python - Pandas: using groupby to get mean for each data category

Question

I have a dataframe that looks like this:

>>> df[['data','category']]
Out[47]: 
          data     category
  0       4610            2
 15       4610            2
 22       5307            7
 23       5307            7
 25       5307            7
...        ...          ...

Both data and category are numeric so I'm able to do this:

>>> df[['data','category']].mean()
Out[48]: 
data        5894.677985
category      13.805886
dtype: float64

And i'm trying to get the mean for each category. It looks straight forward but when I do this:

>>> df[['data','category']].groupby('category').mean()

or

>>> df.groupby('category')['data'].mean()

It returns an error like this:

DataError: No numeric types to aggregate

There's no error if I replace both functions above with .count().

What do I do wrongly? What's the correct way to get the mean of each category?

Answer 1

Can you do a df.dtypes ? In the example below type is Int as it works fine.

    import pandas as pd

    ##group by 1 columns
    df = pd.DataFrame({' data': [4610, 4611, 4612, 4613], 'Category': [2, 2,    7, 7]})
    print df.groupby('Category'). mean()


    ##Mutiple columns to group by
    df1 = pd.DataFrame({' data': [4610, 4611, 4612, 4613], 'Category': [2,    2, 7, 7], 'Category2' : ['A','B','A','B']})
    key=['Category','Category2']
    print df1.groupby( key).mean()

 Category Category2       
 2        A           4610
          B           4611
 7        A           4612
          B           4613