< Sachin Verma /> Personal Blog

Data Visualisation

Reddit is awesome.

While I was browsing Reddit, I came across some posts from the subreddit /r/Dataisbeautiful. So, I decided to make use of data sets available online to visualise it into something meaningful.

Then after some search, I found this site: Kaggle Datasets. This site contains open datasets on everything from government, health, and science to popular games and dating trends. After this, finding a relevant data set wasn’t much difficult, and I settled on this particular one: Top 500 Indian Cities. It contains population data for top 500 Indian cities, including male/female population, literacy rate, sex ratio, total graduates, etc.

So I wrote a program using Python and Matplotlib.

There was a column named “child_sex_ratio” for age group 0-6.

I decided to go ahead and rank top 10 cities which were showing an improvement in sex ratio.

I grabbed the CSV file and stripped the required information using Python. Calculated the difference between child sex ratio and overall sex ratio for all 500 cities, ranked them and plotted top 10 on a bar graph.

This was the result.

*Higher is better

See the Pen Sex ratio #1 by Sachin Verma (@sachinverma) on CodePen.

My own city “Shimla” ranks 9th on this list, which is no doubt a good sign. The sex ratio of children aged 0-6 is 890 whereas overall sex ratio is 818, which is an increase of 8.8%.

Bally city from West Bengal ranked #1 on this list, with an increase of 37.31% (863 to 1185).

Some facts: Two out of 10 of these cities are from Maharashtra, and two are from Assam.

Now it was the time to see ten cities with worst sex ratios.

Results

*Value closer to 0 is better

See the Pen Sex ratio #1 by Sachin Verma (@sachinverma) on CodePen.

Kozhikode from Kerala topped this list with a percentage decrease of 12.17% (1093 to 960).

Some facts: Four out of 10 of these cities are from Kerala. Two are from Maharashtra

This was my first go at data visualisation using Python. Technically I am not ranking cities purely on the basis of sex ratio, but on the basis of improvement and decline in the ratios. It’s not a proper metric to conclude anything, but it was fun doing this.

I am still in the learning phase of Python, which I will continue after my semester exams. The program I wrote was in no way optimised and written perfectly, so feel free to criticise and suggest some improvements.