Finding the common rows between two DataFrames
- The merge() function serves as the entry point for all standard database join operations between DataFrame objects. Merge function is similar to SQL inner join, we find the common rows between two dataframes.
- The concat() function does all the heavy lifting of performing concatenation operations along with an axis od Pandas objects while performing optional set logic (union or intersection) of the indexes (if any) on the other axes.
Example 1: Using merge function
Python3
df = df1.merge(df2, how = 'inner' ,indicator=False) df |
Output:
Example 2: Using concat function
We add the second dataframe(df2) below the first dataframe(df1) by using concat function. Then we groupby the new dataframe using columns and then we see which rows have a count greater than 1. These are the common rows. This is how we can use-
Python3
df = pd.concat([df1, df2]) df = df.reset_index(drop=True) df_group = df.groupby(list(df.columns)) idx = [x[0] for x in df_group.groups.values() if len(x) > 1] df.reindex(idx) |
Output:
Pandas – Find the Difference between two Dataframes
In this article, we will discuss how to compare two DataFrames in pandas. First, let’s create two DataFrames.
Creating two dataframes
Python3
import pandas as pd # first dataframe df1 = pd.DataFrame({ 'Age': ['20', '14', '56', '28', '10'], 'Weight': [59, 29, 73, 56, 48]}) display(df1) # second dataframe df2 = pd.DataFrame({ 'Age': ['16', '20', '24', '40', '22'], 'Weight': [55, 59, 73, 85, 56]}) display(df2) |
Output:
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