【Python】高大上！5個(gè)python高級(jí)可視化圖表！

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 np  import matplotlib.pyplot as plt  from mplhexbin import HexBin    # 假設(shè)的模擬數(shù)據(jù)  np.random.seed(0)  # 確保結(jié)果可復(fù)現(xiàn)  n_points = 10000  x = np.random.rand(n_points) * 100  # 空氣質(zhì)量指數(shù)（AQI）范圍從0到100  y = 5 * np.sin(x * np.pi / 50) + np.random.randn(n_points) * 15  # 假設(shè)的醫(yī)院就診數(shù)量，與AQI相關(guān)但具有噪聲    # 創(chuàng)建一個(gè)新的圖形  fig, ax = plt.subplots(figsize=(10, 8))    # 使用HexBin創(chuàng)建六邊形分箱圖  hb = HexBin(ax, gridsize=20, cmap='viridis', extent=[0, 100, -30, 50])  # 設(shè)置網(wǎng)格大小、顏色映射和范圍  hb.hexbin(x, y, mincnt=1)  # 繪制六邊形分箱圖，mincnt設(shè)置最小計(jì)數(shù)閾值    # 添加標(biāo)題和軸標(biāo)簽  ax.set_title('空氣質(zhì)量指數(shù)（AQI）與醫(yī)院就診數(shù)量的關(guān)系')  ax.set_xlabel('空氣質(zhì)量指數(shù)（AQI）')  ax.set_ylabel('醫(yī)院就診數(shù)量')    # 顯示圖形  plt.colorbar(hb.cmap, ax=ax, label='數(shù)據(jù)點(diǎn)數(shù)量')  # 添加顏色條并設(shè)置標(biāo)簽  plt.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()