Using pandas.Series.at_time() to select values at a specific time

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


The pandas.Series.at_time() method is a convenient tool in the pandas library for selecting values within a datetime Series at a specific time. It is incredibly useful in time series analysis, where focusing on particular periods of the day is common. In this tutorial, we will explore how to use at_time() method across various scenarios, enhancing our data selection capabilities in time series data.

Before diving into the examples, ensure you have pandas installed:

pip install pandas

Let’s start with the basics and gradually move towards more complex examples.

Basic Example

We begin by creating a simple time series with datetime index:

import pandas as pd
import numpy as np
# Creating a date range
rng = pd.date_range('2023-01-01 00:00', periods=24, freq='H')
# Creating a series with the date range as the index
series = pd.Series(np.random.rand(len(rng)), index=rng)

Now, let’s say we want to select all values that occurred at 10:00. Here is how you can do it:

selected = series.at_time('10:00')

Upon execution, you will see the value(s) in your series that occur at 10:00.

Dealing with Multiple Days

Often, you’ll deal with time series spanning multiple days. Using at_time() remains straightforward:

rng = pd.date_range('2023-01-01', periods=7, freq='D')
series = pd.Series(np.random.rand(len(rng)), index=rng.hour)

To select values at a specific time across multiple days, we can ensure our series has a datetime index, spanning the desired range.

Examples with Real-world Data

Let’s move to a more advanced scenario using real-world stock market data. Here, the focus is on analyzing trading volume at market open.

# Assume 'df' is a DataFrame with stock data, including 'Volume' and datetime index

This will plot the trading volume at the market open for each day present in the dataset.

Handling Missing Data

In real-world datasets, missing data at specific times is common. Pandas handles this elegantly, returning NaN for any timestamp without data. Adjusting our analysis to account for this may involve using methods like fillna() or dropna() in conjunction with at_time().

Time Zones

When dealing with global data, time zones become a crucial factor. Before using at_time(), ensure your time series data is localized correctly. Use tz_localize() and tz_convert() for accurate selection:

series = series.tz_localize('UTC').tz_convert('America/New_York')
selected = series.at_time('09:30')

This ensures that the selection is made based on the New York time zone, relevant for NYSE data analytics, for example.

Combining with Other Selection Methods

at_time() can be combined with other selection methods like .loc, .iloc, or .between_time() for more refined data extraction strategies. This blend of methods can be particularly useful in crafting advanced time series analysis pipelines.


In this tutorial, we’ve explored how to use the pandas.Series.at_time() method to select values at a specific time across various scenarios. We started with basic examples and progressed to more complex, real-world applications. The ability to pinpoint data at specific times is invaluable in time series analysis, offering clarity and insight into temporal patterns. By mastering at_time() and combining it with other pandas’ time series capabilities, you’ll enhance your data analysis skill set significantly.