Renaming columns in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to rename the column 'old_name' to 'new_name' using pandas.

Data Analysis Python

df = df.rename(columns=[1])

Drag options to blanks, or click blank then click option'

A{'old_name': 'new_name'}

B['old_name', 'new_name']

C'old_name': 'new_name'

D('old_name', 'new_name')

Attempts:

3 left

💡 Hint

Common Mistakes

Using a list or tuple instead of a dictionary.

Not wrapping the mapping in curly braces.

To rename columns in pandas, you pass a dictionary to the columns parameter where keys are old names and values are new names.

2fill in blank

medium

Complete the code to rename multiple columns: 'A' to 'Alpha' and 'B' to 'Beta'.

Data Analysis Python

df = df.rename(columns=[1])

Drag options to blanks, or click blank then click option'

A{'Alpha': 'A', 'Beta': 'B'}

B{'A': 'Alpha', 'B': 'Beta'}

C['A', 'Alpha', 'B', 'Beta']

D('A', 'Alpha', 'B', 'Beta')

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping keys and values in the dictionary.

Using a list or tuple instead of a dictionary.

Use a dictionary with old column names as keys and new names as values to rename multiple columns.

3fill in blank

hard

Fix the error in the code to rename the column 'score' to 'points'.

Data Analysis Python

df.rename(columns=[1], inplace=True)

Drag options to blanks, or click blank then click option'

A('score', 'points')

B{'points': 'score'}

C['score', 'points']

D{'score': 'points'}

Attempts:

3 left

💡 Hint

Common Mistakes

Reversing the key-value order in the dictionary.

Using a list or tuple instead of a dictionary.

The dictionary keys must be the old column names and values the new names. Also, inplace=True modifies the DataFrame directly.

4fill in blank

hard

Fill both blanks to rename columns 'x' to 'X' and 'y' to 'Y', and assign the result to a new DataFrame.

Data Analysis Python

new_df = df.[1](columns=[2])

Drag options to blanks, or click blank then click option'

Arename

Bdrop

C{'x': 'X', 'y': 'Y'}

D{'X': 'x', 'Y': 'y'}

Attempts:

3 left

💡 Hint

Common Mistakes

Using drop instead of rename.

Swapping keys and values in the dictionary.

Use rename method with a dictionary mapping old to new column names to create a new DataFrame with renamed columns.

5fill in blank

hard

Fill all three blanks to rename columns 'a' to 'alpha', 'b' to 'beta', and only keep rows where 'alpha' is greater than 10.

Data Analysis Python

df_renamed = df.[1](columns=[2])
filtered_df = df_renamed[df_renamed[[3]] > 10]

Drag options to blanks, or click blank then click option'

Arename

B{'a': 'alpha', 'b': 'beta'}

C'alpha'

D'a'

Attempts:

3 left

💡 Hint

Common Mistakes

Filtering using the old column name 'a' instead of 'alpha'.

Swapping keys and values in the rename dictionary.

First rename columns using rename with the correct dictionary, then filter rows where the new column 'alpha' is greater than 10.

Practice

1. What does the df.rename(columns={'old': 'new'}) function do in pandas?

easy

A. It sorts the DataFrame by the column 'old'.

B. It deletes the column named 'old' from the DataFrame.

C. It adds a new column named 'new' with values from 'old'.

D. It changes the column name from 'old' to 'new' in the DataFrame.

Solution

1. Step 1: Understand the rename function

   The rename method changes column names based on a dictionary mapping old names to new names.

2. Step 2: Identify the effect of the dictionary

   The dictionary {'old': 'new'} means the column named 'old' will be renamed to 'new'.

