NumPy is a favorite and valuable Python library for scientific computing that provides functionality as the name suggests, with arrays, numerical computation, and other scientific computing tasks. It has a number of functions and methods for working with arrays, including mathematical, logical, and statistical operations.
1. numpy.all(): This function returns True if all elements of the input array evaluate to True.
2. numpy.any(): This function returns True if any elements of the input array evaluate to True.
3. numpy.argmax(): This function returns the indices of the maximum value along an axis.
4. numpy.argmin(): This function returns the indices of the minimum value along an axis.
5. numpy.argsort(): This function returns the indices that would sort an array.
6. numpy.around(): This function returns an array with the elements rounded to the given number of decimals.
7. numpy.average(): This function returns the weighted average of the array elements.
8. numpy.bincount(): This function counts the number of occurrences of each value in an array.
9. numpy.clip(): This function clips the values in an array to the given range.
10. numpy.concatenate(): This function concatenates an array of arrays into a single array.
11. numpy.corrcoef(): This function returns a matrix of correlation coefficients calculated from a given array.
12. numpy.cov(): This function returns the covariance matrix of the array elements.
13. numpy.cross(): This function returns the vector cross product of two arrays.
14. numpy.diag(): This function returns a diagonal array from the given array.
15. numpy.diagflat(): This function returns a 2-D array with the elements of the input array as the diagonal elements.
16. numpy.diagonal(): This function returns the diagonal elements of the array.
17. numpy.dot(): This function returns the dot product of two arrays.
18. numpy.empty(): This function returns a new array of given shape and type, without initializing the elements.
19. numpy.eye(): This function returns a 2-D array with ones on the diagonal and zeros elsewhere.
20. numpy.fill_diagonal(): This function fills the diagonal elements of an array with the given value.
21. numpy.flip(): This function reverses the order of the elements in an array along the given axis.
22. numpy.flipud(): This function reverses the order of the elements in a 2-D array along the vertical axis.
23. numpy.fliplr(): This function reverses the order of the elements in a 2-D array along the horizontal axis.
24. numpy.hsplit(): This function splits an array into multiple sub-arrays horizontally.
25. numpy.identity(): This function returns an identity array of the given size.
26. numpy.insert(): This function inserts a given value in an array before the given index along the given axis.
27. numpy.invert(): This function inverts a binary array.
28. numpy.max(): This function returns the maximum value of the array.
29. numpy.mean(): This function returns the mean of the array elements.
30. numpy.median(): This function returns the median of the array elements.
31. numpy.min(): This function returns the minimum value of the array.
32. numpy.nonzero(): This function returns the indices of the non-zero elements in an array.
33. numpy.ones(): This function returns an array of ones of the given shape and type.
34. numpy.outer(): This function returns the outer product of two arrays.
35. numpy.prod(): This function returns the product of the array elements.
36. numpy.ptp(): This function returns the range of the array.
37. numpy.ravel(): This function returns a flattened array.
38. numpy.repeat(): This function repeats the elements of an array.
39. numpy.reshape(): This function returns an array with the same data but different shape.
40. numpy.resize(): This function returns a new array with the specified size.
41. numpy.round(): This function rounds the elements of an array to the given number of decimals.
42. numpy.searchsorted(): This function finds the indices of the elements in an array such that, if the corresponding elements are inserted into the array, the order of the array is preserved.
43. numpy.sort(): This function returns a sorted array.
44. numpy.split(): This function splits an array into multiple sub-arrays.
45. numpy.sum(): This function returns the sum of the array elements.
46. numpy.swapaxes(): This function swaps the two axes of an array.
47. numpy.take(): This function returns the elements of an array at the given indices.
48. numpy.trace(): This function calculates the sum of the diagonal elements of a 2-D array.
49. numpy.transpose(): This function returns a transposed array.
50. numpy.unique(): This function returns a sorted list of unique elements in an array.
51. numpy.arange() – Returns an array with evenly spaced values within a given interval.
52. numpy.ndarray.shape – Returns a tuple of integers representing the size of the array.
53. numpy.ndarray.reshape – Reshapes the array without changing its data.
54. numpy.ndarray.T – Transposes the array.
55. numpy.empty – Creates an array filled with random values.
56. numpy.linspace – Returns evenly spaced numbers over a specified interval.
57. numpy.logspace – Returns evenly spaced numbers on a logarithmic scale over a specified interval.
58. numpy.random.rand – Creates an array of random values from a uniform distribution.
59. numpy.random.randn – Creates an array of random values from a normal distribution.
60. numpy.random.randint – Creates an array of random integers from a specified range.
61. numpy.random.choice – Creates an array of randomly selected elements from an input array.
62. numpy.sort – Sorts the elements of an array.
63. numpy.argsort – Returns the indices that would sort an array.
64. numpy.where – Returns the indices of elements that satisfy a given condition.
65. numpy.unique – Returns the unique elements of an array.
66. numpy.bincount – Counts the number of occurrences of each value in an array.
67. numpy.concatenate – Joins two or more arrays together.
68. numpy.vstack – Stacks arrays in sequence vertically (row wise).
69. numpy.hstack – Stacks arrays in sequence horizontally (column wise).
70. numpy.dstack – Stacks arrays in sequence depth wise (along third dimension).
71. numpy.split – Splits an array into multiple sub-arrays.
72. numpy.hsplit – Splits an array into multiple sub-arrays horizontally (column wise).
73. numpy.vsplit – Splits an array into multiple sub-arrays vertically (row wise).
74. numpy.argwhere() – Returns the indices of elements in an input array where the given condition is satisfied.
75. numpy.around() – Evenly round to the given number of decimals.
76. numpy.bincount() – Count number of occurrences of each value in array of non-negative ints.
77. numpy.choose() – Use an index array to construct a new array from a set of choices.
78. numpy.clip() – Clip (limit) the values in an array.
79. numpy.concatenate() – Join a sequence of arrays along an existing axis.
80. numpy.corrcoef() – Return Pearson product-moment correlation coefficients.
81. numpy.cov() – Estimate a covariance matrix, given data and weights.
82. numpy.cumsum() – Return the cumulative sum of the elements along a given axis.
83. numpy.diag() – Extract a diagonal or construct a diagonal array.
84. numpy.diagflat() – Create a two-dimensional array with the flattened input as a diagonal.
85. numpy.diagonal() – Return specified diagonals.
86. numpy.diff() – Calculate the n-th discrete difference along given axis.
87. numpy.dot() – Dot product of two arrays.
88. numpy.dstack() – Stack arrays in sequence depth wise (along third axis).
89. numpy.fill_diagonal() – Fill the main diagonal of the given array of any dimensionality.
90. numpy.fliplr() – Flip array in the left/right direction.
91. numpy.flipud() – Flip array in the up/down direction.
92. numpy.hsplit() – Split array into multiple sub-arrays horizontally (column-wise).
93. numpy.hstack() – Stack arrays in sequence horizontally (column wise).
94. numpy.insert() – Insert values along the given axis before the given indices.
95. numpy.interp() – one-dimensional linear interpolation.
96. numpy.invert() – Compute bit-wise inversion, or bit-wise NOT, element-wise.
97. numpy.lexsort() – Perform an indirect sort using a sequence of keys.
98. numpy.max() – Return the maximum of an array or maximum along an axis.
99. numpy.maximum() – Element-wise maximum of array elements.
100. numpy.mean() – Compute the arithmetic mean along the specified axis.
101. numpy.median() – Compute the median along the specified axis.
102. numpy.min() – Return the minimum of an array or minimum along an axis.
103. numpy.minimum() – Element-wise minimum of array elements.
104. numpy.nonzero() – Return the indices of the elements that are non-zero.
105. numpy.percentile() – Compute the qth percentile of the data along the specified axis.
106. numpy.prod() – Return the product of array elements over a given axis.
107. numpy.ptp() – Range of values (maximum – minimum) along an axis.
108. numpy.put() – Replaces specified elements of an array with given values.
109. numpy.ravel() – Return a contiguous flattened array.
110. numpy.repeat() – Repeat elements of an array.
111. numpy.reshape() – Gives a new shape to an array without changing its data.
112. numpy.resize() – Return a new array with the specified shape.
113. numpy.round() – Round an array to the given number of decimals.
114. numpy.searchsorted() – Find indices where elements should be inserted to maintain order.
115. numpy.sort() – Return a sorted copy of an array.
116. numpy.squeeze() – Remove single-dimensional entries from the shape of an array.
117. numpy.std() – Compute the standard deviation along the specified axis.
118. numpy.sum() – Sum of array elements over a given axis.
119. numpy.swapaxes() – Interchange two axes of an array.
120. numpy.take() – Take elements from an array along an axis.
121. numpy.allclose: Test whether two arrays are element-wise equal within a tolerance.
122. numpy.alltrue: Return True if all elements are nonzero, True or False.
123. numpy.any: Test whether any array element along a given axis evaluates to True.
124. numpy.append: Append values to the end of an array.
125. numpy.arange: Return evenly spaced values within a given interval.
126. numpy.arccos: Trigonometric inverse cosine, element-wise.
127. numpy.arccosh: Hyperbolic inverse cosine, element-wise.
128. numpy.arcsin: Trigonometric inverse sine, element-wise.
129. numpy.arcsinh: Hyperbolic inverse sine, element-wise.
130. numpy.arctan: Trigonometric inverse tangent, element-wise.
131. numpy.arctan2: Element-wise arc tangent of x1/x2 choosing the quadrant correctly.
132. numpy.arctanh: Hyperbolic inverse tangent, element-wise.
133. numpy.argmax: Return indices of the maximum values along an axis.
134. numpy.argmin: Return indices of the minimum values along an axis.
135. numpy.argsort: Returns the indices that would sort an array.
136. numpy.around: Evenly round to the given number of decimals.
137. numpy.array: Create an array.
138. numpy.array2string: Return a string representation of an array.
139. numpy.asarray: Convert input to an array.
140. numpy.asfarray: Convert input to a floating point ndarray.
141. numpy.asfortranarray: Convert input to an ndarray with column-major memory order.
142. numpy.asmatrix: Interpret the input as a matrix.
143. numpy.asscalar: Convert an array of size 1 to its scalar equivalent.
144. numpy.atleast_1d: View inputs as arrays with at least one dimension.
145. numpy.atleast_2d: View inputs as arrays with at least two dimensions.
146. numpy.atleast_3d: View inputs as arrays with at least three dimensions.
147. numpy.bartlett: Bartlett window.
148. numpy.base_repr: Return a string representation of a number in the given base system.
149. numpy.binary_repr: Return the binary representation of the input number as a string.
150. numpy.bincount: Count number of occurrences of each value in array of non-negative ints.
151. numpy.blackman: Blackman window.
152. numpy.bmat: Build a matrix object from a string, nested sequence, or array.
153. numpy.bool: Return a new array of given shape filled with False.
154. numpy.bool8: Return a new array of given shape filled with False.
155. numpy.broadcast: Produce an object that mimics broadcasting.
156. numpy.ceil: Return the ceiling of the input, element-wise.
157. numpy.column_stack: Stack 1-D arrays as columns into a 2-D array.
158. numpy.common_type: Returns the common type of two or more arrays.
159. numpy.compare_chararrays: Compare two character arrays.
160. numpy.compress: Return selected slices of an array along given axis.
161. numpy.concatenate: Join a sequence of arrays along an existing axis.
162. numpy.conjugate: Return the complex conjugate, element-wise.
163. numpy.convolve: N-dimensional convolution.
164. numpy.copy: Return an array copy of the given object.
165. numpy.corrcoef: Return Pearson product-moment correlation coefficients.
166. numpy.correlate: Cross-correlation of two 1-dimensional sequences.
167. numpy.cos: Trigonometric cosine, element-wise.
168. numpy.cosh: Hyperbolic cosine, element-wise.
169. numpy.cov: Estimate a covariance matrix, given data and weights.
170. numpy.cross: Return the cross product of two (arrays of) vectors.
171. numpy.cumprod: Return the cumulative product of elements along a given axis.
172. numpy.cumsum: Return the cumulative sum of the elements along a given axis.
173. numpy.datetime_as_string: Return a datetime object as a string.
174. numpy.deg2rad: Convert angles from degrees to radians.
175. numpy.degrees: Convert angles from radians to degrees.
176. numpy.delete: Return a new array with sub-arrays along an axis deleted.
177. numpy.diag: Return the diagonal (or off-diagonal) elements of a square matrix as a 1D array, or convert a 1D array into a square matrix with zeros on the off-diagonal.
178. numpy.diag_indices: Return the indices to access the main diagonal of an array.
179. numpy.diag_indices_from: Return the indices to access the main diagonal of an array.
180. numpy.diagonal: Return specified diagonals.
181. numpy.digitize: Return the indices of the bins to which each value in input array belongs.
182. numpy.divide: Divide arguments element-wise.
183. numpy.dot: Dot product of two arrays.
184. numpy.dtype: Create a data type object.
185. numpy.ediff1d: The differences between consecutive elements of an array.
186. numpy.empty: Return a new array of given shape and type, without initializing entries.
187. numpy.empty_like: Return a new array with the same shape and type as a given array.
188. numpy.equal: Test element-wise for equality within a tolerance.
189. numpy.exp: Calculate the exponential of all elements in the input array.
190. numpy.expand_dims: Expand the shape of an array.
191. numpy.expm1: Calculate exp(x) – 1 for all elements in the array.
192. numpy.extract: Return the elements of an array that satisfy some condition.
193. numpy.eye: Return a 2-D array with ones on the diagonal and zeros elsewhere.
194. numpy.fabs: Compute the absolute values element-wise for complex numbers.
195. numpy.fft: Return discrete Fourier transform of real or complex sequence.
196. numpy.fill_diagonal: Fill the main diagonal of the given array of any dimensionality.
197. numpy.find_common_type: Find the common type of two or more arrays.
198. numpy.fix: Round to nearest integer towards zero.
199. numpy.flatnonzero: Return indices that are non-zero in the flattened version of the input array.
200. numpy.fliplr: Flip array in the left/right direction.
201. numpy.flipud: Flip array in the up/down direction.
202. numpy.flatten: Return a copy of the array collapsed into one dimension.
203. numpy.floor: Return the floor of the input, element-wise.
204. numpy.floor_divide: Return the largest integer smaller or equal to the division of the inputs.
205. numpy.fmod: Return the element-wise remainder of division.
206. numpy.frombuffer: Interpret a buffer as a 1-dimensional array.
207. numpy.fromfile: Construct an array from data in a text or binary file.
208. numpy.fromfunction: Construct an array by executing a function over each coordinate.
209. numpy.fromiter: Create an array from an iterator.
210. numpy.fromstring: A new 1-D array initialized from text data in a string.
211. numpy.genfromtxt: Load data from a text file, with missing values handled as specified.
212. numpy.getbufsize: Return the default buffer size used in ufuncs.
213. numpy.geterr: Get the current way of treating floating point errors.
214. numpy.geterrcall: Return the current callback for floating point errors.
215. numpy.geterrobj: Return the current object for floating point errors.
216. numpy.gradient: Return the gradient of an N-dimensional array.
217. numpy.greater: Return the truth value of (x1 > x2) element-wise.
218. numpy.greater_equal: Return the truth value of (x1 >= x2) element-wise.
219. numpy.hamming: Hamming window.
220. numpy.hanning: Hanning window.
221. numpy.histogram: Compute the histogram of a set of data.
222. numpy.histogram2d: Compute the bi-dimensional histogram of two data samples.
223. numpy.histogramdd: Compute the multidimensional histogram of some data.
224. numpy.hsplit: Split an array into multiple sub-arrays horizontally (column-wise).
225. numpy.hstack: Stack arrays in sequence horizontally (column wise).
226. numpy.indices: Return an array representing the indices of a grid.
227. numpy.inner: Inner product of two arrays.
228. numpy.insert: Insert values into an array.
229. numpy.interp: One-dimensional linear interpolation.
230. numpy.invert: Compute bit-wise inversion, or bit-wise NOT, element-wise.
