How Can I Sort a Pandas DataFrame by a Specific Column?
Sorting Pandas DataFrame by One Column
Sorting a Pandas DataFrame can be crucial for organizing and analyzing data. This post explores a method to sort a DataFrame based on the values in a specific column, illustrated through an example.
Consider the following DataFrame with a column named "month_number" containing months represented by numbers from 1 to 12:
0 1 month_number 0 354.7 April 4 1 55.4 August 8 2 176.5 December 12 3 95.5 February 2 4 85.6 January 1 5 152 July 7 6 238.7 June 6 7 104.8 March 3 8 283.5 May 5 9 278.8 November 11 10 249.6 October 10 11 212.7 September 9
To sort this DataFrame by the "month_number" column, we can use the sort_values method. Here's how:
sorted_df = df.sort_values('month_number')
The sorted_df now contains the rows sorted in ascending order based on the values in the "month_number" column:
0 1 month_number 4 85.6 January 1 3 95.5 February 2 7 104.8 March 3 0 354.7 April 4 8 283.5 May 5 6 238.7 June 6 5 152.0 July 7 1 55.4 August 8 11 212.7 September 9 10 249.6 October 10 9 278.8 November 11 2 176.5 December 12
We can also sort the DataFrame by multiple columns using a list of column labels. For example, to sort by "month_number" first and then by "column 0", we can use:
sorted_df = df.sort_values(['month_number', '0'])
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