How Can I Perform Custom Sorting of Pandas DataFrame Columns Using a Dictionary?

Custom Sorting in Pandas DataFrame

Custom sorting in Pandas allows you to rearrange the rows of a DataFrame based on a specified order or criterion. When a DataFrame contains a column with values that need to be sorted according to a custom mapping, such as converting month names to numerical indices, you can leverage custom sorting techniques provided by Pandas.

Solution for Custom Sorting Using a Dictionary:

To achieve custom sorting using a dictionary, follow these steps:

1. Create a Pandas DataFrame with the desired column containing month names.
2. Create a dictionary that maps the month names to their corresponding numerical indices, e.g. custom_dict = {'March':0, 'April':1, 'Dec':3}.
3. Apply the apply() function to the column, using a lambda function to map each month name to its index using the dictionary, e.g. df['m'].apply(lambda x: custom_dict[x]).
4. Sort the DataFrame by the intermediate series created in step 3, e.g. df.sort_values('intermediary_series').

Example:

import pandas as pd

# Custom dictionary mapping month names to indices
custom_dict = {'March':0, 'April':1, 'Dec':3}

# Create a DataFrame with a column containing month names
df = pd.DataFrame([[1, 2, 'March'],[5, 6, 'Dec'],[3, 4, 'April']], columns=['a','b','m'])

# Apply the custom sorting
df['intermediary_series'] = df['m'].apply(lambda x: custom_dict[x])
df.sort_values('intermediary_series')

This approach allows you to sort the DataFrame based on the desired order specified in the custom dictionary.

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