Why data I/O matters in Pandas - See It in Action

Why data I/O matters

📖 Scenario: You work in a small shop that keeps sales records in simple files. You want to learn how to read and write data files using pandas so you can analyze sales easily.

🎯 Goal: Learn how to load data from a CSV file into a pandas DataFrame, save a DataFrame to a new CSV file, and understand why reading and writing data files is important in data science.

📋 What You'll Learn

Use pandas to read CSV files

Create a pandas DataFrame from a dictionary

Save a DataFrame to a CSV file

Print DataFrame contents

💡 Why This Matters

🌍 Real World

Data scientists often get data from files or databases and save results back to files. Knowing how to read and write data files is essential for working with real data.

💼 Career

Most data science jobs require handling data input/output efficiently to prepare data for analysis and share results with others.

1

Create sales data dictionary

Create a dictionary called sales_data with these exact entries: 'Product': ['Apple', 'Banana', 'Carrot'], 'Quantity': [10, 5, 7], and 'Price': [0.5, 0.3, 0.2].

Pandas

# Your code here

Need a hint?

Use a dictionary with keys 'Product', 'Quantity', and 'Price'. Each key should have a list of values.

2

Create pandas DataFrame from dictionary

Import pandas as pd and create a DataFrame called df from the sales_data dictionary.

Pandas

sales_data = {
    'Product': ['Apple', 'Banana', 'Carrot'],
    'Quantity': [10, 5, 7],
    'Price': [0.5, 0.3, 0.2]
}
# Your code here

Need a hint?

Use pd.DataFrame() to convert the dictionary to a DataFrame.

3

Save DataFrame to CSV file

Save the DataFrame df to a CSV file named 'sales.csv' without the index column.

Pandas

import pandas as pd

sales_data = {
    'Product': ['Apple', 'Banana', 'Carrot'],
    'Quantity': [10, 5, 7],
    'Price': [0.5, 0.3, 0.2]
}

df = pd.DataFrame(sales_data)
# Your code here

Need a hint?

Use df.to_csv() with index=False to save without row numbers.

4

Read CSV file and print DataFrame

Read the CSV file 'sales.csv' into a new DataFrame called new_df using pandas, then print new_df.

Pandas

import pandas as pd

sales_data = {
    'Product': ['Apple', 'Banana', 'Carrot'],
    'Quantity': [10, 5, 7],
    'Price': [0.5, 0.3, 0.2]
}

df = pd.DataFrame(sales_data)

df.to_csv('sales.csv', index=False)
# Your code here

Need a hint?

Use pd.read_csv() to load the file, then print() the DataFrame.