Select DataFrame rows between two dates

python

dataframe

datetime

pandas

byAlex Kataev·Mar 5, 2025

Select rows within a date range using pandas with this technique:

import pandas as pd

# Your DataFrame
df = pd.DataFrame({'date': pd.to_datetime(['2023-01-01', '2023-01-15', '2023-02-01']), 'value': [10, 20, 30]})

# Filter between '2023-01-10' and '2023-01-31'
# Like dating, but with DataFrames!
result = df[(df['date'] >= '2023-01-10') & (df['date'] <= '2023-01-31')]

print(result)

Optimizing large DataFrames

When it comes to acting upon large data sets:

Use the parse_dates parameter when using pd.read_csv. This converts dates into datetime64[ns] on import.
Consider making the date column a DatetimeIndex. It gives pandas an upper hand at time series wrangling.

DatetimeIndex: Your secret weapon

Assuming your DataFrame has a date column:

Consider it for promotion as DatetimeIndex. This promotion makes slicing the DataFrame as easy as df['2023-01-10':'2023-01-31'].
The DatetimeIndex understands the temporal sequence, unlocking faster querying and advanced manipulation.

Comprehensive date range selection

When you're setting the start and end:

You have multiple options, like pd.Timestamp, datetime.datetime, or plain strings.
Note that in Python's list indexing, the end is exclusive. In contrast, Pandas indexing is inclusive for both extremes.
For a cleaner syntax, consider the pd.Series.between, complete with inclusive argument!

A stitch in time saves nine

To avoid grief later:

Make sure you have a date column in your DataFrame. If not, it's time to parse or convert.
Date formats matter. Discrepancies can lead to unexpected results.
Boolean masks are versatile for dealing with dynamic ranges or complex conditions.

Decision time: Choosing the right method

Each case calls for a unique approach:

Combine boolean masks with logical operators to chisel out complex date ranges like a sculptor.
Apply pd.date_range if you're checking against a particular range of dates.
It's best to swerve around isin with timestamps - it's a slow lane for performance.

Hulk-smashing large datasets

In dealing with massive data:

Hasten operations with a good old pre-parse of the date columns.
Repeated filtering? Saved boolean masks are your buddies.
An efficient use of indexes and accurate data types can shave off important microseconds - a quick win!