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

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

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

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

Hint

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

Practice

1. What does pandas.read_csv() do in data science?

easy

A. It reads data from a CSV file into a DataFrame.

B. It saves a DataFrame to a CSV file.

C. It creates a new empty DataFrame.

D. It deletes a CSV file from disk.

Solution

1. Step 1: Understand the function name

   The function name read_csv suggests it reads CSV files.

2. Step 2: Recall pandas I/O functions

   read_csv loads data from a CSV file into a pandas DataFrame for analysis.

3. Final Answer:

   It reads data from a CSV file into a DataFrame. -> Option A

4. Quick Check:

   read_csv = read data [OK]

Hint: Remember: read_csv reads data in, to_csv writes data out [OK]

Common Mistakes:

• Confusing read_csv with to_csv
• Thinking it creates empty DataFrame
• Assuming it deletes files

2. Which of the following is the correct syntax to save a DataFrame df to a CSV file named data.csv?

easy

A. df.to_csv('data.csv')

B. df.read_csv('data.csv')

C. pandas.save_csv(df, 'data.csv')

D. df.save('data.csv')

Solution

1. Step 1: Identify the correct pandas method for saving

   The method to save a DataFrame to CSV is to_csv.

2. Step 2: Check the syntax

   The syntax is df.to_csv('filename.csv'), which matches df.to_csv('data.csv').

3. Final Answer:

   df.to_csv('data.csv') -> Option A

4. Quick Check:

   to_csv saves DataFrame to CSV [OK]

Hint: Use df.to_csv('filename.csv') to save data [OK]

Common Mistakes:

• Using read_csv instead of to_csv
• Using non-existent methods like save_csv or save
• Wrong method call order

3. What will be the output of this code?

import pandas as pd
from io import StringIO
csv_data = 'name,age\nAlice,30\nBob,25'
df = pd.read_csv(StringIO(csv_data))
print(df.shape)

medium

A. (3, 3)

B. (3, 2)

C. (2, 3)

D. (2, 2)

Solution

1. Step 1: Understand the CSV data

   The CSV string has 2 rows of data (Alice and Bob) and 2 columns (name, age).

2. Step 2: Check DataFrame shape

   The shape is (rows, columns) = (2, 2).

3. Final Answer:

   (2, 2) -> Option D

4. Quick Check:

   2 rows and 2 columns = (2, 2) [OK]

Hint: Count rows and columns in CSV to find shape [OK]

Common Mistakes:

• Counting header as a data row
• Mixing rows and columns in shape
• Confusing number of columns

4. Identify the error in this code snippet that tries to read a CSV file:

import pandas as pd
df = pd.read_csv('data.txt')

medium

A. read_csv cannot read text files

B. No error, code is correct

C. File extension should be .csv, not .txt

D. Missing import statement

Solution

1. Step 1: Examine the code for syntax issues

   The code has proper import and correct read_csv call. No syntax errors.

2. Step 2: Understand file handling

   pandas read_csv reads files based on content, not extension. 'data.txt' works if it contains CSV data.

3. Final Answer:

   No error, code is correct -> Option B

4. Quick Check:

   read_csv ignores file extension [OK]

Hint: File extension (.csv or .txt) doesn't matter for read_csv if format is correct [OK]

Common Mistakes:

• Insisting on .csv extension only
• Believing read_csv can't handle .txt files
• Overlooking the present import statement

5. You have a DataFrame df with columns ['A', 'B', 'C']. You want to save only columns A and C to a CSV file named subset.csv. Which code correctly does this?

hard

A. df.to_csv('subset.csv', columns=['A', 'C'])

B. df.to_csv('subset.csv', cols=['A', 'C'])

C. df[['A', 'C']].to_csv('subset.csv', index=False)

D. df.to_csv('subset.csv').select(['A', 'C'])

Solution

1. Step 1: Recall to_csv column selection

   Slicing df[['A', 'C']] creates a subset DataFrame with only desired columns.

2. Step 2: Proper saving syntax

   df[['A', 'C']].to_csv('subset.csv', index=False) saves exactly A and C without index column.

3. Step 3: Evaluate alternatives

   df.to_csv('subset.csv', columns=['A', 'C']) saves index + A + C (extra column). df.to_csv('subset.csv', cols=['A', 'C']) invalid cols param. df.to_csv('subset.csv').select(['A', 'C']) invalid method chain.

4. Final Answer:

   df[['A', 'C']].to_csv('subset.csv', index=False) -> Option C

5. Quick Check:

   df[cols].to_csv(index=False) excludes index [OK]

Hint: df[['A','C']].to_csv('subset.csv', index=False) for only those columns [OK]

Common Mistakes:

• Using columns= without index=False (adds index column)
• Using invalid cols parameter
• Chaining non-existent .select() method