Practice Exercises for Working with Excel in Python

Overview

In this exercise section, you'll get to apply what you've learned about working with Excel files in Python. These exercises are designed to reinforce the main concepts you've learned in the sample. Ensure you go through each step, and remember, practice is the key to mastering any skill.

Core Concepts

Loading Excel files

Python, with the help of libraries like pandas, can read Excel files directly into DataFrames.

Example:

import pandas as pd
df = pd.read_excel('data/adult.xlsx', index_col=0)

Writing Excel files

pandas can also write DataFrames to Excel files. You can further customize the appearance of the Excel file with XlsxWriter.

Example:

writer = pd.ExcelWriter('data/pandas_output.xlsx', engine='xlsxwriter')
df.to_excel(writer, sheet_name='Sheet1', index=False)

Finding missing data

You can detect missing data in your DataFrame, calculate the mean of missing data in columns, and visualize the distribution of missing data.

Examples:

df.isna()
df.isna().sum()

Analyzing Text data

pandas allows you to filter and visualize text (string) data in your DataFrame.

Examples:

df.education.value_counts().plot.barh()

Numerical Analysis

Correlations between numerical columns, histograms, and scatter plots can be made to analyze relationships and distributions.

Examples:

df.corr()
df.age.hist(bins=20)

Creating scatter plots

Scatter plots can visualize relationships between two numerical columns.

Example:

df.plot.scatter(x='education-num', y='capital-gain')

Exercises

1. Load another Excel file of your choice and display the first 5 rows. If you don't have an Excel file, download a dataset in CSV format and save it as an Excel file before loading.
2. For the loaded Excel file, check if there are any missing values in each column. If yes, replace them with appropriate values (e.g., mean or median for numerical columns).
3. Create a bar chart for any categorical column in the loaded Excel file.

Remember, the key here is to practice. If you encounter issues, use online resources and documentation to debug and move forward. Happy coding!