Numerous growing enterprises are using the MongoDB database to store document-based data as it provides flexibility and data stability. And with the Python language driver, companies can take their data from MongoDB and perform various data analyses via the Pandas library.
Panda is a Python library that is frequently used for data analysis and visualization. Using Pandas DataFrame, you can take advantage of the various methods of the Pandas library and transform your data to discover essential insights to boost your company’s performance.
In the article, you learn about the MongoDB database, Pandas library, and the Pandas MongoDB connection using Python.
Table of Contents
- What is Pandas Library?
- What is MongoDB?
- Pandas MongoDB Connection
Understanding of Big Data and Python.
What is Pandas Library?
The Pandas library is an open-source Python package for data analysis and machine learning workflows. Developed in 2008 by Wes McKinney, it is one of the most used data wrangling packages inside the Python ecosystem. Data wrangling cleans unstructured and complex data for easy access and quick analysis.
The Pandas library primarily works with relational or labeled data intuitively by providing numerous data structures and methods for transforming data. It is built on top of another famous Python library, NumPy, which supports multi-dimensional arrays and works seamlessly with DataFrame. Pandas and NumPy are also inputs for Matplotlib’s data visualization functions, SciPy’s statistical analyses, and Scikit-learn’s machine learning workflows.
Key Features of Pandas
- Transforming DataFrames: With the Pandas library, you can combine, reshape and transform data using Series and DataFrame. For reshaping the DataFrames, transpose, ‘groupby’, and stacking functions add or remove dimensions in the array, making it suitable for data analysis.
- Data Cleaning: Pandas offer a wide range of built-in functions to clean and manipulate data before analysis. You can drop insufficient or unnecessary rows and columns and fill in missing values to improve the readability of the data you are working with, making the results more accurate. With the applymap(), you can save cleaning time as it cleans the entire dataset elements-wise by applying a function.
- Data Visualization: The Pandas library is mainly used for data analysis but can also be used for visualization. Using Pandas’ plot() method for data visualization is highly beneficial as you can serialize or build a pipeline of analysis and plotting functions. You can also use the Matplotlib library along with Pandas and NumPy to create MATLAB-like visualizations.
Scale your data integration effortlessly with Hevo’s Fault-Tolerant No Code Data Pipeline
As the ability of businesses to collect data explodes, data teams have a crucial role to play in fueling data-driven decisions. Yet, they struggle to consolidate the scattered data in their warehouse to build a single source of truth. Broken pipelines, data quality issues, bugs and errors, and lack of control and visibility over the data flow make data integration a nightmare.
1000+ data teams rely on Hevo’s Data Pipeline Platform to integrate data from over 150+ sources in a matter of minutes. Billions of data events from sources as varied as SaaS apps, Databases, File Storage and Streaming sources can be replicated in near real-time with Hevo’s fault-tolerant architecture. What’s more – Hevo puts complete control in the hands of data teams with intuitive dashboards for pipeline monitoring, auto-schema management, and custom ingestion/loading schedules.
All of this combined with transparent pricing and 24×7 support makes us the most loved data pipeline software on review sites.
Take our 14-day free trial to experience a better way to manage data pipelines.Get started for Free with Hevo!
What is MongoDB?
Developed in 2017, MongoDB is an open-source database management program that works effectively with big datasets and is an excellent alternative to traditional databases. The model architecture of MongoDB is designed to support easy data accessibility and stability with the help of its horizontal scaling features. Instead of storing data in tables, MongoDB uses BSON, binary data representation, and can retrieve data in the JSON format making its structure highly flexible. In this format, the native objects are mapped directly, and developers do not need to worry about data normalization. MongoDB database was built for users whose primary goal is to create web and business applications that require continuous transformations.
Key Features of MongoDB
- Data Stability: MongoDB stores the user’s data in a primary server and multiple secondary nodes, unlike traditional databases. The replication of data by deploying numerous servers in MongoDB increases the data stability and availability, giving a good user experience. Replication helps optimal load balancing for growing enterprise applications that deal with millions of client requests concurrently.
- Horizontal Scaling: MongoDB’s horizontal scalability features consist of replication and sharding that assist in large-scale load balancing. With all of these horizontal scaling features, MongoDB is a lightweight, robust database ideal for handling the growing requirements of a continually evolving application.
- Quick Query Execution: MongoDB provides a wide range of indices and features for sorting complex datasets effectively. These indices are created as per demand to accommodate real-time searching of intricate query patterns and growing application requirements.
Pandas MongoDB Connection
The requirements for growing web-based and business applications change as the number of users increases. To handle growing demands, enterprises need to rely on flexible technologies like the MongoDB database to manage their data efficiently. MongoDB has an adaptable schema and provides real-time data discovery while handling data.
