Data Science
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, considered one of the common tasks of knowledge analysts is to see what has modified in the info. And you’ll be able to do 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 grasp what has modified within the system, I compared the 2 DataFrames and that’s where this story’s inspiration comes from.
You could find such DataFrame comparison applications mostly in data validation, data change detection, testing, and debugging. So, it’s important to understand how 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 check two DataFrames in pandas. You may 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 check two DataFrames, let’s create two DataFrames with minor differences in them.
import pandas as pddf = 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]…