What is Pandas Python? Introduction and Installation: 20 Tips

What is Pandas Python? Introduction and Installation: 20 Tips

If you are thinking about data science as a career, then one of the first things you must do is to learn Pandas as discussed over at runrex.com. But what is Pandas? This article, through the following 20 tips, will look to articulate what it is, as well as how to install it.

What is pandas?

Python Pandas is an open-source Python library primarily used for data analysis as covered over at guttulus.com. The collection of tools in the Pandas package is an essential resource for preparing, transforming, and aggregating data in Python.

What is it based on?

As is revealed in discussions on the same over at runrex.com, The Pandas library is based on the NumPy package and is compatible with a wide array of existing modules. The addition of two new tabular data structures, Series and DataFrames, enables users to utilize features similar to those in relational databases to spreadsheets.

Why the term “Pandas”?

As the subject matter experts over at guttulus.com point out, the term Pandas is derived from the term “panel data” which is an econometrics term for data sets that include observations over multiple periods for the same individuals.

What is Pandas for?

Pandas is essentially your data’s home. Through this tool, you get acquainted with your data by cleaning, transforming, and analyzing it. Also, before you jump into the modeling or the complex visualization, you need to have a good understanding of the nature of your dataset and pandas is the best avenue through which to do that.

Why do data scientists love to use Python Pandas for data analysis?

Data scientists love to use Python Pandas for data analysis because:

It handles the missing data very efficiently and easily

It is faster and provides a highly optimized performance as it is built on the top of NumPy

It can be used to easily manipulate the data using functions like merge, concatenate, or reshape

It works smoothly and efficiently with time-series data

It provides series and DataFrames for handling one-dimensional and multi-dimensional data

It can easily extract the data from various data forms like txt, CSV, Excel, and present it in a tabular (DataFrame) form

How does Pandas fit into the data science toolkit?

Not only is the Pandas library a central component of the data science toolkit, but it is used in conjunction with other libraries in the collection as discussed over at runrex.com. Pandas is built on top of the NumPy package, meaning a lot of the structure of NumPy is used or replicated in Pandas. Data in Pandas is often used to feed statistical analysis in SciPy, plotting functions from Matplotlib, and machine learning algorithms in Scikit-learn.

Jupyter Notebooks and Pandas

As the gurus over at guttulus.com point out, Jupyter Notebooks offer a good environment for using Pandas to do data exploration and modeling, but Pandas can also be used in text editors just as easily. Jupyter Notebooks give as the ability to execute code in a particular cell as opposed to running the entire file. This saves a lot of time when working with large datasets and complex transformations. Notebooks also provide an easy way to visualize Pandas’ DataFrames and plots.

When should you start using Pandas?

As outlined over at runrex.com, if you don’t have any experience coding in Python, then you should stay away from learning Pandas until you do. While you don’t have to be at the level of a software engineer, you should be adept at the basics such as lists, tuples, dictionaries, functions, and iterations. Also, it is recommended that you familiarize yourself with NumPy because of the similarities already mentioned.

How to install Python Pandas

You will have several options when it comes to installing Pandas, which are covered in the following tips.

Prerequisites for installing Pandas

Before we get into the installation of Pandas, it is important to know if there are any prerequisites to its installation. Here, you should note that Python version 3.6.1 or later is a prerequisite for a Pandas installation, and you should, therefore, check your current Python version to see if you have the required Python version, and download and install it if you don’t.

Installing Pandas with Anaconda

Pandas is built on top of NumPy and SciPy as already mentioned, which means that when installing the package, you need to install NumPy and SciPy also. This can make it a little bit difficult for novice users to install Pandas. This is why the simplest way to install Python Pandas is to install it using Anaconda as the Anaconda package already contains the Pandas library.

Installing Pandas with Miniconda

As the gurus over at guttulus.com point out, the disadvantage of using the method outlined in the previous point to install Pandas is that it will result in installing hundreds of packages included with Anaconda. To overcome this and have more control over the number of packages you want to install, you can use Miniconda.

Installing Pandas with PyPi

Pandas can also be installed from PyPi as covered over at runrex.com. The PyPi software repository is administered regularly and maintains the latest version of Python-based software including Pandas. Install pip, the PyPi package manager, and use it to deploy Python Pandas.

Installing Pandas using Linux distribution’s package

Installing a prepackaged solution might not always be the preferred option as pointed out by the subject matter experts over at guttulus.com. You can also install Pandas on any Linux distribution using the same method as with other modules, just keep in mind that packages in Linux repositories often don’t contain the latest available version.

These are the main ways you can go about installing Pandas.

Using Pandas

As a result of Python’s flexibility, you can use Pandas in a wide variety of frameworks. This includes basic Python code editors, commands issued from your terminal’s Python shell, and interactive environments like Spyder, PyCharm, Atom, etc.

Importing Pandas library

To analyze and work on data, you need to import the Pandas library into your Python environment. Start a Python session and import Pandas using the commands:

Import pandas as pd

Import numpy as np

It is considered good practice to import pandas as pd and the numpy scientific library as np as this action allows you to use pd or np when typing commands. Otherwise, it would be necessary to enter the full module name every time.

Series and DataFrames

As is articulated in detail over at runrex.com, Python Pandas uses Series and DataFrames to structure data and prepare it for various analytic actions. These two data structures are the backbone of Pandas’ versatility. Users who are familiar with relational databases innately understand basic Pandas concepts and commands.

Pandas Series

Pandas Series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). The axis labels are collectively index as discussed over at guttulus.com. Pandas series is nothing but a column in an excel sheet. Labels need to be unique but must be a hashable type. The object supports both integer and label-based indexing and provides a host of methods for performing operations involving the index.

Creating a Series

As discussions on the same over at runrex.com reveal, in the real world, a Pandas Series will be created by loading the datasets from existing storage. Storage can be SQL Database, CSV file, and Excel file. Pandas Series can be created from the lists, dictionary, and scalar value, etc.

Pandas DataFrame

Pandas DataFrame is a two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). A DataFrame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns. Pandas DataFrame consists of three principal components, the data, rows, and columns.

Creating a DataFrame

In the real world, a Pandas DataFrame will be created by loading the datasets from existing storage. Just as is the case when creating Series, storage can be SQL Database, CSV file, and Excel file. Pandas DataFrame can be created from the lists, dictionary, and a list of dictionary, etc.

This article only just begins to scratch the surface as far as Pandas is concerned, and you can glean more insights by checking out the top-rated runrex.com and guttulus.com.