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Python: Read a given CSV file as a dictionary

Python CSV File Reading and Writing: Exercise-4 with Solution

Write a Python program to read a given CSV file as a dictionary.

Sample Solution:

Python Code :

import csv
data = csv.DictReader(open("departments.csv"))
print("CSV file as a dictionary:\n")
for row in data:
   print(row)

departments.csv

department_id, department_name,  manager_id,  location_id
10, Administration, 200, 1700
20, Marketing, 201, 1800
30, Purchasing, 114, 1700
40, Human Resources, 203, 2400
50, Shipping, 121, 1500
60, IT, 103, 1400
70, Public Relations, 204, 2700
80, Sales, 145, 2500

Sample Output:

CSV file as a dictionary:

OrderedDict([('department_id', '10'), (' department_name', ' Administration'), ('  manager_id', ' 200'), ('  location_id', ' 1700')])
OrderedDict([('department_id', '20'), (' department_name', ' Marketing'), ('  manager_id', ' 201'), ('  location_id', ' 1800')])
OrderedDict([('department_id', '30'), (' department_name', ' Purchasing'), ('  manager_id', ' 114'), ('  location_id', ' 1700')])
OrderedDict([('department_id', '40'), (' department_name', ' Human Resources'), ('  manager_id', ' 203'), ('  location_id', ' 2400')])
OrderedDict([('department_id', '50'), (' department_name', ' Shipping'), ('  manager_id', ' 121'), ('  location_id', ' 1500')])
OrderedDict([('department_id', '60'), (' department_name', ' IT'), ('  manager_id', ' 103'), ('  location_id', ' 1400')])
OrderedDict([('department_id', '70'), (' department_name', ' Public Relations'), ('  manager_id', ' 204'), ('  location_id', ' 2700')])
OrderedDict([('department_id', '80'), (' department_name', ' Sales'), ('  manager_id', ' 145'), ('  location_id', ' 2500')])

Python Code Editor:


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Python: Tips of the Day

Creates a dictionary with the same keys as the provided dictionary and values generated by running the provided function for each value:

Example:

def tips_map_values(obj, fn):
  ret = {}
  for key in obj.keys():
    ret[key] = fn(obj[key])
  return ret
users = {
  'Owen': { 'user': 'Owen', 'age': 29 },
  'Eddie': { 'user': 'Eddie', 'age': 15 }
}

print(tips_map_values(users, lambda u : u['age'])) # {'Owen': 29, 'Eddie': 15}

Output:

{'Owen': 29, 'Eddie': 15}