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Nobel Prize winners data analysis

The Nobel Prize is a set of annual international awards bestowed in several categories by Swedish and Norwegian institutions in recognition of academic, cultural, or scientific advances. The five Nobel prizes (Chemistry, Literature, Peace, Physics, and Physiology or Medicine) were established in 1895 and first awarded in 1901 by the will of the Swedish chemist, engineer and industrialist Alfred Nobel. The prizes are widely regarded as the most prestigious awards available in their respective fields. Later on, in 1968, Sveriges Riksbank, Sweden's central bank, established the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel. The award is based on a donation received by the Nobel Foundation in 1968 from Sveriges Riksbank on the occasion of the bank's 300th anniversary. The first Prize in Economic Sciences was awarded in 1969. The Royal Swedish Academy of Sciences awards the Nobel Prize in Chemistry, the Nobel Prize in Physics, and the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel; the Nobel Assembly at the Karolinska Institute awards the Nobel Prize in Physiology or Medicine; the Swedish Academy grants the Nobel Prize in Literature; and the Norwegian Nobel Committee awards the Nobel Peace Prize.(Courtesy : Wikipedia).

In this notebook, we will be analysing the Nobel Prize winners data from 1901 to 2019. The datasets are obtained from Kaggle and contain the data of every individual who was awarded since 1901.

How to run the code

This is an executable Jupyter notebook hosted on Jovian.ml, a platform for sharing data science projects. You can run and experiment with the code in a couple of ways: using free online resources (recommended) or on your own computer.

Option 1: Running using free online resources (1-click, recommended)

The easiest way to start executing this notebook is to click the "Run" button at the top of this page, and select "Run on Binder". This will run the notebook on mybinder.org, a free online service for running Jupyter notebooks. You can also select "Run on Colab" or "Run on Kaggle".

Option 2: Running on your computer locally
  1. Install Conda by following these instructions. Add Conda binaries to your system PATH, so you can use the conda command on your terminal.

  2. Create a Conda environment and install the required libraries by running these commands on the terminal:

conda create -n zerotopandas -y python=3.8 
conda activate zerotopandas
pip install jovian jupyter numpy pandas matplotlib seaborn opendatasets --upgrade
  1. Press the "Clone" button above to copy the command for downloading the notebook, and run it on the terminal. This will create a new directory and download the notebook. The command will look something like this:
jovian clone notebook-owner/notebook-id
  1. Enter the newly created directory using cd directory-name and start the Jupyter notebook.
jupyter notebook

You can now access Jupyter's web interface by clicking the link that shows up on the terminal or by visiting http://localhost:8888 on your browser. Click on the notebook file (it has a .ipynb extension) to open it.

Downloading the Dataset

The dataset will be downloaded from Kaggle. The data is in CSV format and consists of 14 columns and 924 rows including header and will be downloaded using opendatasets. So, let us first install opendatasets for the same.

!pip install jovian opendatasets --upgrade --quiet

Let's begin by downloading the data, and listing the files within the dataset.