如何在不使用外部库（例如 Numpy、Pandas）的情况下读取 CSV 文件？

Question

This is a question that usually appears in interviews.

I know how to read csv files using Pandas .

However I am struggling to find a way to read files without using external libraries.

Does Python come with any module that would help read csv files?

Answer 1

You most likely will need a library to read a CSV file. While you could potentially open and parse the data yourself, this would be tedious and time consuming. Luckily python comes with a standard csv module that you won't have to pip install! You can read your file in like this:

import csv

with open('file.csv', 'r') as file:
    my_reader = csv.reader(file, delimiter=',')
    for row in my_reader:
        print(row)

This will show you that each row is being read in as a list. You can then process it based on index! There are other ways to read in data too as described at https://docs.python.org/3/library/csv.html one of which will create a dictionary instead of a list!

update

You linked your github for the project I took the snip

product_id,product_name,aisle_id,department_id
9327,Garlic Powder,104,13
17461,Air Chilled Organic Boneless Skinless Chicken Breasts,35,12
17668,Unsweetened Chocolate Almond Breeze Almond Milk,91,16
28985,Michigan Organic Kale,83,4
32665,Organic Ezekiel 49 Bread Cinnamon Raisin,112,3
33120,Organic Egg Whites,86,16
45918,Coconut Butter,19,13
46667,Organic Ginger Root,83,4
46842,Plain Pre-Sliced Bagels,93,3

Saved it as file.csv and ran it with the above code I posted. Result:

['product_id', 'product_name', 'aisle_id', 'department_id']
['9327', 'Garlic Powder', '104', '13']
['17461', 'Air Chilled Organic Boneless Skinless Chicken Breasts', '35', '12']
['17668', 'Unsweetened Chocolate Almond Breeze Almond Milk', '91', '16']
['28985', 'Michigan Organic Kale', '83', '4']
['32665', 'Organic Ezekiel 49 Bread Cinnamon Raisin', '112', '3']
['33120', 'Organic Egg Whites', '86', '16']
['45918', 'Coconut Butter', '19', '13']
['46667', 'Organic Ginger Root', '83', '4']
['46842', 'Plain Pre-Sliced Bagels', '93', '3']

This does what you have asked in your question. I am not going to do your project for you, you should be able to work it from here.

Answer 2

Had a similar requirement and came up with this solution; a function that converts csv to json (needed json for readability and to make querying the data easier without having access to Pandas). If the headers arguement of the function is True , the first row of the csv is used keys in the json, otherwise value indices are used as keys.

from csv import reader as csv_reader

def csv_to_json(csv_path: str, headers: bool) -> list:
  '''Convert data from a csv to json'''
  # store json data
  json_data = []
  
  try:
    with open(csv_path, 'r') as file:
      reader = csv_reader(file)
      # set column names using first row
      if headers:
        columns = next(reader)
      
      # convert csv to json
      for row in reader:
        row_data = {}
        for i in range(len(row)):
          # set key names
          if headers:
            row_key = columns[i].lower()
          else: 
            row_key = i
          # set key/value
          row_data[row_key] = row[i]
        # add data to json store 
        json_data.append(row_data)
        
  # error handling
  except Exception as e:
    print(repr(e))
    
  return json_data

Given a csv containing the following

+------+-------+------+
| Year | Month | Week |
+------+-------+------+
| 2020 |    11 |   11 |
| 2020 |    12 |   12 |
+------+-------+------+

The output with headers is

[
  {"year": 2020, "month": 11, "week": 11},
  {"year": 2020, "month": 12, "week": 12}
]

The ouput without headers is

[
  {"0": 2020, "1": 11, "2": 11},
  {"0": 2020, "1": 12, "2": 12}
]

Answer 3

When one's production environment is limited by memory, being able to read and manage data without importing additional libraries may be helpful.

In order to achieve that, the built in csv module does the work.

import csv

There are at least two ways one might do that: using csv.Reader() or using csv.DictReader() .

csv.Reader() allows you to access CSV data using indexes and is ideal for simple CSV files (Source ).

csv.DictReader() on the other hand is friendlier and easy to use, especially when working with large CSV files (Source ).

Here's how to do it with csv.Reader()

>>> import csv
>>> with open('eggs.csv', newline='') as csvfile:
...     spamreader = csv.reader(csvfile, delimiter=' ', quotechar='|')
...     for row in spamreader:
...         print(', '.join(row))
Spam, Spam, Spam, Spam, Spam, Baked Beans
Spam, Lovely Spam, Wonderful Spam

Here's how to do it with csv.DictReader()

>>> import csv
>>> with open('names.csv', newline='') as csvfile:
...     reader = csv.DictReader(csvfile)
...     for row in reader:
...         print(row['first_name'], row['last_name'])
...
Eric Idle
John Cleese

>>> print(row)
{'first_name': 'John', 'last_name': 'Cleese'}

For another example,check Real Python's page here .

Answer 4

Recently I got a very similar question that was made more complicated than this one on making a data structure without using pandas. This is the only relevant question I have found so far. If I take this question, then what I was asked was: put the product id as keys to a dictionary and then put list of tuples of aisle and department ids as values (in python). The dictionary is the required dataframe. Of course I could not do it in 15 min (rather in 2 hours). It is hard for me to think of outside of numpy and pandas.

I have the following solutions, which also answers this question in the beginning. Probably not ideal but got what I needed.
Hopefully this helps too.

import csv
file =  open('data.csv', 'r')
reader = csv.reader(file)

items = []  # put the rows in csv to a list
aisle_dept_id = []  # to have a tuple of aisle and dept ids
mydict = {} # porudtc id as keys and list of above tuple as values in a dictionary

product_id, aisle_id, department_id, product_name = [], [], [], []

for row in reader:
    items.append(row)

for i  in range(1, len(items)):
    product_id.append(items[i][0])
    aisle_id.append(items[i][1])
    department_id.append(items[i][2])
    product_name.append(items[i][3])

for item1, item2 in zip(aisle_id, department_id):
    aisle_dept_id.append((item1, item2))
for item1, item2 in zip(product_id, aisle_dept_id):
    mydict.update({item1: [item2]})

With the output,

mydict:
{'9327': [('104', '13')],
 '17461': [('35', '12')],
 '17668': [('91', '16')],
 '28985': [('83', '4')],
 '32665': [('112', '3')],
 '33120': [('86', '16')],
 '45918': [('19', '13')],
 '46667': [('83', '4')],
 '46842': [('93', '3')]}