.gamma(shape: number, scale?: number \| undefined, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a Gamma distribution.
`geometric`method	`np.random.RandomState#geometric` signature `rs.geometric(p: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from the geometric distribution.
`get_state`method	`np.random.RandomState#getState` signature `rs.getState(): bigint[]`	Return a tuple representing the internal state of the generator.
`gumbel`method	`np.random.RandomState#gumbel` signature `rs.gumbel(loc?: number \| undefined, scale?: number \| undefined, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a Gumbel distribution.
`hypergeometric`method	`np.random.RandomState#hypergeometric` signature `rs.hypergeometric(ngood: number, nbad: number, nsample: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a Hypergeometric distribution.
`laplace`method	`np.random.RandomState#laplace` signature `rs.laplace(loc?: number \| undefined, scale?: number \| undefined, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from the Laplace or double exponential distribution with specified location (or mean) and scale (decay).
`logistic`method	`np.random.RandomState#logistic` signature `rs.logistic(loc?: number \| undefined, scale?: number \| undefined, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a logistic distribution.
`lognormal`method	`np.random.RandomState#lognormal` signature `rs.lognormal(mean?: number \| undefined, sigma?: number \| undefined, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a log-normal distribution.
`logseries`method	`np.random.RandomState#logseries` signature `rs.logseries(p: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a logarithmic series distribution.
`multinomial`method	`np.random.RandomState#multinomial` signature `rs.multinomial(n: number, pvals: readonly number[], size?: Size \| null \| undefined): NDArray`	Draw samples from a multinomial distribution.
`multivariate_normal`method	not implemented	Draw random samples from a multivariate normal distribution.
`negative_binomial`method	`np.random.RandomState#negativeBinomial` signature `rs.negativeBinomial(n: number, p: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a negative binomial distribution.
`noncentral_chisquare`method	`np.random.RandomState#noncentralChisquare` signature `rs.noncentralChisquare(df: number, nonc: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a noncentral chi-square distribution.
`noncentral_f`method	`np.random.RandomState#noncentralF` signature `rs.noncentralF(dfnum: number, dfden: number, nonc: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from the noncentral F distribution.
`normal`method	`np.random.RandomState#normal` signature `rs.normal(loc?: number \| { loc?: number \| undefined; scale?: number \| undefined; size?: Size \| undefined; } \| undefined, scale?: number \| undefined, size?: Size \| undefined): Out`	Draw random samples from a normal (Gaussian) distribution.
`pareto`method	`np.random.RandomState#pareto` signature `rs.pareto(a: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a Pareto II or Lomax distribution with specified shape.
`permutation`method	`np.random.RandomState#permutation` signature `rs.permutation(x: NDArray \| NestedArray): NDArray`	Randomly permute a sequence, or return a permuted range.
`poisson`method	`np.random.RandomState#poisson` signature `rs.poisson(lam?: number \| undefined, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a Poisson distribution.
`power`method	`np.random.RandomState#power` signature `rs.power(a: number, size?: Size \| null \| undefined): number \| NDArray`	Draws samples in [0, 1] from a power distribution with positive exponent a - 1.
`rand`method	`np.random.RandomState#rand` signature `rs.rand(...dims: number[]): Out`	Random values in a given shape.
`randint`method	`np.random.RandomState#randint` signature `rs.randint(low: number \| bigint \| { low: number \| bigint; high?: number \| bigint \| null \| undefined; size?: Size \| undefined; dtype?: DTypeLike \| undefined; }, high?: number \| bigint \| null \| undefined, size?: Size \| undefined, dt?: DTypeLike \| undefined): Out`	Return random integers from `low` (inclusive) to `high` (exclusive).
`randn`method	`np.random.RandomState#randn` signature `rs.randn(...dims: number[]): Out`	Return a sample (or samples) from the "standard normal" distribution.
`random`method	`np.random.RandomState#random` signature `rs.random(size?: Size \| null \| undefined): Out`	Return random floats in the half-open interval [0.0, 1.0). Alias for `random_sample` to ease forward-porting to the new random API.
`random_integers`method	`np.random.RandomState#random_integers` signature `rs.random_integers(low: number, high?: number \| null \| undefined, size?: Size \| null \| undefined): number \| NDArray`	Random integers of type `numpy.int_` between `low` and `high`, inclusive.
`random_sample`method	`np.random.RandomState#randomSample` signature `rs.randomSample(size?: Size \| null \| undefined): Out`	Return random floats in the half-open interval [0.0, 1.0).
`rayleigh`method	`np.random.RandomState#rayleigh` signature `rs.rayleigh(scale?: number \| undefined, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a Rayleigh distribution.
`seed`method	`np.random.RandomState#seed` signature `rs.seed(seed?: Seed \| null \| undefined): void`	Reseed a legacy MT19937 BitGenerator
`set_state`method	`np.random.RandomState#setState` signature `rs.setState(words: bigint[]): void`	Set the internal state of the generator from a tuple.
`shuffle`method	`np.random.RandomState#shuffle` signature `rs.shuffle(x: NDArray): void`	Modify a sequence in-place by shuffling its contents.
`standard_cauchy`method	`np.random.RandomState#standardCauchy` signature `rs.standardCauchy(size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a standard Cauchy distribution with mode = 0.
`standard_exponential`method	`np.random.RandomState#standardExponential` signature `rs.standardExponential(size?: Size \| null \| undefined): number \| NDArray`	Draw samples from the standard exponential distribution.
`standard_gamma`method	`np.random.RandomState#standardGamma` signature `rs.standardGamma(shape: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a standard Gamma distribution.
`standard_normal`method	`np.random.RandomState#standardNormal` signature `rs.standardNormal(size?: Size \| null \| undefined): Out`	Draw samples from a standard Normal distribution (mean=0, stdev=1).
`standard_t`method	`np.random.RandomState#standardT` signature `rs.standardT(df: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a standard Student's t distribution with `df` degrees of freedom.
`triangular`method	`np.random.RandomState#triangular` signature `rs.triangular(left: number, mode: number, right: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from the triangular distribution over the interval `[left, right]`.
`uniform`method	`np.random.RandomState#uniform` signature `rs.uniform(low?: number \| { low?: number \| undefined; high?: number \| undefined; size?: Size \| undefined; } \| undefined, high?: number \| undefined, size?: Size \| undefined): Out`	Draw samples from a uniform distribution.
`vonmises`method	`np.random.RandomState#vonmises` signature `rs.vonmises(mu: number, kappa: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a von Mises distribution.
`wald`method	`np.random.RandomState#wald` signature `rs.wald(mean: number, scale: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a Wald, or inverse Gaussian, distribution.
`weibull`method	`np.random.RandomState#weibull` signature `rs.weibull(a: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a Weibull distribution.
`zipf`method	`np.random.RandomState#zipf` signature `rs.zipf(a: number, size?: Size \| null \| undefined): number \| NDArray`	Draw samples from a Zipf distribution.

numpy.char# 52 / 52

NumPy	numera	Summary
`add`ufunc	`np.char.add` signature `np.char.add(a: StringLike, b: StringLike): StringArray`	Add arguments element-wise.
`array`function	`np.char.array` documented signature `np.char.array(data: StringLike, dtype?: StringDType \| undefined): StringArray`	Create a `~numpy.char.chararray`.
`asarray`function	`np.char.asarray` signature `np.char.asarray(data: StringLike, dtype?: StringDType \| undefined): StringArray`	Convert the input to a `~numpy.char.chararray`, copying the data only if necessary.
`capitalize`function	`np.char.capitalize` signature `np.char.capitalize(a: StringLike): StringArray`	Return a copy of `a` with only the first character of each element capitalized.
`center`function	`np.char.center` signature `np.char.center(a: StringLike, width: number, fillchar?: string \| undefined): StringArray`	Return a copy of `a` with its elements centered in a string of length `width`.
`count`function	`np.char.count` signature `np.char.count(a: StringLike, sub: StringLike, start?: number \| undefined, end?: number \| undefined): NDArray`	Returns an array with the number of non-overlapping occurrences of substring `sub` in the range [`start`, `end`).
`decode`function	`np.char.decode` signature `np.char.decode(a: StringLike, encoding?: string \| undefined, _errors?: string \| undefined): StringArray`	Calls :meth:`bytes.decode` element-wise.
`encode`function	`np.char.encode` signature `np.char.encode(a: StringLike, encoding?: string \| undefined, _errors?: string \| undefined): StringArray`	Calls :meth:`str.encode` element-wise.
`endswith`function	`np.char.endswith` signature `np.char.endswith(a: StringLike, suffix: StringLike, start?: number \| undefined, end?: number \| undefined): NDArray`	Returns a boolean array which is `True` where the string element in `a` ends with `suffix`, otherwise `False`.
`equal`function	`np.char.equal` signature `np.char.equal(a: StringLike, b: StringLike): NDArray`	Return (x1 == x2) element-wise.
`expandtabs`function	`np.char.expandtabs` signature `np.char.expandtabs(a: StringLike, tabsize?: number \| undefined): StringArray`	Return a copy of each string element where all tab characters are replaced by one or more spaces.
`find`function	`np.char.find` signature `np.char.find(a: StringLike, sub: StringLike, start?: number \| undefined, end?: number \| undefined): NDArray`	For each element, return the lowest index in the string where substring `sub` is found, such that `sub` is contained in the range [`start`, `end`).
`greater`function	`np.char.greater` signature `np.char.greater(a: StringLike, b: StringLike): NDArray`	Return (x1 > x2) element-wise.
`greater_equal`function	`np.char.greater_equal` signature `np.char.greater_equal(a: StringLike, b: StringLike): NDArray`	Return (x1 >= x2) element-wise.
`index`function	`np.char.index` signature `np.char.index(a: StringLike, sub: StringLike, start?: number \| undefined, end?: number \| undefined): NDArray`	Like `find`, but raises :exc:`ValueError` when the substring is not found.
`isalnum`ufunc	`np.char.isalnum` signature `np.char.isalnum(a: StringLike): NDArray`	Returns true for each element if all characters in the string are alphanumeric and there is at least one character, false otherwise.
`isalpha`ufunc	`np.char.isalpha` signature `np.char.isalpha(a: StringLike): NDArray`	Returns true for each element if all characters in the data interpreted as a string are alphabetic and there is at least one character, false otherwise.
`isdecimal`ufunc	`np.char.isdecimal` signature `np.char.isdecimal(a: StringLike): NDArray`	For each element, return True if there are only decimal characters in the element.
`isdigit`ufunc	`np.char.isdigit` signature `np.char.isdigit(a: StringLike): NDArray`	Returns true for each element if all characters in the string are digits and there is at least one character, false otherwise.
`islower`ufunc	`np.char.islower` signature `np.char.islower(a: StringLike): NDArray`	Returns true for each element if all cased characters in the string are lowercase and there is at least one cased character, false otherwise.
`isnumeric`ufunc	`np.char.isnumeric` signature `np.char.isnumeric(a: StringLike): NDArray`	For each element, return True if there are only numeric characters in the element.
`isspace`ufunc	`np.char.isspace` signature `np.char.isspace(a: StringLike): NDArray`	Returns true for each element if there are only whitespace characters in the string and there is at least one character, false otherwise.
`istitle`ufunc	`np.char.istitle` signature `np.char.istitle(a: StringLike): NDArray`	Returns true for each element if the element is a titlecased string and there is at least one character, false otherwise.
`isupper`ufunc	`np.char.isupper` signature `np.char.isupper(a: StringLike): NDArray`	Return true for each element if all cased characters in the string are uppercase and there is at least one character, false otherwise.
`join`function	`np.char.join` signature `np.char.join(sep: StringLike, seq: StringLike): StringArray`	Return a string which is the concatenation of the strings in the sequence `seq`.
`less`function	`np.char.less` signature `np.char.less(a: StringLike, b: StringLike): NDArray`	Return (x1 < x2) element-wise.
`less_equal`function	`np.char.less_equal` signature `np.char.less_equal(a: StringLike, b: StringLike): NDArray`	Return (x1 <= x2) element-wise.
`ljust`function	`np.char.ljust` signature `np.char.ljust(a: StringLike, width: number, fillchar?: string \| undefined): StringArray`	Return an array with the elements of `a` left-justified in a string of length `width`.
`lower`function	`np.char.lower` signature `np.char.lower(a: StringLike): StringArray`	Return an array with the elements converted to lowercase.
`lstrip`function	`np.char.lstrip` signature `np.char.lstrip(a: StringLike, chars?: string \| null \| undefined): StringArray`	For each element in `a`, return a copy with the leading characters removed.
`mod`function	`np.char.mod` signature `np.char.mod(a: StringLike, values: unknown): StringArray`	Return (a % i), that is pre-Python 2.6 string formatting (interpolation), element-wise for a pair of array_likes of str or unicode.
`multiply`function	`np.char.multiply` signature `np.char.multiply(a: StringLike, i: number \| NDArray): StringArray`	Return (a * i), that is string multiple concatenation, element-wise.
`not_equal`function	`np.char.not_equal` signature `np.char.not_equal(a: StringLike, b: StringLike): NDArray`	Return (x1 != x2) element-wise.
`partition`function	`np.char.partition` signature `np.char.partition(a: StringLike, sep: StringLike): StringArray[]`	Partition each element in `a` around `sep`.
`replace`function	`np.char.replace` signature `np.char.replace(a: StringLike, old_: StringLike, new_: StringLike, count_?: number \| undefined): StringArray`	For each element in `a`, return a copy of the string with occurrences of substring `old` replaced by `new`.
`rfind`function	`np.char.rfind` signature `np.char.rfind(a: StringLike, sub: StringLike, start?: number \| undefined, end?: number \| undefined): NDArray`	For each element, return the highest index in the string where substring `sub` is found, such that `sub` is contained in the range [`start`, `end`).
`rindex`function	`np.char.rindex` signature `np.char.rindex(a: StringLike, sub: StringLike, start?: number \| undefined, end?: number \| undefined): NDArray`	Like `rfind`, but raises :exc:`ValueError` when the substring `sub` is not found.
`rjust`function	`np.char.rjust` signature `np.char.rjust(a: StringLike, width: number, fillchar?: string \| undefined): StringArray`	Return an array with the elements of `a` right-justified in a string of length `width`.
`rpartition`function	`np.char.rpartition` signature `np.char.rpartition(a: StringLike, sep: StringLike): StringArray[]`	Partition (split) each element around the right-most separator.
`rsplit`function	`np.char.rsplit` signature `np.char.rsplit(a: StringLike, sep?: string \| null \| undefined, maxsplit?: number \| undefined): StringArray[]`	For each element in `a`, return a list of the words in the string, using `sep` as the delimiter string.
`rstrip`function	`np.char.rstrip` signature `np.char.rstrip(a: StringLike, chars?: string \| null \| undefined): StringArray`	For each element in `a`, return a copy with the trailing characters removed.
`slice`function	`np.char.slice` signature `np.char.slice(a: StringLike, start?: number \| undefined, stop?: number \| undefined, step?: number \| undefined): StringArray`	Slice the strings in `a` by slices specified by `start`, `stop`, `step`. Like in the regular Python `slice` object, if only `start` is specified then it is interpreted as the `stop`.
`split`function	`np.char.split` signature `np.char.split(a: StringLike, sep?: string \| null \| undefined, maxsplit?: number \| undefined): StringArray[]`	For each element in `a`, return a list of the words in the string, using `sep` as the delimiter string.
`splitlines`function	`np.char.splitlines` signature `np.char.splitlines(a: StringLike, keepends?: boolean \| undefined): StringArray[]`	For each element in `a`, return a list of the lines in the element, breaking at line boundaries.
`startswith`function	`np.char.startswith` signature `np.char.startswith(a: StringLike, prefix: StringLike, start?: number \| undefined, end?: number \| undefined): NDArray`	Returns a boolean array which is `True` where the string element in `a` starts with `prefix`, otherwise `False`.
`str_len`ufunc	`np.char.str_len` signature `np.char.str_len(a: StringLike): NDArray`	Returns the length of each element. For byte strings, this is the number of bytes, while, for Unicode strings, it is the number of Unicode code points.
`strip`function	`np.char.strip` signature `np.char.strip(a: StringLike, chars?: string \| null \| undefined): StringArray`	For each element in `a`, return a copy with the leading and trailing characters removed.
`swapcase`function	`np.char.swapcase` signature `np.char.swapcase(a: StringLike): StringArray`	Return element-wise a copy of the string with uppercase characters converted to lowercase and vice versa.
`title`function	`np.char.title` signature `np.char.title(a: StringLike): StringArray`	Return element-wise title cased version of string or unicode.
`translate`function	`np.char.translate` signature `np.char.translate(a: StringLike, table: Map<string, string \| null>): StringArray`	For each element in `a`, return a copy of the string where all characters occurring in the optional argument `deletechars` are removed, and the remaining characters have been mapped through the given translation table.
`upper`function	`np.char.upper` signature `np.char.upper(a: StringLike): StringArray`	Return an array with the elements converted to uppercase.
`zfill`function	`np.char.zfill` signature `np.char.zfill(a: StringLike, width: number): StringArray`	Return the numeric string left-filled with zeros. A leading sign prefix (`+`/`-`) is handled by inserting the padding after the sign character rather than before.

numpy.emath# 9 / 9

NumPy	numera	Summary
`arccos`function	`np.emath.arccos` documented signature `np.emath.arccos(x: ArrayLike): NDArray`	Compute the inverse cosine of x.
`arcsin`function	`np.emath.arcsin` documented signature `np.emath.arcsin(x: ArrayLike): NDArray`	Compute the inverse sine of x.
`arctanh`function	`np.emath.arctanh` documented signature `np.emath.arctanh(x: ArrayLike): NDArray`	Compute the inverse hyperbolic tangent of `x`.
`log`function	`np.emath.log` documented signature `np.emath.log(x: ArrayLike): NDArray`	Compute the natural logarithm of `x`.
`log10`function	`np.emath.log10` documented signature `np.emath.log10(x: ArrayLike): NDArray`	Compute the logarithm base 10 of `x`.
`log2`function	`np.emath.log2` documented signature `np.emath.log2(x: ArrayLike): NDArray`	Compute the logarithm base 2 of `x`.
`logn`function	`np.emath.logn` documented signature `np.emath.logn(n: ArrayLike, x: ArrayLike): NDArray`	Take log base n of x.
`power`function	`np.emath.power` documented signature `np.emath.power(x: ArrayLike, p: ArrayLike): NDArray`	Return x to the power p, (x**p).
`sqrt`function	`np.emath.sqrt` documented signature `np.emath.sqrt(x: ArrayLike): NDArray`	Compute the square root of x.

numpy.fft# 18 / 18

NumPy	numera	Summary
`fft`function	`np.fft.fft` documented signature `np.fft.fft(a: ArrayLike, n?: number \| FftOptions \| null \| undefined, axis?: number \| undefined, norm?: FftNorm \| null \| undefined, out?: NDArray \| null \| undefined): NDArray`	Compute the one-dimensional discrete Fourier Transform.
`fft2`function	`np.fft.fft2` signature `np.fft.fft2(a: ArrayLike, s?: number[] \| FftNOptions \| null \| undefined, axes?: number[] \| null \| undefined, norm?: FftNorm \| null \| undefined, out?: NDArray \| null \| undefined): NDArray`	Compute the 2-dimensional discrete Fourier Transform.
`fftfreq`function	`np.fft.fftfreq` documented signature `np.fft.fftfreq(n: number, d?: number \| undefined): NDArray`	Return the Discrete Fourier Transform sample frequencies.
`fftn`function	`np.fft.fftn` documented signature `np.fft.fftn(a: ArrayLike, s?: number[] \| FftNOptions \| null \| undefined, axes?: number[] \| null \| undefined, norm?: FftNorm \| null \| undefined, out?: NDArray \| null \| undefined): NDArray`	Compute the N-dimensional discrete Fourier Transform.
`fftshift`function	`np.fft.fftshift` documented signature `np.fft.fftshift(x: ArrayLike, axes?: number \| number[] \| null \| undefined): NDArray`	Shift the zero-frequency component to the center of the spectrum.
`hfft`function	`np.fft.hfft` documented signature `np.fft.hfft(a: ArrayLike, n?: number \| FftOptions \| null \| undefined, axis?: number \| undefined, norm?: FftNorm \| null \| undefined, out?: NDArray \| null \| undefined): NDArray`	Compute the FFT of a signal that has Hermitian symmetry, i.e., a real spectrum.
`ifft`function	`np.fft.ifft` documented signature `np.fft.ifft(a: ArrayLike, n?: number \| FftOptions \| null \| undefined, axis?: number \| undefined, norm?: FftNorm \| null \| undefined, out?: NDArray \| null \| undefined): NDArray`	Compute the one-dimensional inverse discrete Fourier Transform.
`ifft2`function	`np.fft.ifft2` signature `np.fft.ifft2(a: ArrayLike, s?: number[] \| FftNOptions \| null \| undefined, axes?: number[] \| null \| undefined, norm?: FftNorm \| null \| undefined, out?: NDArray \| null \| undefined): NDArray`	Compute the 2-dimensional inverse discrete Fourier Transform.
`ifftn`function	`np.fft.ifftn` signature `np.fft.ifftn(a: ArrayLike, s?: number[] \| FftNOptions \| null \| undefined, axes?: number[] \| null \| undefined, norm?: FftNorm \| null \| undefined, out?: NDArray \| null \| undefined): NDArray`	Compute the N-dimensional inverse discrete Fourier Transform.
`ifftshift`function	`np.fft.ifftshift` documented signature `np.fft.ifftshift(x: ArrayLike, axes?: number \| number[] \| null \| undefined): NDArray`	The inverse of `fftshift`. Although identical for even-length `x`, the functions differ by one sample for odd-length `x`.
`ihfft`function	`np.fft.ihfft` documented signature `np.fft.ihfft(a: ArrayLike, n?: number \| FftOptions \| null \| undefined, axis?: number \| undefined, norm?: FftNorm \| null \| undefined, out?: NDArray \| null \| undefined): NDArray`	Compute the inverse FFT of a signal that has Hermitian symmetry.
`irfft`function	`np.fft.irfft` documented signature `np.fft.irfft(a: ArrayLike, n?: number \| FftOptions \| null \| undefined, axis?: number \| undefined, norm?: FftNorm \| null \| undefined, out?: NDArray \| null \| undefined): NDArray`	Computes the inverse of `rfft`.
`irfft2`function	`np.fft.irfft2` signature `np.fft.irfft2(a: ArrayLike, s?: number[] \| FftNOptions \| null \| undefined, axes?: number[] \| null \| undefined, norm?: FftNorm \| null \| undefined, out?: NDArray \| null \| undefined): NDArray`	Computes the inverse of `rfft2`.
`irfftn`function	`np.fft.irfftn` signature `np.fft.irfftn(a: ArrayLike, s?: number[] \| FftNOptions \| null \| undefined, axes?: number[] \| null \| undefined, norm?: FftNorm \| null \| undefined, out?: NDArray \| null \| undefined): NDArray`	Computes the inverse of `rfftn`.
`rfft`function	`np.fft.rfft` documented signature `np.fft.rfft(a: ArrayLike, n?: number \| FftOptions \| null \| undefined, axis?: number \| undefined, norm?: FftNorm \| null \| undefined, out?: NDArray \| null \| undefined): NDArray`	Compute the one-dimensional discrete Fourier Transform for real input.
`rfft2`function	`np.fft.rfft2` signature `np.fft.rfft2(a: ArrayLike, s?: number[] \| FftNOptions \| null \| undefined, axes?: number[] \| null \| undefined, norm?: FftNorm \| null \| undefined, out?: NDArray \| null \| undefined): NDArray`	Compute the 2-dimensional FFT of a real array.
`rfftfreq`function	`np.fft.rfftfreq` documented signature `np.fft.rfftfreq(n: number, d?: number \| undefined): NDArray`	Return the Discrete Fourier Transform sample frequencies (for usage with rfft, irfft).
`rfftn`function	`np.fft.rfftn` documented signature `np.fft.rfftn(a: ArrayLike, s?: number[] \| FftNOptions \| null \| undefined, axes?: number[] \| null \| undefined, norm?: FftNorm \| null \| undefined, out?: NDArray \| null \| undefined): NDArray`	Compute the N-dimensional discrete Fourier Transform for real input.

