Pandas is a powerful open-source data analysis and data manipulation library for Python. It provides high-performance data structures, called DataFrames and Series, for managing and analyzing structured data. The library provides several methods for combining and concatenating DataFrames. One such method is the concat function. In this article, we will discuss the pandas concat function and how to reset the index after concatenation.
The Pandas concat function
The concat function in pandas allows you to concatenate multiple DataFrames along either the rows (axis=0) or columns (axis=1). The basic syntax for the concat function is:
pandas.concat(objs, axis=0, join='outer', ignore_index=False, keys=None, levels=None, names=None, verify_integrity=False, sort=None, copy=True)
The objs parameter takes a list of DataFrames to be concatenated. The axis parameter specifies whether to concatenate along the rows or columns. The join parameter specifies how to handle overlapping index values. The ignore_index parameter, if set to True, will reset the index of the resulting DataFrame.
Example 1: Concatenating DataFrames along rows
Let's create two DataFrames, df1 and df2, and concatenate them along the rows.
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 df1 and df2 along rows
result = pd.concat([df1, df2])
print(result)
The output will be:
A B C D
0 A0 B0 C0 D0
1 A1 B1 C1 D1
2 A2 B2 C2 D2
3 A3 B3 C3 D3
4 A4 B4 C4 D4
5 A5 B5 C5 D5
6 A6 B6 C6 D6
7 A7 B7 C7 D7
Example 2: Concatenating DataFrames along columns
Let's create two DataFrames, df1 and df2, and concatenate them along
the columns.
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 df1 and df2 along columns
result = pd.concat([df1, df2], axis=1)
print(result)
The output will be:
A B C D
0 A0 B0 C0 D0
1 A1 B1 C1 D1
2 A2 B2 C2 D2
3 A3 B3 C3 D3
Resetting the Index
After concatenation, you may want to reset the index of the resulting DataFrame. The reset_index function in pandas allows you to reset the index and optionally create a new column to store the old index values.
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 df1 and df2 along rows
result = pd.concat([df1, df2])
# Reset the index
result = result.reset_index(drop=True)
print(result)
The output will be:
A B C D
0 A0 B0 C0 D0
1 A1 B1 C1 D1
2 A2 B2 C2 D2
3 A3 B3 C3 D3
4 A4 B4 C4 D4
5 A5 B5 C5 D5
6 A6 B6 C6 D6
7 A7 B7 C7 D7
In this example, we reset the index by passing the
Popular questions
- What is
pd.concatin pandas?
pd.concat is a function in the pandas library that allows you to concatenate two or more pandas DataFrames along either rows (axis=0) or columns (axis=1).
- How do you concatenate two DataFrames along the rows in pandas?
You can concatenate two DataFrames along the rows by passing a list of the DataFrames to the pd.concat function and setting the axis parameter to 0.
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 df1 and df2 along rows
result = pd.concat([df1, df2])
print(result)
- How do you concatenate two DataFrames along the columns in pandas?
You can concatenate two DataFrames along the columns by passing a list of the DataFrames to the pd.concat function and setting the axis parameter to 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 df1 and df2 along columns
result = pd.concat([df1, df2], axis=1)
print(result)
- What is
reset_indexin pandas?
reset_index is a method in the pandas library that allows you to reset the index of a DataFrame and optionally create a new column to store the old index values.
- How do you reset the index of a DataFrame in pandas and drop the old index?
You can reset the index of a DataFrame and drop the old index by calling the reset_index method on the DataFrame and passing the drop parameter set to True.
import pandas as pd
# Create a DataFrame
df = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3'],
'C': ['C0', 'C1', 'C2', 'C
### Tag
DataFrame
