Most Popular Python libraries for the data scientist

Here are 10 of the most useful Python libraries for data analysis, along with a brief explanation and sample code for each library:

NumPy (Numerical Python)

NumPy is a library for numerical computing in Python. It provides efficient array and matrix operations, linear algebra, random number generation, and more.

import numpy as np
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
c = a + b
print(c)  # Output: [5 7 9]

pandas

pandas is a powerful data manipulation library that provides data structures like DataFrames and Series for handling and analyzing data in a flexible and efficient way.

import pandas as pd
data = {'A': [1, 2, 3], 'B': [4, 5, 6]}
df = pd.DataFrame(data)
print(df)

matplotlib

matplotlib is a plotting library for creating static, animated, and interactive visualizations in Python.

import matplotlib.pyplot as plt
x = [1, 2, 3]
y = [4, 5, 6]
plt.plot(x, y)
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Sample Plot')
plt.show()

seaborn

seaborn is a statistical data visualization library built on top of matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

import seaborn as sns
import pandas as pd
data = {'A': [1, 2, 3], 'B': [4, 5, 6]}
df =…