During the second world war, the first-ever computer was invented by Alan Turing that successfully cracked the German communication code. In a paper that he wrote in 1950, Turing had asked a simple question: “Can machines think?”

*Language Used: Python,

Libraries Used: pandas, NumPy, matplotlib, seaborn*

As a reader(and someone who is very picky when it comes to books), I often struggle while determining my next read. Of course, there are various sites that suggest books and share book reviews & ratings, but it can get a bit challenging to choose a book just from those text reviews. The key points we are looking for may not be immediately apparent and this often ends up creating more dilemmas. For instance, if you wanted a list of books under the fiction and fantasy genre, with the highest average…

Stack Overflow’s annual Developer Survey has been one of the largest, if not the largest, surveys of coders and programmers worldwide for almost a decade now. In the year 2020, this survey focused on being more representative of the diversity of programmers worldwide and it was taken by approximately 65,000 people.

I will be performing a complete exploratory data analysis on this dataset. This time, I have used a helper library called ‘opendatasets’ to download the required dataset. This library contains a collection of curated datasets and provides a helper function for direct download.

#importing the required library and downloading…

NumPy, its importance, and some useful Numpy functions:

NumPy, short for Numerical Python, is one of the most important foundational packages for numerical computing in Python. One of the reasons why NumPy is so important for numerical computations in Python is because it is designed for efficiency on large arrays of data. It helps in performing complex computations on entire arrays without the need for Python for loops. Similarly, it is much faster and uses significantly less memory.

Following are 6 different NumPy functions that will be quite useful in the Data Analysis field. The functions are:

  • numpy.linspace
  • numpy.repeat
  • numpy.std
  • numpy.percentile
  • numpy.reshape
  • numpy.swapaxes
!pip install jovian --upgrade -q

As part of my course project on Data Analysis with Python, I had to first find a real-world dataset and perform an exploratory data analysis on it. Without much thought, I decided to work on the most trending topic in today’s world — Covid-19. I downloaded the latest dataset on Covid-19 from https://ourworldindata.org/coronavirus-source-data which gave a complete list of information for all the countries starting from February 24, 2020. Similarly, I downloaded another dataset from https://www.kaggle.com/fernandol/countries-of-the-world. This dataset contained other basic information of the countries(not covid related). I wanted to merge certain columns from both these datasets for my analysis.


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