Python Plotly: An Easy Way to Visualize Geographical Data
Are you interested in visualizing your geographical data using Python? If yes, then you are in the right place.
Python Plotly is a powerful library that makes it easy to plot graphs, charts, and maps. In this article, we will show you how to plot geographical data on a map using Python Plotly.
The article is divided into two parts. In the first part, we will show you how to plot any geographical data using Python Plotly.
In the second part, we will specifically discuss how to plot the COVID-19 Asia data on a map.
Part 1:
Plotting Geographical Data on a Map using Python Plotly
Importing Necessary Libraries
The first step is to import the necessary libraries. We will be using Pandas and Plotly.express.
Pandas is used for data manipulation, and Plotly.express is used for data visualization. “`
import pandas as pd
import plotly.express as px
“`
Downloading and Reading Dataset
Next, we need to download and read the dataset. For example, let’s say we want to plot the COVID-19 dataset on a map.
We can download the dataset from a reliable source, like the World Health Organization (WHO). “`
df = pd.read_csv(‘https://covid.ourworldindata.org/data/owid-covid-data.csv’)
“`
Plotting the COVID-19 Dataset on a Map
Now, we can plot the COVID-19 dataset on a map. We will use the choropleth map, which is a map that colors different regions based on a numerical value.
In this case, we will color the regions based on the number of new cases. “`
fig = px.choropleth(df,
locations=’iso_code’,
color=’new_cases’,
animation_frame=’date’)
fig.show()
“`
Complete code to Plot Geographical Data using Python Plotly
Here is the complete code to plot geographical data using Python Plotly:
“`
import pandas as pd
import plotly.express as px
# Download and read the dataset
df = pd.read_csv(‘https://covid.ourworldindata.org/data/owid-covid-data.csv’)
# Plot the COVID-19 dataset on a map
fig = px.choropleth(df,
locations=’iso_code’,
color=’new_cases’,
animation_frame=’date’)
fig.show()
“`
Part 2: Plotting COVID-19 Asia Data on a Map
Downloading and Reading Dataset
To plot the COVID-19 Asia data on a map, we first need to download and read the dataset. We can download the dataset from the same source we used in Part 1.
“`
df = pd.read_csv(‘https://covid.ourworldindata.org/data/owid-covid-data.csv’)
“`
Selecting Entries with the Continent as Asia
Next, we need to select the entries with the continent as Asia. We can do this using the pandas loc method.
“`
asia_df = df.loc[df[‘continent’] == ‘Asia’]
“`
Plotting the COVID-19 Asia Data
Finally, we can plot the COVID-19 Asia data on a map. We will use the same choropleth map we used in Part 1, but we will set the map scope to Asia.
“`
fig = px.choropleth(asia_df,
locations=’iso_code’,
color=’new_cases’,
animation_frame=’date’,
scope=’asia’)
fig.show()
“`
Conclusion
In this article, we discussed how to plot geographical data on a map using Python Plotly. We provided a step-by-step guide to plot any geographical data on a map and also specifically discussed how to plot the COVID-19 Asia data on a map.
Python Plotly is a powerful library that can make data visualization easy and visually appealing. With this guide, you can start plotting your own geographical data on a map using Python Plotly.
Python Plotly: An Easy Way to Visualize Geographical Data – An In-depth Look
In the previous section, we showed you how to plot geographical data on a map using Python Plotly. We covered the basics of importing necessary libraries, downloading and reading a dataset, and plotting the data.
In this section, we will take an in-depth look at the different techniques and tools available to plot geographical data using Python Plotly. Python Plotly is a powerful library that allows you to create interactive data visualizations with just a few lines of code.
With Plotly, you can plot your data in various forms, including line charts, scatter plots, bar graphs, and maps. However, in this article, we will focus solely on mapping geographical data.
To get started with Plotly, you need to install it using pip on your computer. You can easily install it using the following command:
“`
!pip install plotly==5.1.0
“`
Importing Necessary Libraries
Once you have installed Plotly, you can begin by importing the necessary libraries. In addition to Pandas and Plotly.express, you may also need the following libraries for mapping specific types of geographical data:
“`
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import geopandas as gpd
“`
Downloading and Reading Dataset
The next step is to download and read the dataset. You can download the dataset from various sources, such as government websites, academic institutions, or repositories like Kaggle.
Once you have downloaded the dataset, you can use the Pandas read_csv method to read the data into a dataframe. “`
df = pd.read_csv(‘https://example.com/data.csv’)
“`
Cleaning the Dataset
Before plotting the data, you may need to clean it. Data cleaning involves identifying and correcting errors or inconsistencies in the data.
