CSV Files

:dart: Learning Objectives

By the end of this lesson, you will be able to:

• Understand what CSV files are and why they're important
• Read CSV files into Pandas DataFrames
• Export data from Pandas to CSV files
• Upload and download CSV files in Google Colab

:information_source: CSV (Comma Separated Values) files are text files that use commas to separate data values. They're one of the most common ways to store and share tabular data. CSV files help you:

• Store tabular data - Like spreadsheets or database tables
• Share data easily - Works with Excel, Google Sheets, and most data programs
• Keep data simple - Just text and commas, no complex formatting

Here's what a CSV file looks like:

:memo: Did you know? Data analysts spend most of their time cleaning and working with existing data, not collecting new data. That's why CSV files are so important!

In this lesson, you'll learn:

1. How to read CSV files into Pandas DataFrames
2. How to export your data as CSV files

:bar_chart: Kaggle is a great place to find datasets! Visit their dataset library at: https://www.kaggle.com/datasets/

:emoji: Get Started: Download the score.csv file here to practice:

• Reading CSV files with Pandas
• Exporting CSV files with Pandas

:emoji: Reading CSV Files with Pandas

Step One: Upload Your File to Google Colab

First, you need to upload your CSV file. Use this code and then choose your file:

python

from google.colab import files
uploaded = files.upload()

Step 2: Import Required Libraries

Before working with CSV files, import NumPy and Pandas:

python

import numpy as np
import pandas as pd

Step 3: Read the CSV File

Use pd.read_csv() to read your file and store it in a variable:

python

all_score = pd.read_csv("score.csv")
```tip

Once you read a CSV file, it becomes a Pandas DataFrame. You can use all the DataFrame methods you've learned!

:emoji: Exporting CSV Files with Pandas

Step One: Select Your Data

First, choose which columns you want to export. For example, let's save just the Player and Total columns:

total_score = all_score[["Player", "Total"]]

Step 2: Export to CSV

Use the to_csv() method to save your DataFrame as a new CSV file:

total_score.to_csv("total_score.csv")

Step 3: Download from Google Colab

To download your new CSV file to your computer, use:

files.download("total_score.csv")

:memo: Note This will download the file directly to your computer's Downloads folder!

:books: Summary

You've learned how to:

• Upload CSV files to Google Colab using files.upload()
• Read CSV files into DataFrames using pd.read_csv()
• Select specific columns from your data
• Export DataFrames to new CSV files using to_csv()
• Download files from Google Colab using files.download()

:emoji: Video Tutorial

:emoji: AI Practice Prompts

Working with CSV Files Using AI

Practice your CSV skills by asking AI to help you:

Basic Tasks:

• "Help me read a CSV file using pandas and display its first few rows."
• "Show me how to export a DataFrame to a CSV file with specific parameters."

Advanced Tasks:

• "Write code to read a CSV file with custom delimiters and encoding."
• "Help me select specific columns when reading a CSV file and save only those columns to a new CSV."

Challenge Yourself:

• Try reading multiple CSV files and combining them
• Export data with custom formatting options
• Handle CSV files with missing data