How Can I Sort a Pandas DataFrame Column Based on a Custom Order Defined in a Dictionary?

Customizing DataFrame Sorting with a Dictionary

When working with pandas DataFrames, you may encounter situations where you need to sort data based on custom criteria. A common scenario is sorting a column with values that represent months based on a predefined order.

To achieve this, one approach is to utilize the Categorical series introduced in Pandas 0.15. By converting the month column to a categorical series and specifying the desired order, you can ensure that sorting operates according to your defined arrangement.

For instance, consider a DataFrame with a column named 'm' containing month names:

import pandas as pd

df = pd.DataFrame([[1, 2, 'March'],[5, 6, 'Dec'],[3, 4, 'April']], columns=['a','b','m'])

To sort the 'm' column using a custom order, create a dictionary with the preferred month ordering:

custom_dict = {'March':0, 'April':1, 'Dec':3}

Next, convert the 'm' column to a categorical series and specify the custom order:

df['m'] = pd.Categorical(df['m'], 
                            ["March", "April", "Dec",], 
                            categories=["March", "April", "Dec"])

Finally, sorting the DataFrame by the 'm' column will now follow the custom order defined in the dictionary:

df.sort_values("m")

This method provides a clear and convenient way to sort data based on customized criteria within a DataFrame.

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