NumPy¶

NumPy: Numerical Python

Doc: https://numpy.org/doc/stable/reference/index.html

ndarray: At the core of the NumPy package, is the ndarray object. This encapsulates n-dimensional arrays of homogeneous data types, with many operations being performed in compiled code for performance.

NaN (Not a Number)

Basic¶

Reference: https://numpy.org/doc/stable/user/absolute_beginners.html

Indexing and slicing¶

Basic array operations¶

1. Ones

   

2. Addition

   

3. Subtraction, multiplication, division

   

4. Aggregation

   

Broadcasting¶

Matrices¶

1. Create

   

2. Indexing

   

3. Aggregation

   

4. Axis

   

5. Addition

   

6. Addition with broadcasting

   

7. ones(), zeros(), random()

   

   

Transposing and reshaping a matrix¶

arr.reshape(), arr.transpose(), arr.T

1. arr.T

   

2. arr.reshape()

   

In [ ]:

import numpy as np

import numpy as np

In [21]:

arr = np.array([3.7, -1.2, -2.6, 0.5, 12.9, 10.1])
print("ndim:", arr.ndim)
print("shape:", arr.shape)
print("dtype:", arr.dtype)
arr

arr = np.array([3.7, -1.2, -2.6, 0.5, 12.9, 10.1]) print("ndim:", arr.ndim) print("shape:", arr.shape) print("dtype:", arr.dtype) arr

ndim: 1
shape: (6,)
dtype: float64

Out[21]:

array([ 3.7, -1.2, -2.6,  0.5, 12.9, 10.1])

In [22]:

arr.astype(np.int32)

arr.astype(np.int32)

Out[22]:

array([ 3, -1, -2,  0, 12, 10], dtype=int32)

In [50]:

arr > 0

arr > 0

Out[50]:

array([ True, False, False,  True,  True,  True])

In [51]:

arr[arr > 0]

arr[arr > 0]

Out[51]:

array([ 3.7,  0.5, 12.9, 10.1])

In [52]:

np.where(arr > 0, 2, -2)

np.where(arr > 0, 2, -2)

Out[52]:

array([ 2, -2, -2,  2,  2,  2])

random¶

In [45]:

np.random.seed(42)
rng = np.random.default_rng(seed=42)
type(rng)

np.random.seed(42) rng = np.random.default_rng(seed=42) type(rng)

Out[45]:

numpy.random._generator.Generator

In [46]:

rng.integers(low=0, high=10, size=(3, 4))

rng.integers(low=0, high=10, size=(3, 4))

Out[46]:

array([[0, 7, 6, 4],
       [4, 8, 0, 6],
       [2, 0, 5, 9]])

In [48]:

rng.standard_normal(size=(3, 4))

rng.standard_normal(size=(3, 4))

Out[48]:

array([[-0.31624259, -0.01680116, -0.85304393,  0.87939797],
       [ 0.77779194,  0.0660307 ,  1.12724121,  0.46750934],
       [-0.85929246,  0.36875078, -0.9588826 ,  0.8784503 ]])

In [ ]: