python 2.7 - Pandas Dataframe: Add mean and std columns to every column -


i add mean , std column every column of dataframe. unfortunately code replaces original columns mean , std ones.

np.random.seed(50) df = pd.dataframe(np.random.randint(0,9,size=(30, 3)), columns=list('abc')) print df

df

     b  c 0   0  0  1 1   4  6  5 2   6  6  5 3   2  7  4 4   3  6  4 5   1  5  0 6   6  3  2 7   3  3  3 8   2  0  3 9   2  0  3 10  0  0  7 11  3  8  7 12  4  4  0 13  0  3  3 14  1  4  5 15  7  0  3 16  5  6  1 17  4  4  4 18  5  4  6 19  3  0  5 20  8  3  6 21  2  8  8 22  5  4  7 23  8  4  4 24  2  1  8 25  7  1  5 26  8  3  3 27  5  3  6 28  8  6  0 29  8  2  1

here's code from:

https://pandas.pydata.org/pandas-docs/stable/computation.html

r = df.rolling(window=5) print 'agg mean , sdt df' print r['a', 'b', 'c'].agg([np.mean, np.std]) print

output

agg mean , sdt df

                   b              c              mean       std mean       std mean       std 0   nan       nan  nan       nan  nan       nan 1   nan       nan  nan       nan  nan       nan 2   nan       nan  nan       nan  nan       nan 3   nan       nan  nan       nan  nan       nan 4   3.0  2.236068  5.0  2.828427  3.8  1.643168 5   3.2  1.923538  6.0  0.707107  3.6  2.073644 6   3.6  2.302173  5.4  1.516575  3.0  2.000000 7   3.0  1.870829  4.8  1.788854  2.6  1.673320 8   3.0  1.870829  3.4  2.302173  2.4  1.516575 9   2.8  1.923538  2.2  2.167948  2.2  1.303840 10  2.6  2.190890  1.2  1.643168  3.6  1.949359 11  2.0  1.224745  2.2  3.492850  4.6  2.190890 12  2.2  1.483240  2.4  3.577709  4.0  3.000000 13  1.8  1.788854  3.0  3.316625  4.0  3.000000 14  1.6  1.816590  3.8  2.863564  4.4  2.966479 15  3.0  2.738613  3.8  2.863564  3.6  2.607681 16  3.4  2.880972  3.4  2.190890  2.4  1.949359 17  3.4  2.880972  3.4  2.190890  3.2  1.483240 18  4.4  2.190890  3.6  2.190890  3.8  1.923538 19  4.8  1.483240  2.8  2.683282  3.8  1.923538 20  5.0  1.870829  3.4  2.190890  4.4  2.073644 21  4.4  2.302173  3.8  2.863564  5.8  1.483240 22  4.6  2.302173  3.8  2.863564  6.4  1.140175 23  5.2  2.774887  3.8  2.863564  6.0  1.581139 24  5.0  3.000000  4.0  2.549510  6.6  1.673320 25  4.8  2.774887  3.6  2.880972  6.4  1.816590 26  6.0  2.549510  2.6  1.516575  5.4  2.073644 27  6.0  2.549510  2.4  1.341641  5.2  1.923538 28  6.0  2.549510  2.8  2.049390  4.4  3.049590 29  7.2  1.303840  3.0  1.870829  3.0  2.549510

and looking columns (and data) being:

     a_mean  a_std  b  b_mean  b_std  c  c_mean  c_std

i cannot find solution 'adding' these columns.

thanks advice.

in [18]: res = df.rolling(5).agg(['mean','std'])  in [19]: res.columns = res.columns.map('_'.join)  in [54]: cols = np.concatenate(list(zip(df.columns, res.columns[0::2], res.columns[1::2])))  in [55]: cols out[55]: array(['a', 'a_mean', 'a_std', 'b', 'b_mean', 'b_std', 'c', 'c_mean', 'c_std'],       dtype='<u6')  in [56]: res.join(df).loc[:, cols] out[56]:      a_mean     a_std  b  b_mean     b_std  c  c_mean     c_std 0   0     nan       nan  0     nan       nan  1     nan       nan 1   4     nan       nan  6     nan       nan  5     nan       nan 2   6     nan       nan  6     nan       nan  5     nan       nan 3   2     nan       nan  7     nan       nan  4     nan       nan 4   3     3.0  2.236068  6     5.0  2.828427  4     3.8  1.643168 5   1     3.2  1.923538  5     6.0  0.707107  0     3.6  2.073644 6   6     3.6  2.302173  3     5.4  1.516575  2     3.0  2.000000 7   3     3.0  1.870829  3     4.8  1.788854  3     2.6  1.673320 8   2     3.0  1.870829  0     3.4  2.302173  3     2.4  1.516575 9   2     2.8  1.923538  0     2.2  2.167948  3     2.2  1.303840 10  0     2.6  2.190890  0     1.2  1.643168  7     3.6  1.949359 11  3     2.0  1.224745  8     2.2  3.492850  7     4.6  2.190890 12  4     2.2  1.483240  4     2.4  3.577709  0     4.0  3.000000 13  0     1.8  1.788854  3     3.0  3.316625  3     4.0  3.000000 14  1     1.6  1.816590  4     3.8  2.863564  5     4.4  2.966479 15  7     3.0  2.738613  0     3.8  2.863564  3     3.6  2.607681 16  5     3.4  2.880972  6     3.4  2.190890  1     2.4  1.949359 17  4     3.4  2.880972  4     3.4  2.190890  4     3.2  1.483240 18  5     4.4  2.190890  4     3.6  2.190890  6     3.8  1.923538 19  3     4.8  1.483240  0     2.8  2.683282  5     3.8  1.923538 20  8     5.0  1.870829  3     3.4  2.190890  6     4.4  2.073644 21  2     4.4  2.302173  8     3.8  2.863564  8     5.8  1.483240 22  5     4.6  2.302173  4     3.8  2.863564  7     6.4  1.140175 23  8     5.2  2.774887  4     3.8  2.863564  4     6.0  1.581139 24  2     5.0  3.000000  1     4.0  2.549510  8     6.6  1.673320 25  7     4.8  2.774887  1     3.6  2.880972  5     6.4  1.816590 26  8     6.0  2.549510  3     2.6  1.516575  3     5.4  2.073644 27  5     6.0  2.549510  3     2.4  1.341641  6     5.2  1.923538 28  8     6.0  2.549510  6     2.8  2.049390  0     4.4  3.049590 29  8     7.2  1.303840  2     3.0  1.870829  1     3.0  2.549510

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