Consulting - Quality
Difference makes the DIFFERENCE
Merge-Join-Concatenate
Class Work
Merging, Joining, and Concatenating¶
There are 3 main ways of combining DataFrames together: Merging, Joining and Concatenating.
Example DataFrames¶
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import pandas as pd
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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])
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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])
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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])
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df1
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df2
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df3
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Concatenation¶
Concatenation basically glues together DataFrames. Keep in mind that dimensions should match along the axis you are concatenating on. You can use pd.concat and pass in a list of DataFrames to concatenate together:
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pd.concat([df1,df2,df3])
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pd.concat([df1,df2,df3],axis=1)
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Example DataFrames¶
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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']})
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left
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right
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Merging¶
The merge function allows you to merge DataFrames together using a similar logic as merging SQL Tables together. For example:
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pd.merge(left,right,how='inner',on='key')
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Or to show a more complicated example:
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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']})
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pd.merge(left, right, on=['key1', 'key2'])
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pd.merge(left, right, how='outer', on=['key1', 'key2'])
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pd.merge(left, right, how='right', on=['key1', 'key2'])
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pd.merge(left, right, how='left', on=['key1', 'key2'])
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Joining¶
Joining is a convenient method for combining the columns of two potentially differently-indexed DataFrames into a single result DataFrame.
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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'])
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left.join(right)
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left.join(right, how='outer')
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End Cell¶
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!pip install nbconvert
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%shell jupyter nbconvert --to html /content/pi_06_MJC
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