numpy.linalg# 32 / 32

NumPy	numera	Summary
`cholesky`function	`np.linalg.cholesky` documented signature `np.linalg.cholesky(a: ArrayLike, opts?: CholeskyOptions \| undefined): NDArray`	Cholesky decomposition.
`cond`function	`np.linalg.cond` documented signature `np.linalg.cond(x: ArrayLike, p?: NormOrder \| undefined): NDArray`	Compute the condition number of a matrix.
`cross`function	`np.linalg.cross` documented signature `np.linalg.cross(x1: ArrayLike, x2: ArrayLike, opts?: { axis?: number \| undefined; } \| undefined): NDArray`	Returns the cross product of 3-element vectors.
`det`function	`np.linalg.det` documented signature `np.linalg.det(a: ArrayLike): NDArray`	Compute the determinant of an array.
`diagonal`function	`np.linalg.diagonal` documented signature `np.linalg.diagonal(x: ArrayLike, opts?: { offset?: number \| undefined; } \| undefined): NDArray`	Returns specified diagonals of a matrix (or a stack of matrices) `x`.
`eig`function	`np.linalg.eig` documented signature `np.linalg.eig(a: ArrayLike): EigResult`	Compute the eigenvalues and right eigenvectors of a square array.
`eigh`function	`np.linalg.eigh` documented signature `np.linalg.eigh(a: ArrayLike): EigResult`	Return the eigenvalues and eigenvectors of a complex Hermitian (conjugate symmetric) or a real symmetric matrix.
`eigvals`function	`np.linalg.eigvals` documented signature `np.linalg.eigvals(a: ArrayLike): NDArray`	Compute the eigenvalues of a general matrix.
`eigvalsh`function	`np.linalg.eigvalsh` documented signature `np.linalg.eigvalsh(a: ArrayLike): NDArray`	Compute the eigenvalues of a complex Hermitian or real symmetric matrix.
`inv`function	`np.linalg.inv` documented signature `np.linalg.inv(a: ArrayLike): NDArray`	Compute the inverse of a matrix.
`LinAlgError`class	`np.linalg.LinAlgError` signature `new np.linalg.LinAlgError(message: string, code?: string \| undefined)`	Generic Python-exception-derived object raised by linalg functions.
`lstsq`function	`np.linalg.lstsq` documented signature `np.linalg.lstsq(a: ArrayLike, b: ArrayLike, rcond?: number \| null \| undefined): LstsqResult`	Return the least-squares solution to a linear matrix equation.
`matmul`function	`np.linalg.matmul` signature `np.linalg.matmul(a: ArrayLike, b: ArrayLike): NDArray`	Computes the matrix product.
`matrix_norm`function	`np.linalg.matrixNorm` documented signature `np.linalg.matrixNorm(x: ArrayLike, opts?: MatrixNormOptions \| undefined): NDArray`	Computes the matrix norm of a matrix (or a stack of matrices) `x`.
`matrix_power`function	`np.linalg.matrixPower` documented signature `np.linalg.matrixPower(a: ArrayLike, n: number): NDArray`	Raise a square matrix to the (integer) power `n`.
`matrix_rank`function	`np.linalg.matrixRank` documented signature `np.linalg.matrixRank(A: ArrayLike, opts?: MatrixRankOptions \| undefined): NDArray`	Return matrix rank of array using SVD method
`matrix_transpose`function	`np.linalg.matrixTranspose` documented signature `np.linalg.matrixTranspose(x: ArrayLike): NDArray`	Transposes a matrix (or a stack of matrices) `x`.
`multi_dot`function	`np.linalg.multiDot` documented signature `np.linalg.multiDot(arrays: readonly ArrayLike[]): NDArray`	Compute the dot product of two or more arrays in a single function call, while automatically selecting the fastest evaluation order.
`norm`function	`np.linalg.norm` documented signature `np.linalg.norm(a: ArrayLike, opts?: NormOptions \| undefined): NDArray`	Matrix or vector norm.
`outer`function	`np.linalg.outer` documented signature `np.linalg.outer(x1: ArrayLike, x2: ArrayLike): NDArray`	Compute the outer product of two vectors.
`pinv`function	`np.linalg.pinv` documented signature `np.linalg.pinv(a: ArrayLike, opts?: PinvOptions \| undefined): NDArray`	Compute the (Moore-Penrose) pseudo-inverse of a matrix.
`qr`function	`np.linalg.qr` documented signature `np.linalg.qr(a: ArrayLike, mode?: QrMode \| undefined): QrResult`	Compute the qr factorization of a matrix.
`slogdet`function	`np.linalg.slogdet` documented signature `np.linalg.slogdet(a: ArrayLike): SlogdetResult`	Compute the sign and (natural) logarithm of the determinant of an array.
`solve`function	`np.linalg.solve` documented signature `np.linalg.solve(a: ArrayLike, b: ArrayLike): NDArray`	Solve a linear matrix equation, or system of linear scalar equations.
`svd`function	`np.linalg.svd` documented signature `np.linalg.svd(a: ArrayLike, opts?: SvdOptions \| undefined): SvdResult`	Singular Value Decomposition.
`svdvals`function	`np.linalg.svdvals` documented signature `np.linalg.svdvals(x: ArrayLike): NDArray`	Returns the singular values of a matrix (or a stack of matrices) `x`. When x is a stack of matrices, the function will compute the singular values for each matrix in the stack.
`tensordot`function	`np.linalg.tensordot` documented signature `np.linalg.tensordot(a: ArrayLike, b: ArrayLike, opts?: TensordotOptions \| undefined): NDArray`	Compute tensor dot product along specified axes.
`tensorinv`function	`np.linalg.tensorinv` documented signature `np.linalg.tensorinv(a: ArrayLike, opts?: TensorinvOptions \| undefined): NDArray`	Compute the 'inverse' of an N-dimensional array.
`tensorsolve`function	`np.linalg.tensorsolve` documented signature `np.linalg.tensorsolve(a: ArrayLike, b: ArrayLike, opts?: TensorsolveOptions \| undefined): NDArray`	Solve the tensor equation `a x = b` for x.
`trace`function	`np.linalg.trace` documented signature `np.linalg.trace(x: ArrayLike, opts?: { offset?: number \| undefined; dtype?: DTypeLike \| null \| undefined; } \| undefined): NDArray`	Returns the sum along the specified diagonals of a matrix (or a stack of matrices) `x`.
`vecdot`function	`np.linalg.vecdot` documented signature `np.linalg.vecdot(x1: ArrayLike, x2: ArrayLike, opts?: VecdotOptions \| undefined): NDArray`	Computes the vector dot product.
`vector_norm`function	`np.linalg.vectorNorm` documented signature `np.linalg.vectorNorm(x: ArrayLike, opts?: VectorNormOptions \| undefined): NDArray`	Computes the vector norm of a vector (or batch of vectors) `x`.

