Using range of values in column to summarize values in another column based on condition for first c-CodePudding

I cannot find any explanation about numpy.where function.

I have dataframe:

     activity     count    
0       1           25   
1       2           38   
2       3           49   
3       4           55                       
4       5           61   
5       6           74   
6       7           82  
7       8           98   
8       9           103   
9       10          4084

and I want to count values:

for rows where activity < 6, sum count (so for index < 5)
for rows where activity =< 6, sum count ( so for index =< 5)

and crate a charts for this two condition which shows sum count values. I have tried:

under_total = df[df.activity < 6]
under_total.loc['under_total'] = under_total.sum(axis = 0)

df['below_expectations'] = np.where(df['actibity'] < 6, df['activity'], df['hoursActive'])

but it seems I dont get it somewhere. I would be greatful for help.

CodePudding user response：

You can compute a group for

intermediate data:

(df.groupby(df['activity'].lt(6)
            .map({True: '<6', False: '≥6'}))
 ['count'].sum()
)

output:

activity
<6     228
≥6    4441
Name: count, dtype: int64

CodePudding user response：

Use