Python Mini Program Series

Apache spark is an open source unified analytics engine for large scale data processing. It is 100 times faster in operation and hence preferred for big data analysis. The set of commands used in spark is bit different from that of pandas. In this analysis we aim to familiarize you with common spark commands that can prove to be handy during exploratory data analysis. We have used Placement dataset from Kaggle for comparison with pandas and spark operations . Watch the video to know more. #CodeWithUs to find out more and do it your self!!  You can find the code at  Python Code - GitHub

Data visualization is an important part of any data science project. This involves creation of attractive dashboards to tell stories about the data. Recently, Microsoft announced that power bi dashboards can be embedded in Jupyter notebook. Here is a simple illustration on how it can be done . #CodeWithUs to find out more and do it your self!!  You can find the code at  Python Code - GitHub

Model deployment is an important part of any data science project. This involves creation of a front end application that can be used by a broader audience to get the result obtained from model using varied inputs. This is made simple in Python by using a web  application  framework - Flask. This article aims at showing a simple demo of model deployment using Flask. #CodeWithUs to find out more and do it your self!!  You can find the code at  Python Code - GitHub

After exploratory data analysis and building hypothesis, we move to predictive model building stage where we try and test many models on the same dataset to compare the performance and to check which one fits the best in the given business and technical constraints. We should start with the base model and then build advanced models or try some bagging/boosting mechanisms to improve the performance. We are  analyzing the health insurance dataset in this case study.  You can follow  this  post to know how to get started. #CodeWithUs to find out more and do it your self!!  You can find the code at  Python Code - GitHub Bonus content - Read this article to know about how to optimize your Python codes

Univariate and Multivariate analysis of the data is an important step during Exploratory Data Analysis phase of the data science project. We can target analyzing numerical and categorical variables separately before performing multivariate analysis. More we know the data, better it becomes during Model Building phase. We are  analyzing health insurance dataset in this case study.  You can follow  this  post to know how to get started. #CodeWithUs to find out more and do it your self!!  You can find the code at  Python Code - GitHub

Data Cleaning and Exploratory Data Analysis are crucial parts in any data science project. It is rightly said that if we give junk input to the model, we will get a junk output. The dataset may have missing values or outliers which should be identified and treated during data cleaning phase. These are only a few examples of un-clean data. A data analyst find hundreds of anomalies in the dataset while analyzing. Exploratory data analysis is done to get the hidden insights from the data. Data Analysts perform univariate and multi-variate analysis to understand the pattern in the dataset.  We are  analyzing health insurance dataset in this case study.  You can follow this post to know how to get started. #CodeWithUs to find out more and do it your self!!  You can find the code at  Python Code - GitHub  

Data Science domain has grown rapidly since past few years with industries realizing that the data stored can be put to best use, to help them grow their business. People who understands the data, can play around with the data and build insights from them are in great demands. You must have heard about roles like Data Scientist, Data Analysts and Machine learning engineers to name a few. Today, we will learn how to analyze the data and to build predictive analytics models using Python.  Here, we have come up with simple and easy to understand tutorial for analyzing health insurance dataset. #CodeWithUs to find out more and do it your self!!  You can find the code at  Python Code - GitHub               

I am sure most of us have scraped data to store the result in excel or csv for analysis. We explored an interesting option with scraping. Instead of scraping the data part, we have scraped the image and downloaded into our local folder. Does this excite you? If yes, go through the video and don't forget to browse the code on Github. You can use the same concept to scrape images from other sites as well. #CodeWithUs to find out more and do it your self!!  You can find the code at  Python Code - GitHub You can also follow us on  Facebook - In It To Learn

Do you know how easy it is to scrape a website? I am sure most of us have used beautifulsoup to web-scrape. But this time, we came across another package that can also be used. The selenium package can be leveraged quite efficiently to scrape websites too. Try it out today! After all, who doesn't like choices. #CodeWithUs to find out more and do it your self!!  You can find the code at  Python Code - GitHub You can also follow us on  Facebook - In It To Learn

