Introduction
In data analysis and manipulation, it is common to combine or concatenate multiple tables to create a single data structure for analysis. Pandas, a popular Python library for data manipulation and analysis, provides a concat()
function that allows you to combine tables either horizontally or vertically. In this article, we will demonstrate how to use the concat()
function in pandas to concatenate tables horizontally and vertically.
Horizontal Concatenation
Horizontal concatenation involves merging two tables by columns, effectively extending the tables by columns. This can be achieved using the concat()
function in pandas by setting the axis
parameter to 1
.
Example: Horizontal Concatenation
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']})
df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],
'B': ['B4', 'B5', 'B6', 'B7'],
'C': ['C4', 'C5', 'C6', 'C7'],
'D': ['D4', 'D5', 'D6', 'D7']})
# Merge the two tables horizontally.
result = pd.concat([df1, df2], axis=1)
print(result)
Vertical Concatenation
Vertical concatenation involves stacking tables on top of each other, effectively extending the tables by rows. This can be achieved using the concat()
function in pandas by setting the axis
parameter to 0
.
Example: Vertical Concatenation
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']})
df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],
'B': ['B4', 'B5', 'B6', 'B7'],
'C': ['C4', 'C5', 'C6', 'C7'],
'D': ['D4', 'D5', 'D6', 'D7']})
# Concatenate the two tables vertically
result = pd.concat([df1, df2], axis=0)
print(result)
Conclusion
In this article, we have discussed how to use the concat()
function in the pandas library to concatenate tables horizontally or vertically by specifying the axis
parameter. By following the examples provided in this article, you should be able to effectively merge tables to create a single, comprehensive dataset for further data analysis and manipulation. Happy coding!
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