You can establish Pandas MongoDB connection and effectively clean, transform, and analyze data. The Pandas MongoDB connection is essential for data analysis and exploration to create meaningful insights which can assist in identifying the problematic areas you have to handle and improve.
Since Pandas library can also perform data visualizations incorporating various colors, graphs, etc., it helps demonstrate and understand the data better.
There are three methods to connect MongoDB to Pandas using Python – PyMongo, MongoEngine, and Djongo.
- PyMongo is a native low-level driver used to perform database queries via Python code and provides more control.
- On the other hand, MongoEngine is a Document Object Mapper that can be used for defining a schema for mapping application objects to document data.
- Djongo is used exclusively for the Django framework, a Python web application.
In this article, you will learn how to establish Pandas MongoDB connection using PyMongo.
Importing a Pandas DataFrame to MongoDB Database
To safely keep the data in your Pandas DataFrame, you should use Python to create a connection with the MongoDB database. You will require a connection string to import/export data and work with MongoDB.
Follow these below-given steps to connect the MongoDB database to the Pandas library properly:
- Create an account on MongoDB.com. If you already have an account, skip this step.
- Navigate to the clusters section and choose the connect option.
- Opt for the “Connect your application” option.
- Select “Python” as your driver and choose the appropriate Python version you have installed on your system.
- Copy the URL but replace the username and password in the connection string.
- Open Jupyter Notebook, and install Pandas and PyMongo libraries using pip.
pip install pandas pip install pymongo
Once you have installed both the libraries, import them.
import pandas as pd from pymongo import MongoClinet
Then make a client variable and paster your connection string with username and password as follows:
client = MongoClient(‘##paste string connection’)
For the connection between the database and DataFrame, use collections as follows:
db = client[‘database_name’] collection = db[‘collection_name’]
Convert the DataFrame into a dictionary as MongoDB only accepts data in JSON format.
data.rest_index(inplace=True) Data_dictionary = data.to_dict(‘dataframe’)
Insert the data into the MongoDB database.
Exporting Data from MongoDB
You should use Python to create a Pandas MongoDB connection for data analysis of data stored in the MongoDB database. Once successfully connected, you can perform data analysis and visualization to gain information about your data. Follow these given instructions to query data from Pandas MongoDB integration:
- Install the Pandas library using pip
python -m pip install pandas from pandas import DataFrame
- Import the get_database module from PyMongo.
from pymongo_test_insert import get_database dbname = get_database()
- Create a new collection.
collection_name = dbname[Database_name]
- Create a variable and store the contents of the database.
item_details = collection_name.find()
- Convert the data into a DataFrame.
# convert the dictionary objects to dataframe items_df = DataFrame(item_details)
In this article, you learn about the MongoDB database, Pandas library, and the Pandas MongoDB connection. MongoDB database is an open source NoSQL database that supports web and enterprise applications requirements by providing well-distributed data across multiple nodes to enhance data accessibility and stability. Its flexibility allows users to change the database schema as per their needs and work with big data for analysis. You can use Pandas for data analysis and machine learning workflow with the data present in the MongoDB database. By connecting MongoDB to Pandas, enterprises can prepare their data before analysis to improve their overall predictions.
Don’t forget to drop your comment or suggestion in the comment section below on Pandas MongoDB Connection.
Apart from MongoDB, you would be using several applications and databases across your business for Marketing, Accounting, Sales, Customer Relationship Management, etc. To get a complete overview of your business performance, it is important to consolidate data from all these sources. To achieve this you need to assign a portion of your Engineering Bandwidth to Integrate Data from all sources, Clean & Transform it, and finally, Load it to a Cloud Data Warehouse or a destination of your choice for further Business Analytics. All of these challenges can be comfortably solved by a Cloud-Based ETL tool such as Hevo Data.Visit our Website to Explore Hevo
Hevo Data, a No-code Data Pipeline can seamlessly transfer data from a vast sea of 100+ sources such as MongoDB & MongoDB Atlas to a Data Warehouse or a Destination of your choice to be visualized in a BI Tool. It is a reliable, completely automated, and secure service that doesn’t require you to write any code!
If you are using MongoDB as your NoSQL Database Management System and searching for a no-fuss alternative to Manual Data Integration, then Hevo can effortlessly automate this for you. Hevo, with its strong integration with 100+ sources & BI tools(Including 40+ Free Sources), allows you to not only export & load data but also transform & enrich your data & make it analysis-ready in a jiffy.
Tell us about your experience of learning about the Pandas MongoDB Connection! Share your thoughts with us in the comments section below.