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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 the 3 DataFrame:



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 two DataFrame objects with identical columns:

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 DataFrame:



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

Merging on Multiply keys



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.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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