- Trending Categories
Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
Physics
Chemistry
Biology
Mathematics
English
Economics
Psychology
Social Studies
Fashion Studies
Legal Studies
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
Found 1204 Articles for Numpy
![AmitDiwan](https://www.tutorialspoint.com/assets/profiles/123055/profile/60_187394-1565938756.jpg)
81 Views
To return a masked array containing the same data, but with a new shape, use the ma.MaskedArray.reshape() method in Numpy. Give a new shape to the array without changing its data. The order is set using the "order" parameter. The 'F' order determines whether the array data should be viewed as in FORTRAN i.e. F (column-major).The new shape should be compatible with the original shape. If an integer is supplied, then the result will be a 1-D array of that length.The order determines whether the array data should be viewed as in C (row-major) or FORTRAN (column-major) order. Returns a ... Read More
![AmitDiwan](https://www.tutorialspoint.com/assets/profiles/123055/profile/60_187394-1565938756.jpg)
90 Views
To return a masked array containing the same data, but with a new shape, use the ma.MaskedArray.reshape() method in Numpy. Give a new shape to the array without changing its data. The order is set using the "order" parameter. The 'C' order determines whether the array data should be viewed as in C (row-major).The new shape should be compatible with the original shape. If an integer is supplied, then the result will be a 1-D array of that length.The order determines whether the array data should be viewed as in C (row-major) or FORTRAN (column-major) order. Returns a masked array ... Read More
![AmitDiwan](https://www.tutorialspoint.com/assets/profiles/123055/profile/60_187394-1565938756.jpg)
111 Views
To return the inner product of two masked arrays, use the ma.inner() method in Python Numpy. Ordinary inner product of vectors for 1-D arrays (without complex conjugation), in higher dimensions a sum product over the last axes.The out parameter suggests, if both the arrays are scalars or both 1-D arrays then a scalar is returned; otherwise an array is returned. out.shape = (*a.shape[:-1], *b.shape[:-1]).A masked array is the combination of a standard numpy.ndarray and a mask. A mask is either nomask, indicating that no value of the associated array is invalid, or an array of booleans that determines for each ... Read More
![AmitDiwan](https://www.tutorialspoint.com/assets/profiles/123055/profile/60_187394-1565938756.jpg)
118 Views
To return the inner product of two masked arrays, use the ma.inner() method in Python Numpy. Ordinary inner product of vectors for 1-D arrays (without complex conjugation), in higher dimensions a sum product over the last axes.The out parameter suggests, if both the arrays are scalars or both 1-D arrays then a scalar is returned; otherwise an array is returned. out.shape = (*a.shape[:-1], *b.shape[:-1]).A masked array is the combination of a standard numpy.ndarray and a mask. A mask is either nomask, indicating that no value of the associated array is invalid, or an array of booleans that determines for each ... Read More
![AmitDiwan](https://www.tutorialspoint.com/assets/profiles/123055/profile/60_187394-1565938756.jpg)
351 Views
To append masked arrays along specific axis, use the ma.append() method in Python Numpy. The axis is set using the "axis" parameter. The values are appended to a copy of the first parameter array. These values are appended to a copy of first parameter array. It must be of the correct shape. If axis is not specified, the second parameter array can be any shape and will be flattened before use. The function returns a copy of array1 with array2 appended to axis. The append does not occur in-place: a new array is allocated and filled. If axis is None, ... Read More
![AmitDiwan](https://www.tutorialspoint.com/assets/profiles/123055/profile/60_187394-1565938756.jpg)
105 Views
To join a sequence of masked arrays along specific axis, use the ma.stack() method in Python Numpy. The axis is set using the "axis" parameter. The axis parameter specifies the index of the new axis in the dimensions of the result. For example, if axis=0 it will be the first dimension and if axis=-1 it will be the last dimension.The out parameter, if provided, is the destination to place the result. The shape must be correct, matching that of what stack would have returned if no out argument were specified.The function returns the stacked array has one more dimension than ... Read More
![AmitDiwan](https://www.tutorialspoint.com/assets/profiles/123055/profile/60_187394-1565938756.jpg)
155 Views
To join a sequence of masked arrays, use the ma.stack()Â method in Python Numpy. The axis parameter specifies the index of the new axis in the dimensions of the result. For example, if axis=0 it will be the first dimension and if axis=-1 it will be the last dimension.The out parameter, if provided, is the destination to place the result. The shape must be correct, matching that of what stack would have returned if no out argument were specified.The function returns the stacked array has one more dimension than the input arrays. It is applied to both the _data and ... Read More
![AmitDiwan](https://www.tutorialspoint.com/assets/profiles/123055/profile/60_187394-1565938756.jpg)
199 Views
To stack masked arrays in sequence vertically (row wise), use the ma.vstack() method in Python Numpy. This is equivalent to concatenation along the first axis after 1-D arrays of shape (N, ) have been reshaped to (1, N). Rebuilds arrays divided by vsplit.This function makes most sense for arrays with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r/g/b channels (third axis). The functions concatenate, stack and block provide more general stacking and concatenation operations.The parameters are the arrays that must have the same shape along all but the first axis. ... Read More
![AmitDiwan](https://www.tutorialspoint.com/assets/profiles/123055/profile/60_187394-1565938756.jpg)
221 Views
To stack masked arrays in sequence horizontally (column wise), use the ma.hstack() method in Python Numpy. his is equivalent to concatenation along the second axis, except for 1-D arrays where it concatenates along the first axis. Rebuilds arrays divided by hsplit.This function makes most sense for arrays with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r/g/b channels (third axis). The functions concatenate, stack and block provide more general stacking and concatenation operations.The parameters are the arrays that must have the same shape along all but the second axis, except 1-D ... Read More
![AmitDiwan](https://www.tutorialspoint.com/assets/profiles/123055/profile/60_187394-1565938756.jpg)
288 Views
To stack masked arrays in sequence depth wise (along third axis), use the ma.dstack() method in Python Numpy. This is equivalent to concatenation along the third axis after 2-D arrays of shape (M, N) have been reshaped to (M, N, 1) and 1-D arrays of shape (N, ) have been reshaped to (1, N, 1). Rebuilds arrays divided by dsplit.This function makes most sense for arrays with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r/g/b channels (third axis). The functions concatenate, stack and block provide more general stacking and concatenation ... Read More