根据另一列的值将列添加到pandas数据框中

Question

This is part of an ongoing series of issues I'm having trying to condense a csv file with multiple rows for each client based on the number of medical services they received. For each service, they have a row. I've included the dataframe at the bottom.

I'm trying to calculate how many times a client (identified with an ID_profile number) got each type of service and add that to a column named for the type of service. So, if a client got 3 Early Intervention Services, I would add the number "3" to the "eisserv" column. Once that is done, I want to combine all the client rows into one.

Where I'm getting stuck is populating 3 different columns with data based off one column. I am trying to iterate through the rows using some strings for the function to compare to. The function works, but for reasons I can't understand, all the strings change to "25" as the function works.

import pandas as pd
df = pd.read_csv('fakeRWclient.csv')

df['PrimaryServiceCategory'] = df['PrimaryServiceCategory'].map({'Referral for Health Care/Supportive Services': '33', 'Health Education/Risk reduction': '25', 'Early Intervention Services (Parts A and B)': '11'})

df['ServiceDate'] = pd.to_datetime(df['ServiceDate'], format="%m/%d/%Y")
df['id_profile'] = df['id_profile'].apply(str)
df['served'] = df['id_profile']  + " " + df['PrimaryServiceCategory']

df['count'] = df['served'].map(df['served'].value_counts())
eis = "11"
ref = "33"
her = "25"
print("Here are the string values")
print(eis)
print(ref)
print(her)
df['herrserv']=""
df['refserv']=""
df['eisserv']=""
for index in df.itertuples():
    for eis in df['PrimaryServiceCategory']:
        df['eisserv'] = df['count']
    for her in df['PrimaryServiceCategory']:
        df['herrserv'] = df['count']
    for ref in df['PrimaryServiceCategory']:
        df['refserv'] = df['count']
print("Here are the string values")
print(eis)
print(ref)
print(her)

Here is the output:

Here are the string values
11
33
25
Here are the string values
25
25
25
  id_profile ServiceDate PrimaryServiceCategory     served  count  herrserv  
\
0        439  2017-12-05                     25     439 25      1         1   
1     444654  2017-01-25                     25  444654 25      2         2   
2      56454  2017-12-05                     33   56454 33      1         1   
3      56454  2017-01-25                     25   56454 25      2         2   
4     444654  2017-03-01                     25  444654 25      2         2   
5      56454  2017-01-01                     25   56454 25      2         2   
6      12222  2017-01-05                     11   12222 11      1         1   
7      12222  2017-01-30                     25   12222 25      3         3   
8      12222  2017-03-01                     25   12222 25      3         3   
9      12222  2017-03-20                     25   12222 25      3         3   

   refserv  eisserv  
0        1        1  
1        2        2  
2        1        1  
3        2        2  
4        2        2  
5        2        2  
6        1        1  
7        3        3  
8        3        3  
9        3        3  

Why do the string values switch? And is this even the right function to do what I'm hoping to do?

Answer 1

You can use pandas.get_dummies after mapping your integers to categories, then merge with your dataframe.

You can add a 'count' column summing the 3 category counts afterwords.

df = pd.DataFrame({'id_profile': [439, 444654, 56454, 56454, 444654, 56454, 12222, 12222, 12222, 12222],
                   'ServiceDate': ['2017-12-05', '2017-01-25', '2017-12-05', '2017-01-25', '2017-03-01', '2017-01-01', '2017-01-05', '2017-01-30', '2017-03-01', '2017-03-20'],
                   'PrimaryServiceCategory': [25, 25, 33, 25, 25, 25, 11, 25, 25, 25]})

d = {11: 'eis', 33: 'ref', 25: 'her'}
df['Service'] = df['PrimaryServiceCategory'].map(d)

df = df.set_index('id_profile')\
       .join(pd.get_dummies(df.drop('PrimaryServiceCategory', 1), columns=['Service'])\
               .groupby(['id_profile']).sum())

#            ServiceDate  PrimaryServiceCategory Service  Service_eis  \
# id_profile                                                            
# 439         2017-12-05                      25     her            0   
# 12222       2017-01-05                      11     eis            1   
# 12222       2017-01-30                      25     her            1   
# 12222       2017-03-01                      25     her            1   
# 12222       2017-03-20                      25     her            1   
# 56454       2017-12-05                      33     ref            0   
# 56454       2017-01-25                      25     her            0   
# 56454       2017-01-01                      25     her            0   
# 444654      2017-01-25                      25     her            0   
# 444654      2017-03-01                      25     her            0   

#             Service_her  Service_ref  
# id_profile                            
# 439                   1            0  
# 12222                 3            0  
# 12222                 3            0  
# 12222                 3            0  
# 12222                 3            0  
# 56454                 2            1  
# 56454                 2            1  
# 56454                 2            1  
# 444654                2            0  
# 444654                2            0  

Answer 2

I have made changes to your existing code only.

    import pandas as pd
    df = pd.read_csv('fakeRWclient.csv')

    df['PrimaryServiceCategory'] = df['PrimaryServiceCategory'].map({'Referral for Health Care/Supportive Services': '33', 'Health Education/Risk reduction': '25', 'Early Intervention Services (Parts A and B)': '11'})

    df['ServiceDate'] = pd.to_datetime(df['ServiceDate'], format="%m/%d/%Y")
    df['id_profile'] = df['id_profile'].apply(str)

    print(df.groupby('id_profile').PrimaryServiceCategory.count())

Above code will give output like this:

id_profile
439       1
12222     4
56454     3
444654    2

Answer 3

The values of eis , ref and her switch to "25" because you are looping over the variable PrimaryServiceCategory , and the last value in that serie is "25". You are using eis , ref and her as the names of the iterator variable, so they change in every loop. I think this is an inefficient way to do it. It's better if you use groupby and transform:

df['count'] = df.groupby(['id_profile','PrimaryServiceCategory']).transform('count')