Reading CSV files with read_csv in Pandas - Mini Project: Build & Apply

Reading CSV files with read_csv

📖 Scenario: You have a CSV file containing sales data from a small store. You want to load this data into Python to analyze it easily.

🎯 Goal: Learn how to read a CSV file into a pandas DataFrame using the read_csv function.

📋 What You'll Learn

Use pandas library

Read a CSV file named sales_data.csv

Store the data in a variable called sales_df

Print the DataFrame to see the data

💡 Why This Matters

🌍 Real World

Reading CSV files is a common first step in analyzing data from spreadsheets, exports, or logs.

💼 Career

Data scientists and analysts often start their work by loading CSV files into pandas for cleaning and analysis.

1

Import pandas and create the CSV file

Import the pandas library as pd and create a CSV file named sales_data.csv with the following content:
Product,Price,Quantity Apple,0.5,10 Banana,0.3,15 Orange,0.7,7

Pandas

# Import pandas as pd
# Create the CSV file sales_data.csv with the given content
# Your code here

Hint

Use import pandas as pd to import pandas.
Use Python's open function to create and write to the CSV file.

2

Set the CSV file path

Create a variable called csv_file and set it to the string 'sales_data.csv' to store the path of the CSV file.

Pandas

import pandas as pd

csv_content = """Product,Price,Quantity
Apple,0.5,10
Banana,0.3,15
Orange,0.7,7"""

with open('sales_data.csv', 'w') as file:
    file.write(csv_content)

# Create a variable csv_file with the value 'sales_data.csv'
# Your code here

Hint

Just assign the string 'sales_data.csv' to the variable csv_file.

3

Read the CSV file into a DataFrame

Use pd.read_csv with the variable csv_file to read the CSV file and store the result in a variable called sales_df.

Pandas

import pandas as pd

csv_content = """Product,Price,Quantity
Apple,0.5,10
Banana,0.3,15
Orange,0.7,7"""

with open('sales_data.csv', 'w') as file:
    file.write(csv_content)

csv_file = 'sales_data.csv'

# Read the CSV file into sales_df using pd.read_csv
# Your code here

Hint

Use sales_df = pd.read_csv(csv_file) to read the CSV file into a DataFrame.

4

Print the DataFrame

Print the variable sales_df to display the contents of the CSV file as a DataFrame.

Pandas

import pandas as pd

csv_content = """Product,Price,Quantity
Apple,0.5,10
Banana,0.3,15
Orange,0.7,7"""

with open('sales_data.csv', 'w') as file:
    file.write(csv_content)

csv_file = 'sales_data.csv'

sales_df = pd.read_csv(csv_file)

# Print the sales_df DataFrame
# Your code here

Hint

Use print(sales_df) to show the DataFrame.

Practice

1. What does the pandas.read_csv() function do?

easy

A. Loads data from a CSV file into a pandas DataFrame

B. Saves a DataFrame to a CSV file

C. Creates a new CSV file from scratch

D. Deletes a CSV file from disk

Solution

1. Step 1: Understand the purpose of read_csv

   The read_csv function is designed to read data stored in CSV files.

2. Step 2: Identify the output of read_csv

   It loads the CSV data into a pandas DataFrame, which is a table-like data structure for analysis.

3. Final Answer:

   Loads data from a CSV file into a pandas DataFrame -> Option A

4. Quick Check:

   read_csv loads CSV data into DataFrame [OK]

Hint: Remember: read_csv reads files, not writes [OK]

Common Mistakes:

• Confusing read_csv with to_csv (saving files)
• Thinking it creates or deletes files
• Assuming it returns a list or dictionary

2. Which of the following is the correct way to read a CSV file named data.csv using pandas?

easy

A. pd.read_csv('data.csv')

B. pd.read('data.csv')

C. pandas.load_csv('data.csv')

D. pd.load_csv('data.csv')

Solution

1. Step 1: Recall the pandas function for reading CSV

   The correct function is read_csv from the pandas library, usually imported as pd.