numpy.ma# 218 / 224

NumPy	numera	Summary
`abs`function	`np.ma.abs` signature `np.ma.abs(a: ArrayLike \| MaskedArray): MaskedArray`	Calculate the absolute value element-wise.
`absolute`function	`np.ma.absolute` signature `np.ma.absolute(a: ArrayLike \| MaskedArray): MaskedArray`	Calculate the absolute value element-wise.
`add`function	`np.ma.add` signature `np.ma.add(a: ArrayLike \| MaskedArray, b: ArrayLike \| MaskedArray): MaskedArray`	Add arguments element-wise.
`all`function	`np.ma.all` signature `np.ma.all(a: ArrayLike \| MaskedArray, opts?: { axis?: number \| null \| undefined; keepdims?: boolean \| undefined; } \| undefined): NDArray \| MaskedArray`	Returns True if all elements evaluate to True.
`allclose`function	`np.ma.allclose` signature `np.ma.allclose(a: MaOperand, b: MaOperand, masked_equal?: boolean \| undefined, rtol?: number \| undefined, atol?: number \| undefined): boolean`	Returns True if two arrays are element-wise equal within a tolerance.
`allequal`function	`np.ma.allequal` signature `np.ma.allequal(a: MaOperand, b: MaOperand, fill_value?: boolean \| undefined): boolean`	Return True if all entries of a and b are equal, using fill_value as a truth value where either or both are masked.
`alltrue`function	`np.ma.alltrue` signature `np.ma.alltrue(a: ArrayLike \| MaskedArray, opts?: { axis?: number \| null \| undefined; keepdims?: boolean \| undefined; } \| undefined): NDArray \| MaskedArray`	Reduce `target` along the given `axis`.
`amax`function	`np.ma.amax` signature `np.ma.amax(a: ArrayLike \| MaskedArray, opts?: { axis?: number \| null \| undefined; keepdims?: boolean \| undefined; } \| undefined): NDArray \| MaskedArray`	Return the maximum of an array or maximum along an axis.
`amin`function	`np.ma.amin` signature `np.ma.amin(a: ArrayLike \| MaskedArray, opts?: { axis?: number \| null \| undefined; keepdims?: boolean \| undefined; } \| undefined): NDArray \| MaskedArray`	Return the minimum of an array or minimum along an axis.
`angle`function	`np.ma.angle` signature `np.ma.angle(a: ArrayLike \| MaskedArray): MaskedArray`	Return the angle of the complex argument.
`anom`function	`np.ma.anom` signature `np.ma.anom(a: MaOperand, opts?: { axis?: number \| null \| undefined; } \| undefined): MaskedArray`	Compute the anomalies (deviations from the arithmetic mean) along the given axis.
`anomalies`function	`np.ma.anomalies` signature `np.ma.anomalies(a: MaOperand, opts?: { axis?: number \| null \| undefined; } \| undefined): MaskedArray`	Compute the anomalies (deviations from the arithmetic mean) along the given axis.
`any`function	`np.ma.any` signature `np.ma.any(a: ArrayLike \| MaskedArray, opts?: { axis?: number \| null \| undefined; keepdims?: boolean \| undefined; } \| undefined): NDArray \| MaskedArray`	Returns True if any of the elements of `a` evaluate to True.
`append`function	`np.ma.append` signature `np.ma.append(a: MaOperand, b: MaOperand, axis?: number \| undefined): MaskedArray`	Append values to the end of an array.
`apply_along_axis`function	not implemented	Apply a function to 1-D slices along the given axis.
`apply_over_axes`function	not implemented	Apply a function repeatedly over multiple axes.
`arange`function	`np.ma.arange` signature `np.ma.arange(start: number, stop?: number \| undefined, step?: number \| undefined, opts?: { dtype?: DTypeLike \| undefined; } \| undefined): MaskedArray`	Return evenly spaced values within a given interval.
`arccos`function	`np.ma.arccos` signature `np.ma.arccos(a: ArrayLike \| MaskedArray): MaskedArray`	Trigonometric inverse cosine, element-wise.
`arccosh`function	`np.ma.arccosh` signature `np.ma.arccosh(a: ArrayLike \| MaskedArray): MaskedArray`	Inverse hyperbolic cosine, element-wise.
`arcsin`function	`np.ma.arcsin` signature `np.ma.arcsin(a: ArrayLike \| MaskedArray): MaskedArray`	Inverse sine, element-wise.
`arcsinh`function	`np.ma.arcsinh` signature `np.ma.arcsinh(a: ArrayLike \| MaskedArray): MaskedArray`	Inverse hyperbolic sine, element-wise.
`arctan`function	`np.ma.arctan` signature `np.ma.arctan(a: ArrayLike \| MaskedArray): MaskedArray`	Trigonometric inverse tangent, element-wise.
`arctan2`function	`np.ma.arctan2` signature `np.ma.arctan2(a: ArrayLike \| MaskedArray, b: ArrayLike \| MaskedArray): MaskedArray`	Element-wise arc tangent of `x1/x2` choosing the quadrant correctly.
`arctanh`function	`np.ma.arctanh` signature `np.ma.arctanh(a: ArrayLike \| MaskedArray): MaskedArray`	Inverse hyperbolic tangent, element-wise.
`argmax`function	`np.ma.argmax` signature `np.ma.argmax(a: ArrayLike \| MaskedArray, opts?: { axis?: number \| null \| undefined; keepdims?: boolean \| undefined; } \| undefined): NDArray`	Returns array of indices of the maximum values along the given axis. Masked values are treated as if they had the value fill_value.
`argmin`function	`np.ma.argmin` signature `np.ma.argmin(a: ArrayLike \| MaskedArray, opts?: { axis?: number \| null \| undefined; keepdims?: boolean \| undefined; } \| undefined): NDArray`	Return array of indices to the minimum values along the given axis.
`argsort`function	`np.ma.argsort` signature `np.ma.argsort(a: ArrayLike \| MaskedArray, opts?: { axis?: number \| undefined; kind?: string \| undefined; } \| undefined): NDArray`	Return an ndarray of indices that sort the array along the specified axis. Masked values are filled beforehand to `fill_value`.
`around`function	`np.ma.around` signature `np.ma.around(a: ArrayLike \| MaskedArray, decimals?: number \| undefined): MaskedArray`	Round an array to the given number of decimals.
`array`function	`np.ma.array` signature `np.ma.array(data: ArrayLike \| MaskedArray, opts?: MaskedArrayOptions \| undefined): MaskedArray`	An array class with possibly masked values.
`asanyarray`function	`np.ma.asanyarray` signature `np.ma.asanyarray(data: ArrayLike \| MaskedArray, dtype?: DTypeLike \| undefined): MaskedArray`	Convert the input to a masked array, conserving subclasses.
`asarray`function	`np.ma.asarray` signature `np.ma.asarray(data: ArrayLike \| MaskedArray, dtype?: DTypeLike \| undefined): MaskedArray`	Convert the input to a masked array of the given data-type.
`atleast_1d`function	`np.ma.atleast_1d` signature `np.ma.atleast_1d(...arys: MaOperand[]): MaskedArray[]`	Convert inputs to arrays with at least one dimension.
`atleast_2d`function	`np.ma.atleast_2d` signature `np.ma.atleast_2d(...arys: MaOperand[]): MaskedArray[]`	View inputs as arrays with at least two dimensions.
`atleast_3d`function	`np.ma.atleast_3d` signature `np.ma.atleast_3d(...arys: MaOperand[]): MaskedArray[]`	View inputs as arrays with at least three dimensions.
`average`function	`np.ma.average` signature `np.ma.average(a: MaOperand, opts?: { axis?: number \| null \| undefined; weights?: NDArray \| number[] \| null \| undefined; returned?: boolean \| undefined; } \| undefined): MaskedArray \| [MaskedArray, NDArray]`	Return the weighted average of array over the given axis.
`bitwise_and`function	`np.ma.bitwise_and` signature `np.ma.bitwise_and(a: ArrayLike \| MaskedArray, b: ArrayLike \| MaskedArray): MaskedArray`	Compute the bit-wise AND of two arrays element-wise.
`bitwise_or`function	`np.ma.bitwise_or` signature `np.ma.bitwise_or(a: ArrayLike \| MaskedArray, b: ArrayLike \| MaskedArray): MaskedArray`	Compute the bit-wise OR of two arrays element-wise.
`bitwise_xor`function	`np.ma.bitwise_xor` signature `np.ma.bitwise_xor(a: ArrayLike \| MaskedArray, b: ArrayLike \| MaskedArray): MaskedArray`	Compute the bit-wise XOR of two arrays element-wise.
`bool_`class	`np.ma.bool_` signature `np.ma.bool_: DType`	Boolean type (True or False), stored as a byte.
`ceil`function	`np.ma.ceil` signature `np.ma.ceil(a: ArrayLike \| MaskedArray): MaskedArray`	Return the ceiling of the input, element-wise.
`choose`function	`np.ma.choose` signature `np.ma.choose(a: MaOperand, choices: MaOperand[]): MaskedArray`	Use an index array to construct a new array from a list of choices.
`clip`function	`np.ma.clip` signature `np.ma.clip(a: MaOperand, a_min: number \| null, a_max: number \| null): MaskedArray`	Clip (limit) the values in an array.
`clump_masked`function	`np.ma.clump_masked` signature `np.ma.clump_masked(a: MaOperand): [number, number][]`	Returns a list of slices corresponding to the masked clumps of a 1-D array. (A "clump" is defined as a contiguous region of the array).
`clump_unmasked`function	`np.ma.clump_unmasked` signature `np.ma.clump_unmasked(a: MaOperand, _axis?: number \| undefined): [number, number][]`	Return list of slices corresponding to the unmasked clumps of a 1-D array. (A "clump" is defined as a contiguous region of the array).
`column_stack`function	`np.ma.column_stack` signature `np.ma.column_stack(tup: MaOperand[]): MaskedArray`	Stack 1-D arrays as columns into a 2-D array.
`common_fill_value`function	`np.ma.common_fill_value` signature `np.ma.common_fill_value(a: MaskedArray, b: MaskedArray): number \| boolean \| MaskedArray`	Return the common filling value of two masked arrays, if any.
`compress`function	`np.ma.compress` signature `np.ma.compress(condition: NDArray \| boolean[], a: MaOperand, axis?: number \| null \| undefined): MaskedArray`	Return `a` where condition is `True`.
`compress_cols`function	`np.ma.compress_cols` signature `np.ma.compress_cols(x: ArrayLike \| MaskedArray): MaskedArray`	Suppress whole columns of a 2-D array that contain masked values.
`compress_nd`function	`np.ma.compress_nd` signature `np.ma.compress_nd(x: MaOperand, axis?: number \| number[] \| undefined): MaskedArray`	Suppress slices from multiple dimensions which contain masked values.
`compress_rowcols`function	`np.ma.compress_rowcols` signature `np.ma.compress_rowcols(x: ArrayLike \| MaskedArray): MaskedArray`	Suppress the rows and/or columns of a 2-D array that contain masked values.
`compress_rows`function	`np.ma.compress_rows` signature `np.ma.compress_rows(x: ArrayLike \| MaskedArray): MaskedArray`	Suppress whole rows of a 2-D array that contain masked values.
`compressed`function	not implemented	Return all the non-masked data as a 1-D array.
`concatenate`function	`np.ma.concatenate` signature `np.ma.concatenate(arrays: (ArrayLike \| MaskedArray)[], axis?: number \| undefined): MaskedArray`	Concatenate a sequence of arrays along the given axis.
`conjugate`function	`np.ma.conjugate` signature `np.ma.conjugate(a: ArrayLike \| MaskedArray): MaskedArray`	Return the complex conjugate, element-wise.
`convolve`function	`np.ma.convolve` signature `np.ma.convolve(a: MaOperand, v: MaOperand, mode?: string \| undefined): MaskedArray`	Returns the discrete, linear convolution of two one-dimensional sequences.
`copy`function	`np.ma.copy` signature `np.ma.copy(a: ArrayLike \| MaskedArray): MaskedArray`	Return a copy of the array.
`corrcoef`function	`np.ma.corrcoef` signature `np.ma.corrcoef(x: MaOperand, y?: MaOperand \| undefined): MaskedArray`	Return Pearson product-moment correlation coefficients.
`correlate`function	`np.ma.correlate` signature `np.ma.correlate(a: MaOperand, v: MaOperand, mode?: string \| undefined): MaskedArray`	Cross-correlation of two 1-dimensional sequences.
`cos`function	`np.ma.cos` signature `np.ma.cos(a: ArrayLike \| MaskedArray): MaskedArray`	Cosine element-wise.
`cosh`function	`np.ma.cosh` signature `np.ma.cosh(a: ArrayLike \| MaskedArray): MaskedArray`	Hyperbolic cosine, element-wise.
`count`function	`np.ma.count` signature `np.ma.count(a: NDArray \| MaskedArray, _axis?: number \| null \| undefined): number`	Count the non-masked elements of the array along the given axis.
`count_masked`function	`np.ma.count_masked` signature `np.ma.count_masked(arr: NDArray \| MaskedArray, _axis?: number \| null \| undefined): number`	Count the number of masked elements along the given axis.
`cov`function	`np.ma.cov` signature `np.ma.cov(m: MaOperand, y?: MaOperand \| undefined): MaskedArray`	Estimate the covariance matrix.
`cumprod`function	`np.ma.cumprod` signature `np.ma.cumprod(a: ArrayLike \| MaskedArray, opts?: { axis?: number \| null \| undefined; dtype?: DTypeLike \| undefined; } \| undefined): MaskedArray`	Return the cumulative product of the array elements over the given axis.
`cumsum`function	`np.ma.cumsum` signature `np.ma.cumsum(a: ArrayLike \| MaskedArray, opts?: { axis?: number \| null \| undefined; dtype?: DTypeLike \| undefined; } \| undefined): MaskedArray`	Return the cumulative sum of the array elements over the given axis.
`default_fill_value`function	`np.ma.default_fill_value` signature `np.ma.default_fill_value(obj: number \| boolean \| NDArray \| MaskedArray): number \| boolean`	Return the default fill value for the argument object.
`diag`function	`np.ma.diag` signature `np.ma.diag(a: MaOperand, k?: number \| undefined): MaskedArray`	Extract a diagonal or construct a diagonal array.
`diagflat`function	`np.ma.diagflat` signature `np.ma.diagflat(a: MaOperand, k?: number \| undefined): MaskedArray`	Create a two-dimensional array with the flattened input as a diagonal.
`diagonal`function	`np.ma.diagonal` signature `np.ma.diagonal(a: MaOperand, offset?: number \| undefined, axis1?: number \| undefined, axis2?: number \| undefined): MaskedArray`	Return specified diagonals. In NumPy 1.9 the returned array is a read-only view instead of a copy as in previous NumPy versions. In a future version the read-only restriction will be removed.
`diff`function	`np.ma.diff` signature `np.ma.diff(a: MaOperand, n?: number \| undefined, axis?: number \| undefined): MaskedArray`	Calculate the n-th discrete difference along the given axis. The first difference is given by `out[i] = a[i+1] - a[i]` along the given axis, higher differences are calculated by using `diff` recursively. Preserves the input mask.
`divide`function	`np.ma.divide` signature `np.ma.divide(a: ArrayLike \| MaskedArray, b: ArrayLike \| MaskedArray): MaskedArray`	Divide arguments element-wise.
`dot`function	`np.ma.dot` signature `np.ma.dot(a: MaOperand, b: MaOperand): MaskedArray`	Return the dot product of two arrays.
`dstack`function	`np.ma.dstack` signature `np.ma.dstack(tup: MaOperand[]): MaskedArray`	Stack arrays in sequence depth wise (along third axis).
`ediff1d`function	`np.ma.ediff1d` signature `np.ma.ediff1d(ary: MaOperand, to_end?: number \| number[] \| null \| undefined, to_begin?: number \| number[] \| null \| undefined): MaskedArray`	Compute the differences between consecutive elements of an array.
`empty`function	`np.ma.empty` signature `np.ma.empty(shape: number \| Shape, opts?: { dtype?: DTypeLike \| undefined; } \| undefined): MaskedArray`	Return a new array of given shape and type, without initializing entries.
`empty_like`function	`np.ma.empty_like` signature `np.ma.empty_like(a: NDArray \| MaskedArray): MaskedArray`	Return a new array with the same shape and type as a given array.
`equal`function	`np.ma.equal` signature `np.ma.equal(a: ArrayLike \| MaskedArray, b: ArrayLike \| MaskedArray): MaskedArray`	Return (x1 == x2) element-wise.
`exp`function	`np.ma.exp` signature `np.ma.exp(a: ArrayLike \| MaskedArray): MaskedArray`	Calculate the exponential of all elements in the input array.
`expand_dims`function	`np.ma.expand_dims` signature `np.ma.expand_dims(a: MaOperand, axis: number): MaskedArray`	Expand the shape of an array.
`fabs`function	`np.ma.fabs` signature `np.ma.fabs(a: ArrayLike \| MaskedArray): MaskedArray`	Compute the absolute values element-wise.
`filled`function	`np.ma.filled` signature `np.ma.filled(a: NDArray \| MaskedArray, fill_value?: number \| boolean \| undefined): NDArray`	Return input as an `~numpy.ndarray`, with masked values replaced by `fill_value`.
`fix_invalid`function	`np.ma.fix_invalid` signature `np.ma.fix_invalid(a: ArrayLike \| MaskedArray, fill_value?: number \| undefined): MaskedArray`	Return input with invalid data masked and replaced by a fill value.
`flatnotmasked_contiguous`function	`np.ma.flatnotmasked_contiguous` signature `np.ma.flatnotmasked_contiguous(a: MaOperand, _axis?: number \| undefined): [number, number][]`	Find contiguous unmasked data in a masked array.
`flatnotmasked_edges`function	`np.ma.flatnotmasked_edges` signature `np.ma.flatnotmasked_edges(a: MaOperand, _axis?: number \| undefined): [number, number] \| null`	Find the indices of the first and last unmasked values.
`flatten_mask`function	`np.ma.flatten_mask` signature `np.ma.flatten_mask(mask: boolean \| NDArray): false \| NDArray`	Returns a completely flattened version of the mask, where nested fields are collapsed.
`flatten_structured_array`function	`np.ma.flatten_structured_array` signature `np.ma.flatten_structured_array(a: ArrayLike \| MaskedArray): MaskedArray`	Flatten a structured array.
`floor`function	`np.ma.floor` signature `np.ma.floor(a: ArrayLike \| MaskedArray): MaskedArray`	Return the floor of the input, element-wise.
`floor_divide`function	`np.ma.floor_divide` signature `np.ma.floor_divide(a: ArrayLike \| MaskedArray, b: ArrayLike \| MaskedArray): MaskedArray`	Return the largest integer smaller or equal to the division of the inputs. It is equivalent to the Python `//` operator and pairs with the Python `%` (`remainder`), function so that `a = a % b + b * (a // b)` up to roundoff.
`fmod`function	`np.ma.fmod` signature `np.ma.fmod(a: ArrayLike \| MaskedArray, b: ArrayLike \| MaskedArray): MaskedArray`	Returns the element-wise remainder of division.
`frombuffer`function	not implemented	Interpret a buffer as a 1-dimensional array.
`fromflex`function	not implemented	Build a masked array from a suitable flexible-type array.
`fromfunction`function	not implemented	Construct an array by executing a function over each coordinate.
`getdata`function	`np.ma.getdata` signature `np.ma.getdata(a: NDArray \| MaskedArray): NDArray`	Return the data of a masked array as an ndarray.
`getmask`function	`np.ma.getmask` signature `np.ma.getmask(a: NDArray \| MaskedArray): false \| NDArray`	Return the mask of a masked array, or nomask.
`getmaskarray`function	`np.ma.getmaskarray` signature `np.ma.getmaskarray(a: NDArray \| MaskedArray): NDArray`	Return the mask of a masked array, or full boolean array of False.
`greater`function	`np.ma.greater` signature `np.ma.greater(a: ArrayLike \| MaskedArray, b: ArrayLike \| MaskedArray): MaskedArray`	Return the truth value of (x1 > x2) element-wise.
`greater_equal`function	`np.ma.greater_equal` signature `np.ma.greater_equal(a: ArrayLike \| MaskedArray, b: ArrayLike \| MaskedArray): MaskedArray`	Return the truth value of (x1 >= x2) element-wise.
`harden_mask`function	`np.ma.harden_mask` signature `np.ma.harden_mask(a: MaskedArray): MaskedArray`	Force the mask to hard, preventing unmasking by assignment.
`hsplit`function	`np.ma.hsplit` signature `np.ma.hsplit(a: MaOperand, n: number): MaskedArray[]`	Split an array into multiple sub-arrays horizontally (column-wise).
`hstack`function	`np.ma.hstack` signature `np.ma.hstack(tup: MaOperand[]): MaskedArray`	Stack arrays in sequence horizontally (column wise).
`hypot`function	`np.ma.hypot` signature `np.ma.hypot(a: ArrayLike \| MaskedArray, b: ArrayLike \| MaskedArray): MaskedArray`	Given the "legs" of a right triangle, return its hypotenuse.
`identity`function	`np.ma.identity` signature `np.ma.identity(n: number, dtype?: DTypeLike \| undefined): MaskedArray`	Return the identity array.
`ids`function	`np.ma.ids` signature `np.ma.ids(a: MaskedArray): [number, number]`	Return the addresses of the data and mask areas.
`in1d`function	`np.ma.in1d` signature `np.ma.in1d(ar1: ArrayLike \| MaskedArray, ar2: ArrayLike \| MaskedArray): MaskedArray`	Test whether each element of an array is also present in a second array.
`indices`function	`np.ma.indices` signature `np.ma.indices(dimensions: number[], dtype?: DTypeLike \| undefined): MaskedArray`	Return an array representing the indices of a grid.
`inner`function	`np.ma.inner` signature `np.ma.inner(a: MaOperand, b: MaOperand): MaskedArray`	Inner product of two arrays.
`innerproduct`function	`np.ma.innerproduct` signature `np.ma.innerproduct(a: MaOperand, b: MaOperand): MaskedArray`	Inner product of two arrays.
`intersect1d`function	`np.ma.intersect1d` signature `np.ma.intersect1d(ar1: ArrayLike \| MaskedArray, ar2: ArrayLike \| MaskedArray): NDArray`	Returns the unique elements common to both arrays.
`is_mask`function	`np.ma.is_mask` signature `np.ma.is_mask(m: unknown): boolean`	Return True if m is a valid, standard mask.
`is_masked`function	`np.ma.is_masked` signature `np.ma.is_masked(a: unknown): boolean`	Determine whether input has masked values.
`isarray`function	`np.ma.isarray` signature `np.ma.isarray(a: unknown): a is MaskedArray`	Test whether input is an instance of MaskedArray.
`isin`function	`np.ma.isin` signature `np.ma.isin(ar1: ArrayLike \| MaskedArray, ar2: ArrayLike \| MaskedArray): MaskedArray`	Calculates `element in test_elements`, broadcasting over `element` only.
`isMA`function	`np.ma.isMA` signature `np.ma.isMA(a: unknown): a is MaskedArray`	Test whether input is an instance of MaskedArray.
`isMaskedArray`function	`np.ma.isMaskedArray` signature `np.ma.isMaskedArray(a: unknown): a is MaskedArray`	Test whether input is an instance of MaskedArray.
`left_shift`function	`np.ma.left_shift` signature `np.ma.left_shift(a: ArrayLike \| MaskedArray, b: ArrayLike \| MaskedArray): MaskedArray`	Shift the bits of an integer to the left.
`less`function	`np.ma.less` signature `np.ma.less(a: ArrayLike \| MaskedArray, b: ArrayLike \| MaskedArray): MaskedArray`	Return the truth value of (x1 < x2) element-wise.
`less_equal`function	`np.ma.less_equal` signature `np.ma.less_equal(a: ArrayLike \| MaskedArray, b: ArrayLike \| MaskedArray): MaskedArray`	Return the truth value of (x1 <= x2) element-wise.
`log`function	`np.ma.log` signature `np.ma.log(a: ArrayLike \| MaskedArray): MaskedArray`	Natural logarithm, element-wise.
`log10`function	`np.ma.log10` signature `np.ma.log10(a: ArrayLike \| MaskedArray): MaskedArray`	Return the base 10 logarithm of the input array, element-wise.
`log2`function	`np.ma.log2` signature `np.ma.log2(a: ArrayLike \| MaskedArray): MaskedArray`	Base-2 logarithm of `x`.
`logical_and`function	`np.ma.logical_and` signature `np.ma.logical_and(a: ArrayLike \| MaskedArray, b: ArrayLike \| MaskedArray): MaskedArray`	Compute the truth value of x1 AND x2 element-wise.
`logical_not`function	`np.ma.logical_not` signature `np.ma.logical_not(a: ArrayLike \| MaskedArray): MaskedArray`	Compute the truth value of NOT x element-wise.
`logical_or`function	`np.ma.logical_or` signature `np.ma.logical_or(a: ArrayLike \| MaskedArray, b: ArrayLike \| MaskedArray): MaskedArray`	Compute the truth value of x1 OR x2 element-wise.
`logical_xor`function	`np.ma.logical_xor` signature `np.ma.logical_xor(a: ArrayLike \| MaskedArray, b: ArrayLike \| MaskedArray): MaskedArray`	Compute the truth value of x1 XOR x2, element-wise.
`MAError`class	`np.ma.MAError` signature `new np.ma.MAError(message: string, code?: string \| undefined)`	Class for masked array related errors.
`make_mask`function	`np.ma.make_mask` signature `np.ma.make_mask(m: MaskLike, copy?: boolean \| undefined, shrink?: boolean \| undefined, dtype?: DTypeLike \| undefined): false \| NDArray`	Create a boolean mask from an array.
`make_mask_descr`function	`np.ma.make_mask_descr` signature `np.ma.make_mask_descr(_ndtype: unknown): "bool"`	Construct a dtype description list from a given dtype.
`make_mask_none`function	`np.ma.make_mask_none` signature `np.ma.make_mask_none(newshape: number \| Shape, dtype?: DTypeLike \| undefined): NDArray`	Return a boolean mask of the given shape, filled with False.
`mask_cols`function	`np.ma.mask_cols` signature `np.ma.mask_cols(a: MaOperand): MaskedArray`	Mask columns of a 2D array that contain masked values.
`mask_or`function	`np.ma.mask_or` signature `np.ma.mask_or(m1: false \| NDArray \| null, m2: false \| NDArray \| null, copy?: boolean \| undefined, shrink?: boolean \| undefined): false \| NDArray`	Combine two masks with the `logical_or` operator.
`mask_rowcols`function	`np.ma.mask_rowcols` signature `np.ma.mask_rowcols(a: ArrayLike \| MaskedArray): MaskedArray`	Mask rows and/or columns of a 2D array that contain masked values.
`mask_rows`function	`np.ma.mask_rows` signature `np.ma.mask_rows(a: MaOperand): MaskedArray`	Mask rows of a 2D array that contain masked values.
`masked`constant	`np.ma.masked` signature `np.ma.masked: MaskedArray`
`masked_all`function	`np.ma.masked_all` signature `np.ma.masked_all(shape: number \| Shape, dtype?: DTypeLike \| undefined): MaskedArray`	Empty masked array with all elements masked.
`masked_all_like`function	`np.ma.masked_all_like` signature `np.ma.masked_all_like(a: NDArray \| MaskedArray): MaskedArray`	Empty masked array with the properties of an existing array.
`masked_array`class	`np.ma.masked_array` signature `np.ma.masked_array(data: ArrayLike \| MaskedArray, opts?: MaskedArrayOptions \| undefined): MaskedArray`	An array class with possibly masked values.
`masked_equal`function	`np.ma.masked_equal` signature `np.ma.masked_equal(a: ArrayLike \| MaskedArray, value: number): MaskedArray`	Mask an array where equal to a given value.
`masked_greater`function	`np.ma.masked_greater` signature `np.ma.masked_greater(a: ArrayLike \| MaskedArray, value: number): MaskedArray`	Mask an array where greater than a given value.
`masked_greater_equal`function	`np.ma.masked_greater_equal` signature `np.ma.masked_greater_equal(a: ArrayLike \| MaskedArray, value: number): MaskedArray`	Mask an array where greater than or equal to a given value.
`masked_inside`function	`np.ma.masked_inside` signature `np.ma.masked_inside(a: ArrayLike \| MaskedArray, v1: number, v2: number): MaskedArray`	Mask an array inside a given interval.
`masked_invalid`function	`np.ma.masked_invalid` signature `np.ma.masked_invalid(a: ArrayLike \| MaskedArray, copy?: boolean \| undefined): MaskedArray`	Mask an array where invalid values occur (NaNs or infs).
`masked_less`function	`np.ma.masked_less` signature `np.ma.masked_less(a: ArrayLike \| MaskedArray, value: number): MaskedArray`	Mask an array where less than a given value.
`masked_less_equal`function	`np.ma.masked_less_equal` signature `np.ma.masked_less_equal(a: ArrayLike \| MaskedArray, value: number): MaskedArray`	Mask an array where less than or equal to a given value.
`masked_not_equal`function	`np.ma.masked_not_equal` signature `np.ma.masked_not_equal(a: ArrayLike \| MaskedArray, value: number): MaskedArray`	Mask an array where not equal to a given value.
`masked_object`function	`np.ma.masked_object` signature `np.ma.masked_object(a: ArrayLike \| MaskedArray, value: unknown, copy?: boolean \| undefined): MaskedArray`	Mask the array `x` where the data are exactly equal to value.
`masked_outside`function	`np.ma.masked_outside` signature `np.ma.masked_outside(a: ArrayLike \| MaskedArray, v1: number, v2: number): MaskedArray`	Mask an array outside a given interval.
`masked_print_option`constant	`np.ma.masked_print_option` signature `np.ma.masked_print_option: MaskedPrintOption`	Handle the string used to represent missing data in a masked array.
`masked_singleton`constant	`np.ma.masked_singleton` signature `np.ma.masked_singleton: MaskedArray`
`masked_values`function	`np.ma.masked_values` signature `np.ma.masked_values(a: ArrayLike \| MaskedArray, value: number, opts?: { rtol?: number \| undefined; atol?: number \| undefined; copy?: boolean \| undefined; } \| undefined): MaskedArray`	Mask using floating point equality.
`masked_where`function	`np.ma.masked_where` signature `np.ma.masked_where(condition: ArrayLike \| boolean[], a: ArrayLike \| MaskedArray, copy?: boolean \| undefined): MaskedArray`	Mask an array where a condition is met.
`MaskedArray`class	`np.ma.masked_array` signature `np.ma.masked_array(data: ArrayLike \| MaskedArray, opts?: MaskedArrayOptions \| undefined): MaskedArray`	An array class with possibly masked values.
`MaskError`class	`np.ma.MaskError` signature `new np.ma.MaskError(message: string, code?: string \| undefined)`	Class for mask related errors.
`MaskType`class	`np.ma.MaskType` signature `np.ma.MaskType: "bool"`	Boolean type (True or False), stored as a byte.
`max`function	`np.ma.max` signature `np.ma.max(a: ArrayLike \| MaskedArray, opts?: { axis?: number \| null \| undefined; keepdims?: boolean \| undefined; } \| undefined): NDArray \| MaskedArray`	Return the maximum along a given axis.
`maximum`function	`np.ma.maximum` signature `np.ma.maximum(a: ArrayLike \| MaskedArray, b: ArrayLike \| MaskedArray): MaskedArray`	Element-wise maximum of array elements.
`maximum_fill_value`function	`np.ma.maximum_fill_value` signature `np.ma.maximum_fill_value(obj: NDArray \| MaskedArray): number \| boolean`	Return the minimum value that can be represented by the dtype of an object.
`mean`function	`np.ma.mean` signature `np.ma.mean(a: ArrayLike \| MaskedArray, opts?: { axis?: number \| null \| undefined; keepdims?: boolean \| undefined; dtype?: DTypeLike \| undefined; } \| undefined): NDArray \| MaskedArray`	Returns the average of the array elements along given axis.
`median`function	`np.ma.median` signature `np.ma.median(a: MaOperand, opts?: { axis?: number \| null \| undefined; keepdims?: boolean \| undefined; } \| undefined): NDArray \| MaskedArray`	Compute the median along the specified axis.
`min`function	`np.ma.min` signature `np.ma.min(a: ArrayLike \| MaskedArray, opts?: { axis?: number \| null \| undefined; keepdims?: boolean \| undefined; } \| undefined): NDArray \| MaskedArray`	Return the minimum along a given axis.
`minimum`function	`np.ma.minimum` signature `np.ma.minimum(a: ArrayLike \| MaskedArray, b: ArrayLike \| MaskedArray): MaskedArray`	Element-wise minimum of array elements.
`minimum_fill_value`function	`np.ma.minimum_fill_value` signature `np.ma.minimum_fill_value(obj: NDArray \| MaskedArray): number \| boolean`	Return the maximum value that can be represented by the dtype of an object.
`mod`function	`np.ma.mod` signature `np.ma.mod(a: ArrayLike \| MaskedArray, b: ArrayLike \| MaskedArray): MaskedArray`	Returns the element-wise remainder of division.
`mr_`constant	`np.ma.mr_` signature `np.ma.mr_(...arrays: (ArrayLike \| MaskedArray)[]): MaskedArray`	Translate slice objects to concatenation along the first axis.
`multiply`function	`np.ma.multiply` signature `np.ma.multiply(a: ArrayLike \| MaskedArray, b: ArrayLike \| MaskedArray): MaskedArray`	Multiply arguments element-wise.
`mvoid`class	`np.ma.mvoid` signature `new np.ma.mvoid(data: NDArray, mask: false \| NDArray)`	Fake a 'void' object to use for masked array with structured dtypes.
`ndenumerate`function	`np.ma.ndenumerate` signature `np.ma.ndenumerate(a: MaOperand): [number[], number \| boolean \| MaskedArray][]`	Multidimensional index iterator.
`ndim`function	`np.ma.ndim` signature `np.ma.ndim(a: ArrayLike \| MaskedArray): number`	Return the number of dimensions of an array.
`negative`function	`np.ma.negative` signature `np.ma.negative(a: ArrayLike \| MaskedArray): MaskedArray`	Numerical negation, element-wise.
`nomask`constant	`np.ma.nomask` signature `np.ma.nomask: false`	bool(value=False, /) --
`nonzero`function	`np.ma.nonzero` signature `np.ma.nonzero(a: MaOperand): NDArray[]`	Return the indices of unmasked elements that are not zero.
`not_equal`function	`np.ma.not_equal` signature `np.ma.not_equal(a: ArrayLike \| MaskedArray, b: ArrayLike \| MaskedArray): MaskedArray`	Return (x1 != x2) element-wise.
`notmasked_contiguous`function	`np.ma.notmasked_contiguous` signature `np.ma.notmasked_contiguous(a: MaOperand, _axis?: number \| undefined): [number, number][]`	Find contiguous unmasked data in a masked array along the given axis.
`notmasked_edges`function	`np.ma.notmasked_edges` signature `np.ma.notmasked_edges(a: MaOperand, _axis?: number \| undefined): [number, number] \| null`	Find the indices of the first and last unmasked values along an axis.
`ones`function	`np.ma.ones` signature `np.ma.ones(shape: number \| Shape, opts?: { dtype?: DTypeLike \| undefined; } \| undefined): MaskedArray`	Return a new array of given shape and type, filled with ones.
`ones_like`function	`np.ma.ones_like` signature `np.ma.ones_like(a: NDArray \| MaskedArray): MaskedArray`	Return an array of ones with the same shape and type as a given array.
`outer`function	`np.ma.outer` signature `np.ma.outer(a: MaOperand, b: MaOperand): MaskedArray`	Compute the outer product of two vectors.
`outerproduct`function	`np.ma.outerproduct` signature `np.ma.outerproduct(a: MaOperand, b: MaOperand): MaskedArray`	Compute the outer product of two vectors.
`polyfit`function	`np.ma.polyfit` signature `np.ma.polyfit(x: MaOperand, y: MaOperand, deg: number): NDArray`	Least squares polynomial fit.
`power`function	`np.ma.power` signature `np.ma.power(a: ArrayLike \| MaskedArray, b: ArrayLike \| MaskedArray): MaskedArray`	Returns element-wise base array raised to power from second array.
`prod`function	`np.ma.prod` signature `np.ma.prod(a: ArrayLike \| MaskedArray, opts?: { axis?: number \| null \| undefined; keepdims?: boolean \| undefined; dtype?: DTypeLike \| undefined; } \| undefined): NDArray \| MaskedArray`	Return the product of the array elements over the given axis.
`product`function	`np.ma.product` signature `np.ma.product(a: ArrayLike \| MaskedArray, opts?: { axis?: number \| null \| undefined; keepdims?: boolean \| undefined; dtype?: DTypeLike \| undefined; } \| undefined): NDArray \| MaskedArray`	Return the product of the array elements over the given axis.
`ptp`function	`np.ma.ptp` signature `np.ma.ptp(a: MaOperand, opts?: { axis?: number \| null \| undefined; keepdims?: boolean \| undefined; } \| undefined): NDArray \| MaskedArray`	Return (maximum - minimum) along the given dimension (i.e. peak-to-peak value).
`put`function	`np.ma.put` signature `np.ma.put(a: MaskedArray, indices: NDArray \| number[], values: ArrayLike \| MaskedArray): void`	Set storage-indexed locations to corresponding values.
`putmask`function	`np.ma.putmask` signature `np.ma.putmask(a: MaskedArray, mask: NDArray \| boolean[], values: MaOperand): void`	Changes elements of an array based on conditional and input values.
`ravel`function	`np.ma.ravel` signature `np.ma.ravel(a: ArrayLike \| MaskedArray): MaskedArray`	Returns a 1D version of self, as a view.
`remainder`function	`np.ma.remainder` signature `np.ma.remainder(a: ArrayLike \| MaskedArray, b: ArrayLike \| MaskedArray): MaskedArray`	Returns the element-wise remainder of division.
`repeat`function	`np.ma.repeat` signature `np.ma.repeat(a: ArrayLike \| MaskedArray, repeats: number \| number[], axis?: number \| null \| undefined): MaskedArray`	Repeat elements of an array.
`reshape`function	`np.ma.reshape` signature `np.ma.reshape(a: ArrayLike \| MaskedArray, newshape: Shape): MaskedArray`	Returns an array containing the same data with a new shape.
`resize`function	`np.ma.resize` signature `np.ma.resize(a: MaOperand, newshape: number \| Shape): MaskedArray`	Return a new masked array with the specified size and shape.
`right_shift`function	`np.ma.right_shift` signature `np.ma.right_shift(a: ArrayLike \| MaskedArray, b: ArrayLike \| MaskedArray): MaskedArray`	Shift the bits of an integer to the right.
`round`function	`np.ma.round` signature `np.ma.round(a: ArrayLike \| MaskedArray, decimals?: number \| undefined): MaskedArray`	Return a copy of a, rounded to 'decimals' places.
`round_`function	`np.ma.round` signature `np.ma.round(a: ArrayLike \| MaskedArray, decimals?: number \| undefined): MaskedArray`	Return a copy of a, rounded to 'decimals' places.
`row_stack`function	`np.ma.row_stack` signature `np.ma.row_stack(tup: MaOperand[]): MaskedArray`	Stack arrays in sequence vertically (row wise).
`set_fill_value`function	`np.ma.set_fill_value` signature `np.ma.set_fill_value(a: MaskedArray, fill_value: number \| boolean): void`	Set the filling value of a, if a is a masked array.
`setdiff1d`function	`np.ma.setdiff1d` signature `np.ma.setdiff1d(ar1: ArrayLike \| MaskedArray, ar2: ArrayLike \| MaskedArray): NDArray`	Set difference of 1D arrays with unique elements.
`setxor1d`function	`np.ma.setxor1d` signature `np.ma.setxor1d(ar1: ArrayLike \| MaskedArray, ar2: ArrayLike \| MaskedArray): NDArray`	Set exclusive-or of 1-D arrays with unique elements.
`shape`function	`np.ma.shape` signature `np.ma.shape(a: ArrayLike \| MaskedArray): number[]`	Return the shape of an array.
`sin`function	`np.ma.sin` signature `np.ma.sin(a: ArrayLike \| MaskedArray): MaskedArray`	Trigonometric sine, element-wise.
`sinh`function	`np.ma.sinh` signature `np.ma.sinh(a: ArrayLike \| MaskedArray): MaskedArray`	Hyperbolic sine, element-wise.
`size`function	`np.ma.size` signature `np.ma.size(a: ArrayLike \| MaskedArray, axis?: number \| undefined): number`	Return the number of elements along a given axis.
`soften_mask`function	`np.ma.soften_mask` signature `np.ma.soften_mask(a: MaskedArray): MaskedArray`	Force the mask to soft (default), allowing unmasking by assignment.
`sometrue`function	`np.ma.sometrue` signature `np.ma.sometrue(a: ArrayLike \| MaskedArray, opts?: { axis?: number \| null \| undefined; keepdims?: boolean \| undefined; } \| undefined): NDArray \| MaskedArray`	Reduce `target` along the given `axis`.
`sort`function	`np.ma.sort` signature `np.ma.sort(a: ArrayLike \| MaskedArray, opts?: { axis?: number \| undefined; kind?: string \| undefined; } \| undefined): MaskedArray`	Return a sorted copy of the masked array.
`sqrt`function	`np.ma.sqrt` signature `np.ma.sqrt(a: ArrayLike \| MaskedArray): MaskedArray`	Return the non-negative square-root of an array, element-wise.
`squeeze`function	`np.ma.squeeze` signature `np.ma.squeeze(a: ArrayLike \| MaskedArray, axis?: number \| readonly number[] \| undefined): MaskedArray`	Remove axes of length one from `a`.
`stack`function	`np.ma.stack` signature `np.ma.stack(arrays: MaOperand[], axis?: number \| undefined): MaskedArray`	Join a sequence of arrays along a new axis.
`std`function	`np.ma.std` signature `np.ma.std(a: ArrayLike \| MaskedArray, opts?: { axis?: number \| null \| undefined; keepdims?: boolean \| undefined; ddof?: number \| undefined; } \| undefined): NDArray \| MaskedArray`	Returns the standard deviation of the array elements along given axis.
`subtract`function	`np.ma.subtract` signature `np.ma.subtract(a: ArrayLike \| MaskedArray, b: ArrayLike \| MaskedArray): MaskedArray`	Subtract arguments, element-wise.
`sum`function	`np.ma.sum` signature `np.ma.sum(a: ArrayLike \| MaskedArray, opts?: { axis?: number \| null \| undefined; keepdims?: boolean \| undefined; dtype?: DTypeLike \| undefined; } \| undefined): NDArray \| MaskedArray`	Return the sum of the array elements over the given axis.
`swapaxes`function	`np.ma.swapaxes` signature `np.ma.swapaxes(a: ArrayLike \| MaskedArray, axis1: number, axis2: number): MaskedArray`	Return a view of the array with `axis1` and `axis2` interchanged.
`take`function	`np.ma.take` signature `np.ma.take(a: ArrayLike \| MaskedArray, indices: NDArray \| number[], axis?: number \| null \| undefined): MaskedArray`
`tan`function	`np.ma.tan` signature `np.ma.tan(a: ArrayLike \| MaskedArray): MaskedArray`	Compute tangent element-wise.
`tanh`function	`np.ma.tanh` signature `np.ma.tanh(a: ArrayLike \| MaskedArray): MaskedArray`	Hyperbolic tangent, element-wise.
`trace`function	`np.ma.trace` signature `np.ma.trace(a: MaOperand, offset?: number \| undefined, axis1?: number \| undefined, axis2?: number \| undefined): NDArray \| MaskedArray`	Return the sum along diagonals of the array.
`transpose`function	`np.ma.transpose` signature `np.ma.transpose(a: ArrayLike \| MaskedArray, axes?: number[] \| undefined): MaskedArray`	Permute the dimensions of an array.
`true_divide`function	`np.ma.true_divide` signature `np.ma.true_divide(a: ArrayLike \| MaskedArray, b: ArrayLike \| MaskedArray): MaskedArray`	Divide arguments element-wise.
`union1d`function	`np.ma.union1d` signature `np.ma.union1d(ar1: ArrayLike \| MaskedArray, ar2: ArrayLike \| MaskedArray): NDArray`	Union of two arrays.
`unique`function	`np.ma.unique` signature `np.ma.unique(a: ArrayLike \| MaskedArray): NDArray`	Finds the unique elements of an array.
`vander`function	`np.ma.vander` signature `np.ma.vander(x: MaOperand, N?: number \| undefined, increasing?: boolean \| undefined): MaskedArray`	Generate a Vandermonde matrix.
`var`function	`np.ma.var` signature `np.ma.var(a: ArrayLike \| MaskedArray, opts?: { axis?: number \| null \| undefined; keepdims?: boolean \| undefined; ddof?: number \| undefined; } \| undefined): NDArray \| MaskedArray`	Compute the variance along the specified axis.
`vstack`function	`np.ma.vstack` signature `np.ma.vstack(tup: MaOperand[]): MaskedArray`	Stack arrays in sequence vertically (row wise).
`where`function	`np.ma.where` signature `np.ma.where(condition: MaOperand, x?: MaOperand \| undefined, y?: MaOperand \| undefined): NDArray \| MaskedArray`	Return a masked array with elements from `x` or `y`, depending on condition.
`zeros`function	`np.ma.zeros` signature `np.ma.zeros(shape: number \| Shape, opts?: { dtype?: DTypeLike \| undefined; } \| undefined): MaskedArray`	Return a new array of given shape and type, filled with zeros.
`zeros_like`function	`np.ma.zeros_like` signature `np.ma.zeros_like(a: NDArray \| MaskedArray): MaskedArray`	Return an array of zeros with the same shape and type as a given array.

