An Approach To Data Analytics Using Python

In today’s article, we’ll explore multitudes of learnings. First, we’ll start with Introduction to Data Analytics. Here, we’ll give brief knowledge about Data Analytics, the processes used to make meaning out of the raw data, and then we’ll get to know various Scientific Libraries in Python as follows,

  • NumPy
  • Pandas
  • Matplotlib

Data Analytics

Data Analytics can be defined as a process by which raw data is examined in order to draw conclusions and make information out of that raw data.

Steps for Data Analytics,

  • Get the Data
    In today’s age of data, it is easier to find data. One can always opt for sample data to experiment with. You can find freely available data in Kaggle and other resources.
  • Clean the Data
    Data Cleaning is the first step in Data Analysis. Most of the data always needs to be processed first. Thus, by modifying for removing data that are wrong or incomplete and irrelevant or are duplicated values, we prepare the data for the next steps.
  • Wrangle the Data
    Data Wrangling can be understood as the process of mapping data or transforming data into the format which is appropriate for our operations.
  • Analyze the Data
    As we learned in the previous article Statistics for Artificial Intelligence and Data Science, we apply the tools in statistics to analyze the data. We need to perform appropriate analysis for accurate findings and thus, being able to use the tools will help us get the outputs we desire.
  • Visualize the Data
    Data Visualization is the process of representing information in a graphical form such that one can easily understand the gist of the data. Humans are innately visual creatures and visualizing the data using process tools in the proper way will express a lot more meaning to the world than the data in tables and array.

Beyond Data Analytics on its own, Data Analysis can also be done in other ways which can be performed using Machine Learning and Deep Learning methods.

  • Machine Learning
    As the name suggests, Machine Learning is the process of making machines learn themselves. We employ multitudes of algorithms such that the systems can itself learn from data, identify the patterns and make decisions on their own. It is a subset of Artificial Intelligence.
  • Deep Learning
    Deep learning is also known as a Deep Neural Network which is a subset of Machine Learning which has networks that are capable of learning unsupervised without human supervision from data alone which might be unlabeled or unstructured.

According to W. Edwards Deming, Data Scientist,

“Without data, you’re just another person with an opinion.“

Why Python for Data Analytics

Python is one of the easiest and widely used programming languages across the globe,

  • Taught as a beginning programming language to students
  • Clear syntax facilitates ease of understanding and code indentation
  • Active communities of libraries and modules developers

Tools for using Python


Anaconda is a distribution for scientific computing which is an easy-to-install free package manager and environment manager and has a collection of over 720 open-source packages offering free community support for R and Python programming languages. It supports Windows, Linux, and Mac OS and also ships with Juypter Notebook.

Data Structures

Python List

Efficiency is one of the key problems with Python’s list data type. The list allows us to have items of non-uniform type, memory location is where each item in the list is stored, with the list containing an “array” of pointers to each of these locations. Because of the way the Python list is implemented, it is computationally expensive to access items in a large list.

In order to overcome this, we use NumPy,


NumPy is a library that supports numerous programming languages including Python for numerical computation. It helps as an extension that adds support for huge, multi-dimensional arrays & matrices. It also consists of a large library of high-level mathematical functions which can be used to operate on these arrays.

An array is of type ndarray(n-dimensional array) in NumPy. Here, all elements are of the same type.

We imported the NumPy library as np and performed array formation and printed out its shape. You can try it out too with Jupyter Notebook from the Anaconda Packager set up easily. No additional code or library calls are needed.


A multidimensional and homogeneous array of fixed-size items is represented by anndarray object. It is far more efficient than the list of Python. It also provides functions that operate on an entire array at once.


While Python supports lists and dictionaries for the manipulation of structured data, it is not well-suited for manipulating numerical tables, such as those stored in CSV files.

As such, you should use Pandas. It stands for Panel Data Analysis. Pandas is a software library that is written for data manipulation and analysis, especially for Python.


Dataframe in Pandas are two-dimensional and heterogeneous tabular data that are mutable in size. Ie. Its size can be changed.

Slicing DataFrame

Slicing DataFrame in Python with Pandas helps select a set of rows and columns. Similar to slicing in the native python function, we start with including the start bound for the slicing and 1 step more than the row we want for the end part.

To Read the Full Article, Check it out at:

Man on a Mission - to create epochal impact

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store