{"id":11348,"date":"2020-12-10T20:42:13","date_gmt":"2020-12-10T20:42:13","guid":{"rendered":"https:\/\/www.askpython.com\/?p=11348"},"modified":"2020-12-10T20:42:14","modified_gmt":"2020-12-10T20:42:14","slug":"plot-geographical-data-python-plotly","status":"publish","type":"post","link":"https:\/\/www.askpython.com\/python\/examples\/plot-geographical-data-python-plotly","title":{"rendered":"Plot Geographical Data on a Map Using Python Plotly"},"content":{"rendered":"\n

In this tutorial, we will learn how to plot geographical data on a map using Python Plotly. For this demonstration, we’ll plot COVID-19 cases from ourworldindata.org<\/a> dataset.<\/p>\n\n\n\n

Steps to Plot Geographical Data on a Map in Python<\/h2>\n\n\n\n

Let’s get started. <\/p>\n\n\n\n

1. Import the required libraries<\/h3>\n\n\n\n

Let’s start with importing the necessary libraries<\/a>. We need to import the following two libraries:<\/p>\n\n\n\n

  1. Pandas<\/a><\/li>
  2. Plotly.express<\/a> <\/li><\/ol>\n\n\n
    \nimport pandas as pd\nimport plotly.express as px\n<\/pre><\/div>\n\n\n
    Now we can move to the next step, that is downloading the dataset. <\/p>\n\n\n\n
    2. Download and read the dataset<\/h3>\n\n\n\n
    To download and load the dataset, use the following piece of code.<\/p>\n\n\n
    \n!wget https:\/\/covid.ourworldindata.org\/data\/owid-covid-data.csv\n<\/pre><\/div>\n\n\n
    Output :<\/p>\n\n\n\n
    \"Download
    Download Dataset<\/figcaption><\/figure><\/div>\n\n\n\n
    Read the CSV dataset in Python<\/a> using the pandas read_csv method:<\/p>\n\n\n
    \ndf = pd.read_csv('owid-covid-data.csv')\ndf.head()\n<\/pre><\/div>\n\n\n
    \"Dataset\"
    Dataset<\/figcaption><\/figure><\/div>\n\n\n\n
    3. Plotting the COVID-19 Dataset on a Map<\/h3>\n\n\n\n
    Now we can use Plotly to plot the data from the dataset above. <\/p>\n\n\n\n
    We are plotting a Choropleth Map<\/strong>. It is a map which uses differences in shading, colouring, or the placing of symbols within predefined areas to indicate the average values of a particular quantity in those areas.<\/p>\n\n\n\n
    We are going to plot the number of new cases each day.<\/strong> Plotly lets us do that through animations. <\/p>\n\n\n\n
    Here’s the code to plot the colors and shades on the map:<\/p>\n\n\n
    \nfig = px.choropleth(df, locations="iso_code",\n                    color="new_cases",\n                    hover_name="location",\n                    animation_frame="date",\n                    title = "Covid Cases plotted using Plotly",                 color_continuous_scale=px.colors.sequential.PuRd)\n\n\nfig["layout"].pop("updatemenus")\nfig.show()\n<\/pre><\/div>\n\n\n
    Output :<\/strong><\/p>\n\n\n\n
    \"Feb\"
    Feb<\/figcaption><\/figure>\n\n\n\n
    \"Plot
    April<\/figcaption><\/figure>\n\n\n\n
    \"Plot
    September <\/figcaption><\/figure>\n\n\n\n
    The output shows how the map looks over three different months of the year. We can see that the virus was spreading rapidly in China in the month of February, in USA in the month of April and in India in the month of September. <\/p>\n\n\n\n
    You can also hover over any region of the map and view the number of new cases. <\/p>\n\n\n\n
    Plotly animations make it convenient to visualize time series data. <\/p>\n\n\n\n
    4. Complete code to Plot Geographical Data using Python Plotly<\/h3>\n\n\n\n
    The complete code for this section in given below :<\/p>\n\n\n
    \nimport pandas as pd\nimport plotly.express as px\n\n#download dataset\n!wget https:\/\/covid.ourworldindata.org\/data\/owid-covid-data.csv\n\n#import dataset\ndf = pd.read_csv('owid-covid-data.csv')\n\n#plot\nfig = px.choropleth(df, locations="iso_code",\n                    color="new_cases",\n                    hover_name="location",\n                    animation_frame="date",\n                    title = "Covid Cases plotted using Plotly",                 color_continuous_scale=px.colors.sequential.PuRd)\n\n\nfig["layout"].pop("updatemenus")\nfig.show()\n\n<\/pre><\/div>\n\n\n
    6. Plotting COVID-19 Asia Data<\/h3>\n\n\n\n
    You can also set the scope of the map to Asia. Let’s see how to plot geographical data for the content of Asia.<\/p>\n\n\n\n
    To set the scope of the plot to Asia, set the parameter ‘scope<\/em>‘ to ‘asia’<\/em>. <\/p>\n\n\n
    \nimport pandas as pd\nimport plotly.express as px\n\n#download dataset\n!wget https:\/\/covid.ourworldindata.org\/data\/owid-covid-data.csv\n\n#import dataset\ndf = pd.read_csv('owid-covid-data.csv')\n\n#select entries with the continent as asia\ndf = df[df.continent == 'Asia']\n\n#plot\nfig = px.choropleth(df, locations="iso_code",\n                    color="new_cases",\n                    hover_name="location",\n                    animation_frame="date",\n                    title = "Daily new COVID cases",\n                    scope ='asia',  color_continuous_scale=px.colors.sequential.PuRd)\n\nfig["layout"].pop("updatemenus")\nfig.show()\n<\/pre><\/div>\n\n\n
    Output : <\/p>\n\n\n\n