numpy.ndarray# 62 / 62

NumPy	numera	Summary
`all`method	`a.all` documented signature `a.all(opts?: AllAnyOptions \| undefined): NDArray`	Returns True if all elements evaluate to True.
`any`method	`a.any` documented signature `a.any(opts?: AllAnyOptions \| undefined): NDArray`	Returns True if any of the elements of `a` evaluate to True.
`argmax`method	`a.argmax` signature `a.argmax(opts?: { axis?: number \| null \| undefined; keepdims?: boolean \| undefined; } \| undefined): NDArray`	Return indices of the maximum values along the given axis.
`argmin`method	`a.argmin` signature `a.argmin(opts?: { axis?: number \| null \| undefined; keepdims?: boolean \| undefined; } \| undefined): NDArray`	Return indices of the minimum values along the given axis.
`argpartition`method	`a.argpartition` signature `a.argpartition(kth: Kth, opts?: PartitionOptions \| undefined): NDArray`	Returns the indices that would partition this array.
`argsort`method	`a.argsort` signature `a.argsort(opts?: SortOptions \| undefined): NDArray`	Returns the indices that would sort this array.
`astype`method	`a.astype` documented signature `a.astype(dt: DTypeLike, opts?: AstypeOptions \| undefined): NDArray`	Copy of the array, cast to a specified type.
`base`attribute	`a.base` documented signature `a.base: NDArray \| null`	Base object if memory is from some other object.
`byteswap`method	`a.byteswap` documented signature `a.byteswap(opts?: { inplace?: boolean \| undefined; } \| undefined): NDArray`	Swap the bytes of the array elements
`choose`method	`a.choose` documented signature `a.choose(choices: NDArray \| readonly Operand[], opts?: ChooseOptions \| undefined): NDArray`	Use an index array to construct a new array from a set of choices.
`clip`method	`a.clip` documented signature `a.clip(min?: Operand \| null \| undefined, max?: Operand \| null \| undefined, opts?: ClipOptions \| undefined): NDArray`	Return an array whose values are limited to `[min, max]`. One of max or min must be given.
`compress`method	`a.compress` documented signature `a.compress(condition: ArrayLike, axis?: number \| AxisOptions \| null \| undefined): NDArray`	Return selected slices of this array along given axis.
`conj`method	`a.conj` signature `a.conj(): NDArray`	Complex-conjugate all elements.
`conjugate`method	`a.conjugate` signature `a.conjugate(): NDArray`	Return the complex conjugate, element-wise.
`copy`method	`a.copy` documented signature `a.copy(opts?: OrderOptions \| undefined): NDArray`	Return a copy of the array.
`cumprod`method	`a.cumprod` signature `a.cumprod(opts?: CumsumOptions \| undefined): NDArray a.cumprod(axis?: number \| CumOptions \| null \| undefined, opts?: CumOptions \| undefined): NDArray`	Return the cumulative product of the elements along the given axis.
`cumsum`method	`a.cumsum` signature `a.cumsum(opts?: CumsumOptions \| undefined): NDArray a.cumsum(axis?: number \| CumOptions \| null \| undefined, opts?: CumOptions \| undefined): NDArray`	Return the cumulative sum of the elements along the given axis.
`diagonal`method	`a.diagonal` documented signature `a.diagonal(opts?: number \| DiagonalOptions \| undefined): NDArray`	Return specified diagonals. In NumPy 1.9 the returned array is a read-only view instead of a copy as in previous NumPy versions. In a future version the read-only restriction will be removed.
`dot`method	`a.dot` documented signature `a.dot(b: ArrayLike): NDArray`	Refer to :func:`numpy.dot` for full documentation.
`dtype`attribute	`a.dtype` documented signature `a.dtype: DType`	Data-type of the array's elements.
`fill`method	`a.fill` documented signature `a.fill(value: number \| bigint \| boolean \| NDArray \| ComplexLike): void`	Fill the array with a scalar value.
`flags`attribute	`a.flags` documented signature `a.flags: ArrayFlags`	Information about the memory layout of the array.
`flat`attribute	`a.flat` documented signature `a.flat: FlatIter`	A 1-D iterator over the array.
`flatten`method	`a.flatten` signature `a.flatten(opts?: OrderOptions \| undefined): NDArray`	Return a copy of the array collapsed into one dimension.
`imag`attribute	`a.imag` signature `a.imag: NDArray`	The imaginary part of the array.
`item`method	`a.item` documented signature `a.item(...index: number[]): number \| boolean \| Complex`	Copy an element of an array to a standard Python scalar and return it.
`itemsize`attribute	`a.itemSize` documented signature `a.itemSize: number`	Length of one array element in bytes.
`max`method	`a.max` signature `a.max(opts?: Omit<MethodReduceOptions, "dtype" \| "ddof"> \| undefined): NDArray`	Return the maximum along a given axis.
`mean`method	`a.mean` signature `a.mean(opts?: Omit<MethodReduceOptions, "initial" \| "ddof"> \| undefined): NDArray`	Returns the average of the array elements along given axis.
`min`method	`a.min` signature `a.min(opts?: Omit<MethodReduceOptions, "dtype" \| "ddof"> \| undefined): NDArray`	Return the minimum along a given axis.
`mT`attribute	`a.mT` documented signature `a.mT: NDArray`	View of the matrix transposed array.
`nbytes`attribute	`a.nbytes` documented signature `a.nbytes: number`	Total bytes consumed by the elements of the array.
`ndim`attribute	`a.ndim` documented signature `a.ndim: number`	Number of array dimensions.
`nonzero`method	`a.nonzero` documented signature `a.nonzero(): NDArray[]`	Return the indices of the elements that are non-zero.
`partition`method	`a.partition` signature `a.partition(kth: Kth, opts?: (Omit<PartitionOptions, "axis"> & { axis?: number \| undefined; }) \| undefined): void`	Partially sorts the elements in the array in such a way that the value of the element in k-th position is in the position it would be in a sorted array. In the output array, all elements smaller than the k-th element are located to the left of this element and all equal or greater are located to its right. The ordering of the elements in the two partitions on the either side of the k-th element in
`prod`method	`a.prod` signature `a.prod(opts?: MethodReduceOptions \| undefined): NDArray`	Return the product of the array elements over the given axis
`put`method	`a.put` documented signature `a.put(ind: ArrayLike, v: ArrayLike, opts?: PutOptions \| undefined): void`	Set `a.flat[n] = values[n]` for all `n` in indices.
`ravel`method	`a.ravel` signature `a.ravel(opts?: OrderOptions \| undefined): NDArray`	Return a flattened array.
`real`attribute	`a.real` signature `a.real: NDArray`	The real part of the array.
`repeat`method	`a.repeat` documented signature `a.repeat(repeats: number \| NDArray \| readonly number[], axis?: number \| null \| undefined): NDArray`	Repeat elements of an array.
`reshape`method	`a.reshape` documented signature `a.reshape(shape: number \| Shape, ...rest: (number \| OrderOptions)[]): NDArray`	Returns an array containing the same data with a new shape.
`resize`method	`a.resize` documented signature `a.resize(newShape: number \| readonly number[], options?: ResizeOptions \| undefined): void a.resize(...dims: number[]): void`	Change shape and size of array in-place.
`round`method	`a.round` documented signature `a.round(decimals?: number \| undefined, opts?: RoundOptions \| undefined): NDArray`	Return `a` with each element rounded to the given number of decimals.
`searchsorted`method	`a.searchsorted` signature `a.searchsorted(v: number \| bigint \| boolean \| NDArray \| Complex \| { readonly re: number; readonly im?: number \| undefined; } \| readonly NestedArray[], opts?: SearchsortedOptions \| undefined): NDArray`	Find indices where elements of `v` should be inserted in `a` to maintain order.
`setflags`method	`a.setflags` documented signature `a.setflags(opts?: { write?: boolean \| null \| undefined; align?: boolean \| null \| undefined; uic?: boolean \| null \| undefined; } \| undefined): void`	Set array flags WRITEABLE, ALIGNED, WRITEBACKIFCOPY, respectively.
`shape`attribute	`a.shape` documented signature `a.shape: number[]`	Tuple of array dimensions.
`size`attribute	`a.size` documented signature `a.size: number`	Number of elements in the array.
`sort`method	`a.sort` signature `a.sort(opts?: (Omit<SortOptions, "axis"> & { axis?: number \| undefined; }) \| undefined): void`	Sort an array in-place. Refer to `numpy.sort` for full documentation.
`squeeze`method	`a.squeeze` signature `a.squeeze(axis?: number \| readonly number[] \| undefined): NDArray`	Remove axes of length one from `a`.
`std`method	`a.std` signature `a.std(opts?: Omit<MethodReduceOptions, "initial"> \| undefined): NDArray`	Returns the standard deviation of the array elements along given axis.
`strides`attribute	`a.strides` documented signature `a.strides: number[]`	Tuple of bytes to step in each dimension when traversing an array.
`sum`method	`a.sum` signature `a.sum(opts?: MethodReduceOptions \| undefined): NDArray`	Return the sum of the array elements over the given axis.
`swapaxes`method	`a.swapAxes` signature `a.swapAxes(axis1: number, axis2: number): NDArray`	Return a view of the array with `axis1` and `axis2` interchanged.
`T`attribute	`a.T` documented signature `a.T: NDArray`	View of the transposed array.
`take`method	`a.take` documented signature `a.take(indices: ArrayLike, axis?: number \| TakeOptions \| null \| undefined, opts?: TakeOptions \| undefined): NDArray`	Return an array formed from the elements of `a` at the given indices.
`tobytes`method	`a.tobytes` documented signature `a.tobytes(opts?: OrderOptions \| undefined): Uint8Array<ArrayBufferLike>`	Construct Python bytes containing the raw data bytes in the array.
`tofile`method	`a.tofile` documented signature `a.tofile(file: string \| URL \| null, options?: TofileOptions \| undefined): any`	Write array to a file as text or binary (default).
`tolist`method	`a.tolist` documented signature `a.tolist(): NestedArray`	Return the array as an `a.ndim`-levels deep nested list of Python scalars.
`trace`method	`a.trace` documented signature `a.trace(opts?: number \| TraceOptions \| undefined): NDArray`	Return the sum along diagonals of the array.
`transpose`method	`a.transpose` documented signature `a.transpose(...axes: number[] \| [readonly number[]]): NDArray`	Returns a view of the array with axes transposed.
`var`method	`a.var` signature `a.var(opts?: Omit<MethodReduceOptions, "initial"> \| undefined): NDArray`	Returns the variance of the array elements, along given axis.
`view`method	`a.view` documented signature `a.view(dt?: DTypeLike \| undefined): NDArray`	New view of array with the same data.

