Python | Merge, Join and Concatenate DataFrames using Panda
A dataframe is a two-dimensional data structure having multiple rows and columns. In a dataframe, the data is aligned in the form of rows and columns only. A dataframe can perform arithmetic as well as conditional operations. It has mutable size.
Below is the implementation using Numpy and Pandas.
Modules needed:
import numpy as np import pandas as pd
Code #1 : DataFrames Concatenation
concat() function does all of the heavy lifting of performing concatenation operations along an axis while performing optional set logic (union or intersection) of the indexes (if any) on the other axes.
# Python program to concatenate # dataframes using Panda # Creating first dataframe 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]) # Creating second dataframe 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]) # Creating third dataframe df3 = pd.DataFrame({'A': ['A8', 'A9', 'A10', 'A11'], 'B': ['B8', 'B9', 'B10', 'B11'], 'C': ['C8', 'C9', 'C10', 'C11'], 'D': ['D8', 'D9', 'D10', 'D11']}, index = [8, 9, 10, 11]) # Concatenating the dataframes pd.concat([df1, df2, df3]) |
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Output:
Code #2 : DataFrames Merge
Pandas provides a single function, merge(), as the entry point for all standard database join operations between DataFrame objects.
# Python program to merge # dataframes using Panda # Dataframe created 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']}) # Merging the dataframes pd.merge(left, right, how ='inner', on ='Key') |
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Code #3 : DataFrames Join
# Python program to join # dataframes using Panda left = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'], 'B': ['B0', 'B1', 'B2', 'B3']}, index = ['K0', 'K1', 'K2', 'K3']) right = pd.DataFrame({'C': ['C0', 'C1', 'C2', 'C3'], 'D': ['D0', 'D1', 'D2', 'D3']}, index = ['K0', 'K1', 'K2', 'K3']) # Joining the dataframes left.join(right) |
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Output:
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