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Rowwise min () and max () fail for a column with NaN

I am trying to take in turn the maximum (and min) of two columns containing dates

from datetime import date
import pandas as pd
import numpy as np    

df = pd.DataFrame({'date_a' : [date(2015, 1, 1), date(2012, 6, 1),
                               date(2013, 1, 1), date(2016, 6, 1)],
                   'date_b' : [date(2012, 7, 1), date(2013, 1, 1), 
                               date(2014, 3, 1), date(2013, 4, 1)]})

df[['date_a', 'date_b']].max(axis=1)
Out[46]: 
0    2015-01-01
1    2013-01-01
2    2014-03-01
3    2016-06-01

as expected. However, if the data frame contains one NaN value, the whole operation fails.

df_nan = pd.DataFrame({'date_a' : [date(2015, 1, 1), date(2012, 6, 1),
                                   np.NaN, date(2016, 6, 1)],
                       'date_b' : [date(2012, 7, 1), date(2013, 1, 1), 
                                   date(2014, 3, 1), date(2013, 4, 1)]})

df_nan[['date_a', 'date_b']].max(axis=1)
Out[49]: 
0   NaN 
1   NaN
2   NaN
3   NaN
dtype: float64

What's going on here? I expected this result

0    2015-01-01
1    2013-01-01
2    NaN
3    2016-06-01

How to achieve this?

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3 answers

I would say that the best solution is to use the appropriate one dtype. Pandas provides a very well integrated datetime dtype. So you are using objectdtypes ...

>>> df
       date_a      date_b
0  2015-01-01  2012-07-01
1  2012-06-01  2013-01-01
2         NaN  2014-03-01
3  2016-06-01  2013-04-01
>>> df.dtypes
date_a    object
date_b    object
dtype: object

But mind you, the problem disappears when you use

>>> df2 = df.apply(pd.to_datetime)
>>> df2
      date_a     date_b
0 2015-01-01 2012-07-01
1 2012-06-01 2013-01-01
2        NaT 2014-03-01
3 2016-06-01 2013-04-01
>>> df2.min(axis=1)
0   2012-07-01
1   2012-06-01
2   2014-03-01
3   2013-04-01
dtype: datetime64[ns]
+8

, date (, NaN) . numeric_only - float. , df_nan :

df_float = pd.DataFrame({'date_a' : [date(2015, 1, 1), date(2012, 6, 1),
                                    1.023, date(2016, 6, 1)],
                        'date_b' : [date(2012, 7, 1), 3.14, 
                                    date(2014, 3, 1), date(2013, 4, 1)]})

print(df_float.max(1))

0   NaN
1   NaN
2   NaN
3   NaN
dtype: float64

false, TypeError, :

print(date(2015, 1, 1) < 1.0)

TypeError                                 Traceback (most recent call last)
<ipython-input-362-ccbf44ddb40a> in <module>()
      1 
----> 2 print(date(2015, 1, 1) < 1.0)

TypeError: unorderable types: datetime.date() < float()

, pandas, , NaN. str df.astype :

out = df_nan.astype(str).max(1)
print(out) 
0    2015-01-01
1    2013-01-01
2           nan
3    2016-06-01
dtype: object

, .

, juan , datetime pd.to_datetime:

out = df_nan.apply(pd.to_datetime, errors='coerce').max(1)
print(out)

0   2015-01-01
1   2013-01-01
2   2014-03-01
3   2016-06-01
dtype: datetime64[ns]
+6

:

>>> df_nan.where(df_nan.T.notnull().all()).max(axis=1)
Out[1]:
0    2015-01-01
1    2013-01-01
2          None
3    2016-06-01
dtype: object

:

  • df_nan.T.notnull().all() , np.nan
  • df_nan.where()
  • .max(axis=1)

This works because the maximum of the array, where all the values ​​are np.nan, is None. This allows you to track rows where there is no value without displaying the maximum result.

But this solution is up to you, otherwise the @ juanpa.arrivillaga solution converting NaNto NaTyou need.

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