How To Sort A Dataframe Based On A Column In Python

In this tutorial, we will learn how to sort a Dataframe in Python based on a specific column. We will be using the Pandas library for this task, which provides simple yet powerful data structures for data analysis in Python.

The ability to sort data is crucial when managing large datasets, as it enables us to quickly view, analyze, and extract insights from it.

Step 1: Import the Pandas library

In order to sort a Dataframe, the first step is to import the Pandas library. If you do not have Pandas installed, you can install it by running !pip install Pandas in your terminal or Jupyter Notebook. Once installed, you can import the library with the following line:

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import pandas as pd

Step 2: Create a Dataframe

Now, we will create a simple Dataframe to perform the sorting operation on. You can either create a Dataframe from scratch or load it from an existing file (such as CSV or Excel).

For this tutorial, let’s create a simple example Dataframe from scratch with a dictionary:

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data = {'Name': ['John', 'Eva', 'Sam', 'Nick', 'Maria'],
        'Age': [34, 43, 23, 29, 27],
        'Score': [88, 75, 94, 56, 63]}
 
df = pd.DataFrame(data)

Here’s what the example Dataframe looks like:

   Name  Age  Score
0  John   34     88
1   Eva   43     75
2   Sam   23     94
3  Nick   29     56
4 Maria   27     63

Step 3: Sort the Dataframe based on the column

To sort the Dataframe based on a specific column, we will use the sort_values() function. This function takes several parameters, but the two most important ones are:

  1. by: The column name or the column index by which you want to sort the Dataframe.
  2. ascending: A boolean value to indicate whether to sort the column in ascending order (default=True) or descending order (False).

Let’s sort our example Dataframe based on the ‘Score’ column:

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sorted_df = df.sort_values(by='Score')

Here’s the sorted Dataframe:

   Name  Age  Score
3  Nick   29     56
4 Maria   27     63
1   Eva   43     75
0  John   34     88
2   Sam   23     94

Step 4: Sort the Dataframe based on the column in descending order

If you want to sort the Dataframe in descending order, you can set the ascending parameter to False. Here’s the example to sort our Dataframe based on the ‘Score’ column in descending order:

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sorted_df_desc = df.sort_values(by='Score', ascending=False)

Here’s the sorted Dataframe in descending order:

   Name  Age  Score
2   Sam   23     94
0  John   34     88
1   Eva   43     75
4 Maria   27     63
3  Nick   29     56

Full code

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import pandas as pd
 
data = {'Name': ['John', 'Eva', 'Sam', 'Nick', 'Maria'],
        'Age': [34, 43, 23, 29, 27],
        'Score': [88, 75, 94, 56, 63]}
 
df = pd.DataFrame(data)
 
sorted_df = df.sort_values(by='Score')
sorted_df_desc = df.sort_values(by='Score', ascending=False)
 
print(sorted_df)
print(sorted_df_desc)

Conclusion

In this tutorial, we learned how to sort a Dataframe based on a specific column using the Pandas library in Python. We saw how to sort the Dataframe in both ascending and descending order. Sorting Dataframes is essential for data analysis and allows us to easily view and extract insights from the data.