Pandas — Merging, Joining & Concatenations

Facilities For Easily Combining Together Series or DataFrame — #PySeries#Episode 13

print(“Hello Pandas — Merging Joining and Concatenating”)
import numpy as np
import pandas as pd

Preparing 3 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=[0,1,2,3])

df3=pd.DataFrame({'A':['A8','A9','A10','A11'],
'B':['B8','B9','B10','B11'],
'C':['C8','C9','C10','C11'],
'D':['D8','D9','D10','D11']},
index=[0,1,2,3])

Concatenation

Basically glues together DataFrames; keep in mind that dimensions should match along the axis we are concatenation on. We can use pd.concat() and pass in a list of DataFrames to concatenation together.

Combine three DataFrame objects with identical columns:

pd.concat([df1,df2,df3])

pd.concat([df1,df2,df3], axis=1)

Merging: Single key

Merging allows us to merge DataFrames together using similar logic as mapping SQL.

2 More DataFrames:

left=pd.DataFrame({'key':['K0','K1','K2','K3'],
'A':['A0','A1','A2','A3'],
'B':['B0','B1','B2','B3'],
})
left

right=pd.DataFrame({'key':['K0','K1','K2','K3'],
'A':['A4','A5','A6','A7'],
'B':['B4','B5','B6','B7'],
})
right

pd.merge(left,right,how='inner',on='key')

Merging: Multiply keys

DataFrames are now merged into a single DataFrame based on the common values present in the id column of both the DataFrames

left = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],
'key2': ['K0', 'K1', 'K0', 'K1'],
'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3']})
left

right = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],
'key2': ['K0', 'K0', 'K0', 'K0'],
'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']})
right

Merging on Multiply keys

pd.merge(left,right,on=['key1','key2'])

pd.merge(left,right,how='outer',on=['key1','key2'])

pd.merge(left,right,how=’right’,on=[‘key1’,’key2'])

pd.merge(left,right,how='left',on=['key1','key2'])

Joining

Joining is a convenient method for combining the two columns of the two potentially differently-indexed DataFrame into a single result DataFrame;

Same as Merging, except the *key* is an index, instead of columns

left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],
'B': ['B0', 'B1', 'B2']},
index=['K0', 'K1', 'K2'])
right = pd.DataFrame({'C': ['C0', 'C2', 'C3'],
'D': ['D0', 'D2', 'D3']},
index=['K0', 'K2', 'K3'])
left

right

left.join(right)

left.join(right, how='outer')

Colab File link:)

Credits & References:

Jose Portilla — Python for Data Science and Machine Learning Bootcamp — Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!

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