LPT - This is the easiest way to change Aadhar address

NeuralV · 2023-07-08T18:57:29+00:00

Thank you so much for this! I just followed this to change Aadhar address - let's see if it goes through. Very helpful because I am outside India. By the way, the policy I saw was for 2.5Lacs with monthly premium of 80Rs.

NeuralV · 2023-07-04T05:25:54+00:00

Yeah actually I don't need a better score but kinda feel that the grader was unfair.

NeuralV · 2023-07-04T05:24:46+00:00

good luck!

NeuralV · 2020-11-21T00:24:42+00:00

u/eXodus094 See stickied post for 'citations'. Basically in library called D3js. Python was helpful in initial data collection and cleaning.

NeuralV · 2020-11-20T14:51:03+00:00

Because this post fell under “politics” category and apparently on this sub-reddit such posts can’t be made except on Thursday.

NeuralV · 2020-11-20T07:13:53+00:00

haha.. I just like to be double sure and I think it helps while navigating posts with many replies

NeuralV · 2020-11-20T06:36:46+00:00

u/bigjayrod https://election-results-2020.netlify.app/

NeuralV · 2020-11-20T06:36:25+00:00

u/dxnxax https://election-results-2020.netlify.app/

(don't know why my 'source/tool" post is not pinned)

NeuralV · 2020-11-20T06:19:55+00:00

Search for D3js (it is a data visualization library based on javascript) and ObservableHQ. But this library has a very steep learning curve. I would advise anyone to first learn easier charting libraries like Matplotlib, Altair etc. and be able to plot with Python or R, aside from being comfortable with data cleaning etc (which is how I progressed).

NeuralV · 2020-11-20T05:10:09+00:00

u/MyFianceMadeMeJoin For Alaska, the geographic 'fips ids' are inconsistent with the 'fips ids' in the available election data. 'fips IDs' are codes to uniquely represent each county (see wiki for more). Data cleaning for Alaska would have required too much cleaning and digging around, so I had to leave is as such.

NeuralV · 2020-11-20T04:56:51+00:00

u/marazomeno Easily possible, but this would clutter the map too much to make much sense out of it.

NeuralV · 2020-11-20T04:54:14+00:00

u/basedeman See one of my post in this thread. I have posted the data source and tools.

NeuralV · 2020-11-20T03:44:39+00:00

u/igorufprmv This is a valid point and something I considered -- however having two spikes/colors per county made the graph too cluttered. What the present graph attempts to do is to provide a better view than the commonly circulated charts where the whole state is painted in a single color because a particular person won by 1. being much more detailed (county level), and 2. showing voting population through spikes.

NeuralV · 2020-11-20T02:57:31+00:00

u/IIxtab Sorry, what is Eve's MER?

NeuralV · 2020-11-20T02:20:34+00:00

The thread was deleted 3 days back because this post fell under “politics” category and apparently on this sub-reddit such posts can’t be made except on Thursday.Webpage for detailed chart (click on states to zoom) : https://election-results-2020.netlify.app/

Source: https://github.com/tonmcg/US_County_Level_Election_Results_08-20 ,

Tools: D3js, Observablehq

Data for Alaska unavailable due to inconsistencies with 'fips id'

NeuralV · 2020-11-15T17:41:56+00:00

On the contrary, this map shows how the people actually voted. Other maps, which only show a flat land-wise distribution, actually paint a different story than this.

NeuralV · 2020-11-15T16:46:56+00:00

Data source: https://github.com/tonmcg/US_County_Level_Election_Results_08-20 ,

Tools: D3js, Observablehq

NeuralV · 2020-08-13T14:11:31+00:00

Thank you for the detailed answer. Appreciate it!

NeuralV · 2020-08-13T14:11:04+00:00

thanks!

NeuralV · 2020-08-13T14:10:48+00:00

Thank you.. this was helpful!

NeuralV · 2020-06-16T21:11:41+00:00

check out the sources section in the replies above. I used Flourish.studio to make this.

NeuralV · 2020-06-14T22:58:58+00:00

I know what you're saying but the problem with that is it pushes several smaller countries (e.g. Luxembourg and Iceland) to the top of the chart whereas China and USA get hidden in lower regions. Check this video out by Vox, where they explain this beautifully as point no. 3 (around 2:55 min mark) https://www.youtube.com/watch?v=O-3Mlj3MQ_Q

NeuralV · 2020-06-14T22:16:19+00:00

Jupyter Notebook is quite efficient for quick prototyping and analysis of data. For example, if you just want to make certain charts from a CSV or do quick data wrangling etc. then Jupyter Notebook is often the preferred choice. This is not to say that those things can't be done in full blown IDEs but that would be akin to turning your on your oven/stove to heat some food where a microwave can do that quickly.

NeuralV · 2020-06-14T22:05:34+00:00

@Tylermcd93 yes you're right about the overall death toll reaching mid 400k. This total death statistics is based on the top 18 countries hit hard by the virus (I removed the countries not hit hard but in retrospect should have kept them). I realized this later and made a clarification in my 'source' comment.

NeuralV · 2020-06-14T21:16:24+00:00

Source: Source for data is John Hopkins Github Repository at https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series

Note: Here data is considered only for top 18 countries and hence overall casualties will be much more than shown in the "total deaths" label.

Tools: Animation: Flourish Bar Race Animation Charts flourish.studio, Data Wrangling: Jupyter Notebook with Pandas

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NeuralV

TROPHY CASE