Pandas Concat Axis-Pandas Dataframe

Pandas Concat Axis

Pandas is a powerful data manipulation library in Python, widely used in data analysis and data science. One of the essential functions in Pandas is concat, which is used to concatenate pandas objects along a particular axis. In this article, we will explore the concat function in-depth, focusing on its use with different axes.

Introduction to Pandas Concat

The concat function in Pandas is primarily used to combine two or more pandas data structures along a particular axis. The function provides flexibility in handling indices and can be used to combine Series, DataFrame, or Panel objects.

Syntax of Concat

The basic syntax of the concat function is as follows:

pandas.concat(objs, axis=0, join='outer', ignore_index=False, keys=None, levels=None, names=None, verify_integrity=False, sort=False, copy=True)

objs: This is a sequence or mapping of Series, DataFrame, or Panel objects.
axis: {0/’index’, 1/’columns’}, default 0. The axis to concatenate along.
join: {‘inner’, ‘outer’}, default ‘outer’. How to handle indexes on other axis(es).
ignore_index: boolean, default False. If True, do not use the index values on the concatenation axis.
keys: sequence, default None. If multiple levels passed, should contain tuples.
verify_integrity: boolean, default False. Check whether the new concatenated axis contains duplicates.
sort: boolean, default False. Sort non-concatenation axis if it is not already aligned.

Example 1: Basic Concatenation of 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])

# Concatenate along the rows
result = pd.concat([df1, df2])
print(result)

Output:

Pandas Concat Axis

Example 2: Concatenation with Axis=1

import pandas as pd

# Create two DataFrames
df1 = pd.DataFrame({
    'A': ['A0', 'A1', 'A2', 'A3'],
    'B': ['B0', 'B1', 'B2', 'B3']
}, index=[0, 1, 2, 3])

df2 = pd.DataFrame({
    'C': ['C0', 'C1', 'C2', 'C3'],
    'D': ['D0', 'D1', 'D2', 'D3']
}, index=[0, 1, 2, 3])

# Concatenate along the columns
result = pd.concat([df1, df2], axis=1)
print(result)

Output:

Pandas Concat Axis

Example 3: Handling Indexes with Ignore Index

import pandas as pd

# Create two DataFrames
df1 = pd.DataFrame({
    'A': ['A0', 'A1', 'A2', 'A3'],
    'B': ['B0', 'B1', 'B2', 'B3']
}, index=[0, 1, 2, 3])

df2 = pd.DataFrame({
    'A': ['A4', 'A5', 'A6', 'A7'],
    'B': ['B4', 'B5', 'B6', 'B7']
}, index=[0, 1, 2, 3])

# Concatenate and ignore the index
result = pd.concat([df1, df2], ignore_index=True)
print(result)

Output:

Pandas Concat Axis

Example 4: Concatenation with Keys

import pandas as pd

# Create two DataFrames
df1 = pd.DataFrame({
    'A': ['A0', 'A1', 'A2', 'A3'],
    'B': ['B0', 'B1', 'B2', 'B3']
}, index=[0, 1, 2, 3])

df2 = pd.DataFrame({
    'A': ['A4', 'A5', 'A6', 'A7'],
    'B': ['B4', 'B5', 'B6', 'B7']
}, index=[4, 5, 6, 7])

# Concatenate with keys
result = pd.concat([df1, df2], keys=['df1', 'df2'])
print(result)

Output:

Pandas Concat Axis

Example 5: Concatenation with Different Indexes

import pandas as pd

# Create two DataFrames
df1 = pd.DataFrame({
    'A': ['A0', 'A1', 'A2', 'A3'],
    'B': ['B0', 'B1', 'B2', 'B3']
}, index=[0, 1, 2, 3])

df2 = pd.DataFrame({
    'C': ['C0', 'C1', 'C2', 'C3'],
    'D': ['D0', 'D1', 'D2', 'D3']
}, index=[2, 3, 4, 5])

# Concatenate along the columns with different indexes
result = pd.concat([df1, df2], axis=1)
print(result)

Output:

Pandas Concat Axis

Example 6: Inner Join on Concatenation

import pandas as pd

# Create two DataFrames
df1 = pd.DataFrame({
    'A': ['A0', 'A1', 'A2', 'A3'],
    'B': ['B0', 'B1', 'B2', 'B3']
}, index=[0, 1, 2, 3])

df2 = pd.DataFrame({
    'A': ['A4', 'A5', 'A6', 'A7'],
    'B': ['B4', 'B5', 'B6', 'B7']
}, index=[2, 3, 4, 5])

# Concatenate with inner join
result = pd.concat([df1, df2], join='inner')
print(result)

Output:

Pandas Concat Axis

Example 7: Concatenation with MultiIndex

import pandas as pd

# Create two DataFrames
df1 = pd.DataFrame({
    'A': ['A0', 'A1', 'A2', 'A3'],
    'B': ['B0', 'B1', 'B2', 'B3']
}, index=[0, 1, 2, 3])

df2 = pd.DataFrame({
    'C': ['C0', 'C1', 'C2', 'C3'],
    'D': ['D0', 'D1', 'D2', 'D3']
}, index=[0, 1, 2, 3])

# Concatenate with MultiIndex
result = pd.concat([df1, df2], keys=['first', 'second'], axis=1)
print(result)

Output:

Pandas Concat Axis

Example 8: Verifying Integrity on Concatenation

import pandas as pd

# Create two DataFrames
df1 = pd.DataFrame({
    'A': ['A0', 'A1', 'A2', 'A3'],
    'B': ['B0', 'B1', 'B2', 'B3']
}, index=[0, 1, 2, 3])

df2 = pd.DataFrame({
    'A': ['A4', 'A5', 'A6', 'A7'],
    'B': ['B4', 'B5', 'B6', 'B7']
}, index=[0, 1, 2, 3])

# Attempt to concatenate and verify integrity
try:
    result = pd.concat([df1, df2], verify_integrity=True)
    print(result)
except ValueError as e:
    print("ValueError:", e)

Output:

Pandas Concat Axis

Example 9: Concatenation with Sorting

import pandas as pd

# Create two DataFrames
df1 = pd.DataFrame({
    'B': ['B0', 'B1', 'B2', 'B3'],
    'A': ['A0', 'A1', 'A2', 'A3']
}, index=[0, 1, 2, 3])

df2 = pd.DataFrame({
    'D': ['D0', 'D1', 'D2', 'D3'],
    'C': ['C0', 'C1', 'C2', 'C3']
}, index=[0, 1, 2, 3])

# Concatenate and sort columns
result = pd.concat([df1, df2], axis=1, sort=True)
print(result)

Output:

Pandas Concat Axis

Example 10: Concatenation with Copy

import pandas as pd

# Create two DataFrames
df1 = pd.DataFrame({
    'A': ['A0', 'A1', 'A2', 'A3'],
    'B': ['B0', 'B1', 'B2', 'B3']
}, index=[0, 1, 2, 3])

df2 = pd.DataFrame({
    'C': ['C0', 'C1', 'C2', 'C3'],
    'D': ['D0', 'D1', 'D2', 'D3']
}, index=[0, 1, 2, 3])

# Concatenate without copying data
result = pd.concat([df1, df2], copy=False)
print(result)

Output:

Pandas Concat Axis

Example 11: Concatenation with Different Column Names

import pandas as pd

# Create two DataFrames
df1 = pd.DataFrame({
    'A': ['A0', 'A1', 'A2', 'A3'],
    'B': ['B0', 'B1', 'B2', 'B3']
}, index=[0, 1, 2, 3])

df2 = pd.DataFrame({
    'C': ['C0', 'C1', 'C2', 'C3'],
    'D': ['D0', 'D1', 'D2', 'D3']
}, index=[0, 1, 2, 3])

# Concatenate along the columns with different column names
result = pd.concat([df1, df2], axis=1)
print(result)

Output:

Pandas Concat Axis

Example 12: Concatenation with Non-Unique Index

import pandas as pd

# Create two DataFrames
df1 = pd.DataFrame({
    'A': ['A0', 'A1', 'A2', 'A3'],
    'B': ['B0', 'B1', 'B2', 'B3']
}, index=[0, 1, 2, 3])

df2 = pd.DataFrame({
    'A': ['A4', 'A5', 'A6', 'A7'],
    'B': ['B4', 'B5', 'B6', 'B7']
}, index=[2, 3, 4, 5])

# Concatenate with non-unique index
result = pd.concat([df1, df2], ignore_index=False)
print(result)

Output:

Pandas Concat Axis

Example 13: Concatenation with DataFrame and Series

import pandas as pd

# Create a DataFrame and a Series
df = pd.DataFrame({
    'A': ['A0', 'A1', 'A2', 'A3'],
    'B': ['B0', 'B1', 'B2', 'B3']
}, index=[0, 1, 2, 3])

series = pd.Series(['S0', 'S1', 'S2', 'S3'], name='S')

# Concatenate DataFrame and Series along columns
result = pd.concat([df, series], axis=1)
print(result)

Output:

Pandas Concat Axis

Example 14: Concatenation with Different DataTypes

import pandas as pd

# Create two DataFrames with different data types
df1 = pd.DataFrame({
    'A': [1, 2, 3, 4],
    'B': [5, 6, 7, 8]
}, index=[0, 1, 2, 3])

df2 = pd.DataFrame({
    'C': [1.1, 2.2, 3.3, 4.4],
    'D': [5.5, 6.6, 7.7, 8.8]
}, index=[0, 1, 2, 3])

# Concatenate along the columns with different data types
result = pd.concat([df1, df2], axis=1)
print(result)

Output:

Pandas Concat Axis

Example 15: Concatenation with Multi-Level Columns

import pandas as pd

# Create two DataFrames with multi-level columns
df1 = pd.DataFrame({
    ('Group1', 'A'): ['A0', 'A1', 'A2', 'A3'],
    ('Group1', 'B'): ['B0', 'B1', 'B2', 'B3']
}, index=[0, 1, 2, 3])

df2 = pd.DataFrame({
    ('Group2', 'C'): ['C0', 'C1', 'C2', 'C3'],
    ('Group2', 'D'): ['D0', 'D1', 'D2', 'D3']
}, index=[0, 1, 2, 3])

# Concatenate along the columns with multi-level columns
result = pd.concat([df1, df2], axis=1)
print(result)

Output:

Pandas Concat Axis

This extensive exploration of the concat function in Pandas demonstrates its versatility and power in handling various data manipulation tasks. By understanding and utilizing the different parameters and options available with concat, you can efficiently combine multiple datasets into a single structure, facilitating easier analysis and manipulation.

Pandas Concat Axis