The most important object defined in NumPy is an N-dimensional array type called ndarray. It describes the collection of items of the same type. Items in the collection can be accessed using a zero-based index. Every item in an ndarray takes the same size of block in the memory. Each element in ndarray is an object of data-type object (called dtype).Read More
NumPy numerical types are instances of dtype (data-type) objects, each having unique characteristics. The dtypes are available as np.bool_, np.float32, etc.Read More
NumPy offers a lot of array creation routines for different circumstances. arange() is one such function based on numerical ranges. It’s often referred to as np.arange() because np is a widely used abbreviation for NumPy.Read More
Contents of ndarray object can be accessed and modified by indexing or slicing, just like Python's in-built container objects. Basic slicing is an extension of Python's basic concept of slicing to n dimensions. A Python slice object is constructed by giving start, stop, and step parameters to the built-in slice function. This slice object is passed to the array to extract a part of array.Read More
There are two types of Advanced Indexing − Integer and Boolean.
Broadcasting refers to the ability of NumPy to treat arrays of different shapes during arithmetic operations. Arithmetic operations on arrays are usually done on corresponding elements.Read More
NumPy package contains an iterator object numpy.nditer. It is an efficient multidimensional iterator object using which it is possible to iterate over an array. Each element of an array is visited using Python’s standard Iterator interface.Read More
Several routines are available in NumPy package for manipulation of elements in ndarray object. You would be learning about - Changing Shape, Transpose Operation, Changing Dimensions, Joining Arrays, Splitting Arrays, Adding/Removing Elements.Read More
NumPy contains a large number of various mathematical operations. NumPy provides standard trigonometric functions, functions for arithmetic operations, handling complex numbers, etc. Trigonometric Functions NumPy has standard trigonometric functions which return trigonometric ratios for a given angle in radians.Read More
Input arrays for performing arithmetic operations such as add(), subtract(), multiply(), and divide() must be either of the same shape or should conform to array broadcasting rules.Read More
NumPy has quite a few useful statistical functions for finding minimum, maximum, percentile standard deviation and variance, etc. from the given elements in the array.Read More
These sorting functions implement different sorting algorithms, each of them characterized by the speed of execution, worst case performance, the workspace required and the stability of algorithms.Read More
Swap the bytes of the array elements. Toggle between low-endian and big-endian data representation by returning a byteswapped array, optionally swapped in-place. Arrays of byte-strings are not swapped. The real and imaginary parts of a complex number are swapped individually.Read More
While executing the functions, some of them return a copy of the input array, while some return the view. When the contents are physically stored in another location, it is called Copy. If on the other hand, a different view of the same memory content is provided, we call it as View.Read More
The ndarray objects can be saved to and loaded from the disk files. The IO functions available are −load() and save() functions handle /numPy binary files (with npy extension) loadtxt() and savetxt() functions handle normal text filesRead More
Matplotlib is a plotting library for Python. It is used along with NumPy to provide an environment that is an effective open source alternative for MatLab. It can also be used with graphics toolkits like PyQt and wxPython.Read More
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