Python is so popular in the data science world and this is so because of its ecosystem and the libraries that make python integration seem so natural. Pandas is one of these libraries.
What exactly is Pandas?
“Pandas” (short for “Panel Data”) is a Python library that includes built-in functions for cleaning, transforming, manipulating, visualizing, and analyzing data.
The import syntax for pandas is as follows:
While Pandas is a fantastic tool for analyzing data, it does have several flaws, particularly when coping with big volumes of data. Pandas is a terrific way to analyze data in Python, but it requires that the data fit in memory for all of the analytics and calculations to be performed.
When working with large amounts of data, this can be difficult because the size of your data can soon exceed the amount of memory available on your machine, making searches slow and unscalable. The use of “Apache Spark” is required to solve this problem, although the focus of this article is on pandas implementation.
What Pandas Are Used For
Here are a few examples of what pandas can be used for:
- Cleaning: Remove duplicates, fill in blanks, and filter rows and columns.
- Description: obtain information about the data collection, compute statistical values, and respond to quick inquiries such as averages, medians, min, max, correlations, and distribution.
- Data storage: load and save data from and to files such as CSV and JSON, or connect to databases directly.
- Transformation: Transform your data by calculating new values, renaming columns, and mutating it.
- Visualization: Create cutting-edge graphics directly from your pandas dataset using matplotlib, seaborn, or other libraries.
Installing Pandas on Linux
Pandas is an external library that must be installed before it can be used in your project. The library’s name is pandas, and you can install it with your preferred Python package manager. Depending on your preference, pipenv or conda can be used, but the technique is the same.
You can go ahead and input the following command directly
It is highly recommended that you use a Jupyter notebook in the process. Do so by using the following command
Now you have pandas installed and ready to be imported for your projects.
Installing Pandas on Windows
You can install pandas on Windows by putting the following into the command prompt:
If the prompt doesn’t recognize ‘pip,’ you may also use “pip3 install pandas”. Before typing the preceding commands, make sure you have Python installed on your system.
Importing pandas as pd
The import pandas command can be used to import the pandas module. However, if we try to use the pandas module functions, we must always use the pandas keyword.
Using pandas in every call might be a simple effort. As a result, an alias for the imported module can be supplied, and in this case, the pd term can be used as an alias for the pandas.
Importing pandas from a CSV file
Assuming we have a file named “NationalParks.csv” and we want to read its data using Pandas, then we can do so by typing the following commands
Calling one function read_csv with the file name is all it takes to import a CSV file. The function head is called on the second line of code, and it prints the first five rows of data.
Importing pandas from a JSON file
Reading JSON files is no more difficult than reading a CSV file; it’s just a matter of calling a function. Let’s pretend we have a file called “NationalParks.json” that we wish to read the data from with Pandas. This will be coded as follows:
So far, we’ve read data using default values, which has worked well in most circumstances. There may be times, though, when you need to adjust the way you read and process the data.
Panda’s reading functions are comprehensive and customizable. The index col parameter, which allows you to specify the column or columns used as the DataFrame’s index, is a crucial and often used parameter.
Setting a Different Alias Than pd
Despite the fact that the alias pd is quite common and is known as the pandas module alias, we can use any alias we wish. Instead of pd, we’ll use p as the pandas module alias in the following example:
Conclusion
Before beginning the visualization process, data scientists must first investigate, clean, and convert their data. Pandas make these activities simple with just a few keystrokes, and they may be used to visualize data instead of utilizing other libraries like matplotlib and seaborn.