Have you ever faced the wrath of large files which are not possible to read. Yes! It's so annoying. Recently came across this situation. We leveraged it to learn more on Python. We developed a file splitting code that can split based on the number you desire. Isn't it cool or is it 😇? #CodeWithUs to find out more and do it your self!!  You can find the code at  Python Code - GitHub You can also follow us on  Facebook - In It To Learn

Knowddy and Hairbrow are bored. They want to involve into something very different yet useful. They came across Dictionary in Python and openpyxl module in Python. Can the key-value pairs of dictionary items of any length be saved in excel? Find out the answer and try to create your own python code from the pseudo code shared below. Here's the Knowddy's and Hairbrow's version of Python code ( GitHub Link ) Visuals are created using  canva

Have you ever wondered if Python has debugger? Can we do away with boring print statements for debugging? Well! The answer is "Yes". Python debugger comes as a part of standard Python distribution. It's a great tool for tracking bugs. With not so much of documentation available online, we have come up with the complete illustration on how you can work with them. Enjoy the video below! #CodeWithUs to find out more and do it your self!!  You can find the code at  Python Code - GitHub You can also follow us on  Facebook - In It To Learn

Here again we have brought something that might interest you. Did you ever want to check the sentiment of a sentence by yourself? You can start by simply calculating the percentage of positive and negatives words in the sentence to come to a conclusion. We have created the basic step for you in the video below. You can add more complex algorithm to it like handling case where "not good" represents "bad" and "not bad" represents "good".  #CodeWithUs to find out more and do it your self!!  You can find the code at  Python Code - GitHub You can also follow us on  Facebook - In It To Learn

How often have you used Python Packages! Packages in Python makes our life easier be it numpy, matplotlib or Pandas.  Do you know you can create your own package in Python and use it across your Projects and automate a lot! #CodeWithUs to find out more and do it your self!!  You can find the code at  Python Code - GitHub You can also follow us on  Facebook - In It To Learn

We are here with yet another exciting program in Python. Do you know, you can check the similarity between the strings using a simple math rule of finding the cosine of angle between the vector? #CodeWithUs to find out more and do it your self!!  You can find the code at  Python Code - GitHub You can also follow us on Facebook - In It To Learn

Here is an opportunity to showcase your knowledge of Python. Comment below the solution of this challenge and brag your Python skills. Stay tuned for the solution soon!! Solution - Problem statement - Generate complex password using Python Python Modules used - random and string You can access the code snippet at Github Repository Follow us on facebook  for more updates

Have you observed that Knowddy and Hairbrow keeps changing their shapes and size? However, rest assure their characteristics - continuous learning remains the same. The same is with the data. You can save them as excel and perform data manipulations or store them as dataframe and perform the analysis. Another way is to push the data into sql tables and write your queries to get the insights. We see that Python is robust enough to allow you to connect to a database and perform all the sql operations. The study buddies have tried and tested this using a light weight database - sqllite.  Do you want to try sqllite connections in Python with them? Below is the pseudo code for you to experiment with and here is the link to their version ( GitHub Code ) All visuals are created using Canva

Knowddy  is bored. Now that she has got a hang of what  all can be done using Python, she wants do some interesting stuffs leveraging her Python knowledge.  Hairbrow  is all excited with this idea!  Try to create your own python code from the pseudo code shared below. Here's the Knowddy's and Hairbrow's version of Python code ( GitHub Link )

Hairbrow is stuck in her task. She has to search all occurrences of a word in a huge text file. As lazy to do a manual work, she approaches her buddy Knowddy for help. Then, they code not only to search words but also to highlight all occurrences and save the newly created output file. Try to create your own python code from the pseudo code shared below. Here's the Knowddy's and Hairbrow's version of Python code ( GitHub Link ) Visuals are created using  canva

After their successful attempt to automate Google search, Knowddy and Hairbrow are up to their next automation task using Python. They are trying to find the top 3 words occurring in a paragraph based on their frequency.  Why don't you try to create a Python program from the Pseudo code that they shared and join their journey of happy learning!! Don't forget to check out their code ( GitHub Link ) Visuals are created using  canva