Python/ Pandas CSV Parsing

Question

I used JotForm Configurable list widget to collect data, but having troubles parsing the resulting data correctly. When I use

testdf = pd.read_csv (\"TestLoad.c         


        

Answer 1

This is useless text that is required to keep an answer from being downvoted by the moderators. Here is the data I used:

"Date","Information","Type"
"2015-12-07","First: Jim, Last: Jones, School: MCAA; First: Jane, Last: Jones,  School: MCAA;","Old"
"2015-12-06","First: Tom, Last: Smith, School: MCAA; First: Tammy, Last: Smith, School: MCAA;","New"

---

import pandas as pd
import numpy as np
import csv
import re
import itertools as it
import pprint
import datetime as dt

records = [] #Construct a complete record for each person

colon_pairs = r"""
    (\w+)   #Match a 'word' character, one or more times, captured in group 1, followed by..
    :       #A colon, followed by...
    \s*     #Whitespace, 0 or more times, followed by...
    (\w+)   #A 'word' character, one or more times, captured in group 2.
"""

colon_pairs_per_person = 3

with open("csv1.csv", encoding='utf-8') as f:
    next(f) #skip header line
    record = {}

    for date, info, the_type in csv.reader(f):
        info_parser = re.finditer(colon_pairs, info, flags=re.X)

        for i, match_obj in enumerate(info_parser):
            key, val = match_obj.groups()
            record[key] = val

            if (i+1) % colon_pairs_per_person == 0: #then done with info for a person
                record['Date'] = dt.datetime.strptime(date, '%Y-%m-%d') #So that you can sort the DataFrame rows by date.
                record['Type'] = the_type

                records.append(record)
                record = {}

pprint.pprint(records)
df = pd.DataFrame(
        sorted(records, key=lambda record: record['Date'])
)
print(df)
df.set_index('Date', inplace=True)
print(df)

--output:--
[{'Date': datetime.datetime(2015, 12, 7, 0, 0),
  'First': 'Jim',
  'Last': 'Jones',
  'School': 'MCAA',
  'Type': 'Old'},
 {'Date': datetime.datetime(2015, 12, 7, 0, 0),
  'First': 'Jane',
  'Last': 'Jones',
  'School': 'MCAA',
  'Type': 'Old'},
 {'Date': datetime.datetime(2015, 12, 6, 0, 0),
  'First': 'Tom',
  'Last': 'Smith',
  'School': 'MCAA',
  'Type': 'New'},
 {'Date': datetime.datetime(2015, 12, 6, 0, 0),
  'First': 'Tammy',
  'Last': 'Smith',
  'School': 'MCAA',
  'Type': 'New'}]

        Date  First   Last School Type
0 2015-12-06    Tom  Smith   MCAA  New
1 2015-12-06  Tammy  Smith   MCAA  New
2 2015-12-07    Jim  Jones   MCAA  Old
3 2015-12-07   Jane  Jones   MCAA  Old

            First   Last School Type
Date                                
2015-12-06    Tom  Smith   MCAA  New
2015-12-06  Tammy  Smith   MCAA  New
2015-12-07    Jim  Jones   MCAA  Old
2015-12-07   Jane  Jones   MCAA  Old

Answer 2

I used regex separators with the python engine so I could specify multiple separators. Then, I used the usecols parameter to specify which columns in the csv file you want in your dataframe. The header will not be read from file, and I skipped the first row since it doesn't have any data. I read in the first and second set of records into 2 dataframes, and then concatenate the 2 data frames.

a = pd.read_csv('sample.csv', sep=',|:|;', skiprows = 1, usecols = (0,2,4,6, 14), header = None, engine='python')
b = pd.read_csv('sample.csv', sep=',|:|;', skiprows = 1, usecols = (0,8,10,12,14), header = None, engine='python')
a.columns = ['Date', 'First', "Last", 'School', 'Type']
b.columns = ['Date', 'First', "Last", 'School', 'Type']
final_data = pd.concat([a,b], axis = 0)

If you need the order preserved, such that the second names appear right below the first name, you can sort using the indices. I use mergesort, because it is a stable sort and this ensures that the first Information record (record on the right) will be above the Information record on the left.

final_data.sort_index(kind='mergesort', inplace = True)
>>>final_data
        Date        First  Last     School  Type
0   "2015-12-06"    Tom    Smith    MCAA    "New"
0   "2015-12-06"    Tammy  Smith    MCAA    "New"
1   "2015-12-06"    Jim    Jones    MCAA    "New"
1   "2015-12-06"    Jane   Jones    MCAA    "New"

Edit: Including the second set of the record into the data. Changed the axis to 0.