![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F2599569%2F93ba988479287aa45988785e3225ad13%2Fnewplot%20(1).png?generation=1604321434065801&alt=media\")
I'm pretty sure I made a mistake here, however, have a look:
\nSo, basically, I aim to extract the top 10 most populous countries of the world and plot them using Choropleth world maps.
\nCODE
\ntop_10_most = city_disclosing.sort_values('Population', ascending = False)[:10]
\nfig = make_subplots(rows = 1, cols = 1,
\n specs = [[{'type' : 'choropleth'} for column in np.arange(1)]for row in np.arange(1)],
\n subplot_titles = ['Country V Population'])
\nfig.add_trace(go.Choropleth(locations = top_10_most.Country,
\n z = top_10_most.Population,
\n locationmode = 'country names',
\n colorscale = px.colors.sequential.Viridis),
\n row = 1, col = 1)
This appears right to me.
\nThe output was :
\n
As you can see neither China nor India - BOTH COUNTRIES CRAZY POPULATION WISE - aren't even highlighted on the map.
\nIs there something wrong with the data??
", "messages": [ { "id": "1067285", "postDate": "11/02/2020 12:53:16", "content": "I'm pretty sure I made a mistake here, however, have a look:
\nSo, basically, I aim to extract the top 10 most populous countries of the world and plot them using Choropleth world maps.
\nCODE
\ntop_10_most = city_disclosing.sort_values('Population', ascending = False)[:10]
\nfig = make_subplots(rows = 1, cols = 1,
\n specs = [[{'type' : 'choropleth'} for column in np.arange(1)]for row in np.arange(1)],
\n subplot_titles = ['Country V Population'])
\nfig.add_trace(go.Choropleth(locations = top_10_most.Country,
\n z = top_10_most.Population,
\n locationmode = 'country names',
\n colorscale = px.colors.sequential.Viridis),
\n row = 1, col = 1)
This appears right to me.
\nThe output was :
\n
As you can see neither China nor India - BOTH COUNTRIES CRAZY POPULATION WISE - aren't even highlighted on the map.
\nIs there something wrong with the data??
", "rawMarkdown": "I'm pretty sure I made a mistake here, however, have a look: \n\n1. CITY_DISCLOSING is a dataframe that is stacked using city_disclosing-2018,2019 and 2020 data frames(VERTICALLY). It's one big dataframe.\n\nSo, basically, I aim to extract the top 10 most populous countries of the world and plot them using Choropleth world maps.\n\n--------------------------------------------\nCODE\n\ntop_10_most = city_disclosing.sort_values('Population', ascending = False)[:10]\n\nfig = make_subplots(rows = 1, cols = 1, \n specs = [[{'type' : 'choropleth'} for column in np.arange(1)]for row in np.arange(1)],\n subplot_titles = ['Country V Population'])\nfig.add_trace(go.Choropleth(locations = top_10_most.Country,\n z = top_10_most.Population,\n locationmode = 'country names',\n colorscale = px.colors.sequential.Viridis),\n row = 1, col = 1)\n\n--------------------------------------------------\n\nThis appears right to me.\n\nThe output was : \n\n.png?generation=1604321434065801&alt=media)\n\nAs you can see neither China nor India - BOTH COUNTRIES CRAZY POPULATION WISE - aren't even highlighted on the map. \n\n**Is there something wrong with the data??**", "votes": null }, { "id": "1067523", "postDate": "11/02/2020 15:14:14", "content": "There should be data for at least 6 cities in China and 13 cities in India. If you share a link to a public notebook then it will be much easier to help you to troubleshoot your code.
", "rawMarkdown": "There should be data for at least 6 cities in China and 13 cities in India. If you share a link to a public notebook then it will be much easier to help you to troubleshoot your code.", "votes": null }, { "id": "1067552", "postDate": "11/02/2020 15:27:25", "content": "Here you go :
\nhttps://www.kaggle.com/fireheart7/city-disclosing-eda
", "rawMarkdown": "Here you go : \n\n**https://www.kaggle.com/fireheart7/city-disclosing-eda**", "votes": null }, { "id": "1067603", "postDate": "11/02/2020 15:45:53", "content": "You can find some cities in India by adding the following line to code cell #8:
\ncity_disclosing = city_disclosing[city_disclosing['Country']=='India'] \ncity_disclosing.head(15)\n![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F1314380%2F57273f5a4ab1fcb091d7d19e3ab3a65b%2FScreen%20Shot%202020-11-02%20at%208.43.46%20AM.png?generation=1604331844954054&alt=media\")
Whereas it looks like maybe you are only plotting the 10 largest cities on your map due to the inclusion of the following line:
\ntop_10_most = city_disclosing.sort_values('Population', ascending = False)[:10]\nThere should be data for at least 6 cities in China and 13 cities in India. If you share a link to a public notebook then it will be much easier to help you to troubleshoot your code.
", "votes": null, "replies": [ { "id": 1067552, "author_name": "fireheart7", "author_url": "", "post_date": "11/02/2020 15:27:25", "content": "Here you go :
\nhttps://www.kaggle.com/fireheart7/city-disclosing-eda
", "votes": null, "replies": [] }, { "id": 1067603, "author_name": "paultimothymooney", "author_url": "", "post_date": "11/02/2020 15:45:53", "content": "You can find some cities in India by adding the following line to code cell #8:
\ncity_disclosing = city_disclosing[city_disclosing['Country']=='India'] \ncity_disclosing.head(15)\n
Whereas it looks like maybe you are only plotting the 10 largest cities on your map due to the inclusion of the following line:
\ntop_10_most = city_disclosing.sort_values('Population', ascending = False)[:10]\n