3. Final Answer:

   It changes the column name from 'old' to 'new' in the DataFrame. -> Option D

4. Quick Check:

   Rename columns = change names [OK]

Hint: Rename columns using a dictionary with old:new pairs [OK]

Common Mistakes:

• Thinking rename deletes columns
• Confusing rename with adding columns
• Assuming rename sorts data

2. Which of the following is the correct syntax to rename column 'A' to 'Alpha' in a pandas DataFrame df?

easy

A. df.rename('A' to 'Alpha')

B. df.rename(columns={'A': 'Alpha'}, inplace=True)

C. df.rename_columns('A', 'Alpha')

D. df.columns.rename('A', 'Alpha')

Solution

1. Step 1: Recall pandas rename syntax

   The correct syntax uses df.rename(columns={old_name: new_name}, inplace=True) to rename columns.

2. Step 2: Match the syntax to options

   df.rename(columns={'A': 'Alpha'}, inplace=True) matches the correct syntax exactly, including inplace=True to change the original DataFrame.

3. Final Answer:

   df.rename(columns={'A': 'Alpha'}, inplace=True) -> Option B

4. Quick Check:

   Use rename with columns dict and inplace [OK]

Hint: Use rename(columns={old:new}, inplace=True) to rename columns [OK]

Common Mistakes:

• Using wrong method names like rename_columns
• Missing columns= keyword
• Forgetting inplace=True to apply changes

3. Given the DataFrame df below:

import pandas as pd
df = pd.DataFrame({'X': [1,2], 'Y': [3,4]})
df.rename(columns={'X': 'A', 'Y': 'B'}, inplace=True)
print(df.columns.tolist())

What is the output?

medium

A. ['A', 'B']

B. ['X', 'Y']

C. ['X', 'B']

D. ['A', 'Y']

Solution

1. Step 1: Understand the rename operation

   The rename changes 'X' to 'A' and 'Y' to 'B' in the DataFrame columns.

2. Step 2: Check inplace=True effect

   Since inplace=True, the original DataFrame columns are updated.

3. Final Answer:

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

4. Quick Check:

   Renamed columns list = ['A', 'B'] [OK]

Hint: inplace=True updates columns immediately [OK]

Common Mistakes:

• Ignoring inplace=True and expecting old names
• Partial renaming confusion
• Printing DataFrame instead of columns

4. What is wrong with this code snippet?

df.rename({'old_name': 'new_name'}, inplace=True)

medium

A. The dictionary keys and values are reversed.

B. inplace=True is not a valid argument for rename.

C. The dictionary should be passed to the columns parameter.

D. The rename method cannot rename columns.

Solution

1. Step 1: Check rename method parameters

   The rename method requires the dictionary to be passed with the keyword columns= to rename columns.

2. Step 2: Identify the error in the code

   The code passes the dictionary as a positional argument without specifying columns=, so it will not rename columns.

3. Final Answer:

   The dictionary should be passed to the columns parameter. -> Option C

4. Quick Check:

   Use columns= to rename columns [OK]

Hint: Always use columns= when renaming columns [OK]

Common Mistakes:

• Passing dictionary without columns= keyword
• Confusing inplace argument
• Thinking rename can't change columns

5. You have a DataFrame df with columns ['temp', 'humidity', 'pressure']. You want to rename only 'temp' to 'temperature' without changing other columns. Which code correctly does this and keeps the change in df?

hard

A. df.rename(columns={'temp': 'temperature'}, inplace=True)

B. df.columns = ['temperature', 'humidity', 'pressure', 'wind']

C. df.rename({'temp': 'temperature'})

D. df.rename(columns={'temp': 'temperature'})

Solution

1. Step 1: Identify correct rename syntax with inplace

   To rename a single column and keep changes in the original DataFrame, use rename(columns={...}, inplace=True).

2. Step 2: Check other options

   df.columns = ['temperature', 'humidity', 'pressure', 'wind'] replaces all columns, which is risky. Options C and D do not use inplace=True, so changes won't persist in df.

3. Final Answer:

   df.rename(columns={'temp': 'temperature'}, inplace=True) -> Option A

4. Quick Check:

   Use inplace=True to keep rename changes [OK]

Hint: Use inplace=True to save rename changes in original DataFrame [OK]

Common Mistakes:

• Replacing all columns instead of renaming one
• Forgetting inplace=True to save changes
• Using rename without columns= keyword