What is Python Pandas?
Pandas is used for data manipulation, analysis and cleaning. Python pandas is well suited for different kinds of data, such as:
- Tabular data with heterogeneously-typed columns
- Ordered and unordered time series data
- Arbitrary matrix data with row & column labels
- Unlabelled data
- Any other form of observational or statistical data sets
Python Pandas Operations
Using Python pandas, you can perform a lot of operations with series, data frames, missing data, group by etc. Some of the common operations for data manipulation are listed below:
Data Collection — Conducting opinion Surveys, scraping the internet, etc.
Data Handling — Viewing data as a table, performing cleaning activities like checking for spellings, removal of blanks and wrong cases, removal of invalid values from data, etc.
Data Visualization — plotting appealing graphs, so anyone who looks at the data can know what story the data tells us.
“Pandas” — short for “Panel Data” (A panel is a 3D container of data) — is a library in python which contains in-built functions to clean, transform, manipulate, visualize and analyze data.
Key Components of Pandas
Pandas Series- A series in Pandas can be thought of as a unidimensional array that is used to handle and manipulate data which is stored in it.
Pandas DataFrame- This is a data structure in Pandas, which is made up of multiple series. Mainly, a Pandas DataFrame can be compared to a two-dimensional array. These are heavily used to store and manipulate data.
Features of Pandas
Python Pandas have a lot of features. The most critical ones would be:
1. Data manipulation: Pandas provides a lot of functions and features to perform various kinds of operations on datasets.
2. Handling Missing Values: Datasets are imperfect and contain a lot of data that is missing. This is handled efficiently by the library.
3. File format support: Various forms of files are supported by Pandas for both input and output purposes.
4. Data cleaning: Data can be very messy. Pandas provide a variety of tools which help in cleaning up data and make it usable for data analysis.
5. Visualize: You can see the results of your data analysis with Pandas, visually. This helps you to understand your results better.
6. Python support: Pandas runs alongside Python. Which gives us access to other libraries for Python, like NumPy, SciPy, and MatPlotLib.
It is one of the essential uses of Pandas. The library is capable of handling huge sets of data. It is suitable for analyzing huge amounts of data. The manipulations capabilities allow us to clean and filter data which we can analyze easily. Some sectors which use data analysis with Pandas are:
Economics: A lot of economics depends on analyzing data and trying to find trends and similarities. Pandas are very helpful in this.
Statistics: Pandas provides a lot of functions to perform various statistical operations.
Web–Analytics: Pandas can help to read and analyze the traffic of a website to provide helpful insight and improve the website in various ways.
It helps to render data for a model to learn and predict results. Without Pandas, machine learning models would not be able to read data efficiently.
The ability to import data and analyze it is extremely essential. Where it is use-
· Recommendations: Only because of machine learning websites like Netflix and Spotify provide excellent recommendations for their users.
· Finance: Machine Learning can be used to predict stocks. Pandas is used to handle data of previous stock market dealings which help to predict the future dealings.
· Natural Language Processing (NLP): Using machine learning to understand the human language and its intricacies.
List of Companies using Pandas:
Every company delving into data science with python has to use Pandas. Some of the notable ones are:
4. JP Morgan Chase
5. Goldman Sachs
8. AQR Capital Management
9. Vital labs
Hopefully, this introduction to Pandas has helped you to understand the power of the library. Pandas is an essential library for any data scientist or machine learning enthusiast. Both of these streams are extremely lucrative and interesting sectors and are booming currently. Therefore learning Pandas has become of utmost importance.