2. Step 2: Match the syntax with the file name

   The syntax is pd.read_csv('filename.csv'), so pd.read_csv('data.csv') is correct.

3. Final Answer:

   pd.read_csv('data.csv') -> Option A

4. Quick Check:

   Correct function name and syntax = pd.read_csv('data.csv') [OK]

Hint: Use pd.read_csv('filename.csv') to load CSV files [OK]

Common Mistakes:

• Using pd.read instead of pd.read_csv
• Using load_csv which does not exist
• Forgetting to put filename in quotes

3. Given the CSV file sample.csv with content:

name,age,city
Alice,30,New York
Bob,25,Los Angeles
Charlie,35,Chicago

What will be the output of this code?

import pandas as pd
df = pd.read_csv('sample.csv', usecols=['name', 'city'])
print(df)

medium

A. DataFrame with all columns: 'name', 'age', 'city'

B. Error because 'usecols' is not a valid argument

C. DataFrame with columns 'name' and 'city' only

D. Empty DataFrame with no columns

Solution

1. Step 1: Understand the usecols parameter in read_csv

   The usecols argument selects only specified columns from the CSV file.

2. Step 2: Apply usecols=['name', 'city'] to the CSV data

   Only the 'name' and 'city' columns will be loaded into the DataFrame, excluding 'age'.

3. Final Answer:

   DataFrame with columns 'name' and 'city' only -> Option C

4. Quick Check:

   usecols filters columns = DataFrame with columns 'name' and 'city' only [OK]

Hint: usecols picks only listed columns from CSV [OK]

Common Mistakes:

• Expecting all columns to load despite usecols
• Thinking usecols causes an error
• Confusing usecols with skiprows

4. What is wrong with this code snippet?

import pandas as pd
df = pd.read_csv('data.csv', sep=';')
print(df.head())

Assuming data.csv uses commas as separators.

medium

A. Missing header argument causes error

B. The separator sep=';' is incorrect for comma-separated data

C. read_csv cannot read files with commas

D. print(df.head()) is invalid syntax

Solution

1. Step 1: Check the separator argument in read_csv

   The code uses sep=';' which means it expects semicolon-separated values.

2. Step 2: Compare with actual CSV separator

   The file uses commas, so the separator should be sep=',' or omitted (default is comma).

3. Final Answer:

   The separator sep=';' is incorrect for comma-separated data -> Option B

4. Quick Check:

   Separator must match file format = The separator sep=';' is incorrect for comma-separated data [OK]

Hint: Match sep to actual CSV delimiter to avoid errors [OK]

Common Mistakes:

• Using wrong separator for the CSV file
• Assuming header argument is always needed
• Thinking print(df.head()) is invalid

5. You have a CSV file records.csv with no header row, and columns are separated by tabs. How do you correctly load it into pandas with column names ['ID', 'Name', 'Score']?

hard

A. pd.read_csv('records.csv', names=['ID', 'Name', 'Score'])

B. pd.read_csv('records.csv', sep=',', header=0, names=['ID', 'Name', 'Score'])

C. pd.read_csv('records.csv', sep='\t', header=0)

D. pd.read_csv('records.csv', sep='\t', header=None, names=['ID', 'Name', 'Score'])

Solution

1. Step 1: Identify the separator and header presence

   The file uses tabs as separators, so sep='\t' is needed. It has no header row, so header=None must be set.

2. Step 2: Assign column names manually

   Since there is no header, use names=['ID', 'Name', 'Score'] to name columns on load.

3. Final Answer:

   pd.read_csv('records.csv', sep='\t', header=None, names=['ID', 'Name', 'Score']) -> Option D

4. Quick Check:

   No header + tab sep + names = pd.read_csv('records.csv', sep='\t', header=None, names=['ID', 'Name', 'Score']) [OK]

Hint: Use header=None and names=... for files without headers [OK]

Common Mistakes:

• Using wrong separator (comma instead of tab)
• Not setting header=None for no header files
• Providing names without header=None