Selecting multiple columns in Pandas

Introduction

We select multiple columns to look at or work with specific parts of a table of data. This helps us focus on the information we need.

When you want to see only a few columns from a big table.

When you need to analyze or change data in certain columns.

When you want to create a smaller table with just the columns you need.

When preparing data for charts or reports that use specific columns.

Syntax

Pandas

df[["column1", "column2", "column3"]]

Use double square brackets [[ ]] to select multiple columns.

The columns inside must be in a list (inside [ ]).

Examples

Selects the columns named 'Name' and 'Age' from the DataFrame.

Pandas

df[["Name", "Age"]]

Selects three columns: 'City', 'Salary', and 'Department'.

Pandas

df[["City", "Salary", "Department"]]

Sample Program

This code creates a table with four columns. Then it selects only the 'Name' and 'City' columns and prints them.

Pandas

import pandas as pd

# Create a simple DataFrame
data = {
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 35],
    "City": ["New York", "Los Angeles", "Chicago"],
    "Salary": [70000, 80000, 90000]
}
df = pd.DataFrame(data)

# Select multiple columns
selected_columns = df[["Name", "City"]]
print(selected_columns)

Important Notes

Remember to use double brackets [[ ]] when selecting multiple columns, not single brackets.

If you use single brackets with a list, it will cause an error.

The order of columns in the list decides the order in the result.

Summary

Select multiple columns by passing a list of column names inside double brackets.

This helps you focus on the data you want to see or work with.

Always check your column names to avoid errors.

Practice

1. What is the correct way to select multiple columns 'A' and 'B' from a pandas DataFrame df?

easy

A. df.loc['A', 'B']

B. df['A', 'B']

C. df[['A', 'B']]

D. df['A' & 'B']

Solution

1. Step 1: Understand pandas column selection syntax

   To select multiple columns, pandas requires a list of column names inside double brackets.

2. Step 2: Apply correct syntax for multiple columns

   Using df[['A', 'B']] correctly selects columns 'A' and 'B'. Other options are invalid syntax or select rows instead.

3. Final Answer:

   df[['A', 'B']] -> Option C

4. Quick Check:

   Multiple columns = list inside double brackets [OK]

Hint: Use double brackets with a list for multiple columns [OK]

Common Mistakes:

• Using single brackets with comma-separated names
• Using bitwise operators like & between column names
• Using .loc incorrectly for column selection

2. Which of the following is the correct syntax to select columns 'X' and 'Y' from DataFrame df?

easy

A. df.loc[:, 'X', 'Y']

B. df.loc['X', 'Y']

C. df['X', 'Y']

D. df[['X', 'Y']]

Solution

1. Step 1: Recall syntax for selecting multiple columns

   Using double brackets with a list of column names is the standard way: df[['X', 'Y']].

2. Step 2: Check other options for correctness

   df.loc[:, 'X', 'Y'] causes TypeError (invalid arguments to loc); df['X', 'Y'] raises KeyError (tuple not a column); df.loc['X', 'Y'] is incorrect syntax.

3. Final Answer:

   df[['X', 'Y']] -> Option D

4. Quick Check:

   Double brackets with list = correct syntax [OK]

Hint: Remember: double brackets with list select multiple columns [OK]

Common Mistakes:

• Using single brackets with multiple column names
• Confusing .loc row and column selection syntax
• Using tuple instead of list inside brackets

3. Given the DataFrame df below:

   A  B  C
0  1  4  7
1  2  5  8
2  3  6  9

What is the output of df[['B', 'C']]?

medium

A. B C
0 4 7
1 5 8
2 6 9

B. A B
0 1 4
1 2 5
2 3 6

C. A C
0 1 7
1 2 8
2 3 9

D. KeyError

Solution

1. Step 1: Identify columns requested

   The code selects columns 'B' and 'C' from the DataFrame.

2. Step 2: Extract columns 'B' and 'C' values

   Columns 'B' and 'C' have values [[4,7],[5,8],[6,9]] for rows 0,1,2 respectively.

3. Final Answer:

   B C 0 4 7 1 5 8 2 6 9 -> Option A

4. Quick Check:

   Selected columns 'B' and 'C' output matches

      B  C
   0  4  7
   1  5  8
   2  6  9

   [OK]

Hint: Selected columns show only those columns with all rows [OK]

Common Mistakes:

• Confusing columns selected and expecting all columns
• Getting KeyError by misspelling column names
• Selecting rows instead of columns

4. What error will occur with this code?

df = pd.DataFrame({'X':[1,2], 'Y':[3,4]})
selected = df['X', 'Y']

medium

A. TypeError

B. KeyError

C. SyntaxError

D. No error, returns both columns

Solution

1. Step 1: Analyze the indexing syntax

   The code uses single brackets with a comma, creating a tuple key ('X', 'Y').

2. Step 2: Understand pandas error type

   This raises KeyError because pandas looks for a column named ('X', 'Y'), which doesn't exist.

3. Final Answer:

   KeyError -> Option B

4. Quick Check:

   Tuple key in single brackets causes KeyError [OK]

Hint: Use list inside double brackets, not comma in single brackets [OK]

Common Mistakes:

• Using comma inside single brackets for multiple columns
• Expecting no error with wrong syntax
• Confusing TypeError with KeyError

5. You have a DataFrame df with columns ['Name', 'Age', 'City', 'Salary']. You want to create a new DataFrame df_selected with only 'Name' and 'Salary'. Which code correctly does this and also resets the index?

hard

A. df_selected = df[['Name', 'Salary']].reset_index(drop=True)

B. df_selected = df.loc[:, ['Name', 'Salary']].reset_index()

C. df_selected = df[['Name', 'Salary']].reset_index()

D. df_selected = df[['Name', 'Salary']].reset_index(drop=False)

Solution

1. Step 1: Select columns 'Name' and 'Salary'

   Use double brackets with list: df[['Name', 'Salary']] to select these columns.

2. Step 2: Reset index without adding old index as column

   Use reset_index(drop=True) to reset index and drop old index column.

3. Step 3: Check other options

   df.loc[:, ['Name', 'Salary']].reset_index() resets index but keeps old index as column. Using reset_index() without drop=True (default behavior) keeps the old index as a new column.

4. Final Answer:

   df_selected = df[['Name', 'Salary']].reset_index(drop=True) -> Option A

5. Quick Check:

   Select columns + reset index drop old index = df_selected = df[['Name', 'Salary']].reset_index(drop=True) [OK]

Hint: Select columns with double brackets, reset index with drop=True [OK]

Common Mistakes:

• Forgetting to use double brackets for multiple columns
• Not using drop=True in reset_index to avoid old index column
• Using .loc without understanding index reset effect