Concatenation - ( Pandas Reading and Writing Files )
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Syntax
pd.concat(objs, axis=0, join='outer', ignore_index=False, keys=None, levels=None, names=None, verify_integrity=False, copy=True)
Example
import pandas as pd
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'c']})
df2 = pd.DataFrame({'A': [4, 5, 6], 'B': ['d', 'e', 'f']})
result = pd.concat([df1, df2])
print(result)
Output
A B
0 1 a
1 2 b
2 3 c
0 4 d
1 5 e
2 6 f
Explanation
Pandas provides the concat()
function to concatenate two or more dataframes along a specific axis. It takes in a list of dataframes to be concatenated and returns a new concatenated dataframe.
In the above example, we create two dataframes df1
and df2
and then use the concat()
function to concatenate them along the default axis 0 (axis=0
). The result is a new dataframe result
containing all the rows of both the dataframes.
Use
Concatenation is used in various data processing tasks where data needs to be combined or merged from different sources. It is particularly useful in combining tables with the same columns.
Important Points
- Pandas provides the
concat()
function to concatenate two or more dataframes - The function takes in a list of dataframes to be concatenated and returns a new concatenated dataframe
- The default axis for concatenation is 0 (rows), but can be changed to 1 (columns) using the
axis
parameter - The
concat()
function also has optional parameters such asjoin
,keys
,levels
, etc.
Summary
In conclusion, the concat()
function in Pandas is an essential tool for combining two or more dataframes. It provides flexibility in choosing the axis for concatenation, and optional parameters for customizing the concatenation process. Concatenation is a common data processing task, and Pandas simplifies this task with its powerful concat()
function.