Reading CSV files with read_csv in Pandas

Introduction

We use read_csv to open and read data stored in CSV files. This helps us work with data easily in Python.

You have a spreadsheet saved as a CSV file and want to analyze it.

You receive data exports from websites or databases in CSV format.

You want to load sample data for practice or learning.

You need to quickly check the contents of a CSV file in Python.

You want to combine data from multiple CSV files for analysis.

Syntax

Pandas

pandas.read_csv(filepath_or_buffer, sep=',', header='infer', names=None, index_col=None, usecols=None, dtype=None, engine=None, nrows=None, skiprows=None, na_values=None, parse_dates=False, ...)

filepath_or_buffer is the path to your CSV file.

You can customize how the file is read using options like sep for separator or header for column names.

Examples

Reads a CSV file named data.csv with default settings.

Pandas

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

Reads a CSV file where columns are separated by semicolons instead of commas.

Pandas

df = pd.read_csv('data.csv', sep=';')

Reads a CSV without headers and assigns custom column names.

Pandas

df = pd.read_csv('data.csv', header=None, names=['A', 'B', 'C'])

Reads only columns 'A' and 'C' from the CSV file.

Pandas

df = pd.read_csv('data.csv', usecols=['A', 'C'])

Sample Program

This program creates a small CSV file, reads it using read_csv, and prints the data as a table.

Pandas

import pandas as pd

# Create a sample CSV file
csv_content = '''Name,Age,City
Alice,30,New York
Bob,25,Los Angeles
Charlie,35,Chicago'''
with open('sample.csv', 'w') as f:
    f.write(csv_content)

# Read the CSV file
df = pd.read_csv('sample.csv')

# Show the data
print(df)

Important Notes

If your CSV file is large, you can read only a few rows using the nrows option.

Use parse_dates=True to automatically convert date columns to date objects.

Always check the first few rows with df.head() to make sure data loaded correctly.

Summary

read_csv loads CSV files into pandas DataFrames for easy data work.

You can customize reading with options like separator, headers, and columns.

Always verify your data after loading to avoid surprises.

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