231. numpy.isclose: Returns a boolean array where two arrays are element-wise equal within a tolerance.
232. numpy.iscomplex: Returns a boolean array where the elements of x are complex numbers.
2. numpy.any(): This function returns True if any elements of the input array evaluate to True.
3. numpy.argmax(): This function returns the indices of the maximum value along an axis.
4. numpy.argmin(): This function returns the indices of the minimum value along an axis.
5. numpy.argsort(): This function returns the indices that would sort an array.
6. numpy.around(): This function returns an array with the elements rounded to the given number of decimals.
7. numpy.average(): This function returns the weighted average of the array elements.
8. numpy.bincount(): This function counts the number of occurrences of each value in an array.
9. numpy.clip(): This function clips the values in an array to the given range.
10. numpy.concatenate(): This function concatenates an array of arrays into a single array.
11. numpy.corrcoef(): This function returns a matrix of correlation coefficients calculated from a given array.
12. numpy.cov(): This function returns the covariance matrix of the array elements.
13. numpy.cross(): This function returns the vector cross product of two arrays.
14. numpy.diag(): This function returns a diagonal array from the given array.
15. numpy.diagflat(): This function returns a 2-D array with the elements of the input array as the diagonal elements.
16. numpy.diagonal(): This function returns the diagonal elements of the array.
17. numpy.dot(): This function returns the dot product of two arrays.
18. numpy.empty(): This function returns a new array of given shape and type, without initializing the elements.
19. numpy.eye(): This function returns a 2-D array with ones on the diagonal and zeros elsewhere.
20. numpy.fill_diagonal(): This function fills the diagonal elements of an array with the given value.
21. numpy.flip(): This function reverses the order of the elements in an array along the given axis.
22. numpy.flipud(): This function reverses the order of the elements in a 2-D array along the vertical axis.
23. numpy.fliplr(): This function reverses the order of the elements in a 2-D array along the horizontal axis.
24. numpy.hsplit(): This function splits an array into multiple sub-arrays horizontally.
25. numpy.identity(): This function returns an identity array of the given size.
26. numpy.insert(): This function inserts a given value in an array before the given index along the given axis.
27. numpy.invert(): This function inverts a binary array.
28. numpy.max(): This function returns the maximum value of the array.
29. numpy.mean(): This function returns the mean of the array elements.
30. numpy.median(): This function returns the median of the array elements.
31. numpy.min(): This function returns the minimum value of the array.
32. numpy.nonzero(): This function returns the indices of the non-zero elements in an array.
33. numpy.ones(): This function returns an array of ones of the given shape and type.
34. numpy.outer(): This function returns the outer product of two arrays.
35. numpy.prod(): This function returns the product of the array elements.
36. numpy.ptp(): This function returns the range of the array.
37. numpy.ravel(): This function returns a flattened array.
38. numpy.repeat(): This function repeats the elements of an array.
39. numpy.reshape(): This function returns an array with the same data but different shape.
40. numpy.resize(): This function returns a new array with the specified size.
41. numpy.round(): This function rounds the elements of an array to the given number of decimals.
42. numpy.searchsorted(): This function finds the indices of the elements in an array such that, if the corresponding elements are inserted into the array, the order of the array is preserved.
43. numpy.sort(): This function returns a sorted array.
44. numpy.split(): This function splits an array into multiple sub-arrays.
45. numpy.sum(): This function returns the sum of the array elements.
46. numpy.swapaxes(): This function swaps the two axes of an array.
47. numpy.take(): This function returns the elements of an array at the given indices.
48. numpy.trace(): This function calculates the sum of the diagonal elements of a 2-D array.
49. numpy.transpose(): This function returns a transposed array.
50. numpy.unique(): This function returns a sorted list of unique elements in an array.
51. numpy.arange() – Returns an array with evenly spaced values within a given interval.
52. numpy.ndarray.shape – Returns a tuple of integers representing the size of the array.
53. numpy.ndarray.reshape – Reshapes the array without changing its data.
54. numpy.ndarray.T – Transposes the array.
55. numpy.empty – Creates an array filled with random values.
56. numpy.linspace – Returns evenly spaced numbers over a specified interval.
