**Course Title:** Modern Python Programming: Best Practices and Trends **Section Title:** Data Science and Visualization with Python **Topic:** Perform data analysis and visualization on a dataset using Pandas and Matplotlib. (Lab topic) **Introduction** In this lab, you'll learn how to perform data analysis and visualization on a real-world dataset using Pandas and Matplotlib. You'll explore a dataset, clean and preprocess it, and then create informative visualizations to gain insights into the data. This lab combines concepts from data manipulation, exploratory data analysis, and data visualization. **Lab Objectives** * Load and clean a dataset using Pandas * Explore and preprocess the data * Create informative visualizations using Matplotlib * Analyze and interpret the results **Dataset** For this lab, we'll use the Boston Housing dataset, which contains information about the housing prices in Boston. You can download the dataset from [Kaggle](https://www.kaggle.com/datasets/boston-housing) or use the built-in dataset in the [Seaborn library](https://seaborn.pydata.org/generated/seaborn.load_dataset.html). **Step 1: Load and Clean the Dataset** First, you'll need to load the dataset into a Pandas DataFrame. You can use the `read_csv` function to load the dataset from a CSV file or the `load_dataset` function from the Seaborn library to load the built-in dataset. ```python import pandas as pd # Load the dataset from a CSV file df = pd.read_csv('boston_housing.csv') # OR load the built-in dataset from Seaborn import seaborn as sns df = sns.load_dataset('boston') ``` Next, you'll need to clean the dataset by handling missing values and encoding categorical variables. You can use the `isnull` function to check for missing values and the `get_dummies` function to encode categorical variables. ```python # Check for missing values print(df.isnull().sum()) # Encode categorical variables df = pd.get_dummies(df, columns=['CHAS']) ``` **Step 2: Explore and Preprocess the Data** Now that you've cleaned the dataset, you can start exploring it. You can use the `head` function to view the first few rows of the dataset, the `info` function to view the data types and count of non-null values, and the `describe` function to view summary statistics. ```python # View the first few rows of the dataset print(df.head()) # View the data types and count of non-null values print(df.info()) # View summary statistics print(df.describe()) ``` You can also use the `corr` function to calculate the correlation between variables and the `pairplot` function from the Seaborn library to create a pairwise plot of the variables. ```python # Calculate the correlation between variables print(df.corr()) # Create a pairwise plot of the variables import seaborn as sns sns.pairplot(df) ``` **Step 3: Create Informative Visualizations** Now that you've explored the dataset, you can start creating informative visualizations. You can use the `plot` function from the Matplotlib library to create a histogram of the housing prices. ```python import matplotlib.pyplot as plt # Create a histogram of the housing prices plt.hist(df['PRICE'], bins=50) plt.xlabel('Housing Price') plt.ylabel('Frequency') plt.title('Histogram of Housing Prices') plt.show() ``` You can also use the `scatter` function from the Matplotlib library to create a scatter plot of the relationship between the housing prices and the number of rooms. ```python # Create a scatter plot of the relationship between the housing prices and the number of rooms plt.scatter(df['RM'], df['PRICE']) plt.xlabel('Number of Rooms') plt.ylabel('Housing Price') plt.title('Relationship between Number of Rooms and Housing Price') plt.show() ``` **Step 4: Analyze and Interpret the Results** Finally, you can analyze and interpret the results of your visualizations. You can use the insights you gained from the visualizations to answer questions about the dataset and make recommendations. **Conclusion** In this lab, you learned how to perform data analysis and visualization on a real-world dataset using Pandas and Matplotlib. You cleaned and preprocessed the dataset, explored it, and created informative visualizations to gain insights into the data. You can apply these skills to your own data analysis projects to gain valuable insights into your data. **Additional Resources** * [Pandas documentation](https://pandas.pydata.org/docs/) * [Matplotlib documentation](https://matplotlib.org/stable/index.html) * [Seaborn documentation](https://seaborn.pydata.org/documentation.html) **What's Next?** In the next topic, we'll cover the introduction to web development frameworks: Flask vs Django. This topic will cover the basics of web development frameworks, including the differences between Flask and Django. You'll learn how to create a simple web application using Flask and Django. **Do you have any questions or need help?** Please leave a comment below if you have any questions or need help with this topic.