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Agricultural Raw Material prices (1990-2020)

This is a dataset is on Agricultural Raw materials prices over a span of 30 years (1990-2020). You can find this dataset on Kaggle. Mainly this dataset contains information about the prices and the percent change in the prices from the previous month. The prices are present for every month. So we are going to analyse this dataset to get the distribution of the prices and the distribution of the change in prices. We also want to gain some insights regarding the months when the prices of most of the materials falls. We will also like to see the trends in the prices.

This dataset contains information about 12 Agricultural Raw Materials.

  • Coarse wool
  • Copra
  • Cotton
  • Fine wool
  • Hardlog
  • Hard sawnwood
  • Hide
  • Plywood
  • Rubber
  • Softlog
  • Soft sawnwood
  • Wood pulp

For each material we have two columns: Prices and %change. Along with all these columns we have another one column named Date which contains month and year of which the price is. This is a course project for the online course at Jovian.ml named Data Analysis with Python: Zero to Pandas.

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

As the dataset is present in the opendatasets Python library so we are going to download it directly from there using the opendatasets.download() command