Iterating through pandas groupby groups

Question:

I have a pandas dataframe school_df that looks like this:

    school_id  date_posted date_completed
0    A          2014-01-01  2014-01-01
1    A          2014-01-01  2014-01-08
2    A          2014-04-29  2014-05-01
3    B          2014-01-01  2014-01-01
4    B          2014-01-20  2014-02-23

Each row represents one project by that school. I’d like to add two columns: for each unique school_id, a count of how many projects were posted before that date and a count of how many projects were completed before that date.

The code below works, but I have ~300,000 unique schools, so it’s taking a long time to run. Is there a faster way to get what I am looking for? Thank you for your assistance!

import pandas as pd
groups = school_df.groupby("school_id")
blank_df = pd.DataFrame()
for g, df in groups:
    df['school_previous_projects'] = df.date_posted.map(lambda x: len(df[df.date_posted < x]))
    df['school_previous_completed'] = df.date_posted.map(lambda x: len(df[df.date_completed < x]))
    blank_df = pd.concat([blank_df, df])
Asked By: Erin

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Answers:

Give this a try. Should be faster than your for loop and two maps. Starting with your frame

    school_id  date_posted date_completed
0    A          2014-01-01  2014-01-01
1    A          2014-01-01  2014-01-08
2    A          2014-04-29  2014-05-01
3    B          2014-01-01  2014-01-01
4    B          2014-01-20  2014-02-23

Then a function. getProjectCounts() uses boolean indexing and a simple count()

def getProjectCounts(row, df):
    filter = (df["school_id"] == row["school_id"])  & (df["date_posted"] < row["date_posted"])
    dp_count = df[filter]["date_posted"].count()
    filter = (df["school_id"] == row["school_id"])  & (df["date_completed"] < row["date_completed"])
    dc_count = df[filter]["date_completed"].count()
    return pd.Series([dp_count, dc_count])

then an apply() with the function to go row by row

school_df[["school_previous_projects","school_previous_completed"]] = school_df.apply(lambda x : getProjectCounts(x, school_df),axis=1)


  school_id date_posted date_completed  school_previous_projects  
0         A  2014-01-01     2014-01-01                         0   
1         A  2014-01-01     2014-01-08                         0   
2         A  2014-04-29     2014-05-01                         2   
3         B  2014-01-01     2014-01-01                         0   
4         B  2014-01-20     2014-02-23                         1   

   school_previous_completed  
0                          0  
1                          1  
2                          2  
3                          0  
4                          1 
Answered By: Bob Haffner

Here is a version using cumcount (I simplified the dates, but still should work):

import pandas as pd
import io


df = pd.DataFrame({'school_id': ['A', 'A', 'A', 'B', 'B'],
                   'date_posted': pd.date_range('2014-01-01', '2014-01-05'),
                   'date_completed': pd.date_range('2014-01-01', '2014-01-05')})

posted = df.set_index('date_posted').groupby('school_id').cumcount()
comp = df.set_index('date_completed').groupby('school_id').cumcount()

df['posted'] = posted.values
df['comp'] = comp.values

print df

Results in:

  date_completed date_posted school_id  posted  comp 
0     2014-01-01  2014-01-01         A       0     0 
1     2014-01-02  2014-01-02         A       1     1 
2     2014-01-03  2014-01-03         A       2     2 
3     2014-01-04  2014-01-04         B       0     0 
4     2014-01-05  2014-01-05         B       1     1 
Answered By: Brian Pendleton