Phython and csvs

Python has a very useful csv library, but I found that these functions made working with csvs much more efficient. The functions are mainly for import and outputting operations with csv files.
The most common use cases are pulling csvs into different data structures in Python and outputting them to either a log file or a csv.

imports

import csv
from csv import DictReader
import pandas as pd

Csv => key & value pair

def readKeyValues(inputCsvFile,keyColumn,ValueColumn):
	#input file reader
  infile = open(inputCsvFile, "r")
  read = csv.reader(infile)
  headers = next(read) # skip header

  returnDictionary={}

	#for each row
  for row in read:
      returnDictionary[row[keyColumn]] = row[ValueColumn]

  return returnDictionary

Csv => dict

def readFileToDict(inputCsvFile):
    with open(inputCsvFile, 'r') as f:
        dict_reader = DictReader(f)
        return list(dict_reader)

dict => csv

def outputDictToFile(file, dict):
    pd.DataFrame(dict).to_csv(file, index=False)

log

def log(file, string):
    file = open(file, 'a')
    file.write(string)
    file.close()

Example Use Case

Here is our first csv with some data for people. This example is a bit trite, but let’s imagine that we need to change the age from a number to a string, so 23 => ‘twenty three’.

people.csv

id,name,age
1,phil,23
2,jack,34
3,jill,31
4,jody,24

One way to achieve this is to create a key value match csv like this:

pivot.csv

age,age
23,'twenty three'
34,'thirty four'
31,'thirty one'
24,'twenty four'

Next we need to import both of these into a python script.

import utility #all of the above functions are stored in this file

people = utility.readFileToDict('./people.csv')
pivot = utility.readKeyValues('./pivot.csv',0,1)

for person in people:
  age = person.get('age')
  person['age'] = pivot[age]

utility.outputDictToFile('./output.csv',people)