5 Essential Python Libraries for Data Analysis

5 Essential Python Libraries for Data Analysis

5 Essential Python Libraries for Data Analysis You Must Learn Python has revolutionized the field of data analysis with its versatility and an extensive range of libraries. If you're venturing into data analysis or looking to enhance your skills, here are five essential libraries you must learn. Let's dive into each step-by-step. 1. Pandas Why Use Pandas? Pandas is the go-to library for exploratory data analysis (EDA) . It simplifies handling and analyzing data, especially in tabular form, through its DataFrame structure. pip install pandas Example to read a CSV file: import pandas as pd df = pd.read_csv('data.csv') print(df.head()) 2. Matplotlib Why Use Matplotlib? Visualization is a critical aspect of data analysis, and Matplotlib is a robust library for creating a wide range of plots. pip install matplotlib Example to create a simple plot: import matplotlib.pyplot as plt plt.plot([1, 2, 3], [4, 5, 6]) plt.title('Simple Plot') plt.show()