CODEWITHSUNDEEP
pythonpandas
AgileDan
AgileDan

Reputation: 361

Pandas applying data subset to new data frame

I have a script where I do munging with dataframes and extract data like the following:

times = pd.Series(df.loc[df['sy_x'].str.contains('AA'), ('t_diff')].quantile([.1, .25, .5, .75, .9]))

I want to add the resulting data from quantile() to a data frame with separate columns for each of those quantiles, lets say the columns are:

   ID pt_1 pt_2 pt_5 pt_7 pt_9
   AA
   BB
   CC

How might I add the quantiles to each row of ID?

new_df = None
for index, value in times.items():
   for col in df[['pt_1', 'pt_2','pt_5','pt_7','pt_9',]]:

..but that feels wrong and not idiomatic. Should I be using loc or iloc? I have a couple more Series that I'll need to add to other columns not shown, but I think I can figure that out once I know

EDIT: Some of the output of times looks like:

0.1  -0.5
0.25 -0.3
0.5   0.0
0.75  2.0
0.90  4.0

Thanks in advance for any insight

Upvotes: 1

Views: 55

Answers (2)

Benoit de Menthière
Benoit de Menthière

Reputation: 743

Try something like:

pd.DataFrame(times.values.T, index=times.keys())

Upvotes: 1

Quang Hoang
Quang Hoang

Reputation: 150735

IIUC, you want a groupby():

# toy data
np.random.seed(1)
df = pd.DataFrame({'sy_x':np.random.choice(['AA','BB','CC'], 100),
                   't_diff': np.random.randint(0,100,100)})

df.groupby('sy_x').t_diff.quantile((0.1,.25,.5,.75,.9)).unstack(1)

Output:

      0.10   0.25  0.50   0.75  0.90
sy_x                                
AA    16.5  22.25  57.0  77.00  94.5
BB     9.1  21.00  58.5  80.25  91.3
CC     9.7  23.25  40.5  65.75  84.1

Upvotes: 1

Related Questions