How Do I Efficiently Extract Year and Month from Pandas Datetime Columns?

Extracting Month and Year from Pandas Datetime Columns

Resampling a Pandas Datetime column to extract year and month individually can pose challenges. To resolve this issue, one can employ a more direct approach:

Method 1:

Insert Year and Month Columns

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

This method creates new columns named 'year' and 'month' that contain the desired values.

Method 2:

Use Datetime Accessor

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

The '.dt' attribute of the Datetime column provides access to attributes such as 'year' and 'month', which can be extracted directly into new columns.

Working with Extracted Values

Once the year and month columns are created, you can combine them or work with them independently. For example:

# Combine year and month into a new column called 'date'
df['date'] = df['year'].astype(str) + '-' + df['month'].astype(str)

# Group data by year and month
groupby = df.groupby(['year', 'month'])

# Filter data for a specific year and month
filtered_data = df[(df['year'] == 2012) & (df['month'] == 12)]

These methods provide flexible ways to extract and manipulate year and month information from Pandas Datetime columns, allowing for efficient data analysis and manipulation.

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