Pandas DataFrame: Grouping rows by hour/day/month/year

Updated: February 21, 2024 By: Guest Contributor Post a comment


Grouping data is a cornerstone task in data analysis, allowing you to summarize or transform datasets in meaningful ways. Pandas, a powerful and widely-used Python library, provides comprehensive functions to group rows based on time intervals like hours, days, months, or years. In this tutorial, we’ll explore how to perform these operations step by step, from basic examples to more advanced applications.

Getting Started

Before diving into the examples, ensure you have Pandas installed. If not, you can install it using pip:

pip install pandas

Also, for the examples below, we will need some datetime functionalities:

import pandas as pd
import numpy as np

Basic Grouping by Hour

Let’s start with a basic example where we’ll group data rows by the hour. First, you need to ensure your DataFrame has a datetime column. Let’s create a sample DataFrame:

rng = pd.date_range('2023-01-01', periods=24, freq='H')
df = pd.DataFrame({ 'date': rng, 'data': np.random.rand(24)})
df['date'] = pd.to_datetime(df['date'])

Now, let’s group by the hour:


Grouping by Day

For daily grouping,

df['date'] = pd.to_datetime(df['date'])
df.set_index('date', inplace=True)

Grouping by Month

Moving to monthly grouping,


Grouping by Year

For yearly grouping,


Advanced Grouping Techniques

Now that we’ve covered basic groupings, let’s explore some advanced techniques. You can use Grouper with additional parameters for more complex tasks. For instance, to group by the day of the week and calculate the average:


You can also group by multiple columns or keys. If you have another categorical column (let’s say ‘type’), you can group by both ‘date’ and ‘type’ like this:

df.groupby([pd.Grouper(freq='M'), 'type']).mean()

Time Zone Management

Dealing with timezones can be complex in data analysis. Pandas offers built-in solutions for this:

df['date'] = df['date'].dt.tz_localize('UTC').dt.tz_convert('America/New_York')

Handling Missing Data

When grouping time-series data, you might encounter missing periods. To handle these, you can use:


Visualizing Grouped Data

Visualization is a powerful way to present your grouped data. You can create charts directly from Pandas:



This tutorial has walked you through the process of grouping Pandas DataFrame rows by hour, day, month, and year, from basic to more advanced techniques. With these tools, you’re well-equipped to analyze time-series data efficiently. Remember, experimentation with your dataset is key to uncovering valuable insights.