The Comprehensive Guide to Pandas: Data Manipulation and Analysis in Python — Part 3

Pandas in Action: Visualization, Custom Functions, and Machine Learning Integration

1. Introduction

Pandas is not only a powerful tool for data manipulation but also for quick exploratory data analysis and visualization. In this part, we will cover how to create compelling visualizations directly from DataFrames, apply custom transformations to your data, and integrate your data workflows with machine learning libraries such as scikit-learn. Whether you’re performing exploratory analysis or preparing data for a predictive model, these techniques are crucial in turning raw data into actionable insights.

2. Data Visualization with Pandas

Pandas offers a suite of built-in plotting capabilities through integration with Matplotlib. This allows you to visualize data quickly without leaving the Pandas environment.

2.1 Basic Plotting with Pandas

Pandas DataFrames and Series come with a .plot() method that acts as a wrapper around Matplotlib functions. Here’s a simple example:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

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