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 dataframedf1 = 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 dataframedf2 = 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 dataframedf3 = 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 dataframespd.concat([df1, df2, df3]) |
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 createdleft = 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') |
Output:
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) |
Output:
