ValueError: cannot reindex from a duplicate axis while shift one column in Pandas

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Given a dataframe df with date index as follows:

              value
2017-03-31      NaN
2017-04-01  27863.7
2017-04-02  27278.5
2017-04-03  27278.5
2017-04-04  27278.5
             ...
2021-10-27      NaN
2021-10-28      NaN
2021-10-29      NaN
2021-10-30      NaN
2021-10-31      NaN

I'm able to shift value column by one year use df['value'].shift(freq=pd.DateOffset(years=1)):

Out:

2018-03-31        NaN
2018-04-01    27863.7
2018-04-02    27278.5
2018-04-03    27278.5
2018-04-04    27278.5
               ...   
2022-10-27        NaN
2022-10-28        NaN
2022-10-29        NaN
2022-10-30        NaN
2022-10-31        NaN

But when I use it to replace orginal value by df['value'] = df['value'].shift(freq=pd.DateOffset(years=1)), it raises an error:

ValueError: cannot reindex from a duplicate axis

Since the code below works smoothly, so I think the issue caused by NaNs in value column:

import pandas as pd
import numpy as np

np.random.seed(2021)
dates = pd.date_range('20130101', periods=720)
df = pd.DataFrame(np.random.randint(0, 100, size=(720, 3)), index=dates, columns=list('ABC'))
df

df.B = df.B.shift(freq=pd.DateOffset(years=1))

I also try with df['value'].shift(freq=relativedelta(years=+1)), but it generates: pandas.errors.NullFrequencyError: Cannot shift with no freq

Someone could help to deal with this issue? Sincere thanks.

pandas python-3.x numpy date
1 Answers

Since the code below works smoothly, so I think the issue caused by NaNs in value column

No I don't think so. It's probably because in your 2nd sample you have only 1 leap year.

Reproducible error with 2 leap years:

# 2018 (366 days), 2019 (365 days) and 2020 (366 days)
dates = pd.date_range('20180101', periods=365*3+1)
df = pd.DataFrame(np.random.randint(0, 100, size=(365*3+1, 3)),
                  index=dates, columns=list('ABC'))

df.B = df.B.shift(freq=pd.DateOffset(years=1))
...
ValueError: cannot reindex from a duplicate axis
...

The example below works:

# 2017 (365 days), 2018 (366 days) and 2019 (365 days)
dates = pd.date_range('20170101', periods=365*3+1)
df = pd.DataFrame(np.random.randint(0, 100, size=(365*3+1, 3)),
                  index=dates, columns=list('ABC'))

df.B = df.B.shift(freq=pd.DateOffset(years=1))

Just look to value_counts:

# 2018 -> 2020
>>> df.B.shift(freq=pd.DateOffset(years=1)).index.value_counts()
2021-02-28    2  # The duplicated index
2020-12-29    1
2021-01-04    1
2021-01-03    1
2021-01-02    1
             ..
2020-01-07    1
2020-01-08    1
2020-01-09    1
2020-01-10    1
2021-12-31    1
Length: 1095, dtype: int64


# 2017 -> 2019
>>> df.B.shift(freq=pd.DateOffset(years=1)).index.value_counts()
2018-01-01    1
2019-12-30    1
2020-01-05    1
2020-01-04    1
2020-01-03    1
             ..
2019-01-07    1
2019-01-08    1
2019-01-09    1
2019-01-10    1
2021-01-01    1
Length: 1096, dtype: int64

Solution

Obviously, the solution is to remove duplicated index, in our case '2021-02-28', by using resample('D') and an aggregate function first, last, min, max, mean, sum or a custom one:

>>> df.B.shift(freq=pd.DateOffset(years=1))['2021-02-28']
2021-02-28    41
2021-02-28    96
Name: B, dtype: int64

>>> df.B.shift(freq=pd.DateOffset(years=1))['2021-02-28'] \
        .resample('D').agg(('first', 'last', 'min', 'max', 'mean', 'sum')).T

       2021-02-28
first        41.0
last         96.0
min          41.0
max          96.0
mean         68.5
sum         137.0

# Choose `last` for example
df.B = df.B.shift(freq=pd.DateOffset(years=1)).resample('D').last()

Note, you can replace .resample(...).func by .loc[lambda x: x.index.duplicated()]

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