Extracting just Month and Year separately from Pandas Datetime column

Extracting just Month and Year separately from Pandas Datetime column

Are you struggling to extract just the month and year from a pandas datetime column? Don't worry, you're not alone! Many pandas users have come across this issue and it can be a bit confusing at first. In this guide, we'll walk through some common issues and provide easy solutions to help you extract just the month and year separately from your datetime column.

The Problem

Let's take a look at the context around the question:

df['ArrivalDate'] =
...
936   2012-12-31
938   2012-12-29
965   2012-12-31
966   2012-12-31
967   2012-12-31
968   2012-12-31
969   2012-12-31
970   2012-12-29
971   2012-12-31
972   2012-12-29
973   2012-12-29
...

The ArrivalDate column contains pandas timestamp objects. The goal is to extract just the year and month from this column, but the attempted solutions haven't been successful so far.

Solution 1: Convert the column to a Pandas DatetimeIndex

One way to solve this problem is by converting the column to a Pandas DatetimeIndex. This will allow us to access the year and month attributes easily.

df.index = pd.to_datetime(df['ArrivalDate'])

Now, the ArrivalDate column becomes the index of the dataframe. We can now resample another column using the index to group by month and calculate the mean, just like the original attempted solution:

df['AnotherColumn'].resample('M').mean()

Solution 2: Accessing year and month using dt accessor

Another simple solution is to use the pandas dt accessor to access the year and month attributes directly from the ArrivalDate column.

df['Year'] = df['ArrivalDate'].dt.year
df['Month'] = df['ArrivalDate'].dt.month

This will create two new columns, Year and Month, which contain the extracted year and month values from the ArrivalDate column respectively.

Final Thoughts

Extracting just the month and year separately from a pandas datetime column can be a bit tricky, but with the right approach, it's a task that can be easily accomplished. In this guide, we've covered two solutions: converting the column to a pandas datetime index and using the dt accessor. You can choose the method that fits your needs and preferences.

Now that you have these solutions at your disposal, go ahead and give it a try with your own data! Feel free to leave a comment if you have any questions or other helpful suggestions.

Get ready to unleash your pandas skills and extract the month and year like a pro! 🐼💪