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3 Easy Ways To Compare Two Pandas DataFrames

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3 Easy Ways To Compare Two Pandas DataFrames

Data Science

Quickly learn the right way to find the common and unusual rows between the 2 pandas DataFrames.

Towards Data Science
Photo by Meghan Hessler on Unsplash

It is a straightforward task — while you use built-in methods in pandas.

In Python Pandas, a DataFrame is the only data structure where you’ll be able to store the info in tabular i.e. row — column form, and work on it to get useful insights.

While working on real-world scenarios, one among the common tasks of knowledge analysts is to see what has modified in the info. And you’ll be able to try this by comparing two sets of knowledge.

Recently, I developed an automatic computer vision system which collects data from 10 devices at two different times and stores it in 2 pandas DataFrames. To know what has modified within the system, I compared the 2 DataFrames and that’s where this story’s inspiration comes from.

You will discover such DataFrame comparison applications mostly in data validation, data change detection, testing, and debugging. So, it’s important to know the way you’ll be able to compare two datasets quickly and simply.

Due to this fact, in this text, I’m going to elucidate the three best, easiest, most reliable, and quickest ways to match two DataFrames in pandas. You possibly can get a fast overview of the story in the next index.

· Compare Pandas DataFrames using equals()
·
Compare Pandas DataFrames using concat()
·
Compare Pandas DataFrames using compare()

Let’s start!

Before starting with the 3 ways to match two DataFrames, let’s create two DataFrames with minor differences in them.

import pandas as pd

df = pd.DataFrame({"device_id": ['D475', 'D175', 'D200', 'D375', 'M475', 'M400', 'M250', 'A150'],
"device_temperature": [35.4, 45.2, 59.3, 49.3, 32.2, 35.7, 36.8, 34.9],
"device_status": ["Inactive", "Active", "Active", "Active", "Active", "Inactive", "Active", "Active"]})

df1 = pd.DataFrame({"device_id": ['D475', 'D175', 'D200', 'D375', 'M475', 'M400', 'M250', 'A150'],
"device_temperature": [39.4, 45.2, 29.3, 49.3, 32.2, 35.7, 36.8, 24.9]…

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