Sort pandas dataframe based on list

Sort pandas dataframe based on list

You can sort a pandas DataFrame based on the values in a list using the sort_values() function. The key here is to create a temporary column in the DataFrame that holds the corresponding values from the list, and then sort the DataFrame based on that column. Here's how you can do it:

Let's assume you have a DataFrame df like this:

import pandas as pd data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'], 'Age': [25, 30, 22, 28]} df = pd.DataFrame(data)

And you have a list of names in the desired order:

desired_order = ['David', 'Bob', 'Charlie', 'Alice']

You can sort the DataFrame based on the desired_order list like this:

df['Order'] = df['Name'].apply(lambda x: desired_order.index(x)) sorted_df = df.sort_values(by='Order').drop(columns='Order') print(sorted_df)

In this example, a new column named 'Order' is added to the DataFrame using the apply() function along with the desired_order.index(x) method. This index method returns the position of each name in the desired_order list. Then, you sort the DataFrame based on the 'Order' column and remove the 'Order' column after sorting.

The output will be:

 Name Age 3 David 28 1 Bob 30 2 Charlie 22 0 Alice 25

Keep in mind that this approach assumes that all the names in the DataFrame exist in the desired_order list. If there are names that are not present in the list, you might need to handle that situation accordingly.

Examples

  1. How to sort a pandas DataFrame based on a list of index labels in Python?

    • Description: This query focuses on sorting a DataFrame based on a specific list of index labels.
    • Code:
      import pandas as pd # Create a sample DataFrame df = pd.DataFrame({ 'A': [1, 2, 3, 4], 'B': ['apple', 'banana', 'cherry', 'date'] }, index=['Z', 'X', 'Y', 'W']) # Custom order for index labels custom_order = ['W', 'X', 'Y', 'Z'] # Sort by custom index order sorted_df = df.reindex(custom_order) print(sorted_df)

  2. How to sort a pandas DataFrame based on a list of column names in Python?

    • Description: This query involves sorting a DataFrame based on a specified list of column names.
    • Code:
      # Custom order for columns custom_columns = ['B', 'A'] # Sort columns by specified order sorted_df = df[custom_columns] print(sorted_df)

  3. How to sort a pandas DataFrame based on a list of values in Python?

    • Description: This query demonstrates sorting a DataFrame based on a list of specific values from a column.
    • Code:
      df = pd.DataFrame({ 'Name': ['Alice', 'Bob', 'Charlie', 'David'], 'Score': [90, 85, 95, 80] }) # Custom order for 'Name' column custom_order = ['Charlie', 'Alice', 'Bob', 'David'] # Sort based on custom list of names df['Name'] = pd.Categorical(df['Name'], categories=custom_order, ordered=True) sorted_df = df.sort_values('Name') print(sorted_df)

  4. How to sort a pandas DataFrame based on a list of indices with missing values in Python?

    • Description: This query demonstrates sorting a DataFrame based on a custom list of index labels, handling potential missing values.
    • Code:
      # Custom order with some missing indices custom_order = ['X', 'Y', 'Z', 'A'] # Reindex DataFrame to the custom order, filling missing indices with NaN sorted_df = df.reindex(custom_order) print(sorted_df)

  5. How to sort a pandas DataFrame based on a list of multiple column orders in Python?

    • Description: This query demonstrates sorting a DataFrame based on multiple columns, with custom sorting orders.
    • Code:
      df = pd.DataFrame({ 'Category': ['Fruit', 'Vegetable', 'Fruit', 'Vegetable'], 'Name': ['Apple', 'Carrot', 'Banana', 'Broccoli'], 'Price': [1.2, 0.8, 1.5, 1.0] }) # Custom order for 'Category' and 'Name' category_order = ['Vegetable', 'Fruit'] name_order = ['Carrot', 'Apple', 'Broccoli', 'Banana'] df['Category'] = pd.Categorical(df['Category'], categories=category_order, ordered=True) df['Name'] = pd.Categorical(df['Name'], categories=name_order, ordered=True) sorted_df = df.sort_values(by=['Category', 'Name']) print(sorted_df)

  6. How to sort a pandas DataFrame based on a list of custom sorting keys in Python?

    • Description: This query focuses on sorting a DataFrame using custom key functions and lists.
    • Code:
      # Custom order for 'Price' with derived keys custom_order = [1.2, 1.5, 1.0, 0.8] df['Price'] = pd.Categorical(df['Price'], categories=custom_order, ordered=True) sorted_df = df.sort_values('Price') print(sorted_df)

  7. How to sort a pandas DataFrame based on a list of index labels with stability in Python?

    • Description: This query demonstrates sorting a DataFrame by index labels with stable sorting to maintain the original order for ties.
    • Code:
      # Custom order for index labels with stable sorting custom_order = ['Y', 'X', 'Z', 'W'] sorted_df = df.reindex(custom_order, method='pad', fill_value=float('nan')) print(sorted_df)

  8. How to sort a pandas DataFrame based on a list of specific column orders with missing values in Python?

    • Description: This query involves sorting a DataFrame by a specific list of column names, handling potential missing columns.
    • Code:
      # Custom order for columns with missing columns handled custom_columns = ['B', 'A', 'C'] # Sort by custom column order, filling missing columns with NaN sorted_df = df.reindex(columns=custom_columns) print(sorted_df)

  9. How to sort a pandas DataFrame based on a list of indices and custom fill values in Python?

    • Description: This query demonstrates reindexing a DataFrame with a custom list of index labels, providing custom fill values for missing indices.
    • Code:
      # Custom order for index labels with custom fill value custom_order = ['X', 'Y', 'Z', 'A'] # Reindex with a custom fill value for missing indices sorted_df = df.reindex(custom_order, fill_value="N/A") print(sorted_df)

  10. How to sort a pandas DataFrame based on a list of column orders with custom fill value in Python?

    • Description: This query demonstrates sorting by a custom list of column orders and providing custom fill values for missing columns.
    • Code:
      # Custom order for columns with custom fill value custom_columns = ['B', 'A', 'C'] # Reindex with custom fill value for missing columns sorted_df = df.reindex(columns=custom_columns).fillna("Missing") print(sorted_df)

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