| -               | -  |
|-----------------|----|
| **NAME**        | Numpy cheatsheet |
| **URL**         | https://quickref.me/numpy |
| **DESCRIPTION** | [NumPy](https://numpy.org/) is the fundamental package for scientific computing with Python. This cheat sheet is a quick reference for NumPy beginners. |

<br/>

Getting started
---------------

### Introduction

You’ll also need to import numpy to get started:

```python
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))` | 3x4 array with all values 1 |
| `np.eye(5)` | 5x5 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)` | 2x3 array with all values 8 |
| `np.random.rand(4,5)` | 4x5 array of random floats between 0–1 |
| `np.random.rand(6,7)*100` | 6x7 array of random floats between 0–100 |
| `np.random.randint(5,size=(2,3))` | 2x3 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 5x6 and fills new values with 0 |


### Adding/removing Elements


| - | - |
|-----|-----|
| `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[5]` | Returns the element at index 5 |
| `arr[2,5]` | Returns the 2D array element on index [2][5]
| `arr[1]=4` | Assigns array element on index 1 the value 4 |
| `arr[1,3]=10` | Assigns array element on index [1][3] 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 |