How Can I Efficiently Extract Year and Month from a Pandas Datetime Column?

Extracting Year and Month from Pandas Datetime Column

Extracting year and month from a Pandas DataFrame's Datetime column can be a straightforward task. Let's revisit the issue outlined above and provide a comprehensive solution.

One approach involves resampling the Datetime column to group it by frequency, in this case by month. However, the provided code will encounter an error since the resampling operation is only valid for DatetimeIndex or PeriodIndex objects.

Another common solution is to apply a lambda function to each element of the Datetime column, slicing the string to extract just the year or month portion. However, this method will fail due to the Timestamp type of the Datetime column elements, which lacks the slicing capability.

Instead, we recommend the following solution:

df['year'] = pd.DatetimeIndex(df['ArrivalDate']).year
df['month'] = pd.DatetimeIndex(df['ArrivalDate']).month

Alternatively, this concise syntax can be used:

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

This operation creates new 'year' and 'month' columns, each containing the year or month values extracted from the original Datetime column. Now you have separate columns with the extracted year and month information, making it easier to work with them for various analysis purposes.

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