numpy# 431 / 431

NumPy	numera	Summary
`abs`ufunc	`np.abs` documented signature `np.abs(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Calculate the absolute value element-wise.
`absolute`ufunc	`np.absolute` documented signature `np.absolute(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Calculate the absolute value element-wise.
`acos`ufunc	`np.acos` documented signature `np.acos(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Trigonometric inverse cosine, element-wise.
`acosh`ufunc	`np.acosh` documented signature `np.acosh(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Inverse hyperbolic cosine, element-wise.
`add`ufunc	`np.add` documented signature `np.add(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Add arguments element-wise.
`all`function	`np.all` documented signature `np.all(a: ArrayLike, opts?: AllAnyOptions \| undefined): NDArray`	Test whether all array elements along a given axis evaluate to True.
`allclose`function	`np.allclose` documented signature `np.allclose(a: CloseOperand, b: CloseOperand, opts?: IscloseOptions \| undefined): boolean`	Returns True if two arrays are element-wise equal within a tolerance.
`amax`function	`np.amax` signature `np.amax(a: ArrayLike, opts?: Omit<ReduceOptions, "dtype"> \| undefined): NDArray`	Return the maximum of an array or maximum along an axis.
`amin`function	`np.amin` signature `np.amin(a: ArrayLike, opts?: Omit<ReduceOptions, "dtype"> \| undefined): NDArray`	Return the minimum of an array or minimum along an axis.
`angle`function	`np.angle` documented signature `np.angle(z: ArrayLike, deg?: boolean \| undefined): NDArray`	Return the angle of the complex argument.
`any`function	`np.any` documented signature `np.any(a: ArrayLike, opts?: AllAnyOptions \| undefined): NDArray`	Test whether any array element along a given axis evaluates to True.
`append`function	`np.append` documented signature `np.append(arr: ArrayLike, values: ArrayLike, axis?: number \| null \| undefined): NDArray`	Append values to the end of an array.
`apply_along_axis`function	`np.applyAlongAxis` documented signature `np.applyAlongAxis<A extends unknown[]>(func1d: (lane: NDArray, ...args: A) => ArrayLike, axis: number, arr: ArrayLike, ...args: A): NDArray`	Apply a function to 1-D slices along the given axis.
`apply_over_axes`function	`np.applyOverAxes` documented signature `np.applyOverAxes(func: (a: NDArray, axis: number) => ArrayLike, a: ArrayLike, axes: number \| readonly number[]): NDArray`	Apply a function repeatedly over multiple axes.
`arange`function	`np.arange` documented signature `np.arange(startOrStop: number, stop?: number \| undefined, step?: number \| undefined, options?: ArrayOptions \| undefined): NDArray`	Return evenly spaced values within a given interval.
`arccos`ufunc	`np.arccos` documented signature `np.arccos(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Trigonometric inverse cosine, element-wise.
`arccosh`ufunc	`np.arccosh` documented signature `np.arccosh(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Inverse hyperbolic cosine, element-wise.
`arcsin`ufunc	`np.arcsin` documented signature `np.arcsin(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Inverse sine, element-wise.
`arcsinh`ufunc	`np.arcsinh` documented signature `np.arcsinh(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Inverse hyperbolic sine, element-wise.
`arctan`ufunc	`np.arctan` documented signature `np.arctan(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Trigonometric inverse tangent, element-wise.
`arctan2`ufunc	`np.arctan2` documented signature `np.arctan2(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Element-wise arc tangent of `x1/x2` choosing the quadrant correctly.
`arctanh`ufunc	`np.arctanh` documented signature `np.arctanh(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Inverse hyperbolic tangent, element-wise.
`argmax`function	`np.argmax` documented signature `np.argmax(a: ArrayLike, opts?: ArgReduceOptions \| undefined): NDArray`	Returns the indices of the maximum values along an axis.
`argmin`function	`np.argmin` documented signature `np.argmin(a: ArrayLike, opts?: ArgReduceOptions \| undefined): NDArray`	Returns the indices of the minimum values along an axis.
`argpartition`function	`np.argpartition` documented signature `np.argpartition(a: ArrayLike, kth: Kth, opts?: PartitionOptions \| undefined): NDArray`	Perform an indirect partition along the given axis using the algorithm specified by the `kind` keyword. It returns an array of indices of the same shape as `a` that index data along the given axis in partitioned order.
`argsort`function	`np.argsort` documented signature `np.argsort(a: ArrayLike, opts?: SortOptions \| undefined): NDArray`	Returns the indices that would sort an array.
`argwhere`function	`np.argwhere` documented signature `np.argwhere(a: ArrayLike): NDArray`	Find the indices of array elements that are non-zero, grouped by element.
`around`function	`np.around` documented signature `np.around(a: ArrayLike, decimals?: number \| undefined, opts?: RoundOptions \| undefined): NDArray`	Round an array to the given number of decimals.
`array`function	`np.array` documented signature `np.array(data: NDArray \| NestedArray, options?: ArrayCopyOptions \| undefined): NDArray`	Create an array.
`array_equal`function	`np.arrayEqual` documented signature `np.arrayEqual(a1: ArrayLike, a2: ArrayLike, opts?: { equalNan?: boolean \| undefined; } \| undefined): boolean`	True if two arrays have the same shape and elements, False otherwise.
`array_equiv`function	`np.arrayEquiv` documented signature `np.arrayEquiv(a1: ArrayLike, a2: ArrayLike): boolean`	Returns True if input arrays are shape consistent and all elements equal.
`array_repr`function	`np.arrayRepr` documented signature `np.arrayRepr(a: AnyArray, opts?: ArrayReprOptions \| undefined): string`	Return the string representation of an array.
`array_split`function	`np.arraySplit` signature `np.arraySplit(a: ArrayLike, indicesOrSections: number \| NDArray \| readonly number[], axis?: number \| undefined): NDArray[]`	Split an array into multiple sub-arrays.
`array_str`function	`np.arrayStr` documented signature `np.arrayStr(a: AnyArray, opts?: ArrayReprOptions \| undefined): string`	Return a string representation of the data in an array.
`array2string`function	`np.array2string` documented signature `np.array2string(a: AnyArray, opts?: Array2StringOptions \| undefined): string`	Return a string representation of an array.
`asanyarray`function	`np.asanyarray` documented signature `np.asanyarray(a: ArrayLike, options?: { dtype?: DTypeLike \| undefined; } \| undefined): NDArray`	Convert the input to an ndarray, but pass ndarray subclasses through.
`asarray`function	`np.asarray` documented signature `np.asarray(data: NDArray \| NestedArray, options?: ArrayOptions \| undefined): NDArray`	Convert the input to an array.
`asarray_chkfinite`function	`np.asarrayChkfinite` documented signature `np.asarrayChkfinite(a: ArrayLike, options?: { dtype?: DTypeLike \| undefined; } \| undefined): NDArray`	Convert the input to an array, checking for NaNs or Infs.
`ascontiguousarray`function	`np.ascontiguousarray` documented signature `np.ascontiguousarray(a: NDArray \| NestedArray, options?: ArrayOptions \| undefined): NDArray`	Return a contiguous array (ndim >= 1) in memory (C order).
`asfortranarray`function	`np.asfortranarray` documented signature `np.asfortranarray(a: NDArray \| NestedArray, options?: ArrayOptions \| undefined): NDArray`	Return an array (ndim >= 1) laid out in Fortran order in memory.
`asin`ufunc	`np.asin` documented signature `np.asin(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Inverse sine, element-wise.
`asinh`ufunc	`np.asinh` documented signature `np.asinh(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Inverse hyperbolic sine, element-wise.
`astype`function	`np.astype` documented signature `np.astype(x: NDArray, dtype: DTypeLike, options?: Pick<AstypeOptions, "copy"> \| undefined): NDArray`	Copies an array to a specified data type.
`atan`ufunc	`np.atan` documented signature `np.atan(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Trigonometric inverse tangent, element-wise.
`atan2`ufunc	`np.atan2` documented signature `np.atan2(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Element-wise arc tangent of `x1/x2` choosing the quadrant correctly.
`atanh`ufunc	`np.atanh` documented signature `np.atanh(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Inverse hyperbolic tangent, element-wise.
`atleast_1d`function	`np.atleast1d` documented signature `np.atleast1d(a: ArrayLike): NDArray np.atleast1d(...arys: ArrayLike[]): NDArray \| NDArray[]`	Convert inputs to arrays with at least one dimension.
`atleast_2d`function	`np.atleast2d` signature `np.atleast2d(a: ArrayLike): NDArray np.atleast2d(...arys: ArrayLike[]): NDArray \| NDArray[]`	View inputs as arrays with at least two dimensions.
`atleast_3d`function	`np.atleast3d` signature `np.atleast3d(a: ArrayLike): NDArray np.atleast3d(...arys: ArrayLike[]): NDArray \| NDArray[]`	View inputs as arrays with at least three dimensions.
`average`function	`np.average` documented signature `np.average(a: ArrayLike, opts: AverageOptions & { returned: true; }): [NDArray, NDArray] np.average(a: ArrayLike, opts?: AverageOptions \| undefined): NDArray`	Compute the weighted average along the specified axis.
`bartlett`function	`np.bartlett` documented signature `np.bartlett(M: number \| bigint \| NDArray): NDArray`	Return the Bartlett window.
`base_repr`function	`np.baseRepr` documented signature `np.baseRepr(number: IntegerLike, base?: number \| undefined, padding?: number \| undefined): string`	Return a string representation of a number in the given base system.
`binary_repr`function	`np.binaryRepr` documented signature `np.binaryRepr(num: IntegerLike, options?: BinaryReprOptions \| undefined): string`	Return the binary representation of the input number as a string.
`bincount`function	`np.bincount` documented signature `np.bincount(x: ArrayLike, opts?: BincountOptions \| undefined): NDArray`	Count number of occurrences of each value in array of non-negative ints.
`bitwise_and`ufunc	`np.bitwiseAnd` documented signature `np.bitwiseAnd(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Compute the bit-wise AND of two arrays element-wise.
`bitwise_count`ufunc	`np.bitwiseCount` documented signature `np.bitwiseCount(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Computes the number of 1-bits in the absolute value of `x`. Analogous to the builtin `int.bit_count` or `popcount` in C++.
`bitwise_invert`ufunc	`np.bitwiseInvert` signature `np.bitwiseInvert(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Compute bit-wise inversion, or bit-wise NOT, element-wise.
`bitwise_left_shift`ufunc	`np.bitwiseLeftShift` documented signature `np.bitwiseLeftShift(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Shift the bits of an integer to the left.
`bitwise_not`ufunc	`np.bitwiseNot` signature `np.bitwiseNot(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Compute bit-wise inversion, or bit-wise NOT, element-wise.
`bitwise_or`ufunc	`np.bitwiseOr` documented signature `np.bitwiseOr(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Compute the bit-wise OR of two arrays element-wise.
`bitwise_right_shift`ufunc	`np.bitwiseRightShift` documented signature `np.bitwiseRightShift(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Shift the bits of an integer to the right.
`bitwise_xor`ufunc	`np.bitwiseXor` documented signature `np.bitwiseXor(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Compute the bit-wise XOR of two arrays element-wise.
`blackman`function	`np.blackman` documented signature `np.blackman(M: number \| bigint \| NDArray): NDArray`	Return the Blackman window.
`block`function	`np.block` documented signature `np.block(arrays: BlockArg): NDArray`	Assemble an nd-array from nested lists of blocks.
`bool`class	`np.bool` signature `np.bool: DType`	Boolean type (True or False), stored as a byte.
`bool_`class	`np.bool` signature `np.bool: DType`	Boolean type (True or False), stored as a byte.
`broadcast_arrays`function	`np.broadcastArrays` documented signature `np.broadcastArrays(...args: ArrayLike[]): NDArray[]`	Broadcast any number of arrays against each other.
`broadcast_shapes`function	`np.broadcastShapes` documented signature `np.broadcastShapes(...shapes: (number \| Shape)[]): number[]`	Broadcast the input shapes into a single shape.
`broadcast_to`function	`np.broadcastTo` documented signature `np.broadcastTo(a: ArrayLike, shape: number \| Shape): NDArray`	Broadcast an array to a new shape.
`busday_count`function	`np.busday_count` documented signature `np.busday_count(begindates: string \| number \| bigint \| DatetimeArray, enddates: string \| number \| bigint \| DatetimeArray, options?: BusdayCountOptions \| undefined): number \| number[]`	Counts the number of valid days between `begindates` and `enddates`, not including the day of `enddates`.
`busday_offset`function	`np.busday_offset` documented signature `np.busday_offset(dates: string \| number \| bigint \| DatetimeArray \| (string \| number \| bigint \| DatetimeArray)[], offsets: number \| number[], options?: BusdayOffsetOptions \| undefined): DatetimeArray \| DatetimeArray[] \| null`	First adjusts the date to fall on a valid day according to the `roll` rule, then applies offsets to the given dates counted in valid days.
`byte`class	`np.byte` documented signature `np.byte: DType`	Signed integer type, compatible with C `char`.
`bytes_`class	`np.bytes_` documented signature `np.bytes_: "bytes"`	A byte string.
`c_`constant	`np.c_` documented signature `np.c_(...items: ConcatItem[]): NDArray`	Translates slice objects to concatenation along the second axis.
`can_cast`function	`np.canCast` documented signature `np.canCast(from: DTypeLike \| { readonly dtype: DType; }, to: DTypeLike, casting?: Casting \| undefined): boolean`	Returns True if cast between data types can occur according to the casting rule.
`cbrt`ufunc	`np.cbrt` documented signature `np.cbrt(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Return the cube-root of an array, element-wise.
`cdouble`class	`np.cdouble` documented signature `np.cdouble: DType`	Complex number type composed of two double-precision floating-point numbers, compatible with Python :class:`complex`.
`ceil`ufunc	`np.ceil` documented signature `np.ceil(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Return the ceiling of the input, element-wise.
`choose`function	`np.choose` documented signature `np.choose(a: ArrayLike, choices: NDArray \| readonly Operand[], opts?: ChooseOptions \| undefined): NDArray`	Construct an array from an index array and a list of arrays to choose from.
`clip`function	`np.clip` documented signature `np.clip(a: ArrayLike, min?: Bound, max?: Bound, opts?: ClipOptions \| undefined): NDArray`	Clip (limit) the values in an array.
`column_stack`function	`np.columnStack` documented signature `np.columnStack(arrays: Sequence): NDArray`	Stack 1-D arrays as columns into a 2-D array.
`common_type`function	`np.commonType` documented signature `np.commonType(...arrays: (NDArray \| NestedArray)[]): DType`	Return a scalar type which is common to the input arrays.
`complex128`class	`np.complex128` signature `np.complex128: DType`	Complex number type composed of two double-precision floating-point numbers, compatible with Python :class:`complex`.
`complex64`class	`np.complex64` signature `np.complex64: DType`	Complex number type composed of two single-precision floating-point numbers.
`compress`function	`np.compress` documented signature `np.compress(condition: ArrayLike, a: ArrayLike, axis?: number \| AxisOptions \| null \| undefined): NDArray`	Return selected slices of an array along given axis.
`concat`function	`np.concat` signature `np.concat(arrays: Sequence, axis?: number \| ConcatenateOptions \| null \| undefined, options?: JoinOptions \| undefined): NDArray`	Join a sequence of arrays along an existing axis.
`concatenate`function	`np.concatenate` documented signature `np.concatenate(arrays: Sequence, axis?: number \| ConcatenateOptions \| null \| undefined, options?: JoinOptions \| undefined): NDArray`	Join a sequence of arrays along an existing axis.
`conj`ufunc	`np.conj` documented signature `np.conj(a: ArrayLike): NDArray`	Return the complex conjugate, element-wise.
`conjugate`ufunc	`np.conjugate` documented signature `np.conjugate(a: ArrayLike): NDArray`	Return the complex conjugate, element-wise.
`convolve`function	`np.convolve` documented signature `np.convolve(a: ArrayLike, v: ArrayLike, mode?: ConvMode \| undefined): NDArray`	Returns the discrete, linear convolution of two one-dimensional sequences.
`copy`function	`np.copy` documented signature `np.copy(a: NDArray \| NestedArray, options?: { order?: MemoryOrder \| null \| undefined; } \| undefined): NDArray`	Return an array copy of the given object.
`copysign`ufunc	`np.copysign` documented signature `np.copysign(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Change the sign of x1 to that of x2, element-wise.
`copyto`function	`np.copyto` documented signature `np.copyto(dst: NDArray, src: number \| bigint \| boolean \| NDArray \| Complex \| { readonly re: number; readonly im?: number \| undefined; } \| readonly NestedArray[], options?: CopytoOptions \| undefined): void`	Copies values from one array to another, broadcasting as necessary.
`corrcoef`function	`np.corrcoef` documented signature `np.corrcoef(x: ArrayLike, opts?: CorrcoefOptions \| undefined): NDArray`	Return Pearson product-moment correlation coefficients.
`correlate`function	`np.correlate` documented signature `np.correlate(a: ArrayLike, v: ArrayLike, mode?: ConvMode \| undefined): NDArray`	Cross-correlation of two 1-dimensional sequences.
`cos`ufunc	`np.cos` documented signature `np.cos(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Cosine element-wise.
`cosh`ufunc	`np.cosh` documented signature `np.cosh(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Hyperbolic cosine, element-wise.
`count_nonzero`function	`np.countNonzero` documented signature `np.countNonzero(a: ArrayLike, opts?: CountNonzeroOptions \| undefined): NDArray`	Counts the number of non-zero values in the array `a`.
`cov`function	`np.cov` documented signature `np.cov(m: ArrayLike, opts?: CovOptions \| undefined): NDArray`	Estimate a covariance matrix, given data and weights.
`cross`function	`np.cross` documented signature `np.cross(a: ArrayLike, b: ArrayLike, opts?: CrossOptions \| undefined): NDArray`	Return the cross product of two (arrays of) vectors.
`csingle`class	`np.csingle` documented signature `np.csingle: DType`	Complex number type composed of two single-precision floating-point numbers.
`cumprod`function	`np.cumprod` documented signature `np.cumprod(a: NDArray \| NestedArray, axis?: number \| null \| undefined, opts?: { dtype?: DTypeLike \| null \| undefined; } \| undefined): NDArray`	Return the cumulative product of elements along a given axis.
`cumsum`function	`np.cumsum` documented signature `np.cumsum(a: NDArray \| NestedArray, axis?: number \| null \| undefined, opts?: { dtype?: DTypeLike \| null \| undefined; } \| undefined): NDArray`	Return the cumulative sum of the elements along a given axis.
`cumulative_prod`function	`np.cumulativeProd` documented signature `np.cumulativeProd(x: ArrayLike, opts?: CumulativeOptions \| undefined): NDArray`	Return the cumulative product of elements along a given axis.
`cumulative_sum`function	`np.cumulativeSum` documented signature `np.cumulativeSum(x: ArrayLike, opts?: CumulativeOptions \| undefined): NDArray`	Return the cumulative sum of the elements along a given axis.
`datetime_as_string`function	`np.datetime_as_string` documented signature `np.datetime_as_string(arr: DatetimeArray, options?: string \| DatetimeAsStringOptions \| undefined): string \| string[]`	Convert an array of datetimes into an array of strings.
`datetime64`class	`np.datetime64` documented signature `np.datetime64(value: DatetimeInput, unit?: string \| undefined): DatetimeArray`	If created from a 64-bit integer, it represents an offset from `1970-01-01T00:00:00`. If created from string, the string can be in ISO 8601 date or datetime format.
`deg2rad`ufunc	`np.deg2rad` documented signature `np.deg2rad(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Convert angles from degrees to radians.
`degrees`ufunc	`np.degrees` documented signature `np.degrees(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Convert angles from radians to degrees.
`delete`function	`np.delete` documented signature `np.delete(arr: ArrayLike, obj: EditIndex, axis?: number \| null \| undefined): NDArray`	Return a new array with sub-arrays along an axis deleted. For a one dimensional array, this returns those entries not returned by `arr[obj]`.
`diag`function	`np.diag` documented signature `np.diag(v: ArrayInput, k?: number \| undefined): NDArray`	Extract a diagonal or construct a diagonal array.
`diag_indices`function	`np.diagIndices` documented signature `np.diagIndices(n: number, ndim?: number \| undefined): NDArray[]`	Return the indices to access the main diagonal of an array.
`diag_indices_from`function	`np.diagIndicesFrom` documented signature `np.diagIndicesFrom(arr: NDArray): NDArray[]`	Return the indices to access the main diagonal of an n-dimensional array.
`diagflat`function	`np.diagflat` documented signature `np.diagflat(v: ArrayInput, k?: number \| undefined): NDArray`	Create a two-dimensional array with the flattened input as a diagonal.
`diagonal`function	`np.diagonal` documented signature `np.diagonal(a: ArrayLike, opts?: number \| DiagonalOptions \| undefined): NDArray`	Return specified diagonals.
`diff`function	`np.diff` documented signature `np.diff(a: ArrayLike, n?: number \| DiffOptions \| undefined, axis?: number \| undefined): NDArray`	Calculate the n-th discrete difference along the given axis.
`digitize`function	`np.digitize` documented signature `np.digitize(x: ArrayLike, bins: ArrayLike, right?: boolean \| undefined): NDArray`	Return the indices of the bins to which each value in input array belongs.
`divide`ufunc	`np.divide` documented signature `np.divide(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Divide arguments element-wise.
`divmod`ufunc	`np.divmod` documented signature `np.divmod(a: Operand, b: Operand, opts?: MultiUfuncOptions \| undefined): [NDArray, NDArray]`	Return element-wise quotient and remainder simultaneously.
`dot`function	`np.dot` documented signature `np.dot(a: ArrayLike, b: ArrayLike): NDArray`	Dot product of two arrays. Specifically,
`double`class	`np.double` documented signature `np.double: DType`	Double-precision floating-point number type, compatible with Python :class:`float` and C `double`.
`dsplit`function	`np.dsplit` signature `np.dsplit(a: ArrayLike, indicesOrSections: number \| NDArray \| readonly number[]): NDArray[]`	Split array into multiple sub-arrays along the 3rd axis (depth).
`dstack`function	`np.dstack` documented signature `np.dstack(arrays: Sequence): NDArray`	Stack arrays in sequence depth wise (along third axis).
`dtype`class	`np.dtype` documented signature `np.dtype(like: DTypeLike): DType`	dtype(dtype, align=False, copy=False, **kwargs) --
`e`constant	`np.e` documented signature `np.e: number`
`ediff1d`function	`np.ediff1d` documented signature `np.ediff1d(a: ArrayLike, opts?: Ediff1dOptions \| undefined): NDArray`	The differences between consecutive elements of an array.
`einsum`function	`np.einsum` documented signature `np.einsum(subscripts: string, ...operands: (ArrayLike \| EinsumOptions)[]): NDArray np.einsum(...args: (ArrayLike \| EinsumOptions \| Sublist)[]): NDArray`	Evaluates the Einstein summation convention on the operands.
`einsum_path`function	`np.einsumPath` documented signature `np.einsumPath(subscripts: string, ...operands: (ArrayLike \| EinsumOptions)[]): [EinsumPath, string] np.einsumPath(...args: (ArrayLike \| EinsumOptions \| Sublist)[]): [EinsumPath, string]`	Evaluates the lowest cost contraction order for an einsum expression by considering the creation of intermediate arrays.
`empty`function	`np.empty` documented signature `np.empty(shape: number \| Shape, options?: CreationOptions \| undefined): NDArray`	Return a new array of given shape and type, without initializing entries.
`empty_like`function	`np.emptyLike` signature `np.emptyLike(a: NDArray, options?: LikeOptions \| undefined): NDArray`	Return a new array with the same shape and type as a given array.
`equal`ufunc	`np.equal` documented signature `np.equal(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Return (x1 == x2) element-wise.
`errstate`class	`np.errstate` documented signature `np.errstate<T>(settings: ErrSettings, fn: () => T): T`	Context manager for floating-point error handling.
`euler_gamma`constant	`np.euler_gamma` documented signature `np.euler_gamma: number`
`exp`ufunc	`np.exp` documented signature `np.exp(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Calculate the exponential of all elements in the input array.
`exp2`ufunc	`np.exp2` documented signature `np.exp2(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Calculate `2**p` for all `p` in the input array.
`expand_dims`function	`np.expandDims` documented signature `np.expandDims(a: NDArray, axis: number \| readonly number[]): NDArray`	Expand the shape of an array.
`expm1`ufunc	`np.expm1` documented signature `np.expm1(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Calculate `exp(x) - 1` for all elements in the array.
`extract`function	`np.extract` documented signature `np.extract(condition: ArrayLike, arr: ArrayLike): NDArray`	Return the elements of an array that satisfy some condition.
`eye`function	`np.eye` documented signature `np.eye(n: number, m?: number \| undefined, options?: EyeOptions \| undefined): NDArray`	Return a 2-D array with ones on the diagonal and zeros elsewhere.
`fabs`ufunc	`np.fabs` documented signature `np.fabs(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Compute the absolute values element-wise.
`False_`constant	`np.False_` documented signature `np.False_: false`	bool(value=False, /) --
`fill_diagonal`function	`np.fillDiagonal` documented signature `np.fillDiagonal(a: NDArray, val: ArrayInput, options?: { wrap?: boolean \| undefined; } \| undefined): void`	Fill the main diagonal of the given array of any dimensionality.
`finfo`class	`np.finfo` documented signature `np.finfo(dt: NDArray \| DTypeLike): FInfo`	Machine limits for floating point types.
`fix`function	`np.fix` documented signature `np.fix(x: ArrayLike, opts?: RoundOptions \| undefined): NDArray`	Round to nearest integer towards zero.
`flatnonzero`function	`np.flatnonzero` documented signature `np.flatnonzero(a: ArrayLike): NDArray`	Return indices that are non-zero in the flattened version of a.
