How to Efficiently Select DataFrame Rows Within a Specific Date Range in Python?

Selecting DataFrame Rows Within a Date Range

In Python, DataFrames are a powerful tool for storing and manipulating tabular data. One commonly encountered task is filtering rows based on a specified date range. To accomplish this, we have the following options:

Method 1: Using a Boolean Mask

If your DataFrame contains a date column with datetime64[ns] data type, you can create a boolean mask to select rows that fall within the desired range:

# Ensure date column is a datetime64 series
df['date'] = pd.to_datetime(df['date']) 

# Create a boolean mask
mask = (df['date'] > start_date) & (df['date'] <p><strong>Method 2: Setting a DatetimeIndex</strong></p><p>Another efficient approach is to set the date column as the index of the DataFrame, creating a DatetimeIndex:</p><pre class="brush:php;toolbar:false">df = df.set_index(['date'])

# Select rows using index slicing
sub_df = df.loc[start_date:end_date]

This method is particularly useful for frequent date-based selections, as it provides faster performance compared to using a boolean mask.

Example:

Consider the following DataFrame:

>>> df
            value  date
0          0.2   2021-06-01
1          0.3   2021-06-05
2          0.4   2021-06-10
3          0.5   2021-06-15

To select rows for the month of June 2021, we can use either method:

Method 1: Boolean Mask

mask = (df['date'] > '2021-06-01') & (df['date'] <p><strong>Method 2: DatetimeIndex</strong></p><pre class="brush:php;toolbar:false">df = df.set_index(['date'])
sub_df = df.loc['2021-06-01':'2021-06-30']

Both methods will return the following sub-DataFrame:

>>> sub_df
            value  date
0          0.2   2021-06-01
1          0.3   2021-06-05
2          0.4   2021-06-10
3          0.5   2021-06-15

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