Tag: Machine Learning

Slicing Pandas Dataframe Columns Cheat Sheet

Here’s my quick cheat-sheet on slicing columns from a Pandas dataframe. Consider this dataset:

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df = pd.DataFrame([
                    ["AA", "747", 1],
                    ["AB", "A380", 1],
                    ["AA", "737", 0],
                    ["AB", "747", 1],
                    ["AA", "737", 0]
                ],
                columns=["Airline", "Aircraft", "Class"])

It has 3 columns. You can use the iloc function to slice columns:

AirlineAircraftClass
AA7471
ABA3801
AA7370
AB7471
AA7370

1st column only:

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df.iloc[:, 0:1].values
 
array([['AA', '747'],
       ['AB', 'A380'],
       ['AA', '737'],
       ['AB', '747'],
       ['AA', '737']], dtype=object)

1st through last columns (i.e. all columns):

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df.iloc[:, :].values
df.iloc[:, 0:].values # Alternate syntax
 
array([['AA', '747', 1],
       ['AB', 'A380', 1],
       ['AA', '737', 0],
       ['AB', '747', 1],
       ['AA', '737', 0]], dtype=object)

2nd column only:

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df.iloc[:, 1:2].values
 
array([['747'],
       ['A380'],
       ['737'],
       ['747'],
       ['737']], dtype=object)

2nd through last columns:

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df.iloc[:, 1:].values
 
array([['747', 1],
       ['A380', 1],
       ['737', 0],
       ['747', 1],
       ['737', 0]], dtype=object)

1st through last-but-one:

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df.iloc[:, :-1].values
df.iloc[:, 0:-1].values # Alternate syntax
 
array([['AA', '747'],
       ['AB', 'A380'],
       ['AA', '737'],
       ['AB', '747'],
       ['AA', '737']], dtype=object)

Last column only:

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df.iloc[:, -1:].values
 
array([[1],
       [1],
       [0],
       [1],
       [0]])