`flip`function	`np.flip` documented signature `np.flip(m: ArrayLike, axis?: Axes \| null \| undefined): NDArray`	Reverse the order of elements in an array along the given axis.
`fliplr`function	`np.fliplr` signature `np.fliplr(m: ArrayLike): NDArray`	Reverse the order of elements along axis 1 (left/right).
`flipud`function	`np.flipud` signature `np.flipud(m: ArrayLike): NDArray`	Reverse the order of elements along axis 0 (up/down).
`float_power`ufunc	`np.floatPower` documented signature `np.floatPower(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	First array elements raised to powers from second array, element-wise.
`float16`class	`np.float16` signature `np.float16: DType`	Half-precision floating-point number type.
`float32`class	`np.float32` signature `np.float32: DType`	Single-precision floating-point number type, compatible with C `float`.
`float64`class	`np.float64` signature `np.float64: DType`	Double-precision floating-point number type, compatible with Python :class:`float` and C `double`.
`floor`ufunc	`np.floor` documented signature `np.floor(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Return the floor of the input, element-wise.
`floor_divide`ufunc	`np.floorDivide` documented signature `np.floorDivide(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Return the largest integer smaller or equal to the division of the inputs. It is equivalent to the Python `//` operator and pairs with the Python `%` (`remainder`), function so that `a = a % b + b * (a // b)` up to roundoff.
`fmax`ufunc	`np.fmax` documented signature `np.fmax(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Element-wise maximum of array elements.
`fmin`ufunc	`np.fmin` documented signature `np.fmin(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Element-wise minimum of array elements.
`fmod`ufunc	`np.fmod` documented signature `np.fmod(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Returns the element-wise remainder of division.
`format_float_positional`function	`np.formatFloatPositional` documented signature `np.formatFloatPositional(x: number \| boolean \| NDArray, opts?: FormatFloatPositionalOptions \| undefined): string`	Format a floating-point scalar as a decimal string in positional notation.
`format_float_scientific`function	`np.formatFloatScientific` documented signature `np.formatFloatScientific(x: number \| boolean \| NDArray, opts?: FormatFloatScientificOptions \| undefined): string`	Format a floating-point scalar as a decimal string in scientific notation.
`frexp`ufunc	`np.frexp` documented signature `np.frexp(x: ArrayLike, opts?: MultiUfuncOptions \| undefined): [NDArray, NDArray]`	Decompose the elements of x into mantissa and twos exponent.
`frombuffer`function	`np.frombuffer` documented signature `np.frombuffer(buffer: ArrayBufferLike \| ArrayBufferView<ArrayBufferLike>, options?: FrombufferOptions \| undefined): NDArray`	Interpret a buffer as a 1-dimensional array.
`fromfile`function	`np.fromfile` documented signature `np.fromfile(file: string \| URL \| Uint8Array<ArrayBufferLike>, options?: FromfileOptions \| undefined): NDArray`	Construct an array from data in a text or binary file.
`fromfunction`function	`np.fromfunction` documented signature `np.fromfunction<R>(fn: (...coords: NDArray[]) => R, shape: Shape, options?: { dtype?: DTypeLike \| undefined; } \| undefined): R`	Construct an array by executing a function over each coordinate.
`fromiter`function	`np.fromiter` documented signature `np.fromiter(iterable: Iterable<number \| bigint \| boolean \| ComplexLike>, dtype: DTypeLike, count?: number \| undefined): NDArray`	Create a new 1-dimensional array from an iterable object.
`fromregex`function	`np.fromregex` documented signature `np.fromregex(file: TextSource, regexp: string \| RegExp, dtype: FieldList): Record<string, NDArray>`	Construct an array from a text file, using regular expression parsing.
`fromstring`function	`np.fromstring` documented signature `np.fromstring(text: string, options?: FromstringOptions \| undefined): NDArray`	A new 1-D array initialized from text data in a string.
`full`function	`np.full` documented signature `np.full(shape: number \| Shape, fillValue: number \| bigint \| boolean \| ComplexLike, options?: CreationOptions \| undefined): NDArray`	Return a new array of given shape and type, filled with `fill_value`.
`full_like`function	`np.fullLike` signature `np.fullLike(a: NDArray, fillValue: number \| bigint \| boolean \| ComplexLike, options?: LikeOptions \| undefined): NDArray`	Return a full array with the same shape and type as a given array.
`gcd`ufunc	`np.gcd` documented signature `np.gcd(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Returns the greatest common divisor of `\|x1\|` and `\|x2\|`
`genfromtxt`function	`np.genfromtxt` documented signature `np.genfromtxt(fname: TextSource, options?: GenfromtxtOptions \| undefined): NDArray`	Load data from a text file, with missing values handled as specified.
`geomspace`function	`np.geomspace` documented signature `np.geomspace(start: ScalarLike, stop: ScalarLike, num?: number \| undefined, options?: GeomspaceOptions \| undefined): NDArray`	Return numbers spaced evenly on a log scale (a geometric progression).
`get_printoptions`function	`np.getPrintoptions` documented signature `np.getPrintoptions(): PrintOptions`	Return the current print options.
`geterr`function	`np.geterr` documented signature `np.geterr(): ErrState`	Get the current way of handling floating-point errors.
`gradient`function	`np.gradient` documented signature `np.gradient(f: ArrayLike, ...args: (Spacing \| GradientOptions)[]): NDArray \| NDArray[]`	Return the gradient of an N-dimensional array.
`greater`ufunc	`np.greater` documented signature `np.greater(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Return the truth value of (x1 > x2) element-wise.
`greater_equal`ufunc	`np.greaterEqual` documented signature `np.greaterEqual(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Return the truth value of (x1 >= x2) element-wise.
`half`class	`np.half` documented signature `np.half: DType`	Half-precision floating-point number type.
`hamming`function	`np.hamming` documented signature `np.hamming(M: number \| bigint \| NDArray): NDArray`	Return the Hamming window.
`hanning`function	`np.hanning` documented signature `np.hanning(M: number \| bigint \| NDArray): NDArray`	Return the Hanning window.
`heaviside`ufunc	`np.heaviside` documented signature `np.heaviside(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Compute the Heaviside step function.
`histogram`function	`np.histogram` documented signature `np.histogram(a: ArrayLike, bins?: BinsArg \| undefined, opts?: HistogramOptions \| undefined): HistogramResult`	Compute the histogram of a dataset.
`histogram_bin_edges`function	`np.histogramBinEdges` documented signature `np.histogramBinEdges(a: ArrayLike, bins?: BinsArg \| undefined, opts?: HistogramOptions \| undefined): NDArray`	Function to calculate only the edges of the bins used by the `histogram` function.
`histogram2d`function	`np.histogram2d` documented signature `np.histogram2d(x: ArrayLike, y: ArrayLike, bins?: BinsArg \| [BinsArg, BinsArg] \| undefined, opts?: Omit<Histogram2dOptions, "bins"> \| undefined): Histogram2dResult`	Compute the bi-dimensional histogram of two data samples.
`histogramdd`function	`np.histogramdd` documented signature `np.histogramdd(sample: ArrayLike, bins?: BinsArg \| BinsArg[] \| undefined, opts?: HistogramddOptions \| undefined): HistogramddResult`	Compute the multidimensional histogram of some data.
`hsplit`function	`np.hsplit` signature `np.hsplit(a: ArrayLike, indicesOrSections: number \| NDArray \| readonly number[]): NDArray[]`	Split an array into multiple sub-arrays horizontally (column-wise).
`hstack`function	`np.hstack` documented signature `np.hstack(arrays: Sequence, options?: VHStackOptions \| undefined): NDArray`	Stack arrays in sequence horizontally (column wise).
`hypot`ufunc	`np.hypot` documented signature `np.hypot(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Given the "legs" of a right triangle, return its hypotenuse.
`i0`function	`np.i0` documented signature `np.i0(x: ArrayLike): NDArray`	Modified Bessel function of the first kind, order 0.
`identity`function	`np.identity` documented signature `np.identity(n: number, options?: ArrayOptions \| undefined): NDArray`	Return the identity array.
`iinfo`class	`np.iinfo` documented signature `np.iinfo(dt: NDArray \| DTypeLike): IInfo`	Machine limits for integer types.
`imag`function	`np.imag` documented signature `np.imag(a: ArrayLike): NDArray`	Return the imaginary part of the complex argument.
`index_exp`constant	`np.indexExp` documented signature `np.indexExp(...specs: (string \| number \| boolean \| NDArray \| readonly [] \| readonly [number \| null] \| readonly [number \| null, number \| null] \| readonly [number \| null, number \| null, number \| null] \| null)[]): IndexSpec[]`	A nicer way to build up index tuples for arrays.
`indices`function	`np.indices` documented signature `np.indices(dimensions: readonly number[], options: IndicesOptions & { sparse: true; }): NDArray[] np.indices(dimensions: readonly number[], options?: IndicesOptions \| undefined): NDArray`	Return an array representing the indices of a grid.
`inf`constant	`np.inf` documented signature `np.inf: number`
`inner`function	`np.inner` documented signature `np.inner(a: ArrayLike, b: ArrayLike): NDArray`	Inner product of two arrays.
`insert`function	`np.insert` documented signature `np.insert(arr: ArrayLike, obj: EditIndex, values: ArrayLike, axis?: number \| null \| undefined): NDArray`	Insert values along the given axis before the given indices.
`int_`class	`np.int_` documented signature `np.int_: DType`	Signed integer type, compatible with C `long`.
`int16`class	`np.int16` signature `np.int16: DType`	Signed integer type, compatible with C `short`.
`int32`class	`np.int32` signature `np.int32: DType`	Signed integer type, compatible with C `int`.
`int64`class	`np.int64` signature `np.int64: DType`	Signed integer type, compatible with C `long`.
`int8`class	`np.int8` signature `np.int8: DType`	Signed integer type, compatible with C `char`.
`intc`class	`np.intc` documented signature `np.intc: DType`	Signed integer type, compatible with C `int`.
`interp`function	`np.interp` documented signature `np.interp(x: ArrayLike, xp: ArrayLike, fp: ArrayLike, opts?: InterpOptions \| undefined): NDArray`	One-dimensional linear interpolation for monotonically increasing sample points.
`intersect1d`function	`np.intersect1d` documented signature `np.intersect1d(a: ArrayLike, b: ArrayLike, opts?: { assumeUnique?: boolean \| undefined; returnIndices?: false \| undefined; } \| undefined): NDArray np.intersect1d(a: ArrayLike, b: ArrayLike, opts: { assumeUnique?: boolean \| undefined; returnIndices: true; }): IntersectResult`	Find the intersection of two arrays.
`intp`class	`np.intp` documented signature `np.intp: DType`	Signed integer type, compatible with C `long`.
`invert`ufunc	`np.invert` documented signature `np.invert(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Compute bit-wise inversion, or bit-wise NOT, element-wise.
`is_busday`function	`np.is_busday` documented signature `np.is_busday(dates: string \| number \| bigint \| DatetimeArray \| (string \| number \| bigint \| DatetimeArray)[], options?: IsBusdayOptions \| undefined): boolean \| boolean[]`	Calculates which of the given dates are valid days, and which are not.
`isclose`function	`np.isclose` documented signature `np.isclose(a: CloseOperand, b: CloseOperand, opts?: IscloseOptions \| undefined): NDArray`	Returns a boolean array where two arrays are element-wise equal within a tolerance.
`iscomplex`function	`np.iscomplex` documented signature `np.iscomplex(a: ArrayLike): NDArray`	Returns a bool array, where True if input element is complex.
`iscomplexobj`function	`np.iscomplexobj` documented signature `np.iscomplexobj(a: ArrayLike): boolean`	Check for a complex type or an array of complex numbers.
`isdtype`function	`np.isdtype` documented signature `np.isdtype(dt: DType, kind: string \| DType \| readonly (string \| DType)[]): boolean`	Determine if a provided dtype is of a specified data type `kind`.
`isfinite`ufunc	`np.isfinite` documented signature `np.isfinite(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Test element-wise for finiteness (not infinity and not Not a Number).
`isfortran`function	`np.isfortran` documented signature `np.isfortran(a: NDArray): boolean`	Check if the array is Fortran contiguous but not C contiguous.
`isin`function	`np.isin` documented signature `np.isin(element: ArrayLike, testElements: ArrayLike, opts?: IsinOptions \| undefined): NDArray`	Calculates `element in test_elements`, broadcasting over `element` only. Returns a boolean array of the same shape as `element` that is True where an element of `element` is in `test_elements` and False otherwise.
`isinf`ufunc	`np.isinf` documented signature `np.isinf(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Test element-wise for positive or negative infinity.
`isnan`ufunc	`np.isnan` documented signature `np.isnan(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Test element-wise for NaN and return result as a boolean array.
`isnat`ufunc	`np.isnat` documented signature `np.isnat(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Test element-wise for NaT (not a time) and return result as a boolean array.
`isneginf`function	`np.isneginf` documented signature `np.isneginf(x: ArrayLike, opts?: { out?: NDArray \| null \| undefined; } \| undefined): NDArray`	Test element-wise for negative infinity, return result as bool array.
`isposinf`function	`np.isposinf` documented signature `np.isposinf(x: ArrayLike, opts?: { out?: NDArray \| null \| undefined; } \| undefined): NDArray`	Test element-wise for positive infinity, return result as bool array.
`isreal`function	`np.isreal` documented signature `np.isreal(a: ArrayLike): NDArray`	Returns a bool array, where True if input element is real.
`isrealobj`function	`np.isrealobj` documented signature `np.isrealobj(a: ArrayLike): boolean`	Return True if x is a not complex type or an array of complex numbers.
`isscalar`function	`np.isscalar` documented signature `np.isscalar(x: unknown): boolean`	Returns True if the type of `element` is a scalar type.
`issubdtype`function	`np.issubdtype` documented signature `np.issubdtype(a: DTypeLike \| AbstractDType, b: DTypeLike \| AbstractDType): boolean`	Returns True if first argument is a typecode lower/equal in type hierarchy.
`ix_`function	`np.ix_` documented signature `np.ix_(...seqs: ArrayInput[]): NDArray[]`	Construct an open mesh from multiple sequences.
`kaiser`function	`np.kaiser` documented signature `np.kaiser(M: number \| bigint \| NDArray, beta: number \| NDArray): NDArray`	Return the Kaiser window.
`kron`function	`np.kron` documented signature `np.kron(a: ArrayLike, b: ArrayLike): NDArray`	Kronecker product of two arrays.
`lcm`ufunc	`np.lcm` documented signature `np.lcm(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Returns the lowest common multiple of `\|x1\|` and `\|x2\|`
`ldexp`ufunc	`np.ldexp` documented signature `np.ldexp(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Returns x1 * 2**x2, element-wise.
`left_shift`ufunc	`np.leftShift` documented signature `np.leftShift(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Shift the bits of an integer to the left.
`less`ufunc	`np.less` documented signature `np.less(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Return the truth value of (x1 < x2) element-wise.
`less_equal`ufunc	`np.lessEqual` documented signature `np.lessEqual(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Return the truth value of (x1 <= x2) element-wise.
`lexsort`function	`np.lexsort` documented signature `np.lexsort(keys: NDArray \| readonly ArrayLike[], opts?: { axis?: number \| null \| undefined; } \| undefined): NDArray`	Perform an indirect stable sort using a sequence of keys.
`linspace`function	`np.linspace` documented signature `np.linspace(start: number, stop: number, num?: number \| undefined, options?: LinspaceOptions \| undefined): NDArray`	Return evenly spaced numbers over a specified interval.
`load`function	`np.load` documented signature `np.load(file: string \| URL \| BytesLike, options?: LoadOptions \| undefined): NDArray \| NpzFile`	Load arrays or pickled objects from `.npy`, `.npz` or pickled files.
`loadtxt`function	`np.loadtxt` documented signature `np.loadtxt(fname: TextSource, options?: LoadtxtOptions \| undefined): NDArray`	Load data from a text file.
`log`ufunc	`np.log` documented signature `np.log(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Natural logarithm, element-wise.
`log10`ufunc	`np.log10` documented signature `np.log10(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Return the base 10 logarithm of the input array, element-wise.
`log1p`ufunc	`np.log1p` documented signature `np.log1p(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Return the natural logarithm of one plus the input array, element-wise.
`log2`ufunc	`np.log2` documented signature `np.log2(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Base-2 logarithm of `x`.
`logaddexp`ufunc	`np.logaddexp` documented signature `np.logaddexp(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Logarithm of the sum of exponentiations of the inputs.
`logaddexp2`ufunc	`np.logaddexp2` documented signature `np.logaddexp2(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Logarithm of the sum of exponentiations of the inputs in base-2.
`logical_and`ufunc	`np.logicalAnd` documented signature `np.logicalAnd(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Compute the truth value of x1 AND x2 element-wise.
`logical_not`ufunc	`np.logicalNot` documented signature `np.logicalNot(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Compute the truth value of NOT x element-wise.
`logical_or`ufunc	`np.logicalOr` documented signature `np.logicalOr(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Compute the truth value of x1 OR x2 element-wise.
`logical_xor`ufunc	`np.logicalXor` documented signature `np.logicalXor(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Compute the truth value of x1 XOR x2, element-wise.
`logspace`function	`np.logspace` documented signature `np.logspace(start: ScalarLike, stop: ScalarLike, num?: number \| undefined, options?: LogspaceOptions \| undefined): NDArray`	Return numbers spaced evenly on a log scale.
`long`class	`np.long` signature `np.long: DType`	Signed integer type, compatible with C `long`.
`mask_indices`function	`np.maskIndices` documented signature `np.maskIndices(n: number, maskFunc: (m: NDArray, k: number) => NDArray, k?: number \| undefined): NDArray[]`	Return the indices to access (n, n) arrays, given a masking function.
`matmul`ufunc	`np.matmul` documented signature `np.matmul(a: ArrayLike, b: ArrayLike): NDArray`	Matrix product of two arrays.
`matrix_transpose`function	`np.matrixTranspose` signature `np.matrixTranspose(x: ArrayLike): NDArray`	Transposes a matrix (or a stack of matrices) `x`.
`matvec`ufunc	`np.matvec` documented signature `np.matvec(x1: ArrayLike, x2: ArrayLike): NDArray`	Matrix-vector dot product of two arrays.
`max`function	`np.max` documented signature `np.max(a: ArrayLike, opts?: Omit<ReduceOptions, "dtype"> \| undefined): NDArray`	Return the maximum of an array or maximum along an axis.
`maximum`ufunc	`np.maximum` documented signature `np.maximum(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Element-wise maximum of array elements.
`may_share_memory`function	`np.mayShareMemory` documented signature `np.mayShareMemory(a: NDArray, b: NDArray): boolean`	Determine if two arrays might share memory
`mean`function	`np.mean` documented signature `np.mean(a: ArrayLike, opts?: Omit<ReduceOptions, "initial"> \| undefined): NDArray`	Compute the arithmetic mean along the specified axis.
`median`function	`np.median` documented signature `np.median(a: ArrayLike, opts?: MedianOptions \| undefined): NDArray`	Compute the median along the specified axis.
`meshgrid`function	`np.meshgrid` documented signature `np.meshgrid(...args: (ArrayInput \| MeshgridOptions)[]): NDArray[]`	Return a tuple of coordinate matrices from coordinate vectors.
`mgrid`constant	`np.mgrid` documented signature `np.mgrid(...slices: GridSlice[]): NDArray`	An instance which returns a dense multi-dimensional "meshgrid".
`min`function	`np.min` documented signature `np.min(a: ArrayLike, opts?: Omit<ReduceOptions, "dtype"> \| undefined): NDArray`	Return the minimum of an array or minimum along an axis.
`min_scalar_type`function	`np.minScalarType` documented signature `np.minScalarType(a: NDArray \| JSScalar): DType`	For scalar `a`, returns the data type with the smallest size and smallest scalar kind which can hold its value. For non-scalar array `a`, returns the vector's dtype unmodified.
`minimum`ufunc	`np.minimum` documented signature `np.minimum(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Element-wise minimum of array elements.
`mintypecode`function	`np.mintypecode` documented signature `np.mintypecode(typechars: string \| readonly (string \| NDArray \| NestedArray \| DType)[], typeset?: string \| undefined, defaultCode?: string \| undefined): string`	Return the character for the minimum-size type to which given types can be safely cast.
`mod`ufunc	`np.mod` documented signature `np.mod(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Returns the element-wise remainder of division.
`modf`ufunc	`np.modf` documented signature `np.modf(x: ArrayLike, opts?: MultiUfuncOptions \| undefined): [NDArray, NDArray]`	Return the fractional and integral parts of an array, element-wise.
`moveaxis`function	`np.moveAxis` documented signature `np.moveAxis(a: NDArray, source: number \| readonly number[], destination: number \| readonly number[]): NDArray`	Move axes of an array to new positions.
`multiply`ufunc	`np.multiply` documented signature `np.multiply(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Multiply arguments element-wise.
`nan`constant	`np.nan` documented signature `np.nan: number`
`nan_to_num`function	`np.nanToNum` documented signature `np.nanToNum(x: ArrayLike, opts?: NanToNumOptions \| undefined): NDArray`	Replace NaN with zero and infinity with large finite numbers (default behaviour) or with the numbers defined by the user using the `nan`, `posinf` and/or `neginf` keywords.
`nanargmax`function	`np.nanargmax` documented signature `np.nanargmax(a: ArrayLike, opts?: ArgReduceOptions \| undefined): NDArray`	Return the indices of the maximum values in the specified axis ignoring NaNs. For all-NaN slices `ValueError` is raised. Warning: the results cannot be trusted if a slice contains only NaNs and -Infs.
`nanargmin`function	`np.nanargmin` documented signature `np.nanargmin(a: ArrayLike, opts?: ArgReduceOptions \| undefined): NDArray`	Return the indices of the minimum values in the specified axis ignoring NaNs. For all-NaN slices `ValueError` is raised. Warning: the results cannot be trusted if a slice contains only NaNs and Infs.
`nancumprod`function	`np.nancumprod` documented signature `np.nancumprod(a: ArrayLike, opts?: CumsumOptions \| undefined): NDArray`	Return the cumulative product of array elements over a given axis treating Not a Numbers (NaNs) as one. The cumulative product does not change when NaNs are encountered and leading NaNs are replaced by ones.
`nancumsum`function	`np.nancumsum` documented signature `np.nancumsum(a: ArrayLike, opts?: CumsumOptions \| undefined): NDArray`	Return the cumulative sum of array elements over a given axis treating Not a Numbers (NaNs) as zero. The cumulative sum does not change when NaNs are encountered and leading NaNs are replaced by zeros.
`nanmax`function	`np.nanmax` documented signature `np.nanmax(a: ArrayLike, opts?: Omit<ReduceOptions, "dtype"> \| undefined): NDArray`	Return the maximum of an array or maximum along an axis, ignoring any NaNs. When all-NaN slices are encountered a `RuntimeWarning` is raised and NaN is returned for that slice.
`nanmean`function	`np.nanmean` documented signature `np.nanmean(a: ArrayLike, opts?: Omit<ReduceOptions, "initial"> \| undefined): NDArray`	Compute the arithmetic mean along the specified axis, ignoring NaNs.
`nanmedian`function	`np.nanmedian` documented signature `np.nanmedian(a: ArrayLike, opts?: MedianOptions \| undefined): NDArray`	Compute the median along the specified axis, while ignoring NaNs.
`nanmin`function	`np.nanmin` documented signature `np.nanmin(a: ArrayLike, opts?: Omit<ReduceOptions, "dtype"> \| undefined): NDArray`	Return minimum of an array or minimum along an axis, ignoring any NaNs. When all-NaN slices are encountered a `RuntimeWarning` is raised and Nan is returned for that slice.
`nanpercentile`function	`np.nanpercentile` documented signature `np.nanpercentile(a: ArrayLike, q: ArrayLike, opts?: QuantileOptions \| undefined): NDArray`	Compute the qth percentile of the data along the specified axis, while ignoring nan values.
`nanprod`function	`np.nanprod` documented signature `np.nanprod(a: ArrayLike, opts?: ReduceOptions \| undefined): NDArray`	Return the product of array elements over a given axis treating Not a Numbers (NaNs) as ones.
`nanquantile`function	`np.nanquantile` documented signature `np.nanquantile(a: ArrayLike, q: ArrayLike, opts?: QuantileOptions \| undefined): NDArray`	Compute the qth quantile of the data along the specified axis, while ignoring nan values. Returns the qth quantile(s) of the array elements.
`nanstd`function	`np.nanstd` documented signature `np.nanstd(a: ArrayLike, opts?: VarOptions \| undefined): NDArray`	Compute the standard deviation along the specified axis, while ignoring NaNs.
`nansum`function	`np.nansum` documented signature `np.nansum(a: ArrayLike, opts?: ReduceOptions \| undefined): NDArray`	Return the sum of array elements over a given axis treating Not a Numbers (NaNs) as zero.
`nanvar`function	`np.nanvar` documented signature `np.nanvar(a: ArrayLike, opts?: VarOptions \| undefined): NDArray`	Compute the variance along the specified axis, while ignoring NaNs.
`ndarray`class	`np.NDArray` signature `new np.NDArray(token: typeof internal, handle: NativeNDArray)`	An array object represents a multidimensional, homogeneous array of fixed-size items. An associated data-type object describes the format of each element in the array (its byte-order, how many bytes it occupies in memory, whether it is an integer, a floating point number, or something else, etc.)
`ndenumerate`class	`np.ndenumerate` documented signature `np.ndenumerate(a: Operand): Generator<[number[], ScalarValue], any, any>`	Multidimensional index iterator.
`ndim`function	`np.ndim` documented signature `np.ndim(a: ArrayLike): number`	Return the number of dimensions of an array.
`ndindex`class	`np.ndindex` documented signature `np.ndindex(...shape: (number \| readonly number[])[]): Generator<number[], any, any>`	An N-dimensional iterator object to index arrays.
`negative`ufunc	`np.negative` documented signature `np.negative(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Numerical negation, element-wise.
`newaxis`constant	`np.newaxis` signature `np.newaxis: "newaxis"`
`nextafter`ufunc	`np.nextafter` documented signature `np.nextafter(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Return the next floating-point value after x1 towards x2, element-wise.
`nonzero`function	`np.nonzero` documented signature `np.nonzero(a: NDArray \| NestedArray): NDArray[]`	Return the indices of the elements that are non-zero.
`not_equal`ufunc	`np.notEqual` documented signature `np.notEqual(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Return (x1 != x2) element-wise.
`ogrid`constant	`np.ogrid` documented signature `np.ogrid(slice: GridSlice): NDArray np.ogrid(...slices: GridSlice[]): NDArray[]`	An instance which returns an open multi-dimensional "meshgrid".
`ones`function	`np.ones` documented signature `np.ones(shape: number \| Shape, options?: CreationOptions \| undefined): NDArray`	Return a new array of given shape and type, filled with ones.
`ones_like`function	`np.onesLike` signature `np.onesLike(a: NDArray, options?: LikeOptions \| undefined): NDArray`	Return an array of ones with the same shape and type as a given array.
`outer`function	`np.outer` documented signature `np.outer(a: ArrayLike, b: ArrayLike): NDArray`	Compute the outer product of two vectors.
`packbits`function	`np.packbits` documented signature `np.packbits(a: ArrayLike, opts?: PackbitsOptions \| undefined): NDArray`	Packs the elements of a binary-valued array into bits in a uint8 array.
`pad`function	`np.pad` documented signature `np.pad(a: ArrayLike, padWidth: PadWidth, mode?: PadMode \| PadFunction \| undefined, options?: (PadOptions & Record<string, unknown>) \| undefined): NDArray`	Pad an array.
`partition`function	`np.partition` documented signature `np.partition(a: ArrayLike, kth: Kth, opts?: PartitionOptions \| undefined): NDArray`	Return a partitioned copy of an array.
`percentile`function	`np.percentile` documented signature `np.percentile(a: ArrayLike, q: ArrayLike, opts?: QuantileOptions \| undefined): NDArray`	Compute the q-th percentile of the data along the specified axis.
`permute_dims`function	`np.permuteDims` signature `np.permuteDims(a: ArrayLike, axes?: readonly number[] \| undefined): NDArray`	Returns an array with axes transposed.
`pi`constant	`np.pi` documented signature `np.pi: number`
`piecewise`function	`np.piecewise` documented signature `np.piecewise(x: ArrayLike, condlist: ArrayLike \| readonly ArrayLike[], funclist: readonly PiecewiseFunc[]): NDArray`	Evaluate a piecewise-defined function.
`place`function	`np.place` documented signature `np.place(arr: NDArray, mask: ArrayLike, vals: ArrayLike): void`	Change elements of an array based on conditional and input values.
`poly`function	`np.poly` documented signature `np.poly(seqOfZeros: ArrayLike): number \| NDArray`	Find the coefficients of a polynomial with the given sequence of roots.
`poly1d`class	`np.poly1d` documented signature `new np.poly1d(cOrR: PolyLike, options?: boolean \| Poly1dOptions \| undefined)`	A one-dimensional polynomial class.
`polyadd`function	`np.polyadd` documented signature `np.polyadd(a1: poly1d, a2: PolyLike): poly1d np.polyadd(a1: PolyLike, a2: poly1d): poly1d np.polyadd(a1: ArrayLike, a2: ArrayLike): NDArray`	Find the sum of two polynomials.
`polyder`function	`np.polyder` documented signature `np.polyder(p: poly1d, m?: number \| undefined): poly1d np.polyder(p: ArrayLike, m?: number \| undefined): NDArray`	Return the derivative of the specified order of a polynomial.
`polydiv`function	`np.polydiv` documented signature `np.polydiv(u: poly1d, v: PolyLike): [poly1d, poly1d] np.polydiv(u: PolyLike, v: poly1d): [poly1d, poly1d] np.polydiv(u: ArrayLike, v: ArrayLike): [NDArray, NDArray]`	Returns the quotient and remainder of polynomial division.
`polyfit`function	`np.polyfit` documented signature `np.polyfit(x: ArrayLike, y: ArrayLike, deg: number, options?: PolyfitOptions \| undefined): NDArray \| (number \| NDArray)[]`	Least squares polynomial fit.
`polyint`function	`np.polyint` documented signature `np.polyint(p: poly1d, m?: number \| undefined, k?: ArrayLike \| null \| undefined): poly1d np.polyint(p: ArrayLike, m?: number \| undefined, k?: ArrayLike \| null \| undefined): NDArray`	Return an antiderivative (indefinite integral) of a polynomial.
`polymul`function	`np.polymul` documented signature `np.polymul(a1: poly1d, a2: PolyLike): poly1d np.polymul(a1: PolyLike, a2: poly1d): poly1d np.polymul(a1: ArrayLike, a2: ArrayLike): NDArray`	Find the product of two polynomials.
`polysub`function	`np.polysub` documented signature `np.polysub(a1: poly1d, a2: PolyLike): poly1d np.polysub(a1: PolyLike, a2: poly1d): poly1d np.polysub(a1: ArrayLike, a2: ArrayLike): NDArray`	Difference (subtraction) of two polynomials.
`polyval`function	`np.polyval` documented signature `np.polyval(p: PolyLike, x: poly1d): poly1d np.polyval(p: PolyLike, x: ArrayLike): NDArray`	Evaluate a polynomial at specific values.
`positive`ufunc	`np.positive` documented signature `np.positive(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Numerical positive, element-wise.
`pow`ufunc	`np.pow` documented signature `np.pow(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	First array elements raised to powers from second array, element-wise.
`power`ufunc	`np.power` documented signature `np.power(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	First array elements raised to powers from second array, element-wise.
`printoptions`function	`np.printoptions` documented signature `np.printoptions<T>(opts: PrintOptionsInput, fn: () => T): T`	Context manager for setting print options.
`prod`function	`np.prod` documented signature `np.prod(a: ArrayLike, opts?: ReduceOptions \| undefined): NDArray`	Return the product of array elements over a given axis.
`promote_types`function	`np.promoteTypes` documented signature `np.promoteTypes(a: DTypeLike, b: DTypeLike): DType`	Returns the data type with the smallest size and smallest scalar kind to which both `type1` and `type2` may be safely cast. The returned data type is always considered "canonical", this mainly means that the promoted dtype will always be in native byte order.
`put`function	`np.put` documented signature `np.put(a: NDArray, ind: ArrayLike, v: ArrayLike, opts?: PutOptions \| undefined): void`	Replaces specified elements of an array with given values.
`put_along_axis`function	`np.putAlongAxis` documented signature `np.putAlongAxis(arr: NDArray, indices: ArrayLike, values: ArrayLike, axis: number \| null): void`	Put values into the destination array by matching 1d index and data slices.
`putmask`function	`np.putmask` documented signature `np.putmask(a: NDArray, mask: ArrayLike, values: ArrayLike): void`	Changes elements of an array based on conditional and input values.
`quantile`function	`np.quantile` documented signature `np.quantile(a: ArrayLike, q: ArrayLike, opts?: QuantileOptions \| undefined): NDArray`	Compute the q-th quantile of the data along the specified axis.
`r_`constant	`np.r_` documented signature `np.r_(...items: ConcatItem[]): NDArray`	Translates slice objects to concatenation along the first axis.
`rad2deg`ufunc	`np.rad2deg` documented signature `np.rad2deg(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Convert angles from radians to degrees.
`radians`ufunc	`np.radians` documented signature `np.radians(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Convert angles from degrees to radians.
`ravel`function	`np.ravel` documented signature `np.ravel(a: NDArray, opts?: OrderOptions \| undefined): NDArray`	Return a contiguous flattened array.
`ravel_multi_index`function	`np.ravelMultiIndex` documented signature `np.ravelMultiIndex(multiIndex: readonly ArrayLike[], dims: number \| Shape, opts?: RavelMultiIndexOptions \| undefined): NDArray`	Converts a tuple of index arrays into an array of flat indices, applying boundary modes to the multi-index.
`real`function	`np.real` documented signature `np.real(a: ArrayLike): NDArray`	Return the real part of the complex argument.
`real_if_close`function	`np.realIfClose` documented signature `np.realIfClose(x: ArrayLike, tol?: number \| undefined): NDArray`	If input is complex with all imaginary parts close to zero, return real parts.
`recarray`class	`np.recarray` documented signature `new np.recarray(shape: number \| number[], opts?: RecArrayOptions \| undefined)`	Construct an ndarray that allows field access using attributes.
`reciprocal`ufunc	`np.reciprocal` documented signature `np.reciprocal(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Return the reciprocal of the argument, element-wise.
`remainder`ufunc	`np.remainder` documented signature `np.remainder(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Returns the element-wise remainder of division.
`repeat`function	`np.repeat` documented signature `np.repeat(a: ArrayLike, repeats: IntList, axis?: number \| null \| undefined): NDArray`	Repeat each element of an array after themselves
`require`function	`np.require` documented signature `np.require(a: ArrayLike, dtype?: DTypeLike \| null \| undefined, requirements?: Requirements \| null \| undefined): NDArray`	Return an ndarray of the provided type that satisfies requirements.
`reshape`function	`np.reshape` documented signature `np.reshape(a: NDArray, shape: number \| Shape, opts?: OrderOptions \| undefined): NDArray`	Returns a reshaped ndarray without changing data.
`resize`function	`np.resize` documented signature `np.resize(a: ArrayLike, newShape: number \| readonly number[]): NDArray`	Return a new array with the specified shape.
`result_type`function	`np.resultType` documented signature `np.resultType(...args: (NDArray \| NestedArray \| DTypeLike)[]): DType`	Returns the type that results from applying the NumPy :ref:`type promotion <arrays.promotion>` rules to the arguments.
`right_shift`ufunc	`np.rightShift` documented signature `np.rightShift(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Shift the bits of an integer to the right.
`rint`ufunc	`np.rint` documented signature `np.rint(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Round elements of the array to the nearest integer.
`roll`function	`np.roll` documented signature `np.roll(a: ArrayLike, shift: Axes, axis?: Axes \| null \| undefined): NDArray`	Roll array elements along a given axis.
`rollaxis`function	`np.rollaxis` documented signature `np.rollaxis(a: ArrayLike, axis: number, start?: number \| undefined): NDArray`	Roll the specified axis backwards, until it lies in a given position.
`roots`function	`np.roots` documented signature `np.roots(p: PolyLike): NDArray`	Return the roots of a polynomial with coefficients given in p.
`rot90`function	`np.rot90` documented signature `np.rot90(m: ArrayLike, k?: number \| undefined, axes?: readonly number[] \| undefined): NDArray`	Rotate an array by 90 degrees in the plane specified by axes.
`round`function	`np.round` documented signature `np.round(a: ArrayLike, decimals?: number \| undefined, opts?: RoundOptions \| undefined): NDArray`	Evenly round to the given number of decimals.
`s_`constant	`np.s_` documented signature `np.s_(spec: string \| number \| boolean \| NDArray \| readonly [] \| readonly [number \| null] \| readonly [number \| null, number \| null] \| readonly [number \| null, number \| null, number \| null] \| null): IndexSpec np.s_(...specs: (string \| number \| boolean \| NDArray \| readonly [] \| readonly [number \| null] \| readonly [number \| null, number \| null] \| readonly [number \| null, number \| null, number \| null] \| null)[]): IndexSpec[]`	A nicer way to build up index tuples for arrays.
`save`function	`np.save` documented signature `np.save(file: FileLike, arr: ArrayLike): any`	Save an array to a binary file in NumPy `.npy` format.
`savetxt`function	`np.savetxt` documented signature `np.savetxt(fname: string \| URL \| null, X: ArrayLike, options?: SavetxtOptions \| undefined): string \| undefined`	Save an array to a text file.
`savez`function	`np.savez` documented signature `np.savez(file: FileLike, ...arrays: (ArrayLike \| NamedArrays)[]): any`	Save several arrays into a single file in uncompressed `.npz` format.
`savez_compressed`function	`np.savezCompressed` documented signature `np.savezCompressed(file: FileLike, ...arrays: (ArrayLike \| NamedArrays)[]): any`	Save several arrays into a single file in compressed `.npz` format.
`searchsorted`function	`np.searchsorted` documented signature `np.searchsorted(a: ArrayLike, v: number \| bigint \| boolean \| NDArray \| Complex \| { readonly re: number; readonly im?: number \| undefined; } \| readonly NestedArray[], opts?: SearchsortedOptions \| undefined): NDArray`	Find indices where elements should be inserted to maintain order.
`select`function	`np.select` documented signature `np.select(condlist: readonly ArrayLike[], choicelist: readonly ArrayLike[], opts?: SelectOptions \| undefined): NDArray`	Return an array drawn from elements in choicelist, depending on conditions.
`set_printoptions`function	`np.setPrintoptions` documented signature `np.setPrintoptions(opts?: PrintOptionsInput \| undefined): void`	Set printing options.
`setdiff1d`function	`np.setdiff1d` documented signature `np.setdiff1d(a: ArrayLike, b: ArrayLike, opts?: { assumeUnique?: boolean \| undefined; } \| undefined): NDArray`	Find the set difference of two arrays.
`seterr`function	`np.seterr` documented signature `np.seterr(settings?: ErrSettings \| undefined): ErrState`	Set how floating-point errors are handled.
`setxor1d`function	`np.setxor1d` documented signature `np.setxor1d(a: ArrayLike, b: ArrayLike, opts?: { assumeUnique?: boolean \| undefined; } \| undefined): NDArray`	Find the set exclusive-or of two arrays.
`shape`function	`np.shape` documented signature `np.shape(a: ArrayLike): number[]`	Return the shape of an array.
`shares_memory`function	`np.sharesMemory` documented signature `np.sharesMemory(a: NDArray, b: NDArray, _opts?: SharesMemoryOptions \| undefined): boolean`	Determine if two arrays share memory.
`short`class	`np.short` documented signature `np.short: DType`	Signed integer type, compatible with C `short`.
`sign`ufunc	`np.sign` documented signature `np.sign(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Returns an element-wise indication of the sign of a number.
`signbit`ufunc	`np.signbit` documented signature `np.signbit(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Returns element-wise True where signbit is set (less than zero).
`sin`ufunc	`np.sin` documented signature `np.sin(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Trigonometric sine, element-wise.
`sinc`function	`np.sinc` documented signature `np.sinc(x: ArrayLike): NDArray`	Return the normalized sinc function.
`single`class	`np.single` documented signature `np.single: DType`	Single-precision floating-point number type, compatible with C `float`.
`sinh`ufunc	`np.sinh` documented signature `np.sinh(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Hyperbolic sine, element-wise.
`size`function	`np.size` documented signature `np.size(a: ArrayLike, axis?: number \| readonly number[] \| null \| undefined): number`	Return the number of elements along a given axis.
`sort`function	`np.sort` documented signature `np.sort(a: ArrayLike, opts?: SortOptions \| undefined): NDArray`	Return a sorted copy of an array.
`sort_complex`function	`np.sortComplex` documented signature `np.sortComplex(a: ArrayLike): NDArray`	Sort a complex array using the real part first, then the imaginary part.
`spacing`ufunc	`np.spacing` documented signature `np.spacing(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Return the distance between x and the nearest adjacent number.
`split`function	`np.split` documented signature `np.split(a: ArrayLike, indicesOrSections: number \| NDArray \| readonly number[], axis?: number \| undefined): NDArray[]`	Split an array into multiple sub-arrays as views into `ary`.
`sqrt`ufunc	`np.sqrt` documented signature `np.sqrt(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Return the non-negative square-root of an array, element-wise.
`square`ufunc	`np.square` documented signature `np.square(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Return the element-wise square of the input.
`squeeze`function	`np.squeeze` documented signature `np.squeeze(a: NDArray, axis?: number \| readonly number[] \| undefined): NDArray`	Remove axes of length one from `a`.
`stack`function	`np.stack` documented signature `np.stack(arrays: Sequence, axis?: number \| StackOptions \| undefined, options?: JoinOptions \| undefined): NDArray`	Join a sequence of arrays along a new axis.
`std`function	`np.std` documented signature `np.std(a: ArrayLike, opts?: VarOptions \| undefined): NDArray`	Compute the standard deviation along the specified axis.
`str_`class	`np.str_` documented signature `np.str_: "str"`	A unicode string.
`subtract`ufunc	`np.subtract` documented signature `np.subtract(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Subtract arguments, element-wise.
`sum`function	`np.sum` documented signature `np.sum(a: ArrayLike, opts?: ReduceOptions \| undefined): NDArray`	Sum of array elements over a given axis.
`swapaxes`function	`np.swapAxes` documented signature `np.swapAxes(a: NDArray, axis1: number, axis2: number): NDArray`	Interchange two axes of an array.
`take`function	`np.take` documented signature `np.take(a: NDArray \| NestedArray, indices: NDArray \| NestedArray, axis?: number \| TakeOptions \| null \| undefined, opts?: TakeOptions \| undefined): NDArray`	Take elements from an array along an axis.
`take_along_axis`function	`np.takeAlongAxis` documented signature `np.takeAlongAxis(arr: ArrayLike, indices: ArrayLike, axis?: number \| null \| undefined): NDArray`	Take values from the input array by matching 1d index and data slices.
`tan`ufunc	`np.tan` documented signature `np.tan(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Compute tangent element-wise.
`tanh`ufunc	`np.tanh` documented signature `np.tanh(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Hyperbolic tangent, element-wise.
`tensordot`function	`np.tensordot` documented signature `np.tensordot(a: ArrayLike, b: ArrayLike, opts?: TensordotOptions \| undefined): NDArray`	Compute tensor dot product along specified axes.
`tile`function	`np.tile` documented signature `np.tile(a: ArrayLike, reps: IntList): NDArray`	Construct an array by repeating A the number of times given by reps.
`timedelta64`class	`np.timedelta64` documented signature `np.timedelta64(value: TimedeltaInput, unit?: string \| undefined): TimedeltaArray`	A timedelta stored as a 64-bit integer.
`trace`function	`np.trace` documented signature `np.trace(a: ArrayLike, opts?: number \| TraceOptions \| undefined): NDArray`	Return the sum along diagonals of the array.
`transpose`function	`np.transpose` documented signature `np.transpose(a: NDArray, axes?: readonly number[] \| undefined): NDArray`	Returns an array with axes transposed.
`trapezoid`function	`np.trapezoid` documented signature `np.trapezoid(y: ArrayLike, opts?: TrapezoidOptions \| undefined): NDArray`	Integrate along the given axis using the composite trapezoidal rule.
`tri`function	`np.tri` documented signature `np.tri(n: number, m?: number \| null \| undefined, options?: TriOptions \| undefined): NDArray`	An array with ones at and below the given diagonal and zeros elsewhere.
`tril`function	`np.tril` documented signature `np.tril(m: ArrayInput, k?: number \| undefined): NDArray`	Lower triangle of an array.
`tril_indices`function	`np.trilIndices` documented signature `np.trilIndices(n: number, k?: number \| undefined, m?: number \| null \| undefined): NDArray[]`	Return the indices for the lower-triangle of an (n, m) array.
`tril_indices_from`function	`np.trilIndicesFrom` documented signature `np.trilIndicesFrom(arr: NDArray, k?: number \| undefined): NDArray[]`	Return the indices for the lower-triangle of arr.
`trim_zeros`function	`np.trimZeros` documented signature `np.trimZeros(filt: ArrayLike, trim?: string \| undefined, axis?: number \| readonly number[] \| null \| undefined): NDArray`	Remove values along a dimension which are zero along all other.
`triu`function	`np.triu` documented signature `np.triu(m: ArrayInput, k?: number \| undefined): NDArray`	Upper triangle of an array.
`triu_indices`function	`np.triuIndices` documented signature `np.triuIndices(n: number, k?: number \| undefined, m?: number \| null \| undefined): NDArray[]`	Return the indices for the upper-triangle of an (n, m) array.
`triu_indices_from`function	`np.triuIndicesFrom` documented signature `np.triuIndicesFrom(arr: NDArray, k?: number \| undefined): NDArray[]`	Return the indices for the upper-triangle of arr.
`True_`constant	`np.True_` documented signature `np.True_: true`	bool(value=False, /) --
`true_divide`ufunc	`np.trueDivide` documented signature `np.trueDivide(a: Operand, b: Operand, opts?: UfuncOptions \| undefined): NDArray`	Divide arguments element-wise.
`trunc`ufunc	`np.trunc` documented signature `np.trunc(a: ArrayLike, opts?: UfuncOptions \| undefined): NDArray`	Return the truncated value of the input, element-wise.
`ubyte`class	`np.ubyte` documented signature `np.ubyte: DType`	Unsigned integer type, compatible with C `unsigned char`.
`uint`class	`np.uint` documented signature `np.uint: DType`	Unsigned signed integer type, 64bit on 64bit systems and 32bit on 32bit systems.
`uint16`class	`np.uint16` signature `np.uint16: DType`	Unsigned integer type, compatible with C `unsigned short`.
`uint32`class	`np.uint32` signature `np.uint32: DType`	Unsigned integer type, compatible with C `unsigned int`.
`uint64`class	`np.uint64` signature `np.uint64: DType`	Unsigned signed integer type, 64bit on 64bit systems and 32bit on 32bit systems.
`uint8`class	`np.uint8` signature `np.uint8: DType`	Unsigned integer type, compatible with C `unsigned char`.
`uintc`class	`np.uintc` documented signature `np.uintc: DType`	Unsigned integer type, compatible with C `unsigned int`.
`uintp`class	`np.uintp` documented signature `np.uintp: DType`	Unsigned signed integer type, 64bit on 64bit systems and 32bit on 32bit systems.
`ulong`class	`np.ulong` documented signature `np.ulong: DType`	Unsigned signed integer type, 64bit on 64bit systems and 32bit on 32bit systems.
`union1d`function	`np.union1d` documented signature `np.union1d(a: ArrayLike, b: ArrayLike): NDArray`	Find the union of two arrays.
`unique`function	`np.unique` documented signature `np.unique(a: ArrayLike, opts?: (UniqueOptions & { returnIndex?: false \| undefined; returnInverse?: false \| undefined; returnCounts?: false \| undefined; }) \| undefined): NDArray np.unique(a: ArrayLike, opts: UniqueOptions): UniqueResult`	Find the unique elements of an array.
`unique_all`function	`np.uniqueAll` documented signature `np.uniqueAll(x: ArrayLike): UniqueAllResult`	Find the unique elements of an array, and counts, inverse, and indices.
`unique_counts`function	`np.uniqueCounts` signature `np.uniqueCounts(x: ArrayLike): UniqueCountsResult`	Find the unique elements and counts of an input array `x`.
`unique_inverse`function	`np.uniqueInverse` signature `np.uniqueInverse(x: ArrayLike): UniqueInverseResult`	Find the unique elements of `x` and indices to reconstruct `x`.
`unique_values`function	`np.uniqueValues` signature `np.uniqueValues(x: ArrayLike): NDArray`	Returns the unique elements of an input array `x`.
`unpackbits`function	`np.unpackbits` documented signature `np.unpackbits(a: ArrayLike, opts?: UnpackbitsOptions \| undefined): NDArray`	Unpacks elements of a uint8 array into a binary-valued output array.
`unravel_index`function	`np.unravelIndex` documented signature `np.unravelIndex(indices: ArrayLike, shape: number \| Shape, opts?: { order?: MemoryOrder \| undefined; } \| undefined): NDArray[]`	Converts a flat index or array of flat indices into a tuple of coordinate arrays.
`unstack`function	`np.unstack` documented signature `np.unstack(x: ArrayLike, axis?: number \| { axis?: number \| undefined; } \| undefined): NDArray[]`	Split an array into a sequence of arrays along the given axis.
`unwrap`function	`np.unwrap` documented signature `np.unwrap(p: ArrayLike, opts?: UnwrapOptions \| undefined): NDArray`	Unwrap by taking the complement of large deltas with respect to the period.
`ushort`class	`np.ushort` documented signature `np.ushort: DType`	Unsigned integer type, compatible with C `unsigned short`.
`vander`function	`np.vander` documented signature `np.vander(x: ArrayInput, n?: number \| null \| undefined, options?: { increasing?: boolean \| undefined; } \| undefined): NDArray`	Generate a Vandermonde matrix.
`var`function	`np.var` documented signature `np.var(a: ArrayLike, opts?: VarOptions \| undefined): NDArray`	Compute the variance along the specified axis.
`vdot`function	`np.vdot` documented signature `np.vdot(a: ArrayLike, b: ArrayLike): NDArray`	Return the dot product of two vectors.
`vecdot`ufunc	`np.vecdot` documented signature `np.vecdot(x1: ArrayLike, x2: ArrayLike, opts?: VecdotOptions \| undefined): NDArray`	Vector dot product of two arrays.
`vecmat`ufunc	`np.vecmat` documented signature `np.vecmat(x1: ArrayLike, x2: ArrayLike): NDArray`	Vector-matrix dot product of two arrays.
`vectorize`class	`np.vectorize` documented signature `np.vectorize(fn: (...args: unknown[]) => unknown, opts?: VectorizeOptions \| undefined): VectorizedFn`	Returns an object that acts like pyfunc, but takes arrays as input.
`vsplit`function	`np.vsplit` signature `np.vsplit(a: ArrayLike, indicesOrSections: number \| NDArray \| readonly number[]): NDArray[]`	Split an array into multiple sub-arrays vertically (row-wise).
`vstack`function	`np.vstack` documented signature `np.vstack(arrays: Sequence, options?: VHStackOptions \| undefined): NDArray`	Stack arrays in sequence vertically (row wise).
`where`function	`np.where` documented signature `np.where(condition: NDArray \| NestedArray): NDArray[] np.where(condition: NDArray \| NestedArray, x: NDArray \| NestedArray, y: NDArray \| NestedArray): NDArray`	Return elements chosen from `x` or `y` depending on `condition`.
`zeros`function	`np.zeros` documented signature `np.zeros(shape: number \| Shape, options?: CreationOptions \| undefined): NDArray`	Return a new array of given shape and type, filled with zeros.
`zeros_like`function	`np.zerosLike` documented signature `np.zerosLike(a: NDArray, options?: LikeOptions \| undefined): NDArray`	Return an array of zeros with the same shape and type as a given array.

