https://github.com/arv-anshul/india-census-2011
I made a dashboard with India's 2011 census data with streamlit, python.
https://github.com/arv-anshul/india-census-2011
census-data dashboard india-census python3 streamlit
Last synced: about 1 month ago
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I made a dashboard with India's 2011 census data with streamlit, python.
- Host: GitHub
- URL: https://github.com/arv-anshul/india-census-2011
- Owner: arv-anshul
- Created: 2023-01-28T16:17:37.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-08-12T05:25:55.000Z (almost 3 years ago)
- Last Synced: 2025-10-07T02:51:10.583Z (8 months ago)
- Topics: census-data, dashboard, india-census, python3, streamlit
- Language: Jupyter Notebook
- Homepage:
- Size: 706 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# India Census 2011 Dashboard
After taking the session of **CampusX DSMP Program**. I decided that this would be a good project to improve my **Data Wrangling and Data Interpretation** ability.
## Data Gathering
### 1. Census data get from kaggle.
- [Dataset 1](https://www.kaggle.com/datasets/sirpunch/indian-census-data-with-geospatial-indexing)
- [Dataset 2](https://www.kaggle.com/datasets/danofer/india-census?select=india-districts-census-2011.csv)
### Disclaimer
- Data is not **appropriate or absolute** to belive. I just used it for practicing purpose.
## Inspired from [CampusX](https://youtube.com/@campusx-official) a Data Science YouTube channel.
## Process of Thinking | Notetaking
### 1. For Populations data :
- Plot a `scatter_mapbox` for each _States and Districts_.
- Plot a `pie chart` for `male-female` distributions in _States_. _Do this only for States._
### 2. For Literacy Data :
- Plot a `scatter_mapbox` for each _States and Districts_.
- Plot a `pie chart` for `male-female` distribution in _States_.
### 3. For Castes Group Data :
- This data contains two caste groups `SC & ST`. So we can plot the only the `pie charts` for each _States_.
- For Districts we can plot the `nested pie plot` for each **States's Districts**.\*
### 4. For Religions Data :
- This contains _maybe_ five religions overall. So we have again plot the `nested pie plot` for each _States and Districts_.
### Common Thoughts :
1. Plot some default `scatter plot` with plotly to display many feature analysis in one graph.
2. After analysing the `Rough Analysis.py` graphs I found that `Litracy` columns does not depict the way it has to. That's why we have to calculate the `litracy rate` of the particulars.
## Feature Engineering
1. In the dataset _Male, Female and Literate_ columns are present instead of _Literacy Rate and Sex Ratio_.
2. The dataset is in _wide formate_ so I turn it into _long formate_ for analysis.
## Created by [arv-anshul](https://github.com/arv-anshul)
Used dataset is not appropiate for real life analysis. I just used it to improve my skills. Find the used datasets [here](#1-census-data-get-from-kaggle).