57. numpy.logspace – Returns evenly spaced numbers on a logarithmic scale over a specified interval.
58. numpy.random.rand – Creates an array of random values from a uniform distribution.
59. numpy.random.randn – Creates an array of random values from a normal distribution.
60. numpy.random.randint – Creates an array of random integers from a specified range.
61. numpy.random.choice – Creates an array of randomly selected elements from an input array.
62. numpy.sort – Sorts the elements of an array.
63. numpy.argsort – Returns the indices that would sort an array.
64. numpy.where – Returns the indices of elements that satisfy a given condition.
65. numpy.unique – Returns the unique elements of an array.
66. numpy.bincount – Counts the number of occurrences of each value in an array.
67. numpy.concatenate – Joins two or more arrays together.
68. numpy.vstack – Stacks arrays in sequence vertically (row wise).
69. numpy.hstack – Stacks arrays in sequence horizontally (column wise).
70. numpy.dstack – Stacks arrays in sequence depth wise (along third dimension).
71. numpy.split – Splits an array into multiple sub-arrays.
72. numpy.hsplit – Splits an array into multiple sub-arrays horizontally (column wise).
73. numpy.vsplit – Splits an array into multiple sub-arrays vertically (row wise).
74. numpy.argwhere() – Returns the indices of elements in an input array where the given condition is satisfied.
75. numpy.around() – Evenly round to the given number of decimals.
76. numpy.bincount() – Count number of occurrences of each value in array of non-negative ints.
77. numpy.choose() – Use an index array to construct a new array from a set of choices.
78. numpy.clip() – Clip (limit) the values in an array.
79. numpy.concatenate() – Join a sequence of arrays along an existing axis.
80. numpy.corrcoef() – Return Pearson product-moment correlation coefficients.
81. numpy.cov() – Estimate a covariance matrix, given data and weights.
82. numpy.cumsum() – Return the cumulative sum of the elements along a given axis.
83. numpy.diag() – Extract a diagonal or construct a diagonal array.
84. numpy.diagflat() – Create a two-dimensional array with the flattened input as a diagonal.
85. numpy.diagonal() – Return specified diagonals.
86. numpy.diff() – Calculate the n-th discrete difference along given axis.
87. numpy.dot() – Dot product of two arrays.
88. numpy.dstack() – Stack arrays in sequence depth wise (along third axis).
89. numpy.fill_diagonal() – Fill the main diagonal of the given array of any dimensionality.
90. numpy.fliplr() – Flip array in the left/right direction.
91. numpy.flipud() – Flip array in the up/down direction.
92. numpy.hsplit() – Split array into multiple sub-arrays horizontally (column-wise).
93. numpy.hstack() – Stack arrays in sequence horizontally (column wise).
94. numpy.insert() – Insert values along the given axis before the given indices.
95. numpy.interp() – one-dimensional linear interpolation.
96. numpy.invert() – Compute bit-wise inversion, or bit-wise NOT, element-wise.
97. numpy.lexsort() – Perform an indirect sort using a sequence of keys.
98. numpy.max() – Return the maximum of an array or maximum along an axis.
99. numpy.maximum() – Element-wise maximum of array elements.
100. numpy.mean() – Compute the arithmetic mean along the specified axis.
101. numpy.median() – Compute the median along the specified axis.
102. numpy.min() – Return the minimum of an array or minimum along an axis.
103. numpy.minimum() – Element-wise minimum of array elements.
104. numpy.nonzero() – Return the indices of the elements that are non-zero.
105. numpy.percentile() – Compute the qth percentile of the data along the specified axis.
106. numpy.prod() – Return the product of array elements over a given axis.
107. numpy.ptp() – Range of values (maximum – minimum) along an axis.
108. numpy.put() – Replaces specified elements of an array with given values.
109. numpy.ravel() – Return a contiguous flattened array.
110. numpy.repeat() – Repeat elements of an array.
111. numpy.reshape() – Gives a new shape to an array without changing its data.
112. numpy.resize() – Return a new array with the specified shape.
113. numpy.round() – Round an array to the given number of decimals.
