*39*

Often you might be interested in comparing the values between two pandas DataFrames to spot their similarities and differences.

This tutorial explains how to do so.

**Example: Comparing Two DataFrames in Pandas**

Suppose we have the following two pandas DataFrames that each contain data about four basketball players:

import pandas as pd #define DataFrame 1 df1 = pd.DataFrame({'player': ['A', 'B', 'C', 'D'], 'points': [12, 15, 17, 24], 'assists': [4, 6, 7, 8]}) df1 player points assists 0 A 12 4 1 B 15 6 2 C 17 7 3 D 24 88 #define DataFrame 2 df2 = pd.DataFrame({'player': ['A', 'B', 'C', 'D'], 'points': [12, 24, 26, 29], 'assists': [7, 8, 10, 13]}) df2 player points assists 0 A 12 7 1 B 24 8 2 C 26 10 3 D 29 13

**Example 1: Find out if the two DataFrames are identical.**

We can first find out if the two DataFrames are identical by using the DataFrame.equals() function:

#see if two DataFrames are identical df1.equals(df2) False

The two DataFrames do not contain the exact same values, so this function correctly returns **False**.

**Example 2: Find the differences in player stats between the two DataFrames.**

We can find the differences between the assists and points for each player by using the pandas subtract() function:

#subtract df1 from df2 df2.set_index('player').subtract(df1.set_index('player')) points assists player A 0 3 B 9 2 C 9 3 D 5 5

The way to interpret this is as follows:

- Player A had the same amount of points in both DataFrames, but they had 3 more assists in DataFrame 2.
- Player B had 9 more points and 2 more assists in DataFrame 2 compared to DataFrame 1.
- Player C had 9 more points and 3 more assists in DataFrame 2 compared to DataFrame 1.
- Player D had 5 more points and 5 more assists in DataFrame 2 compared to DataFrame 1.

**Example 3: Find all rows that only exist in one DataFrame.**

We can use the following code to obtain a complete list of rows that only appear in one DataFrame:

#outer merge the two DataFrames, adding an indicator column called 'Exist' diff_df = pd.merge(df1, df2, how='outer', indicator='Exist') #find which rows don't exist in both DataFrames diff_df = diff_df.loc[diff_df['Exist'] != 'both'] diff_df player points assists Exist 0 A 12 4 left_only 1 B 15 6 left_only 2 C 17 7 left_only 3 D 24 8 left_only 4 A 12 7 right_only 5 B 24 8 right_only 6 C 26 10 right_only 7 D 29 13 right_only

In this case, the two DataFrames share no identical rows so there are 8 total rows that only appear in one of the DataFrames.

The column titled “Exist” conveniently tells us which DataFrame each row uniquely appears in.