Top 5 Python 数据可视化技术

import holoviews as hvfrom holoviews import optsimport pandas as pdimport numpy as nphv.extension('bokeh')
# Sample matrix representing the export volumes between 5 countriesexport_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 DataFramedf = pd.DataFrame(export_data, index=labels, columns=labels)df = df.stack().reset_index()
df.columns = ['source', 'target', 'value']
# Creating a Chord objectchord = hv.Chord(df)
# Styling the Chord diagramchord.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 plotchord

import plotly.express as pximport 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()

import numpy as npimport matplotlib.pyplot as pltfrom mplhexbin import HexBin
# Simulated datanp.random.seed(0)  # Ensure reproducibilityn_points = 10000x = np.random.rand(n_points) * 100  # Air Quality Index (AQI) range from 0 to 100y = 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 figurefig, ax = plt.subplots(figsize=(10, 8))
# Use HexBin to create a hexagonal bin plothb = HexBin(ax, gridsize=20, cmap='viridis', extent=[0, 100, -30, 50])  # Set grid size, colormap, and rangehb.hexbin(x, y, mincnt=1)  # Draw the hexagonal bin plot, mincnt sets the minimum count threshold
# Add title and axis labelsax.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 figureplt.colorbar(hb.cmap, ax=ax, label='Number of Data Points')  # Add color bar and set labelplt.show()

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 objectfig = 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()

import altair as altfrom 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()