python - Compiling a condition (once) for multicolumns pandas dataframe -


i put condition 1 column in pandas dataframe, works. doesn't work when apply columns. example have following dataframe called temps:

temps               t  t_0  t_1 2012-07-16  260  250  210 2012-07-17  230  251  212 2012-07-18  265  220  250 2012-07-19  270  260  210

it works when specified 1 column:

df_new = temps['t'][(temps['t'].values>(temps['t'].mean()-1.0*temps['t'].std()))         & (temps['t'].values<(temps['t'].mean()+1.0*temps['t'].std()))]  df_new                 t 2012-07-16    260 2012-07-18    265 2012-07-19    270

i appreciate if guide me how can once columns? following doesn't work of course.

df_new = temps[(temps.values>(temps.mean()-1.0*temps.std()))         & (temps.values<(temps.mean()+1.0*temps.std()))]

the expected output is:

df_new                  t   t_0   t_1 2012-07-16     210   250   210 2012-07-19     210   260   210

or 

df_new                  t   t_0   t_1 2012-07-16     210   250   210 2012-07-17     nan   251   212 2012-07-18     265   nan   nan 2012-07-19     210   260   210

thank in advance

here's lazy way make code work least change possible:

temps2 = temps.copy() col in temps2.columns:     temps2[col] = temps[col][(temps[col].values>(temps[col].mean()-1.0*temps[col].std()))                  & (temps[col].values<(temps[col].mean()+1.0*temps[col].std()))]                t  t_0  t_1 2012-07-16  260  250  210 2012-07-17  nan  251  212 2012-07-18  265  nan  nan 2012-07-19  270  260  210

and here's more elegant way:

zscore = temps.apply( lambda x: (x - x.mean()) / x.std() )                     t       t_0       t_1 2012-07-16  0.208683  0.272618 -0.533286 2012-07-17 -1.460778  0.330011 -0.431708 2012-07-18  0.486926 -1.449180  1.498279 2012-07-19  0.765169  0.846551 -0.533286  temps[ abs(zscore) < 1 ]                t  t_0  t_1 2012-07-16  260  250  210 2012-07-17  nan  251  212 2012-07-18  265  nan  nan 2012-07-19  270  260  210

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