 # Numpy cheatsheet

This Numpy cheatsheet is a complete guide for learning Numpy, suitable for beginners to advanced Numpy reference.

## Getting Started

Import numpy to get started:

``````import numpy as np
``````

### Importing/exporting

 `np.loadtxt('file.txt')` From a text file `np.genfromtxt('file.csv',delimiter=',')` From a CSV file `np.savetxt('file.txt',arr,delimiter=' ')` Writes to a text file `np.savetxt('file.csv',arr,delimiter=',')` Writes to a CSV file

### Creating Arrays

 `np.array([1,2,3])` One dimensional array `np.array([(1,2,3),(4,5,6)])` Two dimensional array `np.zeros(3)` 1D array of length 3 all values 0 `np.ones((3,4))` 3×4 array with all values 1 `np.eye(5)` 5×5 array of 0 with 1 on diagonal (Identity matrix) `np.linspace(0,100,6)` Array of 6 evenly divided values from 0 to 100 `np.arange(0,10,3)` Array of values from 0 to less than 10 with step 3 (eg [0,3,6,9]) `np.full((2,3),8)` 2×3 array with all values 8 `np.random.rand(4,5)` 4×5 array of random floats between 0–1 `np.random.rand(6,7)*100` 6×7 array of random floats between 0–100 `np.random.randint(5,size=(2,3))` 2×3 array with random ints between 0–4

### Inspecting Properties

 `arr.size` Returns number of elements in arr `arr.shape` Returns dimensions of arr (rows,columns) `arr.dtype` Returns type of elements in arr `arr.astype(dtype)` Convert arr elements to type dtype `arr.tolist()` Convert arr to a Python list `np.info(np.eye)` View documentation for np.eye

### Copying/sorting/reshaping

 `np.copy(arr)` Copies arr to new memory `arr.view(dtype)` Creates view of arr elements with type dtype `arr.sort()` Sorts arr `arr.sort(axis=0)` Sorts specific axis of arr `two_d_arr.flatten()` Flattens 2D array two_d_arr to 1D `arr.T` Transposes arr (rows become columns and vice versa) `arr.reshape(3,4)` Reshapes arr to 3 rows, 4 columns without changing data `arr.resize((5,6))` Changes arr shape to 5×6 and fills new values with 0

 `np.append(arr,values)` Appends values to end of arr `np.insert(arr,2,values)` Inserts values into arr before index 2 `np.delete(arr,3,axis=0)` Deletes row on index 3 of arr `np.delete(arr,4,axis=1)` Deletes column on index 4 of arr

### Combining/splitting

 `np.concatenate((arr1,arr2),axis=0)` Adds arr2 as rows to the end of arr1 `np.concatenate((arr1,arr2),axis=1)` Adds arr2 as columns to end of arr1 `np.split(arr,3)` Splits arr into 3 sub-arrays `np.hsplit(arr,5)` Splits arr horizontally on the 5th index

### Indexing/slicing/subsetting

 `arr` Returns the element at index 5 `arr[2,5]` Returns the 2D array element on index  `arr=4` Assigns array element on index 1 the value 4 `arr[1,3]=10` Assigns array element on index  the value 10 `arr[0:3]` Returns the elements at indices 0,1,2 (On a 2D array: returns rows 0,1,2) `arr[0:3,4]` Returns the elements on rows 0,1,2 at column 4 `arr[:2]` Returns the elements at indices 0,1 (On a 2D array: returns rows 0,1) `arr[:,1]` Returns the elements at index 1 on all rows `arr<5` Returns an array with boolean values `(arr1<3) & (arr2>5)` Returns an array with boolean values `~arr` Inverts a boolean array `arr[arr<5]` Returns array elements smaller than 5

### Vector Math

 `np.add(arr1,arr2)` Elementwise add arr2 to arr1 `np.subtract(arr1,arr2)` Elementwise subtract arr2 from arr1 `np.multiply(arr1,arr2)` Elementwise multiply arr1 by arr2 `np.divide(arr1,arr2)` Elementwise divide arr1 by arr2 `np.power(arr1,arr2)` Elementwise raise arr1 raised to the power of arr2 `np.array_equal(arr1,arr2)` Returns True if the arrays have the same elements and shape `np.sqrt(arr)` Square root of each element in the array `np.sin(arr)` Sine of each element in the array `np.log(arr)` Natural log of each element in the array `np.abs(arr)` Absolute value of each element in the array `np.ceil(arr)` Rounds up to the nearest int `np.floor(arr)` Rounds down to the nearest int `np.round(arr)` Rounds to the nearest int

### Scalar Math

 `np.add(arr,1)` Add 1 to each array element `np.subtract(arr,2)` Subtract 2 from each array element `np.multiply(arr,3)` Multiply each array element by 3 `np.divide(arr,4)` Divide each array element by 4 (returns np.nan for division by zero) `np.power(arr,5)` Raise each array element to the 5th power

### Statistics

 `np.mean(arr,axis=0)` Returns mean along specific axis `arr.sum()` Returns sum of arr `arr.min()` Returns minimum value of arr `arr.max(axis=0)` Returns maximum value of specific axis `np.var(arr)` Returns the variance of array `np.std(arr,axis=1)` Returns the standard deviation of specific axis `arr.corrcoef()` Returns correlation coefficient of array

Last Updated on June 11, 2023 by admin

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