https://github.com/victorowinoke/data-science-challenge--dap-eldohub
This is my solution for the EldoHub DAP data science challenge
https://github.com/victorowinoke/data-science-challenge--dap-eldohub
challenges-solved data-science data-structures interview-questions python
Last synced: 3 months ago
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This is my solution for the EldoHub DAP data science challenge
- Host: GitHub
- URL: https://github.com/victorowinoke/data-science-challenge--dap-eldohub
- Owner: VictorOwinoKe
- Created: 2020-12-15T22:15:51.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2020-12-16T06:46:35.000Z (over 5 years ago)
- Last Synced: 2025-03-04T22:27:32.517Z (over 1 year ago)
- Topics: challenges-solved, data-science, data-structures, interview-questions, python
- Language: Jupyter Notebook
- Homepage:
- Size: 183 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
### Data-Science-Challenge--DAP-EldoHub
This is my solution for the EldoHub DAP data science challenge
## The Challenge
Consider a dataset providing information on the functionality of infrastructure resources, for each water point it includes the name of the village it is in and its functional state. Implement a data processing module in python which takes a dataset URL as input and returns:
- The number of water points that are functional,
- The number of water points per community,
- The rank for each community by the percentage of broken water points.
@Dataset URL
Data: https://raw.githubusercontent.com/onaio/ona-tech/master/data/water_points.json
### MY SOLUTION
# Introduction
As a Data Scientist and AI programmer, you do most of the works on the JSON data. You usually fetch the JSON data from a particular URL and visualizes it. But traversing into a JSON data is always a challenging task especially for beginners.
The full-form of JSON is JavaScript Object Notation. It means that a script (executable) file which is made of text in a programming language, is used to store and transfer the data. Python supports JSON through a built-in package called json. To use this feature, we import the json package in Python script. The text in JSON is done through quoted string which contains value in key-value mapping within . It is similar to the dictionary in Python. JSON shows an API similar to users of Standard Library marshal and pickle modules and Python natively supports JSON features.
-Initially, I use pandas library which provides an inbuilt function (pd.read_json()) to read the json file
-Then convert the json file into pandas data frame using the function DataFrame (At this point am ready to use my dataset to analysis)
- The water point dataset that contains many columns, the relevant ones are:
-communities villages
-water functioning
# 1. Number of Functional Water Point
functioning 590
**************************
broken 40
abandoned 36
newly_constructed 28
under_construction 16
na_dn 2
NB: 590 Water point are functioning
# 2. Number of Water Points per Community
# 3. Community Ranking
# 4. Conclusion
High percentage of the water points are functional and few are brocken.
A good number are either abandoned or under construction or newly_constructed
Google Collab Notebook: https://colab.research.google.com/drive/1R47uK41R-oqj7dSemaw1baOHSfgov8jT?usp=sharing