Cleaning may involve removing duplicates, filling in missing values, or standardizing data types.
Plotting Geographical Data
One of the most versatile ways to plot geographical data using Python Plotly is by using the choropleth map. A choropleth map is a map that colors different regions based on a numerical value.
For example, in the COVID-19 dataset, we colored different countries based on the number of new cases. To plot a choropleth map, you will need to specify the location column, the numerical value to color the regions, and the animation column.
Additionally, you can further customize the map by setting the map scope, the color scale, and adding a title. “`
fig = px.choropleth(df,
locations=’iso_code’,
color=’new_cases’,
animation_frame=’date’,
scope=’world’,
color_continuous_scale=’reds’,
title=’COVID-19 New Cases by Country’)
fig.show()
“`
We specify the location column using the `locations` parameter and the numerical value to color the regions using the `color` parameter.
The `animation_frame` parameter is used to create an animated map that shows the data over time. We set the `scope` parameter to “world” to show all countries, but we could also set it to a specific region like “asia” or “europe.” The `color_continuous_scale` parameter is used to set the color scale for the regions, and the `title` parameter is used to give the chart a title.
In addition to the choropleth map, you can also plot geographical data using other types of maps, such as scatter maps and symbol maps. Scatter maps plot markers on a map, while symbol maps replace the markers with symbols that represent the data.
These types of maps are useful when you want to plot specific locations rather than regions. “`
fig = px.scatter_mapbox(df,
lat=’latitude’,
lon=’longitude’,
color=’new_cases’,
hover_name=’location’,
zoom=2,
mapbox_style=’carto-positron’,
center=dict(lat=0, lon=0),
title=’COVID-19 New Cases by Location’)
fig.show()
“`
In the above example, we use the `scatter_mapbox` function to plot a scatter map.
Here, we specify the latitude and longitude columns using the `lat` and `lon` parameters, respectively. We then color the markers based on the numerical value using the `color` parameter and set the columns to display when hovering over the markers using the `hover_name` parameter.
Finally, we set the map’s style using the `mapbox_style` parameter, the center coordinates using the `center` parameter, and the initial zoom level using the `zoom` parameter.
Mapping Customized Geographical Data
In addition to using pre-existing datasets, you can also map customized geographical data using Python Plotly. For example, you may have a custom dataset that contains information about parks, landmarks, or other points of interest that you want to plot on a map.
To plot customized geographical data, you first need to convert your data into a geodataframe. A geodataframe is like a pandas dataframe but with an additional geometry column that stores the shapes and coordinates of your data.
You can create a geodataframe using the from_features method from the geopandas library. “`
gdf = gpd.GeoDataFrame.from_features(json_data)
“`
Next, you can use Plotly.graph_objects to create a plotly figure.
This requires specifying the geographical data source and the type of map to plot. Once you have a figure, you can use various plotly properties to style the map, such as the color scale, the marker size, and the map center.
“`
fig = go.Figure(go.Scattermapbox(
lat=gdf.geometry.y,
lon=gdf.geometry.x,
hoverinfo=’text’,
mode=’markers’,
marker=go.scattermapbox.Marker(
size=10,
color=’red’
),
text=gdf.name
))
fig.update_layout(
mapbox=dict(
center=dict(
lat=40.718,
lon=-73.954
),
zoom=12
),
title=’Customized Geographical Data’
)
fig.show()
“`
In this example, we create a scattermapbox by passing the latitude and longitude columns from our geodataframe. We also specify the marker size, color, and text using various parameters.
Finally, we use the `update_layout` method to set the map’s center and zoom level and give it a title.
Final Thoughts
In conclusion, Python Plotly is a powerful library that makes it easy to plot geographical data on a map. With its various features, you can create interactive and visually appealing maps for different types of data, including COVID-19 data, customized data, and other geographical datasets.
By following the steps outlined in this article, you can start visualizing your data on maps using Python Plotly. Python Plotly is an essential tool for visualizing geographical data.
This article demonstrated how to plot geographical data on a map using Python Plotly, highlighting two main topics: plotting any geographical data and plotting COVID-19 Asia data. The article covered importing necessary libraries, downloading and reading datasets, cleaning data, and plotting geographical data.
Tools such as choropleth maps, scatter maps, and symbol maps were explained in detail. Furthermore, customized geographical data can be mapped using Python Plotly by converting data into a geodataframe.
Lastly, this article provides a comprehensive guide on visualizing geographical data on a map using Python Plotly.