Cheatography

# NumPy Cheat Sheet by Lavanya-M22

NumPy cheatsheet by Lavanya and

### Why NumPy?

 NumPy is an open-s­ource numerical Python library used for working with arrays. It aims to provide an array object that is upto 50x faster than tradit­ional python list takes signif­icantly less amount of memory as compared to python lists.

### How to Install Numpy

 ``````pip install numpy or conda install numpy``````

### Importing Library

 ``import numpy as np``

### Attributes of ndarray

 ndarray.shape Tuple of array shape ndarra­y.ndim Number of array dimensions as interger ndarra­y.size Number of elements in the array ndarra­y,dtype Data type of array’s elements ndarra­y.base To check if object has its own memory

### Slicing

 arr Returns the element at index 0 arr[1,2] Returns array element on index  arr[0:3] Returns the elements at indices on outer dimension arr[0:3,2] Returns the elements on rows 0,1,2 at column 2 arr

### Statistics

 np.mean(arr,axis=0/1) Compute the arithmetic mean along the specified axis. arr.sum() Sum of array elements over a given axis arr.min() Return the minimum along a given axis arr.max() Return the maximum along a given axis np.var­(arr) Compute the variance along the specified axis np.std­(arr) Compute the standard deviation along the specified axis. arr.co­rrc­oef() Return Pearson produc­t-m­oment correl­ation coeffi­cients

### Creating Arrays

 np.arr­ay(­object) Creates an array np.arr­ay(­[1,­2,3]) 1D array np.array([(1,2,3),(4,5,6)]) 2D array np.zer­os(­shape) Return a new array of given shape and type, filled with zeros np.one­s(s­hape) Return a new array of given shape and type, filled with ones np.eye(no. of rows) Return a 2-D array with ones on the diagonal and zeros elsewhere np.ara­nge­(st­art­,st­op,­step) Return evenly spaced values within a given interval. np.ran­dom.ra­nd(­shape) Return array of random floats between 0–1 of fiven shape np.random.randint(low,high) Return random integers from low (inclu­sive) to high (exclu­sive) np.linspace(start, stop, n) Returns n evenly spaced numbers over a specified interval

### commonly used methods

 np.sor­t(arr) Returns a sorted copy of the array np.arg­sor­t(arr) Returns the indices that would sort an array np.res­ize(a, new_shape) Return a new array with the specified shape np.dot­(arr1, arr2) Dot product of two arrays arr.copy() Returns a copy of the array arr.view() New view of array with the same data arr.fl­atten() Return a copy of the array collapsed into 1D arr.re­sha­pe(­new­_shape) Returns an array containing the same data with a new shape

### Math operators

 np.add(arr_1, arr_2) Add arguments elemen­t-wise np.subtract(arr_1, arr_2) Subtract arguments, elemen­t-wise np.multiply(arr_1, arr_2) Multiply arguments, elemen­t-wise np.divide(arr_1, arr_2) Divide arguments, elemen­t-wise np.power(arr_1, arr_2) First array elements raised to powers from second array, elemen­t-wise np.sqr­t(arr) Return the non-ne­gative square­-root of an array, elemen­t-wise np.log­(arr) Natural logarithm, elemen­t-wise np.cei­l(arr) Rounds up to the nearest int , elemen­t-wise np.flo­or(arr) Rounds down to the nearest int ,eleme­nt-wise np.abs­(arr) Absolute value of each element in the array np.rou­nd(arr) Rounds to the nearest int

 NumPy Official docume­nataion w3schools NumPy Tutorial NumPy Illust­rated: The Visual Guide to NumPy NumPy: creating and manipu­lating numerical data