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Specifying "skip NA" when calculating the average value of a column in a data frame created using Pandas

I study the package Pandasby replicating the output from some vignettes of R. Now I use the package dplyrfrom R as an example:

http://cran.rstudio.com/web/packages/dplyr/vignettes/introduction.html

R script

planes <- group_by(hflights_df, TailNum)
delay <- summarise(planes,
  count = n(),
  dist = mean(Distance, na.rm = TRUE))
delay <- filter(delay, count > 20, dist < 2000)

Python script

planes = hflights.groupby('TailNum')
planes['Distance'].agg({'count' : 'count',
                        'dist' : 'mean'})

How can I explicitly tell python what NAto skip?

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2 answers

This is a trick since you are not doing this . Pandas automatically excludes numbers NaNfrom aggregation functions. Consider mine df:

    b   c   d  e
a               
2   2   6   1  3
2   4   8 NaN  7
2   4   4   6  3
3   5 NaN   2  6
4 NaN NaN   4  1
5   6   2   1  8
7   3   2   4  7
9   6   1 NaN  1
9 NaN NaN   9  3
9   3   4   6  1

count() NaN, mean(). , NaN, - NaN. , NaN:

In[335]: df.groupby('a').mean()
Out[333]: 
          b    c    d         e
a                              
2  3.333333  6.0  3.5  4.333333
3  5.000000  NaN  2.0  6.000000
4       NaN  NaN  4.0  1.000000
5  6.000000  2.0  1.0  8.000000
7  3.000000  2.0  4.0  7.000000
9  4.500000  2.5  7.5  1.666667

:

In[340]: df.groupby('a')['b'].agg({'foo': np.mean})
Out[338]: 
        foo
a          
2  3.333333
3  5.000000
4       NaN
5  6.000000
7  3.000000
9  4.500000

: , dataframe.mean API NaN, .

+6

, foobar, , , skipna. , :

def custom_mean(df):
    return df.mean(skipna=False)

group.agg({"your_col_name_to_be_aggregated":custom_mean})

! , , , , .

, , ! doc.

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