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

Select DataFrame Rows Between Two Dates

Introduction

When working with time-series data, it is often necessary to select specific rows based on date ranges. This article explores two methods for achieving this in pandas DataFrames.

Method 1: Boolean Mask

1. Ensure the date column is a Series with dtype datetime64[ns]:

   df['date'] = pd.to_datetime(df['date'])

2. Create a boolean mask using comparison operators with the start and end dates:

   mask = (df['date'] > start_date) & (df['date'] <= end_date)

3. Select the sub-DataFrame using the mask:

   df.loc[mask]

4. Optionally, re-assign the sub-DataFrame to df.

Method 2: DatetimeIndex

1. Set the date column as the index:

   df = df.set_index(['date'])

2. Slice the DataFrame using date ranges:

   df.loc[start_date:end_date]

Example

Consider a DataFrame with a date column. The following code uses the boolean mask method to select rows between '2000-06-01' and '2000-06-10':

import pandas as pd

df = pd.DataFrame({
    'date': pd.date_range('2000-1-1', periods=200, freq='D'),
    'value': np.random.rand(200)
})

mask = (df['date'] > '2000-06-01') & (df['date'] <= '2000-06-10')
result_df = df[mask]

The result includes rows from June 1st to 10th, 2000.

Comparison

• The boolean mask method is more flexible and allows for more complex date comparisons.
• The DatetimeIndex method is faster for repetitive date range selections.
• Using parse_dates in pd.read_csv can save the need for converting the date column to datetime64s.

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