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This article mainly introduces how to use Python to achieve visual analysis of global volcanic distribution, the article is very detailed, has a certain reference value, interested friends must read it!

Preparatory work

As before, we first import the modules that need to be used in the process of data analysis and read the data set, this time the data set is from the kaggle website, mainly compiled by the famous Smithsonian Institution in the United States.

Import pandas as pdimport folium.plugins as pluginsimport folium df_volcano = pd.read_csv ("volcano.csv") df_volcano.head ()

Output

The dataset contains these pieces of data

Df_volcano.columns

Output

Index (['volcano_number',' volcano_name', 'primary_volcano_type'

'last_eruption_year', 'country',' region', 'subregion',' latitude'

'longitude', 'elevation',' tectonic_settings', 'evidence_category'

'major_rock_1', 'major_rock_2',' major_rock_3', 'major_rock_4'

'major_rock_5', 'minor_rock_1',' minor_rock_2', 'minor_rock_3'

'minor_rock_4', 'minor_rock_5',' population_within_5_km'

'population_within_10_km', 'population_within_30_km'

'population_within_100_km']

Dtype='object')

Visualization of the distribution of global volcanic belts

We map the distribution of volcanoes around the world by calling the folium module, as follows

Volcano_map = folium.Map () # add data for each row of volcanoes to for i in range (0, df_volcano.shape [0]): volcano = df_ volcano [I] folium.Marker ([volcano ['latitude'], volcano [' longitude']], popup=volcano ['volcano_name']). Add_to (volcano_map) volcano_map

Output

Roughly speaking, the logic of the above code is to instantiate a Map () object first, and then traverse each row of data, mainly aiming at the latitude and longitude data in the data set, and putting a label on the map, we click on each label and automatically pop up the name of the corresponding volcano.

Of course, the visualization result is not very beautiful. Let's take a look at the distribution of volcanoes around the world through a simple histogram. The code is as follows.

Fig, (ax1, ax2) = plt.subplots (1,2, figsize= (16,4)) volcano_country = pd.DataFrame (df_volcano.groupby (['country']). Size (). Sort_values (0, ascending=True) volcano_country.columns = [' Count'] volcano_country.tail (10) .plot (kind='barh', legend=False) Ax=ax1) ax1.set_title ('Number of Volcanoes per Country') ax1.set_ylabel (' Country') ax1.set_xlabel ('Count') volcano_region = pd.DataFrame (df_volcano.groupby ([' region']). Size (). Sort_values (0, ascending=True) volcano_region.columns = ['Count'] volcano_region.tail (10) .plot (kind='barh', legend=False) Ax=ax2) ax2.set_title ('Number of Volcanoes per Region') ax2.set_ylabel (' Region') ax2.set_xlabel ('Count') plt.tight_layout () plt.show ()

Output

It can be seen that volcanoes are mainly concentrated in the United States, Indonesia and Japan, while geographically speaking, there are more volcanoes in South America, Japan, Taiwan and Indonesia.

Visual optimization of the distribution of global volcanic belts

Next, let's optimize the previously drawn map of the global volcanic distribution, call the CircleMarker method in the folium module, and set the color and size of the marker.

Volcano_map = folium.Map (zoom_start=10) groups = folium.FeatureGroup ('') # add data for each row of volcanoes to for i in range (0, df_volcano.shape [0]): volcano = df_ volcano [I] groups.add_child (folium.CircleMarker ([volcano ['latitude'], volcano [' longitude']], popup=volcano ['volcano_name'], radius=3, color='blue' Fill=True, fill_color='blue',fill_opacity=0.8)) volcano_map.add_child (groups) volcano_map.add_child (folium.LatLngPopup ())

Output

Actual combat of map visualization

Then let's take a look at the place where the volcano erupted. The Republic of Tang is located in the southwest Pacific Ocean, belonging to Oceania, at about 175 °west longitude and 20 °south latitude.

Import folium.plugins as pluginsimport folium m = folium.Map ([- 21.178986,-175.198242], zoom_start=10, control_scale=True, width='80%') m

Output

The first parameter obviously represents longitude and latitude, while the zoom_start parameter represents the degree of zoom, if we need to further enlarge the drawing of the chart, we can adjust this parameter, while the width parameter represents the width of the final chart.

Put a mark on the map

We can also mark the map drawn, such as drawing a circle, the code is as follows

M = folium.Map ([- 21.178986,-175.198242], zoom_start=12, control_scale=True, width='80%') folium.Circle (location = [- 21.177986,-175.199242], radius = 1500, color = "purple") .add_to (m) m

Output

Or color the circled area with the following code

M = folium.Map ([- 21.178986,-175.198242], zoom_start=12, control_scale=True, width='80%') folium.Circle (location = [- 21.177986,-175.199242], radius = 1500, color = "purple", fill = True, fill_color = "red"). Add_to (m) m

Output

The above is all the contents of the article "how to use Python to visually analyze the global distribution of volcanoes". Thank you for reading! Hope to share the content to help you, more related knowledge, welcome to follow the industry information channel!

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