Reading CSV files with read_csv in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of the read_csv function in pandas?

The read_csv function is used to load data from a CSV file into a pandas DataFrame, making it easy to analyze and manipulate the data in Python.

beginner

How do you specify a different delimiter in read_csv if your file is not comma-separated?

You can use the sep parameter to specify a different delimiter. For example, pd.read_csv('file.csv', sep=';') reads a semicolon-separated file.

beginner

What does the header=None argument do in read_csv?

It tells pandas that the CSV file has no header row, so pandas will assign default column names as numbers starting from 0.

intermediate

How can you read only specific columns from a CSV file using read_csv?

Use the usecols parameter with a list of column names or indices you want to load. For example, pd.read_csv('file.csv', usecols=['Name', 'Age']).

beginner

What happens if you use read_csv on a file path that does not exist?

Pandas will raise a FileNotFoundError, indicating that the file path is invalid or the file does not exist.

Which pandas function is used to load data from a CSV file?

A read_csv

B load_csv

C open_csv

D import_csv

The correct function to read CSV files in pandas is read_csv.

How do you tell read_csv that your CSV file has no header row?

A no_header=True

B header=0

C header=None

D skip_header=True

Using header=None tells pandas the file has no header row.

If your CSV file uses tabs instead of commas, which parameter should you change in read_csv?

A delimiter='\t'

B delimiter='tab'

C separator='tab'

D sep='\t'

The sep parameter specifies the delimiter. For tabs, use sep='\t'.

How can you load only the columns 'Name' and 'Age' from a CSV file?

A select=['Name', 'Age']

B usecols=['Name', 'Age']

C columns=['Name', 'Age']

D cols=['Name', 'Age']

The usecols parameter lets you specify which columns to load.

What error occurs if the CSV file path is wrong when using read_csv?

A FileNotFoundError

B ValueError

C TypeError

D KeyError

A FileNotFoundError is raised if the file path does not exist.

Explain how to use pandas read_csv to load a CSV file that uses semicolons as separators and has no header row.

Think about how to tell pandas about the separator and header.

Describe how you would read only specific columns from a CSV file using pandas.

Focus on the parameter that controls which columns to load.

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