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.csv")
The data is read in as two records and the details are stored in the "Information" column. I understand why it is parsed the way it is, but I would like to break out the details into multiple records as noted below.
Any help would be appreciated.
Sample CSV
"Date","Information","Type"
"2015-12-06","First: Tom, Last: Smith, School: MCAA; First: Tammy, Last: Smith, School: MCAA;","New"
"2015-12-06","First: Jim, Last: Jones, School: MCAA; First: Jane, Last: Jones, School: MCAA;","New"
Current Result
Date Information Type
2015-12-06 First: Tom, Last: Smith, School: MCAA; First: Tammy, Last: Smith, School: MCAA; New
2015-12-06 First: Jim, Last: Jones, School: MCAA; First: Jane, Last: Jones, School: MCAA; New
Desired Result
Date First Last School Type
2015-12-06 Tom Smith MCAA New
2015-12-06 Tammy Smith MCAA New
2015-12-06 Jim Jones MCAA New
2015-12-06 Jane Jones MCAA New
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
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.