numpy.polynomial# 6 / 6

NumPy	numera	Summary
`Chebyshev`class	`np.polynomial.chebyshev` documented signature `np.polynomial.chebyshev: Record<string, unknown>`	A Chebyshev series class.
`Hermite`class	`np.polynomial.hermite` signature `np.polynomial.hermite: Record<string, unknown>`	A Hermite series class.
`HermiteE`class	`np.polynomial.hermite_e` signature `np.polynomial.hermite_e: Record<string, unknown>`	A HermiteE series class.
`Laguerre`class	`np.polynomial.laguerre` signature `np.polynomial.laguerre: Record<string, unknown>`	A Laguerre series class.
`Legendre`class	`np.polynomial.legendre` signature `np.polynomial.legendre: Record<string, unknown>`	A Legendre series class.
`Polynomial`class	`np.polynomial.polynomial` signature `np.polynomial.polynomial: Record<string, unknown>`	A power series class.

numpy.random# 59 / 60

NumPy	numera	Summary
`beta`function	`np.random.beta` signature `np.random.beta(a: number, b: number, size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from a Beta distribution.
`binomial`function	`np.random.binomial` signature `np.random.binomial(n: number, p: number, size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from a binomial distribution.
`BitGenerator`class	`np.random.BitGenerator` documented signature `new np.random.BitGenerator()`	Base Class for generic BitGenerators, which provide a stream of random bits based on different algorithms. Must be overridden.
`bytes`function	`np.random.bytes` signature `np.random.bytes(length: number): Uint8Array<ArrayBufferLike>`	Return random bytes.
`chisquare`function	`np.random.chisquare` signature `np.random.chisquare(df: number, size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from a chi-square distribution.
`choice`function	`np.random.choice` signature `np.random.choice(a: NDArray \| NestedArray, size?: Size \| { size?: Size \| undefined; replace?: boolean \| undefined; p?: unknown; } \| null \| undefined, replace?: boolean \| undefined, p?: unknown): number \| boolean \| NDArray \| Complex`	Generates a random sample from a given 1-D array
`default_rng`function	`np.random.defaultRng` documented signature `np.random.defaultRng(seed?: Seed \| Generator \| null \| undefined): Generator`	Construct a new Generator with the default BitGenerator (PCG64).
`dirichlet`function	`np.random.dirichlet` signature `np.random.dirichlet(alpha: readonly number[], size?: Size \| null \| undefined): NDArray`	Draw samples from the Dirichlet distribution.
`exponential`function	`np.random.exponential` signature `np.random.exponential(scale?: number \| undefined, size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from an exponential distribution.
`f`function	`np.random.f` signature `np.random.f(dfnum: number, dfden: number, size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from an F distribution.
`gamma`function	`np.random.gamma` signature `np.random.gamma(shape: number, scale?: number \| undefined, size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from a Gamma distribution.
`Generator`class	`np.random.Generator` signature `new np.random.Generator(bg: BitGenerator \| NativeBitGenerator)`	Container for the BitGenerators.
`geometric`function	`np.random.geometric` signature `np.random.geometric(p: number, size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from the geometric distribution.
`get_state`function	`np.random.get_state` documented signature `np.random.get_state(): bigint[]`	Return a tuple representing the internal state of the generator.
`gumbel`function	`np.random.gumbel` signature `np.random.gumbel(loc?: number \| undefined, scale?: number \| undefined, size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from a Gumbel distribution.
`hypergeometric`function	`np.random.hypergeometric` signature `np.random.hypergeometric(ngood: number, nbad: number, nsample: number, size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from a Hypergeometric distribution.
`laplace`function	`np.random.laplace` signature `np.random.laplace(loc?: number \| undefined, scale?: number \| undefined, size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from the Laplace or double exponential distribution with specified location (or mean) and scale (decay).
`logistic`function	`np.random.logistic` signature `np.random.logistic(loc?: number \| undefined, scale?: number \| undefined, size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from a logistic distribution.
`lognormal`function	`np.random.lognormal` signature `np.random.lognormal(mean?: number \| undefined, sigma?: number \| undefined, size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from a log-normal distribution.
`logseries`function	`np.random.logseries` signature `np.random.logseries(p: number, size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from a logarithmic series distribution.
`MT19937`class	`np.random.MT19937` signature `new np.random.MT19937(seed?: SeedLike \| SeedSequence \| null \| undefined)`	Container for the Mersenne Twister pseudo-random number generator.
`multinomial`function	`np.random.multinomial` signature `np.random.multinomial(n: number, pvals: readonly number[], size?: Size \| null \| undefined): NDArray`	Draw samples from a multinomial distribution.
`multivariate_normal`function	not implemented	Draw random samples from a multivariate normal distribution.
`negative_binomial`function	`np.random.negative_binomial` signature `np.random.negative_binomial(n: number, p: number, size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from a negative binomial distribution.
`noncentral_chisquare`function	`np.random.noncentral_chisquare` signature `np.random.noncentral_chisquare(df: number, nonc: number, size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from a noncentral chi-square distribution.
`noncentral_f`function	`np.random.noncentral_f` signature `np.random.noncentral_f(dfnum: number, dfden: number, nonc: number, size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from the noncentral F distribution.
`normal`function	`np.random.normal` signature `np.random.normal(loc?: number \| { loc?: number \| undefined; scale?: number \| undefined; size?: Size \| undefined; } \| undefined, scale?: number \| undefined, size?: Size \| undefined): number \| boolean \| NDArray \| Complex`	Draw random samples from a normal (Gaussian) distribution.
`pareto`function	`np.random.pareto` signature `np.random.pareto(a: number, size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from a Pareto II or Lomax distribution with specified shape.
`PCG64`class	`np.random.PCG64` signature `new np.random.PCG64(seed?: SeedLike \| SeedSequence \| null \| undefined)`	BitGenerator for the PCG-64 pseudo-random number generator.
`PCG64DXSM`class	`np.random.PCG64DXSM` documented signature `new np.random.PCG64DXSM(seed?: SeedLike \| SeedSequence \| null \| undefined)`	BitGenerator for the PCG-64 DXSM pseudo-random number generator.
`permutation`function	`np.random.permutation` signature `np.random.permutation(x: NDArray \| NestedArray): NDArray`	Randomly permute a sequence, or return a permuted range.
`Philox`class	`np.random.Philox` documented signature `new np.random.Philox(seed?: SeedLike \| SeedSequence \| null \| undefined)`	Container for the Philox (4x64) pseudo-random number generator.
`poisson`function	`np.random.poisson` signature `np.random.poisson(lam?: number \| undefined, size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from a Poisson distribution.
`power`function	`np.random.power` signature `np.random.power(a: number, size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draws samples in [0, 1] from a power distribution with positive exponent a - 1.
`rand`function	`np.random.rand` signature `np.random.rand(...dims: number[]): number \| boolean \| NDArray \| Complex`	Random values in a given shape.
`randint`function	`np.random.randint` signature `np.random.randint(low: number \| bigint \| { low: number \| bigint; high?: number \| bigint \| null \| undefined; size?: Size \| undefined; dtype?: DTypeLike \| undefined; }, high?: number \| bigint \| null \| undefined, size?: Size \| undefined, dt?: DTypeLike \| undefined): number \| boolean \| NDArray \| Complex`	Return random integers from `low` (inclusive) to `high` (exclusive).
`randn`function	`np.random.randn` signature `np.random.randn(...dims: number[]): number \| boolean \| NDArray \| Complex`	Return a sample (or samples) from the "standard normal" distribution.
`random`function	`np.random.random` signature `np.random.random(size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Return random floats in the half-open interval [0.0, 1.0). Alias for `random_sample` to ease forward-porting to the new random API.
`random_integers`function	`np.random.random_integers` documented signature `np.random.random_integers(low: number, high?: number \| null \| undefined, size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Random integers of type `numpy.int_` between `low` and `high`, inclusive.
`random_sample`function	`np.random.randomSample` signature `np.random.randomSample(size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Return random floats in the half-open interval [0.0, 1.0).
`RandomState`class	`np.random.RandomState` signature `new np.random.RandomState(seed?: Seed \| null \| undefined)`	Container for the slow Mersenne Twister pseudo-random number generator. Consider using a different BitGenerator with the Generator container instead.
`ranf`function	`np.random.ranf` documented signature `np.random.ranf(size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	This is an alias of `random_sample`. See `random_sample` for the complete documentation.
`rayleigh`function	`np.random.rayleigh` signature `np.random.rayleigh(scale?: number \| undefined, size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from a Rayleigh distribution.
`sample`function	`np.random.sample` documented signature `np.random.sample(size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	This is an alias of `random_sample`. See `random_sample` for the complete documentation.
`seed`function	`np.random.seed` documented signature `np.random.seed(seed?: Seed \| null \| undefined): void`	Reseed the singleton RandomState instance.
`SeedSequence`class	`np.random.SeedSequence` documented signature `new np.random.SeedSequence(entropy?: SeedLike \| null \| undefined, options?: SeedSequenceOptions \| undefined)`	SeedSequence mixes sources of entropy in a reproducible way to set the initial state for independent and very probably non-overlapping BitGenerators.
`set_state`function	`np.random.set_state` documented signature `np.random.set_state(words: bigint[]): void`	Set the internal state of the generator from a tuple.
`SFC64`class	`np.random.SFC64` documented signature `new np.random.SFC64(seed?: SeedLike \| SeedSequence \| null \| undefined)`	BitGenerator for Chris Doty-Humphrey's Small Fast Chaotic PRNG.
`shuffle`function	`np.random.shuffle` signature `np.random.shuffle(x: NDArray): void`	Modify a sequence in-place by shuffling its contents.
`standard_cauchy`function	`np.random.standard_cauchy` signature `np.random.standard_cauchy(size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from a standard Cauchy distribution with mode = 0.
`standard_exponential`function	`np.random.standard_exponential` signature `np.random.standard_exponential(size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from the standard exponential distribution.
`standard_gamma`function	`np.random.standard_gamma` signature `np.random.standard_gamma(shape: number, size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from a standard Gamma distribution.
`standard_normal`function	`np.random.standardNormal` signature `np.random.standardNormal(size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from a standard Normal distribution (mean=0, stdev=1).
`standard_t`function	`np.random.standard_t` signature `np.random.standard_t(df: number, size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from a standard Student's t distribution with `df` degrees of freedom.
`triangular`function	`np.random.triangular` signature `np.random.triangular(left: number, mode: number, right: number, size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from the triangular distribution over the interval `[left, right]`.
`uniform`function	`np.random.uniform` signature `np.random.uniform(low?: number \| { low?: number \| undefined; high?: number \| undefined; size?: Size \| undefined; } \| undefined, high?: number \| undefined, size?: Size \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from a uniform distribution.
`vonmises`function	`np.random.vonmises` signature `np.random.vonmises(mu: number, kappa: number, size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from a von Mises distribution.
`wald`function	`np.random.wald` signature `np.random.wald(mean: number, scale: number, size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from a Wald, or inverse Gaussian, distribution.
`weibull`function	`np.random.weibull` signature `np.random.weibull(a: number, size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from a Weibull distribution.
`zipf`function	`np.random.zipf` signature `np.random.zipf(a: number, size?: Size \| null \| undefined): number \| boolean \| NDArray \| Complex`	Draw samples from a Zipf distribution.

numpy.rec# 7 / 7

NumPy	numera	Summary
`array`function	`np.rec.array` documented signature `np.rec.array(obj: recarray \| ArrayLike[] \| unknown[][], opts?: RecArrayOptions \| undefined): recarray`	Construct a record array from a wide-variety of objects.
`find_duplicate`function	`np.rec.find_duplicate` documented signature `np.rec.find_duplicate(list: unknown[]): unknown[]`	Find duplication in a list, return a list of duplicated elements
`format_parser`class	`np.rec.format_parser` signature `new np.rec.format_parser(formats: string \| string[], names?: string \| string[] \| null \| undefined, _titles?: unknown, _aligned?: boolean \| undefined, _byteorder?: string \| null \| undefined)`	Class to convert formats, names, titles description to a dtype.
`fromarrays`function	`np.rec.fromarrays` documented signature `np.rec.fromarrays(arrayList: ArrayLike[], opts?: RecArrayOptions \| undefined): recarray`	Create a record array from a (flat) list of arrays
`fromrecords`function	`np.rec.fromrecords` documented signature `np.rec.fromrecords(recList: ArrayLike \| unknown[][], opts?: RecArrayOptions \| undefined): recarray`	Create a recarray from a list of records in text form.
`recarray`class	`np.rec.recarray` documented signature `new np.rec.recarray(shape: number \| number[], opts?: RecArrayOptions \| undefined)`	Construct an ndarray that allows field access using attributes.
`record`class	`np.rec.record` signature `new np.rec.record(shape: number \| number[], opts?: RecArrayOptions \| undefined)`	A data-type scalar that allows field access as attribute lookup.

numpy.strings# 46 / 46

NumPy	numera	Summary
`add`ufunc	`np.strings.add` documented signature `np.strings.add(a: StringLike, b: StringLike): StringArray`	Add arguments element-wise.
`capitalize`function	`np.strings.capitalize` documented signature `np.strings.capitalize(a: StringLike): StringArray`	Return a copy of `a` with only the first character of each element capitalized.
`center`function	`np.strings.center` signature `np.strings.center(a: StringLike, width: number, fillchar?: string \| undefined): StringArray`	Return a copy of `a` with its elements centered in a string of length `width`.
`count`function	`np.strings.count` signature `np.strings.count(a: StringLike, sub: StringLike, start?: number \| undefined, end?: number \| undefined): NDArray`	Returns an array with the number of non-overlapping occurrences of substring `sub` in the range [`start`, `end`).
`decode`function	`np.strings.decode` signature `np.strings.decode(a: StringLike, encoding?: string \| undefined, _errors?: string \| undefined): StringArray`	Calls :meth:`bytes.decode` element-wise.
`encode`function	`np.strings.encode` signature `np.strings.encode(a: StringLike, encoding?: string \| undefined, _errors?: string \| undefined): StringArray`	Calls :meth:`str.encode` element-wise.
`endswith`function	`np.strings.endswith` signature `np.strings.endswith(a: StringLike, suffix: StringLike, start?: number \| undefined, end?: number \| undefined): NDArray`	Returns a boolean array which is `True` where the string element in `a` ends with `suffix`, otherwise `False`.
`equal`ufunc	`np.strings.equal` documented signature `np.strings.equal(a: StringLike, b: StringLike): NDArray`	Return (x1 == x2) element-wise.
`expandtabs`function	`np.strings.expandtabs` signature `np.strings.expandtabs(a: StringLike, tabsize?: number \| undefined): StringArray`	Return a copy of each string element where all tab characters are replaced by one or more spaces.
`find`function	`np.strings.find` signature `np.strings.find(a: StringLike, sub: StringLike, start?: number \| undefined, end?: number \| undefined): NDArray`	For each element, return the lowest index in the string where substring `sub` is found, such that `sub` is contained in the range [`start`, `end`).
`greater`ufunc	`np.strings.greater` signature `np.strings.greater(a: StringLike, b: StringLike): NDArray`	Return the truth value of (x1 > x2) element-wise.
`greater_equal`ufunc	`np.strings.greater_equal` signature `np.strings.greater_equal(a: StringLike, b: StringLike): NDArray`	Return the truth value of (x1 >= x2) element-wise.
`index`function	`np.strings.index` signature `np.strings.index(a: StringLike, sub: StringLike, start?: number \| undefined, end?: number \| undefined): NDArray`	Like `find`, but raises :exc:`ValueError` when the substring is not found.
`isalnum`ufunc	`np.strings.isalnum` signature `np.strings.isalnum(a: StringLike): NDArray`	Returns true for each element if all characters in the string are alphanumeric and there is at least one character, false otherwise.
`isalpha`ufunc	`np.strings.isalpha` signature `np.strings.isalpha(a: StringLike): NDArray`	Returns true for each element if all characters in the data interpreted as a string are alphabetic and there is at least one character, false otherwise.
`isdecimal`ufunc	`np.strings.isdecimal` signature `np.strings.isdecimal(a: StringLike): NDArray`	For each element, return True if there are only decimal characters in the element.
`isdigit`ufunc	`np.strings.isdigit` signature `np.strings.isdigit(a: StringLike): NDArray`	Returns true for each element if all characters in the string are digits and there is at least one character, false otherwise.
`islower`ufunc	`np.strings.islower` signature `np.strings.islower(a: StringLike): NDArray`	Returns true for each element if all cased characters in the string are lowercase and there is at least one cased character, false otherwise.
`isnumeric`ufunc	`np.strings.isnumeric` signature `np.strings.isnumeric(a: StringLike): NDArray`	For each element, return True if there are only numeric characters in the element.
`isspace`ufunc	`np.strings.isspace` signature `np.strings.isspace(a: StringLike): NDArray`	Returns true for each element if there are only whitespace characters in the string and there is at least one character, false otherwise.
`istitle`ufunc	`np.strings.istitle` signature `np.strings.istitle(a: StringLike): NDArray`	Returns true for each element if the element is a titlecased string and there is at least one character, false otherwise.
`isupper`ufunc	`np.strings.isupper` signature `np.strings.isupper(a: StringLike): NDArray`	Return true for each element if all cased characters in the string are uppercase and there is at least one character, false otherwise.
`less`ufunc	`np.strings.less` signature `np.strings.less(a: StringLike, b: StringLike): NDArray`	Return the truth value of (x1 < x2) element-wise.
`less_equal`ufunc	`np.strings.less_equal` signature `np.strings.less_equal(a: StringLike, b: StringLike): NDArray`	Return the truth value of (x1 <= x2) element-wise.
`ljust`function	`np.strings.ljust` signature `np.strings.ljust(a: StringLike, width: number, fillchar?: string \| undefined): StringArray`	Return an array with the elements of `a` left-justified in a string of length `width`.
`lower`function	`np.strings.lower` documented signature `np.strings.lower(a: StringLike): StringArray`	Return an array with the elements converted to lowercase.
`lstrip`function	`np.strings.lstrip` signature `np.strings.lstrip(a: StringLike, chars?: string \| null \| undefined): StringArray`	For each element in `a`, return a copy with the leading characters removed.
`mod`function	`np.strings.mod` signature `np.strings.mod(a: StringLike, values: unknown): StringArray`	Return (a % i), that is pre-Python 2.6 string formatting (interpolation), element-wise for a pair of array_likes of str or unicode.
`multiply`function	`np.strings.multiply` documented signature `np.strings.multiply(a: StringLike, i: number \| NDArray): StringArray`	Return (a * i), that is string multiple concatenation, element-wise.
`not_equal`ufunc	`np.strings.not_equal` signature `np.strings.not_equal(a: StringLike, b: StringLike): NDArray`	Return (x1 != x2) element-wise.
`partition`function	`np.strings.partition` signature `np.strings.partition(a: StringLike, sep: StringLike): StringArray[]`	Partition each element in `a` around `sep`.
`replace`function	`np.strings.replace` signature `np.strings.replace(a: StringLike, old_: StringLike, new_: StringLike, count_?: number \| undefined): StringArray`	For each element in `a`, return a copy of the string with occurrences of substring `old` replaced by `new`.
`rfind`function	`np.strings.rfind` signature `np.strings.rfind(a: StringLike, sub: StringLike, start?: number \| undefined, end?: number \| undefined): NDArray`	For each element, return the highest index in the string where substring `sub` is found, such that `sub` is contained in the range [`start`, `end`).
`rindex`function	`np.strings.rindex` signature `np.strings.rindex(a: StringLike, sub: StringLike, start?: number \| undefined, end?: number \| undefined): NDArray`	Like `rfind`, but raises :exc:`ValueError` when the substring `sub` is not found.
`rjust`function	`np.strings.rjust` signature `np.strings.rjust(a: StringLike, width: number, fillchar?: string \| undefined): StringArray`	Return an array with the elements of `a` right-justified in a string of length `width`.
`rpartition`function	`np.strings.rpartition` signature `np.strings.rpartition(a: StringLike, sep: StringLike): StringArray[]`	Partition (split) each element around the right-most separator.
`rstrip`function	`np.strings.rstrip` signature `np.strings.rstrip(a: StringLike, chars?: string \| null \| undefined): StringArray`	For each element in `a`, return a copy with the trailing characters removed.
`slice`function	`np.strings.slice` signature `np.strings.slice(a: StringLike, start?: number \| undefined, stop?: number \| undefined, step?: number \| undefined): StringArray`	Slice the strings in `a` by slices specified by `start`, `stop`, `step`. Like in the regular Python `slice` object, if only `start` is specified then it is interpreted as the `stop`.
`startswith`function	`np.strings.startswith` signature `np.strings.startswith(a: StringLike, prefix: StringLike, start?: number \| undefined, end?: number \| undefined): NDArray`	Returns a boolean array which is `True` where the string element in `a` starts with `prefix`, otherwise `False`.
`str_len`ufunc	`np.strings.str_len` documented signature `np.strings.str_len(a: StringLike): NDArray`	Returns the length of each element. For byte strings, this is the number of bytes, while, for Unicode strings, it is the number of Unicode code points.
`strip`function	`np.strings.strip` signature `np.strings.strip(a: StringLike, chars?: string \| null \| undefined): StringArray`	For each element in `a`, return a copy with the leading and trailing characters removed.
`swapcase`function	`np.strings.swapcase` signature `np.strings.swapcase(a: StringLike): StringArray`	Return element-wise a copy of the string with uppercase characters converted to lowercase and vice versa.
`title`function	`np.strings.title` signature `np.strings.title(a: StringLike): StringArray`	Return element-wise title cased version of string or unicode.
`translate`function	`np.strings.translate` signature `np.strings.translate(a: StringLike, table: Map<string, string \| null>): StringArray`	For each element in `a`, return a copy of the string where all characters occurring in the optional argument `deletechars` are removed, and the remaining characters have been mapped through the given translation table.
`upper`function	`np.strings.upper` documented signature `np.strings.upper(a: StringLike): StringArray`	Return an array with the elements converted to uppercase.
`zfill`function	`np.strings.zfill` signature `np.strings.zfill(a: StringLike, width: number): StringArray`	Return the numeric string left-filled with zeros. A leading sign prefix (`+`/`-`) is handled by inserting the padding after the sign character rather than before.

numpy.testing# 14 / 14

NumPy	numera	Summary
`assert_`function	`np.testing.assert_` signature `np.testing.assert_(val: unknown, msg?: string \| (() => string) \| undefined): void`	Assert that works in release mode. Accepts callable msg to allow deferring evaluation until failure.
`assert_allclose`function	`np.testing.assertAllclose` documented signature `np.testing.assertAllclose(actual: ArrayLike, desired: ArrayLike, opts?: AllcloseOptions \| undefined): void`	Raises an AssertionError if two objects are not equal up to desired tolerance.
`assert_almost_equal`function	`np.testing.assertAlmostEqual` signature `np.testing.assertAlmostEqual(actual: Operand, desired: Operand, opts?: AlmostEqualOptions \| undefined): void`	Raises an AssertionError if two items are not equal up to desired precision.
`assert_approx_equal`function	`np.testing.assertApproxEqual` signature `np.testing.assertApproxEqual(actual: number, desired: number, opts?: ApproxEqualOptions \| undefined): void`	Raises an AssertionError if two items are not equal up to significant digits.
`assert_array_almost_equal`function	`np.testing.assertArrayAlmostEqual` signature `np.testing.assertArrayAlmostEqual(actual: ArrayLike, desired: ArrayLike, opts?: AlmostEqualOptions \| undefined): void`	Raises an AssertionError if two objects are not equal up to desired precision.
`assert_array_almost_equal_nulp`function	`np.testing.assertArrayAlmostEqualNulp` signature `np.testing.assertArrayAlmostEqualNulp(x: ArrayLike, y: ArrayLike, nulp?: number \| undefined): void`	Compare two arrays relatively to their spacing.
`assert_array_compare`function	`np.testing.assertArrayCompare` signature `np.testing.assertArrayCompare(comparison: (x: NDArray, y: NDArray) => boolean \| NDArray, actual: ArrayLike, desired: ArrayLike, opts?: AssertArrayCompareOptions \| undefined): void`
`assert_array_equal`function	`np.testing.assertArrayEqual` documented signature `np.testing.assertArrayEqual(actual: ArrayLike, desired: ArrayLike, opts?: AssertOptions \| undefined): void`	Raises an AssertionError if two array_like objects are not equal.
`assert_array_less`function	`np.testing.assertArrayLess` signature `np.testing.assertArrayLess(x: ArrayLike, y: ArrayLike, opts?: AssertOptions \| undefined): void`	Raises an AssertionError if two array_like objects are not ordered by less than.
`assert_array_max_ulp`function	`np.testing.assertArrayMaxUlp` documented signature `np.testing.assertArrayMaxUlp(a: ArrayLike, b: ArrayLike, opts?: MaxUlpOptions \| undefined): NDArray`	Check that all items of arrays differ in at most N Units in the Last Place.
`assert_equal`function	`np.testing.assertEqual` documented signature `np.testing.assertEqual(actual: unknown, desired: unknown, opts?: AssertOptions \| undefined): void`	Raises an AssertionError if two objects are not equal.
`assert_raises`function	`np.testing.assertRaises` documented signature `np.testing.assertRaises<A extends unknown[]>(errorClass: ErrorCtor, fn: (...args: A) => unknown, ...args: A): Error`	Fail unless an exception of class exception_class is thrown by callable when invoked with arguments args and keyword arguments kwargs. If a different type of exception is thrown, it will not be caught, and the test case will be deemed to have suffered an error, exactly as for an unexpected exception.
`assert_raises_regex`function	`np.testing.assertRaisesRegex` signature `np.testing.assertRaisesRegex<A extends unknown[]>(errorClass: ErrorCtor, pattern: string \| RegExp, fn: (...args: A) => unknown, ...args: A): Error`	Fail unless an exception of class exception_class and with message that matches expected_regexp is thrown by callable when invoked with arguments args and keyword arguments kwargs.
`assert_string_equal`function	`np.testing.assertStringEqual` documented signature `np.testing.assertStringEqual(actual: string, desired: string): void`	Test if two strings are equal.