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Practical Data Wrangling

You're reading from   Practical Data Wrangling Expert techniques for transforming your raw data into a valuable source for analytics

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781787286139
Length 204 pages
Edition 1st Edition
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Author (1):
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Allan Visochek Allan Visochek
Author Profile Icon Allan Visochek
Allan Visochek
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Table of Contents (10) Chapters Close

Preface 1. Programming with Data FREE CHAPTER 2. Introduction to Programming in Python 3. Reading, Exploring, and Modifying Data - Part I 4. Reading, Exploring, and Modifying Data - Part II 5. Manipulating Text Data - An Introduction to Regular Expressions 6. Cleaning Numerical Data - An Introduction to R and RStudio 7. Simplifying Data Manipulation with dplyr 8. Getting Data from the Web 9. Working with Large Datasets

Interfacing with MongoDB from Python


Python can connect to MongoDB using the pymongo module. To start off with, I will create a file called process_large_data.py and import the pymongo and csv modules. You will need to import both pymongo and MongoClient as I have done in the demonstration:

import csv
import pymongo
from pymongo import MongoClient

MongoClient takes care of establishing a connection and interfacing with the database system. The following steps in process_large_data.py will create an object assigned to the collection variable, which can be used to insert documents into the database:

....
from pymongo import MongoClient

## create a MongoClient object,
## used to connect and interface
## with mongodb
client = MongoClient()

## these two lines create a collection
## object which allows you to interface
## with a particular collection
db = client.weather
collection = db["records2"]

The only thing left to do now is to read the data line by line and insert each row as a record into...

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