numpy.md

layout	title
../../layouts/CheatSheet.astro	Numpy Cheatsheet

Synopsis

Numpy is the fundamental package for scientific computing with Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more.

Installation

```bash
# Install
pip install numpy
```

Basic Commands

```python
# Import
import numpy as np
```


```python
    # Create array
    np.array([1, 2, 3])
    np.array([[1, 2, 3], [4, 5, 6]])
    np.array([1, 2, 3], ndmin=2)
    np.array([1, 2, 3], dtype=complex)
```

Array

```python
# Create array
np.array([1, 2, 3])
np.array([[1, 2, 3], [4, 5, 6]])
np.array([1, 2, 3], ndmin=2)
np.array([1, 2, 3], dtype=complex)
```

Output:

```python
array([1, 2, 3])
array([[1, 2, 3],
       [4, 5, 6]])
array([[1, 2, 3]])
array([1.+0.j, 2.+0.j, 3.+0.j])
```

Array Indexing

```python
# Create array
a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
```

Output:

```python
array([1, 2, 3, 4, 5, 6, 7, 8, 9])
```

Array Slicing

```python
# Create array
a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
```

Output:

```python
array([1, 2, 3, 4, 5, 6, 7, 8, 9])
```

Array Reshaping

```python
# Create array
a = np.array([[1, 2, 3], [4, 5, 6]])
```

Output:

```python
array([[1, 2, 3],
       [4, 5, 6]])
```

Array Concatenation

```python
# Create array
a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6], [7, 8]])
```

Output:

```python
array([[1, 2],
       [3, 4]])
array([[5, 6],
       [7, 8]])
```

Array Splitting

```python
# Create array
a = np.array([[1, 2, 3], [4, 5, 6]])
```

Output:

```python
array([[1, 2, 3],
       [4, 5, 6]])
```

Array Copying

```python
# Create array
a = np.array([1, 2, 3, 4, 5])
```

Output:

```python
array([1, 2, 3, 4, 5])
```

Array Sorting

```python
# Create array
a = np.array([[1, 4], [3, 1]])
```

Output:

```python
array([[1, 4],
       [3, 1]])
```

Array Searching

```python
# Create array
a = np.array([1, 2, 3, 2, 3, 4, 3, 4, 5, 6])
```

Output:

```python
array([1, 2, 3, 2, 3, 4, 3, 4, 5, 6])
```

Array Iterating

```python
# Create array
a = np.arange(0, 60, 5)
a = a.reshape(3, 4)
```

Output:

```python
array([[ 0,  5, 10, 15],
       [20, 25, 30, 35],
       [40, 45, 50, 55]])
```

Array Joining

```python
# Create array
a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6], [7, 8]])
```

Output:

```python
array([[1, 2],
       [3, 4]])
array([[5, 6],
       [7, 8]])
```

Array Stacking

```python
# Create array
a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6], [7, 8]])
```

Output:

```python
array([[1, 2],
       [3, 4]])
array([[5, 6],
       [7, 8]])
```

Array Splitting

```python
# Create array
a = np.arange(9)
```

Output:

```python
array([0, 1, 2, 3, 4, 5, 6, 7, 8])
```

Array Repeating

```python
# Create array
a = np.array([1, 2, 3])
```

Output:

```python
array([1, 2, 3])
```

Array Manipulation

```python
# Create array
a = np.array([[1, 2, 3], [4, 5, 6]])
```

Output:

```python
array([[1, 2, 3],
       [4, 5, 6]])
```

Array Math

    ```python
    # Create array
    a = np.array([1, 2, 3, 4])
    b = np.array([4, 3, 2, 1])
    ```
Output:

    ```python
    array([1, 2, 3, 4])
    array([4, 3, 2, 1])
    ```

Array Statistics

```python
# Create array
a = np.array([[3, 7, 5], [8, 4, 3], [2, 4, 9]])
```

Output:

```python
array([[3, 7, 5],
       [8, 4, 3],
       [2, 4, 9]])
```