Pandas: Select rows since last day/week/month in a DataFrame

Introduction

Pandas is an open-source, BSD-licensed library providing high-performance, easy-to-use data structures, and data analysis tools for the Python programming language. One of the core features of Pandas is its ability to handle time series data effortlessly. In this tutorial, we’ll delve into how you can select rows based on time frames such as the last day, week, or month in a DataFrame.

Getting Started

First, ensure you have Pandas installed. You can install Pandas using pip:

pip install pandas

For this tutorial, we’ll also use the datetime module to work with dates. It’s a standard Python module, so there’s no need for installation.

Creating a Sample DataFrame

Let’s start by creating a DataFrame with timestamped data. We’ll use this as our example throughout the tutorial:

import pandas as pd
from datetime import datetime, timedelta

# Create a sample DataFrame
data = {'date': ['2023-01-01', '2023-01-15', '2023-02-01', '2023-03-01',
 '2023-03-15', '2023-03-29'], 'value': [10, 15, 20, 25, 30, 35]}
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])
print(df)

This code snippet creates a DataFrame with a ‘date’ column containing dates and a ‘value’ column with some numerical values. Notice how we convert the ‘date’ column to Datetime format using pd.to_datetime().

Selecting Rows from the Last Day

To select rows from the last day, we calculate the most recent date in our DataFrame and subtract a day from it:

last_day = df['date'].max() - pd.Timedelta(days=1)
last_day_rows = df[df['date'] > last_day]
print(last_day_rows)

This code calculates the most recent day in the ‘date’ column and selects rows where the date is greater than this day minus one. The output will show rows from the last day, if any.

Selecting Rows from the Last Week

Similar to selecting rows from the last day, we can select rows from the last week by subtracting seven days from the most recent date in our DataFrame:

last_week = df['date'].max() - pd.Timedelta(days=7)
last_week_rows = df[df['date'] > last_week]
print(last_week_rows)

This snippet selects ranges that fall within the last week. Depending on your DataFrame’s dates, the output will show all rows from the past seven days.

Selecting Rows from the Last Month

For selecting rows from the last month, we use a slightly different approach. Since months vary in length, we’ll use the pd.DateOffset() function:

last_month = df['date'].max() - pd.DateOffset(months=1)
last_month_rows = df[df['date'] > last_month]
print(last_month_rows)

This calculation considers the variable length of months and selects rows from the previous month based on the most recent date in the DataFrame. The exact output will vary based on your DataFrame’s dates.

Advanced Selection Using Relative Time Frames

You can go a step further by making your time frame selection relative to the current date rather than the most recent date in the DataFrame. Here’s how to select rows from the last week relative to the current date:

current_date = pd.to_datetime('today')
last_week_relative = current_date - pd.Timedelta(days=7)
relative_last_week_rows = df[df['date'] > last_week_relative]
print(relative_last_week_rows)

This approach allows you to select data ranges relative to today’s date, which is particularly useful for periodically updating reports or analyses.

Conclusion

In this tutorial, we’ve explored various methods for selecting rows in a Pandas DataFrame based on time frames such as the last day, week, or month. Using Pandas’ powerful date and time functionalities allows you to handle time series data effectively, making data analysis tasks simpler and more intuitive. Remember, the examples shown are customizable based on your specific data set and analysis needs. Happy coding!