Pandas Concat vs Merge-Pandas Dataframe

Pandas Concat vs Merge

In data analysis, combining data from different sources into a single coherent data structure is a common task. Pandas, a powerful data manipulation library in Python, provides several functions to perform these operations, notably concat() and merge(). This article will explore the differences between these two functions, their use cases, and how to apply them effectively in various scenarios.

Understanding Pandas `concat()`

The concat() function in Pandas is used to concatenate pandas objects along a particular axis with optional set logic along the other axes. It can be used to concatenate Series, DataFrame, or Panel objects.

Example 1: Basic Concatenation of Two DataFrames

import pandas as pd

# Create two DataFrames
df1 = pd.DataFrame({
    'A': ['A0', 'A1', 'A2', 'A3'],
    'B': ['B0', 'B1', 'B2', 'B3'],
    'C': ['C0', 'C1', 'C2', 'C3'],
    'D': ['D0', 'D1', 'D2', 'D3']
}, index=[0, 1, 2, 3])

df2 = pd.DataFrame({
    'A': ['A4', 'A5', 'A6', 'A7'],
    'B': ['B4', 'B5', 'B6', 'B7'],
    'C': ['C4', 'C5', 'C6', 'C7'],
    'D': ['D4', 'D5', 'D6', 'D7']
}, index=[4, 5, 6, 7])

result = pd.concat([df1, df2])
print(result)

Output:

Pandas Concat vs Merge

Example 2: Concatenation with Axis Specified

import pandas as pd

# Create two DataFrames
df1 = pd.DataFrame({
    'A': ['A0', 'A1', 'A2', 'A3'],
    'B': ['B0', 'B1', 'B2', 'B3'],
    'C': ['C0', 'C1', 'C2', 'C3'],
    'D': ['D0', 'D1', 'D2', 'D3']
}, index=[0, 1, 2, 3])

df2 = pd.DataFrame({
    'A': ['A4', 'A5', 'A6', 'A7'],
    'B': ['B4', 'B5', 'B6', 'B7'],
    'C': ['C4', 'C5', 'C6', 'C7'],
    'D': ['D4', 'D5', 'D6', 'D7']
}, index=[4, 5, 6, 7])

result = pd.concat([df1, df2], axis=1)
print(result)

Output:

Pandas Concat vs Merge

Example 3: Concatenation with Index Reorganization

import pandas as pd

# Create two DataFrames
df1 = pd.DataFrame({
    'A': ['A0', 'A1', 'A2', 'A3'],
    'B': ['B0', 'B1', 'B2', 'B3'],
    'C': ['C0', 'C1', 'C2', 'C3'],
    'D': ['D0', 'D1', 'D2', 'D3']
}, index=[0, 1, 2, 3])

df2 = pd.DataFrame({
    'A': ['A4', 'A5', 'A6', 'A7'],
    'B': ['B4', 'B5', 'B6', 'B7'],
    'C': ['C4', 'C5', 'C6', 'C7'],
    'D': ['D4', 'D5', 'D6', 'D7']
}, index=[4, 5, 6, 7])

result = pd.concat([df1, df2], ignore_index=True)
print(result)

Output:

Pandas Concat vs Merge

Understanding Pandas `merge()`

The merge() function is used to merge DataFrame or named Series objects with a database-style join. The join is done on columns or indexes. If joining columns on columns, the DataFrame indexes will be ignored.

Example 4: Basic Merge Operation

import pandas as pd

# Create two DataFrames
df1 = pd.DataFrame({
    'A': ['A0', 'A1', 'A2', 'A3'],
    'B': ['B0', 'B1', 'B2', 'B3'],
    'C': ['C0', 'C1', 'C2', 'C3'],
    'D': ['D0', 'D1', 'D2', 'D3']
}, index=[0, 1, 2, 3])

df2 = pd.DataFrame({
    'A': ['A4', 'A5', 'A6', 'A7'],
    'B': ['B4', 'B5', 'B6', 'B7'],
    'C': ['C4', 'C5', 'C6', 'C7'],
    'D': ['D4', 'D5', 'D6', 'D7']
}, index=[4, 5, 6, 7])

left = pd.DataFrame({
    'key': ['K0', 'K1', 'K2', 'K3'],
    'A': ['A0', 'A1', 'A2', 'A3'],
    'B': ['B0', 'B1', 'B2', 'B3']
})

right = pd.DataFrame({
    'key': ['K0', 'K1', 'K2', 'K3'],
    'C': ['C0', 'C1', 'C2', 'C3'],
    'D': ['D0', 'D1', 'D2', 'D3']
})

result = pd.merge(left, right, on='key')
print(result)

Output:

