Why Pandas .divide() method adding the divisor as a column with NAN values?

Question

I am trying to divide a Pandas timeseries data frame with another data frame with exact matching datetime index, using Pandas.divide() method. When I do so instead of the expected element wise division, the divisor column is getting as a column the data frame being divided with NAN values. Why is that? I am unable to figure out. Please note that if I use a scalar to divide there is no problem. Appreciate inputs. Below are the info on the data frames .

print(week1_range)
            Max TemperatureF  Min TemperatureF
Date                                          
2013-07-01                79                66
2013-07-02                84                66
2013-07-03                86                71
2013-07-04                86                70
2013-07-05                86                69
2013-07-06                89                70
2013-07-07                77                70
-----------------------------------------
print(week1_range.info())
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 7 entries, 2013-07-01 to 2013-07-07
Data columns (total 2 columns):
 #   Column            Non-Null Count  Dtype
---  ------            --------------  -----
 0   Max TemperatureF  7 non-null      int64
 1   Min TemperatureF  7 non-null      int64
dtypes: int64(2)
memory usage: 168.0 bytes
None
-----------------------------------------------
print(week1_mean)
           Mean TemperatureF
Date                         
2013-07-01                 72
2013-07-02                 74
2013-07-03                 78
2013-07-04                 77
2013-07-05                 76
2013-07-06                 78
2013-07-07                 72
----------------------------------------------------
print(week1_mean.info())
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 7 entries, 2013-07-01 to 2013-07-07
Data columns (total 1 columns):
 #   Column             Non-Null Count  Dtype
---  ------             --------------  -----
 0   Mean TemperatureF  7 non-null      int64
dtypes: int64(1)
memory usage: 112.0 bytes
None
-------------------------------------------
print(week1_range.divide(week1_mean, axis='rows'))
            Max TemperatureF  Mean TemperatureF  Min TemperatureF
Date                                                             
2013-07-01               NaN                NaN               NaN
2013-07-02               NaN                NaN               NaN
2013-07-03               NaN                NaN               NaN
2013-07-04               NaN                NaN               NaN
2013-07-05               NaN                NaN               NaN
2013-07-06               NaN                NaN               NaN
2013-07-07               NaN                NaN               NaN

Asked By: Srinivas

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Source

Answer 1

That’s because you’re dividing two dataframes with differents columns names.

You need to squeeze the second dataframe so pandas does not try/need to align the columns :

out = week1_range.div(week1_mean.squeeze(), axis='rows')

#or simply select the column
#out = week1_range.div(week1_mean['Mean TemperatureF'], axis='rows')

#or use to_numpy() as per @Corralien
#out = week1_range.div(week1_mean.to_numpy(), axis='rows')

Output :

print(out)

            Max TemperatureF  Min TemperatureF
Date                                          
2013-07-01          1.097222          0.916667
2013-07-02          1.135135          0.891892
2013-07-03          1.102564          0.910256
2013-07-04          1.116883          0.909091
2013-07-05          1.131579          0.907895
2013-07-06          1.141026          0.897436
2013-07-07          1.069444          0.972222

Answered By: Timeless

Why Pandas .divide() method adding the divisor as a column with NAN values?

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