Name: Python Mini Project 2023
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

Python Mini Project 2023

Source

https://www.kaggle.com/code/sarmaz/global-food-prices-data-analysis-and-visualize/data

Project

In this project, you will perform EDA, visualize and make calculations from African food prices data using matplotlib, seaborn, and pandas. The dataset values were obtained from the Source above.

Information

Data includes country, locality, market, goods purchased, price & currency used, quantity exchanged, and month/year of purchase.

Column Description

adm0_id: country id

adm0_name: country name

adm1_id: locality id

adm1_name: locality name

mkt_id: market id

mkt_name: market name

cm_id: commodity purchase id

cm_name: commodity purchased

cur_id: currency id

cur_name: name of currency

pt_id: market type id

pt_name: market type (Retail/Wholesale/Producer/Farm Gate)

um_id: measurement id

um_name: unit of goods measurement

mp_month: month recorded

mpyear: year recorded

mpprice: price paid

mp_commoditysource: Source supplying price information

Task

Perform Data Cleaning and Transformation
Perform EDA on the dataset
List 5 research questions from the dataset
Analyze the trends in the dataset to answer the questions listed
Visualize the trends in the dataset
All steps should be clearly described in your notebook
Be sure to use the full range of analytics options learnt in class

Submission

Upload the completed notebook to your personal github account, thereafter send the link with your Full Name, and Title to the submission email: jonathanogwumike@gmail.com

Bonus: Create a streamlit App at the end of your analysis and share in a separate github repository

Tables

Africa Food Prices

@kaggle.jogwums_python_mini_project_2022.africa_food_prices

7.81 MB
956,779 rows
19 columns

CREATE TABLE africa_food_prices (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "country_id" DOUBLE,
  "country" VARCHAR,
  "state_id" BIGINT,
  "state" VARCHAR,
  "market_id" BIGINT,
  "market" VARCHAR,
  "produce_id" BIGINT,
  "produce" VARCHAR,
  "currency_id" DOUBLE,
  "currency" VARCHAR,
  "pt_id" BIGINT,
  "market_type" VARCHAR,
  "um_unit_id" BIGINT,
  "quantity" VARCHAR,
  "month" BIGINT,
  "year" BIGINT,
  "price" DOUBLE,
  "mp_commoditysource" VARCHAR
);