CODEWITHSUNDEEP
pythonregexpandas
Danny Coveney
Danny Coveney

Reputation: 57

Parsing Multiple Text Fields Using Regex and Compiling into Pandas DataFrame

I am attempting to parse a text file using python and regex to construct a specific pandas data frame. Below is a sample from the text file I am parsing and the ideal pandas DataFrame I am seeking. 

Sample Text

Washington, DC  November 27, 2019
USDA Truck Rate Report

WA_FV190 

FIRST PRICE RANGE FOR WEEK OF NOVEMBER 20-26 2019                                                                                   
SECOND PRICE MOSTLY FOR TUESDAY NOVEMBER 26 2019                                                                                    

PERCENTAGE OF CHANGE FROM TUESDAY NOVEMBER 19 2019 SHOWN IN ().                                                                     

In areas where rates are based on package rates, per-load rates were                                                                
derived by multiplying the package rate by the number of packages in                                                                
the most usual load in a 48-53 foot trailer.

CENTRAL AND WESTERN ARIZONA                                                                                                         
-- LETTUCE, BROCCOLI, CAULIFLOWER, ROMAINE AND LEAF LETTUCE   SLIGHT SHORTAGE 
--                                                    

ATLANTA           5100   5500                                                                                                       
BALTIMORE         6300   6600                                                                                                       
BOSTON            7000   7300                                                                                                       
CHICAGO           4500   4900                                                                                                       
DALLAS            3400   3800                                                                                                       
MIAMI             6400   6700                                                                                                       
NEW YORK          6600   6900                                                                                                       
PHILADELPHIA      6400   6700 

                   2019           2018                                                                                              

              NOV 17-23      NOV 18-24                                                                                              

U.S.             25,701         22,956                                                                                              
IMPORTS          13,653         15,699                                                                                              
           ------------ --------------                                                                                              
sum              39,354         38,655

The ideal output should look something like:

Region                        CommodityGroup          InboundCity  Low   High   
CENTRAL AND WESTERN ARIZONA   LETTUCE, BROCCOLI,ETC   ATLANTA      5100  5500
CENTRAL AND WESTERN ARIZONA   LETTUCE, BROCCOLI,ETC   BALTIMORE    6300  6600
CENTRAL AND WESTERN ARIZONA   LETTUCE, BROCCOLI,ETC   BOSTON       7000  7300
CENTRAL AND WESTERN ARIZONA   LETTUCE, BROCCOLI,ETC   CHICAGO      4500  4900
CENTRAL AND WESTERN ARIZONA   LETTUCE, BROCCOLI,ETC   DALLAS       3400  3800
CENTRAL AND WESTERN ARIZONA   LETTUCE, BROCCOLI,ETC   MIAMI        6400  6700
CENTRAL AND WESTERN ARIZONA   LETTUCE, BROCCOLI,ETC   NEW YORK     6600  6900
CENTRAL AND WESTERN ARIZONA   LETTUCE, BROCCOLI,ETC   PHILADELPHIA 6400  6700

With my limited understanding of creating regex statements, this is the closest I have come to successfully isolating the desired text: regex tester for USDA data

I have been trying to replicate the solution from How to parse complex text files using Python?1 where applicable but my regex experience is severely lacking. Any help you can provide will greatly appreciated!

Upvotes: 2

Views: 188

Answers (1)

Andrej Kesely
Andrej Kesely

Reputation: 195438

I came up with this regex (txt is your text from the question):

import re
import numpy as np
import pandas as pd

data = {'Region':[], 'CommodityGroup':[], 'InboundCity':[], 'Low':[], 'High':[]}
for region, commodity_group, values in re.findall(r'([A-Z ]+)\n--(.*?)--\n(.*?)\n\n', txt, flags=re.S|re.M):

    for val in values.strip().splitlines():
        val = re.sub(r'(\d)\s{8,}.*', r'\1', val)
        inbound_city, low, high = re.findall(r'([A-Z ]+)\s*(\d*)\s+(\d+)', val)[0]
        data['Region'].append(region)
        data['CommodityGroup'].append(commodity_group)
        data['InboundCity'].append(inbound_city)
        data['Low'].append(np.nan if low == '' else int(low))
        data['High'].append(int(high))

df = pd.DataFrame(data)
print(df)

Prints:

                        Region                                     CommodityGroup   InboundCity   Low  High
0  CENTRAL AND WESTERN ARIZONA  LETTUCE, BROCCOLI, CAULIFLOWER, ROMAINE AND LE...       ATLANTA  5100  5500
1  CENTRAL AND WESTERN ARIZONA  LETTUCE, BROCCOLI, CAULIFLOWER, ROMAINE AND LE...     BALTIMORE  6300  6600
2  CENTRAL AND WESTERN ARIZONA  LETTUCE, BROCCOLI, CAULIFLOWER, ROMAINE AND LE...        BOSTON  7000  7300
3  CENTRAL AND WESTERN ARIZONA  LETTUCE, BROCCOLI, CAULIFLOWER, ROMAINE AND LE...       CHICAGO  4500  4900
4  CENTRAL AND WESTERN ARIZONA  LETTUCE, BROCCOLI, CAULIFLOWER, ROMAINE AND LE...        DALLAS  3400  3800
5  CENTRAL AND WESTERN ARIZONA  LETTUCE, BROCCOLI, CAULIFLOWER, ROMAINE AND LE...         MIAMI  6400  6700
6  CENTRAL AND WESTERN ARIZONA  LETTUCE, BROCCOLI, CAULIFLOWER, ROMAINE AND LE...      NEW YORK  6600  6900
7  CENTRAL AND WESTERN ARIZONA  LETTUCE, BROCCOLI, CAULIFLOWER, ROMAINE AND LE...  PHILADELPHIA  6400  6700

EDIT: Now should work even for your big document from the regex101

Upvotes: 1

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