How can I sort a Pandas DataFrame by calendar month order?

General Discussions
1

I have a DataFrame where one column contains month names (like “January”, “February”, etc.), but they’re not in calendar order. To fix this, I added a new column that maps each month name to its corresponding number (1–12).

Now I want to sort the DataFrame by that numeric column so the months appear in proper calendar sequence. What’s the correct way to do this using pandas sort by column?

Here’s a simplified version of the DataFrame:

        0          1     2
0   354.7      April   4.0
1    55.4     August   8.0
...

How do I sort it by column 2 so the DataFrame reflects the correct month order?

2

Hey, I’ve done this kind of thing when visualizing monthly data in dashboards. If you already have a column (say, column 2) with the month numbers, you can sort the DataFrame using pandas sort by column like this:

df = df.sort_values(by=2)

That’s it, Pandas will sort the rows based on the numeric month column in ascending order. If your month column is named instead of indexed, just replace 2 with the column name.

3

Just to add up, Here’s a cleaner way I’ve used when I don’t want to rely on a separate numeric column.

Pandas lets you define custom ordering using CategoricalDtype, which works great for months:


import pandas as pd

months = ["January", "February", "March", "April", "May", "June",
          "July", "August", "September", "October", "November", "December"]


df[1] = pd.Categorical(df[1], categories=months, ordered=True)
df = df.sort_values(by=1)  # pandas sort by column name

This way, you don’t need to maintain the numeric column separately. It’s great if your data changes frequently or you’re reading it from CSVs.

4

This is how I approached it when cleaning messy data in a CSV export. I first mapped month names to numbers and then used pandas sort by column to order them:

month_map = {
    "January": 1, "February": 2, "March": 3, "April": 4,
    "May": 5, "June": 6, "July": 7, "August": 8,
    "September": 9, "October": 10, "November": 11, "December": 12
}

df[‘month_num’] = df[1].map(month_map) df = df.sort_values(by=‘month_num’)



Once sorted, you can even drop the `month_num` column if you don’t need it. This method gives you full control and makes it obvious how sorting happens behind the scenes.