Merging DataFrames based on a common column in Pandas

Python Pandas Numpy: Exercise-7 with Solution

Merge two Pandas DataFrames based on a common column.

Sample Solution:

Python Code:

import pandas as pd

# Create two sample DataFrames
df1 = pd.DataFrame({'ID': [1, 2, 3], 'Name': ['Teodosija', 'Sutton', 'Taneli']})
df2 = pd.DataFrame({'ID': [2, 3, 4], 'Age': [25, 30, 22]})

# Merge DataFrames based on the 'ID' column
merged_df = pd.merge(df1, df2, on='ID')

# Display the merged DataFrame
print(merged_df)

Output:

   ID    Name  Age
0   2  Sutton   25
1   3  Taneli   30

Explanation:

In the exerciser above -

• First we create two sample DataFrames, "df1" and "df2", with a common column 'ID'.
• The pd.merge function is used to merge the DataFrames based on the 'ID' column.
• The on='ID' parameter specifies the common column on which the merge operation is performed.
• The resulting DataFrame (merged_df) contains columns from both DataFrames, and rows are matched based on the values in the 'ID' column.

Flowchart:

Python Code Editor:

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