Python - Merge DataFrames of different length

To merge DataFrames of different lengths, we use the merge() method with different join types. The left join keeps all rows from the left DataFrame and matches rows from the right DataFrame where possible.

Creating DataFrames of Different Lengths

Let's create two DataFrames with different lengths to demonstrate merging ?

import pandas as pd

# Create DataFrame1 with length 4
dataFrame1 = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Audi', 'Jaguar'],
    "Price": [50000, 45000, 48000, 60000]
})

print("DataFrame1 ...\n", dataFrame1)
print("DataFrame1 length =", len(dataFrame1))

DataFrame1 ...
      Car  Price
0     BMW  50000
1   Lexus  45000
2    Audi  48000
3  Jaguar  60000
DataFrame1 length = 4

import pandas as pd

# Create DataFrame2 with length 6
dataFrame2 = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Audi', 'Mercedes', 'Jaguar', 'Bentley'],
    "Year": [2020, 2019, 2021, 2022, 2020, 2023]
})

print("DataFrame2 ...\n", dataFrame2)
print("DataFrame2 length =", len(dataFrame2))

DataFrame2 ...
       Car  Year
0      BMW  2020
1    Lexus  2019
2     Audi  2021
3 Mercedes  2022
4   Jaguar  2020
5  Bentley  2023
DataFrame2 length = 6

Merging DataFrames with Left Join

A left join keeps all rows from the left DataFrame and adds matching data from the right DataFrame ?

import pandas as pd

# Create DataFrames
dataFrame1 = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Audi', 'Jaguar'],
    "Price": [50000, 45000, 48000, 60000]
})

dataFrame2 = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Audi', 'Mercedes', 'Jaguar', 'Bentley'],
    "Year": [2020, 2019, 2021, 2022, 2020, 2023]
})

# Merge DataFrames using left join
mergedRes = dataFrame2.merge(dataFrame1, on='Car', how='left')
print("Merged DataFrame (Left Join)...\n", mergedRes)

Merged DataFrame (Left Join)...
       Car  Year   Price
0      BMW  2020 50000.0
1    Lexus  2019 45000.0
2     Audi  2021 48000.0
3 Mercedes  2022     NaN
4   Jaguar  2020 60000.0
5  Bentley  2023     NaN

Different Merge Types

You can use different join types depending on your needs ?

import pandas as pd

dataFrame1 = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Audi', 'Jaguar'],
    "Price": [50000, 45000, 48000, 60000]
})

dataFrame2 = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Audi', 'Mercedes', 'Jaguar', 'Bentley'],
    "Year": [2020, 2019, 2021, 2022, 2020, 2023]
})

# Inner join - only matching rows
inner_merge = dataFrame1.merge(dataFrame2, on='Car', how='inner')
print("Inner Join:\n", inner_merge)

# Outer join - all rows from both DataFrames
outer_merge = dataFrame1.merge(dataFrame2, on='Car', how='outer')
print("\nOuter Join:\n", outer_merge)

Inner Join:
      Car  Price  Year
0     BMW  50000  2020
1   Lexus  45000  2019
2    Audi  48000  2021
3  Jaguar  60000  2020

Outer Join:
       Car   Price  Year
0      BMW 50000.0  2020
1    Lexus 45000.0  2019
2     Audi 48000.0  2021
3   Jaguar 60000.0  2020
4 Mercedes     NaN  2022
5  Bentley     NaN  2023

Comparison of Merge Types

Conclusion

Use merge() with the appropriate how parameter to combine DataFrames of different lengths. Left join preserves all rows from the left DataFrame, while inner join only keeps matching rows.