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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