Using pandas.Series.between_time() to select values between 2 times

Updated: February 18, 2024 By: Guest Contributor Post a comment


In the world of data analysis, time series data is ubiquitous, ranging from financial stock prices to IoT sensor readings. pandas, a powerful Python data analysis toolkit, is equipped with numerous functions to handle time series data efficiently. One such function is between_time(), which is incredibly useful when you need to filter your dataset to only include data between specific times. This tutorial will dive into how to use the pandas.Series.between_time() method through progressive examples, from basic usage to more advanced scenarios.


Before we start, ensure you have the latest version of pandas installed in your working environment:

pip install pandas

Additionally, understanding Python’s datetime library will be beneficial since time-related operations are heavily dependent on it.

Basic Usage of between_time()

Let’s start with a basic example to understand how to use between_time(). Assume you have a Series of datetime indexed stock prices:

import pandas as pd
import numpy as np

dates = pd.date_range('2023-01-01', periods=24, freq='H')
prices = np.random.rand(24) * 100  # Random stock prices
series = pd.Series(prices, index=dates)


This prints out the first few entries of our Series:

2023-01-01 00:00:00    57.832
2023-01-01 01:00:00    68.394
2023-01-01 02:00:00    48.284
2023-01-01 03:00:00    49.792
2023-01-01 04:00:00    98.506

To select series values between 10 AM and 5 PM, you can use between_time():

filtered_series = series.between_time('10:00', '17:00')

This will produce a subset of the original series that falls within the specified timeframe:

2023-01-01 10:00:00    ...
2023-01-01 17:00:00    ...

Handling Time Zones

Time zone management is crucial when working with time series data that spans different time zones. pandas provides tools to localize and convert time zones. Let’s see how between_time() can be applied in such scenarios:

dates = pd.date_range('2023-01-01', periods=24, freq='H', tz='UTC')
series = pd.Series(np.random.rand(24) * 100, index=dates)
series = series.tz_convert('America/New_York')

filtered_series = series.between_time('10:00', '17:00')

This example converts the Series from UTC to Eastern Time (America/New_York) before applying between_time(), showcasing how the method seamlessly integrates with pandas’ time zone functionality.

Combining between_time() with Other Methods

pandas between_time() can be used in conjunction with other methods for more powerful and comprehensive data handling. Let’s explore an example where between_time() is used together with resample() to analyze hourly data:

dates = pd.date_range('2023-01-01', periods=24, freq='H')
series = pd.Series(np.random.rand(24) * 100, index=dates)

# Filter the series for working hours and calculate average hourly price
working_hours_series = series.between_time('09:00', '17:00')
avg_hourly_price = working_hours_series.resample('H').mean()

This example highlights the power of combining between_time() with resample() to compute the average price during working hours.

Advanced Use Cases

For those looking to dive deeper, between_time() can also handle more complex scenarios, such as filtering across days or handling overnight ranges. Here’s an example of filtering values that fall between an overnight range:

night_series = series.between_time('22:00', '02:00')

Note, this requires your DateTimeIndex to cross over midnight, which highlights the flexibility of between_time() in accommodating various data scenarios.


The pandas.Series.between_time() method is a powerful tool for time series data analysis, allowing for the easy selection of values between two times. Through its integration with pandas’ broader functionality, such as timezone handling and resampling, it offers a versatile solution for data filtering and analysis. Whether you’re dealing with stock prices, sensor data, or any time series dataset, between_time() can streamline your data processing tasks, making your data analysis more efficient and insightful.