In general use cases, we upload the CSV files to the system to store huge amount of data by uploading single file. For example, in e-commerce sites we just write thousands of products details in a CSV file and just upload it.
In python, we can read the data of a CSV file in 2 ways. One by using normal csv.reader and the other by using CSV.DictReader. To learn more about CSV's normal reader and dictreader check https://docs.python.org/2/library/csv.html.
Using CSV reader we can read data by using column indexes and with DictReader we can read the data by using column names. Using the normal reader if the column indexes change then the data extraction goes wrong, to over come this we'll go for DictReder. With DictReader we can read the data using column names.
Example CSV file format of products.csv:
With the following snippet we can read the above CSV file with DictReader.
import csv with open('products.csv') as csvfile: reader = csv.DictReader(csvfile) for row in reader: print(row['Title'], row['UPC'])
The above snippet will print title and upc details of the product. But there is a disadvantage of this if the row headers are gone wrong in terms of case sensitivity it will raise an error.
For ex: If Title is written as TITLE or 'Title ' the above snippet raise KeyError.
To overcome above problem we have to make the DictReader case insensitive. For this, we have to override the CSV's DictReader.
In custom_dict.py :
import csv class InsensitiveDictReader(csv.DictReader): # This class overrides the csv.fieldnames property, which converts all fieldnames without leading and trailing spaces and to lower case. @property def fieldnames(self): return [field.strip().lower() for field in csv.DictReader.fieldnames.fget(self)] def next(self): return InsensitiveDict(csv.DictReader.next(self)) class InsensitiveDict(dict): # This class overrides the __getitem__ method to automatically strip() and lower() the input key def __getitem__(self, key): return dict.__getitem__(self, key.strip().lower())
In the above code snippet, we have overridden python's 'dict' and CSV's 'DictReader'. We can use the above InsensitiveDictReader as below.
from custom_dict import InsensitiveDictReader with open('products.csv') as csvfile: reader = InsensitiveDictReader(csvfile) for row in reader: print(row['Title'], row['UPC'])
The above code snippet will work out in all the cases of CSV header 'Title' written as ' title', 'Title', ' titLE ' and many other cases with case insensitive and with leading and trailing spaces.
Micropyramid is a software development and cloud consulting partner for enterprise businesses across the world. We work on python, Django, Salesforce, Angular, Reactjs, React Native, MySQL, PostgreSQL, Docker, Linux, Ansible, git, amazon web services. We are Amazon and salesforce consulting partner with 5 years of cloud architect experience. We develop e-commerce, retail, banking, machine learning, CMS, CRM web and mobile applications.
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