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To solve the problem about pivot() of Pandas
source link: http://www.donghao.org/2021/01/08/to-solve-the-problem-about-pivot-of-pandas/
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To solve the problem about pivot() of Pandas
Below is an example from pandas official document for pivot():
import pandas as pd
df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two', 'two'],
'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
'baz': [1, 2, 3, 4, 5, 6],
'zoo': ['x', 'y', 'z', 'q', 'w', 't']})
result = df.pivot(index='foo', columns='bar', values='baz')
print(result)Python
xxxxxxxxxx
import pandas as pd
df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two', 'two'],'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
'baz': [1, 2, 3, 4, 5, 6],
'zoo': ['x', 'y', 'z', 'q', 'w', 't']})
result = df.pivot(index='foo', columns='bar', values='baz')
print(result)
the result will be
bar A B C foo one 1 2 3 two 4 5 6
Python
xxxxxxxxxx
bar A B C
foo
one 1 2 3
two 4 5 6
But if there are duplicate rows in the Dataframe, it will report error:
ValueError: Index contains duplicate entries, cannot reshape
Python
xxxxxxxxxx
ValueError: Index contains duplicate entries, cannot reshape
To fix this, we can just drop_duplicates() first and then pivot():
result = df.drop_duplicates().pivot(index='foo', columns='bar', values='baz')
Python
xxxxxxxxxx
result = df.drop_duplicates().pivot(index='foo', columns='bar', values='baz')
As matter of fact, there are situations that drop_duplicates() couldn’t fix:
df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two', 'two'],
'bar': ['A', 'A', 'C', 'A', 'B', 'C'],
'baz': [1, 2, 3, 4, 5, 6],
'zoo': ['x', 'y', 'z', 'q', 'w', 't']})Python
xxxxxxxxxx
df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two', 'two'],'bar': ['A', 'A', 'C', 'A', 'B', 'C'],
'baz': [1, 2, 3, 4, 5, 6],
'zoo': ['x', 'y', 'z', 'q', 'w', 't']})
Now we will need to use groupby() and unstack() to replace pivot():
result = (df.groupby(["foo", "bar"])
.baz
.first()
.unstack()
)Python
xxxxxxxxxx
result = (df.groupby(["foo", "bar"])
.baz
.first()
.unstack()
)
And the result is
bar A B C foo one 1.0 NaN 3.0 two 4.0 5.0 6.0
Python
xxxxxxxxxx
bar A B C
foo
one 1.0 NaN 3.0
two 4.0 5.0 6.0
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