Top 5 Python 数据可视化技术

import holoviews as hv
from holoviews import opts
import pandas as pd
import numpy as np
hv.extension('bokeh')

# Sample matrix representing the export volumes between 5 countries
export_data = np.array([[0, 50, 30, 20, 10],   
                        [10, 0, 40, 30, 20],   
                        [20, 10, 0, 35, 25],   
                        [30, 20, 10, 0, 40],   
                        [25, 15, 30, 20, 0]]) 

labels = ['USA', 'China', 'Germany', 'Japan', 'India']

# Creating a pandas DataFrame
df = pd.DataFrame(export_data, index=labels, columns=labels)
df = df.stack().reset_index()

df.columns = ['source', 'target', 'value']

# Creating a Chord object
chord = hv.Chord(df)

# Styling the Chord diagram
chord.opts(
    opts.Chord(
        cmap='Category20', edge_cmap='Category20', 
        labels='source', label_text_font_size='10pt',  
        edge_color='source', node_color='index', 
        width=700, height=700 
    )
).select(value=(5, None)) 

# Display the plot
chord
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import plotly.express as px
import numpy as np

df = px.data.gapminder().query("year == 2007")

fig = px.sunburst(df, path=['continent', 'country'], 
                  values='pop',
                  color='lifeExp', 
                  hover_data=['iso_alpha'],
                  color_continuous_scale='RdBu',
                  color_continuous_midpoint=np.average(df['lifeExp'], weights=df['pop']))
fig.show()
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import numpy as np
import matplotlib.pyplot as plt
from mplhexbin import HexBin

# Simulated data
np.random.seed(0)  # Ensure reproducibility
n_points = 10000
x = np.random.rand(n_points) * 100  # Air Quality Index (AQI) range from 0 to 100
y = 5 * np.sin(x * np.pi / 50) + np.random.randn(n_points) * 15  # Simulated hospital visits, related to AQI but with noise

# Create a new figure
fig, ax = plt.subplots(figsize=(10, 8))

# Use HexBin to create a hexagonal bin plot
hb = HexBin(ax, gridsize=20, cmap='viridis', extent=[0, 100, -30, 50])  # Set grid size, colormap, and range
hb.hexbin(x, y, mincnt=1)  # Draw the hexagonal bin plot, mincnt sets the minimum count threshold

# Add title and axis labels
ax.set_title('Relationship between Air Quality Index (AQI) and Hospital Visits')
ax.set_xlabel('Air Quality Index (AQI)')
ax.set_ylabel('Hospital Visits')

# Show the figure
plt.colorbar(hb.cmap, ax=ax, label='Number of Data Points')  # Add color bar and set label
plt.show()
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import plotly.graph_objects as go

labels = ["Coal", "Solar", "Wind", "Nuclear", "Residential", "Industrial", "Commercial"]

source = [0, 1, 2, 3, 0, 1, 2, 3] 
target = [4, 4, 4, 4, 5, 5, 5, 5] 
value = [25, 10, 40, 20, 30, 15, 25, 35] 

# Create the Sankey diagram object
fig = go.Figure(data=[go.Sankey(
    node=dict(
        pad=15,  
        thickness=20, 
        line=dict(color="black", width=0.5),
        label=labels 
    ),
    link=dict(
        source=source,  
        target=target, 
        value=value  
    ))])

fig.update_layout(title_text="Energy Flow in Model City", font_size=12)
fig.show()
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import altair as alt
from vega_datasets import data

source = data.unemployment_across_industries.url

alt.Chart(source).mark_area().encode(
    alt.X('yearmonth(date):T',
        axis=alt.Axis(format='%Y', domain=False, tickSize=0)
    ),
    alt.Y('sum(count):Q', stack='center', axis=None),
    alt.Color('series:N',
        scale=alt.Scale(scheme='category20b')
    )
).interactive()
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