Changing multiple column names but not all of them pandas python

When working with pandas in Python, it is common to come across situations where you need to change the names of multiple columns in a dataframe. However, you may not want to change the names of all the columns, but only a specific subset. In this article, we will explore three different ways to solve this problem.

Option 1: Using the rename() method

The first option is to use the rename() method provided by pandas. This method allows you to specify a dictionary where the keys are the current column names and the values are the new column names. By passing this dictionary to the rename() method, you can change the names of multiple columns at once.

import pandas as pd

# Create a sample dataframe
data = {'col1': [1, 2, 3],
        'col2': [4, 5, 6],
        'col3': [7, 8, 9]}
df = pd.DataFrame(data)

# Define the dictionary with the column name changes
column_names = {'col1': 'new_col1',
                'col2': 'new_col2'}

# Use the rename() method to change the column names
df.rename(columns=column_names, inplace=True)

# Print the updated dataframe
print(df)

This will output:

   new_col1  new_col2  col3
0         1         4     7
1         2         5     8
2         3         6     9

Option 2: Using the set_axis() method

The second option is to use the set_axis() method provided by pandas. This method allows you to specify a list of new column names and assign it to the columns attribute of the dataframe. By passing the list of new column names to the set_axis() method, you can change the names of multiple columns.

import pandas as pd

# Create a sample dataframe
data = {'col1': [1, 2, 3],
        'col2': [4, 5, 6],
        'col3': [7, 8, 9]}
df = pd.DataFrame(data)

# Define the new column names
new_column_names = ['new_col1', 'new_col2', 'col3']

# Use the set_axis() method to change the column names
df.set_axis(new_column_names, axis=1, inplace=True)

# Print the updated dataframe
print(df)

This will output:

   new_col1  new_col2  col3
0         1         4     7
1         2         5     8
2         3         6     9

Option 3: Using a list comprehension

The third option is to use a list comprehension to iterate over the column names and change only the ones you want. This approach provides more flexibility as you can apply any custom logic to determine which column names to change.

import pandas as pd

# Create a sample dataframe
data = {'col1': [1, 2, 3],
        'col2': [4, 5, 6],
        'col3': [7, 8, 9]}
df = pd.DataFrame(data)

# Define the column names to change
columns_to_change = ['col1', 'col2']

# Use a list comprehension to change the column names
df.columns = ['new_' + col if col in columns_to_change else col for col in df.columns]

# Print the updated dataframe
print(df)

This will output:

   new_col1  new_col2  col3
0         1         4     7
1         2         5     8
2         3         6     9

After exploring these three options, it is clear that the best option depends on the specific requirements of your task. If you have a predefined mapping of old column names to new column names, using the rename() method (Option 1) is a straightforward and efficient solution. However, if you want more control over the column name changes or need to apply custom logic, using a list comprehension (Option 3) provides the flexibility you need. The set_axis() method (Option 2) is also a viable option if you prefer a more concise syntax.

Ultimately, the choice between these options will depend on the complexity of your task and your personal preference.

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4 Responses

    1. I totally disagree! Option 1 is way more practical and reliable. Option 3 might seem flashy, but it lacks substance. Slow and steady wins the race, my friend. 🐢💪

  1. Option 3 is like a ninja move! Who needs rename() or set_axis() when you have list comprehension? #PythonPower

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