Pandas Concat vs Merge

Example 5: Merge with Different Key Names

import pandas as pd

# Create two DataFrames
df1 = pd.DataFrame({
    'A': ['A0', 'A1', 'A2', 'A3'],
    'B': ['B0', 'B1', 'B2', 'B3'],
    'C': ['C0', 'C1', 'C2', 'C3'],
    'D': ['D0', 'D1', 'D2', 'D3']
}, index=[0, 1, 2, 3])

df2 = pd.DataFrame({
    'A': ['A4', 'A5', 'A6', 'A7'],
    'B': ['B4', 'B5', 'B6', 'B7'],
    'C': ['C4', 'C5', 'C6', 'C7'],
    'D': ['D4', 'D5', 'D6', 'D7']
}, index=[4, 5, 6, 7])

left = pd.DataFrame({
    'left_key': ['K0', 'K1', 'K2', 'K3'],
    'A': ['A0', 'A1', 'A2', 'A3']
})

right = pd.DataFrame({
    'right_key': ['K0', 'K1', 'K2', 'K3'],
    'B': ['B0', 'B1', 'B2', 'B3']
})

result = pd.merge(left, right, left_on='left_key', right_on='right_key')
print(result)

Output:

Pandas Concat vs Merge

Comparing `concat()` and `merge()`

While both concat() and merge() are used for combining data, they serve different purposes and are used in different scenarios.

Example 6: Concatenation vs. Merge

import pandas as pd

# Create two DataFrames
df1 = pd.DataFrame({
    'A': ['A0', 'A1', 'A2', 'A3'],
    'B': ['B0', 'B1', 'B2', 'B3'],
    'C': ['C0', 'C1', 'C2', 'C3'],
    'D': ['D0', 'D1', 'D2', 'D3']
}, index=[0, 1, 2, 3])

df2 = pd.DataFrame({
    'A': ['A4', 'A5', 'A6', 'A7'],
    'B': ['B4', 'B5', 'B6', 'B7'],
    'C': ['C4', 'C5', 'C6', 'C7'],
    'D': ['D4', 'D5', 'D6', 'D7']
}, index=[4, 5, 6, 7])

left = pd.DataFrame({
    'left_key': ['K0', 'K1', 'K2', 'K3'],
    'A': ['A0', 'A1', 'A2', 'A3']
})

right = pd.DataFrame({
    'right_key': ['K0', 'K1', 'K2', 'K3'],
    'B': ['B0', 'B1', 'B2', 'B3']
})

# Using concat
concat_result = pd.concat([df1, df2])

# Using merge
merge_result = pd.merge(df1, df2, on='A', how='inner')
print(concat_result)
print(merge_result)

Output:

Pandas Concat vs Merge

Advanced Usage of `concat()`

Example 7: Concatenation with MultiIndex

import pandas as pd

df1 = pd.DataFrame({
    'A': ['A0', 'A1', 'A2', 'A3'],
    'B': ['B0', 'B1', 'B2', 'B3'],
    'key': ['K0', 'K1', 'K2', 'K3']
}).set_index(['key'])

df2 = pd.DataFrame({
    'C': ['C0', 'C1', 'C2', 'C3'],
    'D': ['D0', 'D1', 'D2', 'D3'],
    'key': ['K0', 'K1', 'K2', 'K3']
}).set_index(['key'])

result = pd.concat([df1, df2], axis=1)
print(result)

Output:

Pandas Concat vs Merge

Example 8: Handling Overlapping Columns

import pandas as pd

df1 = pd.DataFrame({
    'A': ['A0', 'A1', 'A2', 'A3'],
    'B': ['B0', 'B1', 'B2', 'B3']
})

df2 = pd.DataFrame({
    'B': ['B4', 'B5', 'B6', 'B7'],
    'C': ['C0', 'C1', 'C2', 'C3']
})

result = pd.concat([df1, df2], sort=False)
print(result)

Output:

Pandas Concat vs Merge

Advanced Usage of `merge()`

Example 9: Merge with Multiple Keys

import pandas as pd

left = pd.DataFrame({
    'key1': ['K0', 'K1', 'K2', 'K3'],
    'key2': ['K0', 'K1', 'K0', 'K1'],
    'A': ['A0', 'A1', 'A2', 'A3']
})

right = pd.DataFrame({
    'key1': ['K0', 'K1', 'K2', 'K3'],
    'key2': ['K0', 'K0', 'K0', 'K0'],
    'B': ['B0', 'B1', 'B2', 'B3']
})

result = pd.merge(left, right, on=['key1', 'key2'], how='inner')
print(result)

Output:

Pandas Concat vs Merge

Pandas Concat vs Merge Conclusion

In this article, we explored the differences and use cases of concat() and merge() in pandas. While concat() is generally used for combining data along an axis, merge() is more suited for complex joins and relational data structures. Understanding when to use each function can significantly enhance your data manipulation capabilities in pandas.

This guide should serve as a starting point for applying these powerful pandas functions to your own data sets.

Pandas Concat vs Merge