CODEWITHSUNDEEP
pythonpandas
Camue
Camue

Reputation: 481

Engineering data structure/ information retrieval and storage

I have a data frame containing different sub areas. All I want is to be able to group them together and count them in another data frame. Here is how my data frame called data looks like:

    Area
0 TEX001
1 TEX002
2 TEX003
3 ARZ004
4 ARZ015
5 MSS034
6 NWY001
7 NWY003

I have tried a loop to retrieve the information I needed using a loop but I only get lots of boolean values.

Here is what I tried:

u_area=list(data['U_area'].unique())
listo=[]

for i in u_area:
      d={}
      d['Sector']= i
      d['Sec_name]=list(data['Area'].str.startswith(i))
      print (x)
      d['Total']= len(d['sites'])
      listo.append(d)

I am hoping to have something a list containing many dictionaries like the below so that I can data frame my list with pandas to have someting like the following:

    Sector   Sec_name              Total
     TEX     TEX001,TEX002,TEX003,  3
     ARZ     ARZ004, ARZ035         2
     MSS     MSS034                 1

what I am doing wrong to have those boolean values??

Upvotes: 3

Views: 66

Answers (2)

smj
smj

Reputation: 1284

Try this:

import pandas as pd

# Test data.

data = pd.DataFrame({'area': ['TEX001', 'TEX002', 'TEX003', 'ARZ004', 'ARZ015', 'MSS034', 'NWY001', 'NWY003']})

# Slice out the sector, assumes this is always the first three characters of "area".

data['sector'] = data['area'].str[:3]

# Group and aggregate.

grouped = data.groupby('sector')['area'].agg(['count', lambda x: ', '.join(x)])

# Clean up the column names.

grouped.columns = ['count', 'sec_name']

print(grouped)

Gives:

        count                sec_name
sector                               
ARZ         2          ARZ004, ARZ015
MSS         1                  MSS034
NWY         2          NWY001, NWY003
TEX         3  TEX001, TEX002, TEX003

Upvotes: 1

BENY
BENY

Reputation: 323316

IIUC with agg and groupby 

yourdf=df.groupby(df['Area'].str[:3])['Area'].agg([','.join,'count'])
yourdf.columns=['Sec_name','Total']
yourdf# if need reset_index adding yourdf.reset_index(inplace=True)
Out[17]: 
                  Sec_name  Total
Area                             
ARZ          ARZ004,ARZ015      2
MSS                 MSS034      1
NWY          NWY001,NWY003      2
TEX   TEX001,TEX002,TEX003      3

Upvotes: 1

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