Agfdhyk

I need a function that given a data frame and a number num constructs a data frame with num rows such that every row has the following value:
- for columns with string values we sample a value from a column in original table
- for columns with floats or ints we find mean value

Here is my code

def rows_aggr(df, num):

    dataframe = None

    for i in range(0, num):

        row = None

        for cname in df.columns.values:

            column = df[cname]

            dfcol = Series.to_frame(column)



            if column.dtype != np.number:

                item = dfcol.sample(n=1)

            else:

                item = dfcol.mean(axis=1)



            if row is None:

                row = item

            else:

                row = pd.concat([row, item], axis=1)



        if dataframe is None:

            dataframe = row

        else:

            dataframe = pd.concat([dataframe, row], axis=0)



    return dataframe

for some reason rows contain nan values and exceed the num ... and this code does not seem to work right. If you know a better way accomplishing what I need - I would be happy to know.

for

df = pd.DataFrame({'col1':list('abcdef'),'col2':range(6)}) and num=3

we would get smth like

c, 2.5

f, 2.5

b, 2.5

assuming and c, f, b were randomly picked

Thank you!

One error seems that the condition column.dtype != np.number does not work. Then there is a problem with index alignment when you do pd.concat([row, item], axis=1), item contains an index number that is not always the same and this add rows with Nan in row. Here is another way to do it.

SETUP

df = pd.DataFrame({'col1':list('abcdef'),'col2':list('ijklmn'),

                   'col3':range(6),'col4':np.arange(10,16)/1.5})

print (df)

  col1 col2  col3       col4

0    a    i     0   6.666667

1    b    j     1   7.333333

2    c    k     2   8.000000

3    d    l     3   8.666667

4    e    m     4   9.333333

5    f    n     5  10.000000

you can use select_dtypes to check if a column is not numeric, and create the dataframe with a dictionary comprehension like:

def rows_aggr(df, num):

    list_col_notnumeric = df.select_dtypes(exclude=[np.number]).columns

    return pd.DataFrame({col: df[col].sample(num).values

                              if col in list_col_notnumeric  

                              else df[col].mean() 

                         for col in df.columns})



print (rows_aggr(df, 3))

  col1 col2  col3      col4

0    d    i   2.5  8.333333

1    a    n   2.5  8.333333

2    c    j   2.5  8.333333