Four tips in numpy

1. introduction

NumPy yes Python One of the most commonly used data science libraries in . It can easily make our daily work simple . This article focuses on Numpy Four skills in , Mastering these skills can make the way we process data more efficient .
Gossip , Let's start straight ！

2. Generate special arrays

In Data Science 、 Deep learning and linear algebra , You usually need to generate special arrays , Such as 0 and 1 Array and matrix , Or identity matrix .

NumPy Make the above operation very simple ！ Let's see how to use Numpy To code , as follows ：

# Creating an array of zeroes
import numpy as np

zeroes = np.zeros(5)
print(zeroes)

# Returns:
# [0. 0. 0. 0. 0.]

In the above code , We used np.zeros() Function to generate an array containing zeros . The above code creates a one-dimensional vector .

Similarly , We can pass in other shape To create other special arrays , as follows ：

# Creating Special Arrays in NumPy
import numpy as np

zeros = np.zeros((2,2))
ones = np.ones((2,2))
identity = np.identity(3)

print(zeros)
print(ones)
print(identity)

# Returns:
# [[0. 0.]
#  [0. 0.]]

# [[1. 1.]
#  [1. 1.]]

# [[1. 0. 0.]
#  [0. 1. 0.]
#  [0. 0. 1.]]

3. Use where Perform conditional filtration

function np.where() Mainly used for screening （ And replace ） The values in the array . This is a powerful function , It is very convenient to find and replace .

next , Let's start with a basic example of filtering data ：

# Filtering Arrays with NumPy where()
import numpy as np

arr = np.array([1,2,3,4,5,6,7,8])
print(np.where(arr > 5))

# Returns:
# (array([5, 6, 7]),)

Similarly , We can use this function to replace the values that meet the filter conditions . Examples are as follows ：

# Replacing Values with NumPy where()
import numpy as np

arr = np.array([1,2,3,4,5,6,7,8])
print(np.where(arr > 5, 1, arr))

# Returns:
# [1 2 3 4 5 1 1 1]

In the example above , We will pass in the second parameter instead of the value satisfying the condition . In the third parameter , We pass the value to be used for any object that does not satisfy the condition .

4. Change the shape of the matrix

Changing the shape of the matrix may be in NumPy One of the most common operations to perform in . for example , When using deep neural networks , It is important to ensure that the array has a specific shape . Like other examples ,NumPy Make the above operation very simple ！

Let's see how to convert a one-dimensional array to a multi-dimensional matrix ：

# Reshaping a NumPy Array
import numpy as np

arr = np.arange(9)
arr = np.reshape(arr, (3,3))

print(arr)

# Returns
# [[0 1 2]
#  [3 4 5]
#  [6 7 8]]

In the example above , We use np.reshape() Convert a one-dimensional array to 3x3 matrix .

Similarly , We can use np.tranpose() Function to transpose a matrix , As shown below ：

# Transposing an Array in NumPy
import numpy as np

arr = np.array([[1,2,3,4]])
print(f'Original Array: \n{arr}')

arr = np.transpose(arr)
print(f'Modified Array: \n{arr}')

# Returns
# Original Array: 
# [[1 2 3 4]]

# Modified Array: 
# [[1]
#  [2]
#  [3]
#  [4]]

5. Calculate unique values

In the last section , Let's learn how to use functions np.unique() To calculate the unique value in the array .

Of course , We can also pass in counts=True Parameters , At this point, the above function will return a tuple containing a unique value and a count .

Examples are as follows ：

# Counting Unique Values in an Array
import numpy as np

arr = [1,1,1,2,2,1,3,4,5,1,2]

values, counts = np.unique(arr, return_counts=True)
print(values)
print(counts)

# Returns:
# [1 2 3 4 5]
# [5 3 1 1 1]

In the example above , The first returned array outputs unique values in the order they appear . The second contains the frequency of occurrence of each value . for example , Array arr Middle number 1 Five times ！

6. summary

In this tutorial , We learned to use NumPy Four important methods of . meanwhile Numpy The library is huge , There are many useful features . I hope this tutorial can let us have a deeper understanding of how to effectively use the library for daily work development .