114. numpy.searchsorted() – Find indices where elements should be inserted to maintain order.
115. numpy.sort() – Return a sorted copy of an array.
116. numpy.squeeze() – Remove single-dimensional entries from the shape of an array.
117. numpy.std() – Compute the standard deviation along the specified axis.
118. numpy.sum() – Sum of array elements over a given axis.
119. numpy.swapaxes() – Interchange two axes of an array.
120. numpy.take() – Take elements from an array along an axis.
121. numpy.allclose: Test whether two arrays are element-wise equal within a tolerance.
122. numpy.alltrue: Return True if all elements are nonzero, True or False.
123. numpy.any: Test whether any array element along a given axis evaluates to True.
124. numpy.append: Append values to the end of an array.
125. numpy.arange: Return evenly spaced values within a given interval.
126. numpy.arccos: Trigonometric inverse cosine, element-wise.
127. numpy.arccosh: Hyperbolic inverse cosine, element-wise.
128. numpy.arcsin: Trigonometric inverse sine, element-wise.
129. numpy.arcsinh: Hyperbolic inverse sine, element-wise.
130. numpy.arctan: Trigonometric inverse tangent, element-wise.
131. numpy.arctan2: Element-wise arc tangent of x1/x2 choosing the quadrant correctly.
132. numpy.arctanh: Hyperbolic inverse tangent, element-wise.
133. numpy.argmax: Return indices of the maximum values along an axis.
134. numpy.argmin: Return indices of the minimum values along an axis.
135. numpy.argsort: Returns the indices that would sort an array.
136. numpy.around: Evenly round to the given number of decimals.
137. numpy.array: Create an array.
138. numpy.array2string: Return a string representation of an array.
139. numpy.asarray: Convert input to an array.
140. numpy.asfarray: Convert input to a floating point ndarray.
141. numpy.asfortranarray: Convert input to an ndarray with column-major memory order.
142. numpy.asmatrix: Interpret the input as a matrix.
143. numpy.asscalar: Convert an array of size 1 to its scalar equivalent.
144. numpy.atleast_1d: View inputs as arrays with at least one dimension.
145. numpy.atleast_2d: View inputs as arrays with at least two dimensions.
146. numpy.atleast_3d: View inputs as arrays with at least three dimensions.
147. numpy.bartlett: Bartlett window.
148. numpy.base_repr: Return a string representation of a number in the given base system.
149. numpy.binary_repr: Return the binary representation of the input number as a string.
150. numpy.bincount: Count number of occurrences of each value in array of non-negative ints.
151. numpy.blackman: Blackman window.
152. numpy.bmat: Build a matrix object from a string, nested sequence, or array.
153. numpy.bool: Return a new array of given shape filled with False.
154. numpy.bool8: Return a new array of given shape filled with False.
155. numpy.broadcast: Produce an object that mimics broadcasting.
156. numpy.ceil: Return the ceiling of the input, element-wise.
157. numpy.column_stack: Stack 1-D arrays as columns into a 2-D array.
158. numpy.common_type: Returns the common type of two or more arrays.
159. numpy.compare_chararrays: Compare two character arrays.
160. numpy.compress: Return selected slices of an array along given axis.
161. numpy.concatenate: Join a sequence of arrays along an existing axis.
162. numpy.conjugate: Return the complex conjugate, element-wise.
163. numpy.convolve: N-dimensional convolution.
164. numpy.copy: Return an array copy of the given object.
165. numpy.corrcoef: Return Pearson product-moment correlation coefficients.
166. numpy.correlate: Cross-correlation of two 1-dimensional sequences.
167. numpy.cos: Trigonometric cosine, element-wise.
168. numpy.cosh: Hyperbolic cosine, element-wise.
169. numpy.cov: Estimate a covariance matrix, given data and weights.
170. numpy.cross: Return the cross product of two (arrays of) vectors.
171. numpy.cumprod: Return the cumulative product of elements along a given axis.
172. numpy.cumsum: Return the cumulative sum of the elements along a given axis.
173. numpy.datetime_as_string: Return a datetime object as a string.
174. numpy.deg2rad: Convert angles from degrees to radians.
175. numpy.degrees: Convert angles from radians to degrees.
176. numpy.delete: Return a new array with sub-arrays along an axis deleted.
177. numpy.diag: Return the diagonal (or off-diagonal) elements of a square matrix as a 1D array, or convert a 1D array into a square matrix with zeros on the off-diagonal.
178. numpy.diag_indices: Return the indices to access the main diagonal of an array.
179. numpy.diag_indices_from: Return the indices to access the main diagonal of an array.
180. numpy.diagonal: Return specified diagonals.
181. numpy.digitize: Return the indices of the bins to which each value in input array belongs.
182. numpy.divide: Divide arguments element-wise.
183. numpy.dot: Dot product of two arrays.
184. numpy.dtype: Create a data type object.
185. numpy.ediff1d: The differences between consecutive elements of an array.
186. numpy.empty: Return a new array of given shape and type, without initializing entries.
187. numpy.empty_like: Return a new array with the same shape and type as a given array.
188. numpy.equal: Test element-wise for equality within a tolerance.
189. numpy.exp: Calculate the exponential of all elements in the input array.
190. numpy.expand_dims: Expand the shape of an array.
191. numpy.expm1: Calculate exp(x) – 1 for all elements in the array.
192. numpy.extract: Return the elements of an array that satisfy some condition.
193. numpy.eye: Return a 2-D array with ones on the diagonal and zeros elsewhere.
194. numpy.fabs: Compute the absolute values element-wise for complex numbers.
195. numpy.fft: Return discrete Fourier transform of real or complex sequence.
196. numpy.fill_diagonal: Fill the main diagonal of the given array of any dimensionality.
197. numpy.find_common_type: Find the common type of two or more arrays.
198. numpy.fix: Round to nearest integer towards zero.
199. numpy.flatnonzero: Return indices that are non-zero in the flattened version of the input array.
200. numpy.fliplr: Flip array in the left/right direction.
201. numpy.flipud: Flip array in the up/down direction.
202. numpy.flatten: Return a copy of the array collapsed into one dimension.
203. numpy.floor: Return the floor of the input, element-wise.
204. numpy.floor_divide: Return the largest integer smaller or equal to the division of the inputs.
205. numpy.fmod: Return the element-wise remainder of division.
206. numpy.frombuffer: Interpret a buffer as a 1-dimensional array.
207. numpy.fromfile: Construct an array from data in a text or binary file.
208. numpy.fromfunction: Construct an array by executing a function over each coordinate.
209. numpy.fromiter: Create an array from an iterator.
210. numpy.fromstring: A new 1-D array initialized from text data in a string.
211. numpy.genfromtxt: Load data from a text file, with missing values handled as specified.
212. numpy.getbufsize: Return the default buffer size used in ufuncs.
213. numpy.geterr: Get the current way of treating floating point errors.
214. numpy.geterrcall: Return the current callback for floating point errors.
215. numpy.geterrobj: Return the current object for floating point errors.
216. numpy.gradient: Return the gradient of an N-dimensional array.
217. numpy.greater: Return the truth value of (x1 > x2) element-wise.
218. numpy.greater_equal: Return the truth value of (x1 >= x2) element-wise.
219. numpy.hamming: Hamming window.
220. numpy.hanning: Hanning window.
221. numpy.histogram: Compute the histogram of a set of data.
222. numpy.histogram2d: Compute the bi-dimensional histogram of two data samples.
223. numpy.histogramdd: Compute the multidimensional histogram of some data.
224. numpy.hsplit: Split an array into multiple sub-arrays horizontally (column-wise).
225. numpy.hstack: Stack arrays in sequence horizontally (column wise).
226. numpy.indices: Return an array representing the indices of a grid.
227. numpy.inner: Inner product of two arrays.
228. numpy.insert: Insert values into an array.
229. numpy.interp: One-dimensional linear interpolation.
230. numpy.invert: Compute bit-wise inversion, or bit-wise NOT, element-wise.
231. numpy.isclose: Returns a boolean array where two arrays are element-wise equal within a tolerance.
232. numpy.iscomplex: Returns a boolean array where the elements of x are complex numbers.