Python for Data Analysis

Welcome back to my new blog ,
Today we are going to touch the programming language python.
Python is a language which we use for programming. And with python the original coding part is starting for data analysis. 
We use two libraries of python for analysis, 2 for scraping and 1 for data visualization. 
We can it's the whole package for data analysis big or small data doesn't matter here. We use it majorly for big datasets only.

Let's see how it works:- 
First we scrape the data, which means to take out data from online sources. This part is not used for internal data. But if you don't have data then you can python for data scraping. 
The libraries we normally use for scraping are: scrape and beautifulSoup. We can pull data from websites or web apps by using them.

Second we prepare, process and analyse data. 
As we discussed before Prepration contain to sort and filter data, Processing is to make data clutter free by cleaning the data along with validation. Analysing data is about taking out insights from data and answering the questions of stakeholders.
The libraries we use for these processes are numpy and pandas. 

Third is visualization of data. The insights we have are only in the form of numbers which is quite difficult for someone to understand who is not familiar with data. For example we can say we have 2 more sales than yesterday or we can say we have 50% more sales than yesterday because yesterday the number of sales was 4. At those places visualization plays a vital role to show data in bar chats, pi charts or other visualizations. Matplotlib is a library we use to create visualisations of the data.

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