{"id":1052432,"date":"2024-12-31T14:24:59","date_gmt":"2024-12-31T06:24:59","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1052432.html"},"modified":"2024-12-31T14:25:01","modified_gmt":"2024-12-31T06:25:01","slug":"python%e4%b8%ad%e5%a6%82%e4%bd%95%e8%ae%be%e7%bd%ae%e8%89%b2%e6%a0%87%e4%b8%ba%e6%b8%90%e5%8f%98","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1052432.html","title":{"rendered":"python\u4e2d\u5982\u4f55\u8bbe\u7f6e\u8272\u6807\u4e3a\u6e10\u53d8"},"content":{"rendered":"

\"python\u4e2d\u5982\u4f55\u8bbe\u7f6e\u8272\u6807\u4e3a\u6e10\u53d8\"<\/p>\n

\u5728Python\u4e2d\u8bbe\u7f6e\u8272\u6807\u4e3a\u6e10\u53d8\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528Matplotlib\u5e93\u3001Seaborn\u5e93\u4ee5\u53caPlotly\u5e93\u7b49\u3002\u6700\u5e38\u7528\u4e14\u7b80\u4fbf\u7684\u65b9\u6cd5\u662f\u4f7f\u7528Matplotlib\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u8272\u5f69\u6620\u5c04\u529f\u80fd\u3001\u53ef\u81ea\u5b9a\u4e49\u7684\u6e10\u53d8\u8272\u6807\u3001\u7075\u6d3b\u7684\u663e\u793a\u63a7\u5236\u3002<\/strong><\/p>\n<\/p>\n

Matplotlib\u5e93<\/strong>\uff1aMatplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u5b83\u652f\u6301\u521b\u5efa\u5404\u79cd\u56fe\u5f62\uff0c\u5e76\u4e14\u63d0\u4f9b\u4e86\u591a\u79cd\u6e10\u53d8\u8272\u6807\u65b9\u6848\u3002\u4f7f\u7528Matplotlib\u8bbe\u7f6e\u8272\u6807\u4e3a\u6e10\u53d8\u975e\u5e38\u7b80\u4fbf\uff0c\u53ef\u4ee5\u901a\u8fc7plt.cm<\/code>\u6a21\u5757\u4e2d\u7684\u5404\u79cd\u989c\u8272\u6620\u5c04\u8868\u6765\u5b9e\u73b0\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528Matplotlib\u8bbe\u7f6e\u6e10\u53d8\u8272\u6807<\/h3>\n<\/p>\n

1. \u5b89\u88c5\u548c\u5bfc\u5165Matplotlib<\/h4>\n<\/p>\n

\u9996\u5148\uff0c\u9700\u8981\u786e\u4fdd\u5b89\u88c5\u4e86Matplotlib\u5e93\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install matplotlib<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5bfc\u5165Matplotlib\u5e93\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u521b\u5efa\u6e10\u53d8\u8272\u6807<\/h4>\n<\/p>\n
\u53ef\u4ee5\u901a\u8fc7\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u4e8c\u7ef4\u6570\u636e\u5e76\u4f7f\u7528imshow<\/code>\u65b9\u6cd5\u6765\u5c55\u793a\u6e10\u53d8\u8272\u6807\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
data = np.random.rand(10, 10)<\/p>\n
plt.imshow(data, cmap='viridis')<\/p>\n
plt.colorbar()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0ccmap<\/code>\u53c2\u6570\u6307\u5b9a\u4e86\u989c\u8272\u6620\u5c04\u8868\uff0c\u8fd9\u91cc\u4f7f\u7528\u7684\u662fviridis<\/code>\uff0c\u5b83\u662fMatplotlib\u4e2d\u9884\u5b9a\u4e49\u7684\u6e10\u53d8\u8272\u6807\u4e4b\u4e00\u3002plt.colorbar()<\/code>\u6dfb\u52a0\u4e86\u4e00\u4e2a\u8272\u6807\u6761\uff0c\u663e\u793a\u4e86\u6570\u636e\u503c\u4e0e\u989c\u8272\u4e4b\u95f4\u7684\u5bf9\u5e94\u5173\u7cfb\u3002<\/p>\n<\/p>\n
3. \u81ea\u5b9a\u4e49\u6e10\u53d8\u8272\u6807<\/h4>\n<\/p>\n
\u5982\u679c\u9884\u5b9a\u4e49\u7684\u6e10\u53d8\u8272\u6807\u4e0d\u6ee1\u8db3\u9700\u6c42\uff0c\u53ef\u4ee5\u81ea\u5b9a\u4e49\u6e10\u53d8\u8272\u6807\uff1a<\/p>\n<\/p>\n
from matplotlib.colors import LinearSegmentedColormap<\/p>\n
colors = ["blue", "green", "yellow", "red"]<\/p>\n
n_bins = [3, 6, 10, 100]  # \u8bbe\u7f6e\u6e10\u53d8\u7684\u7ec6\u817b\u7a0b\u5ea6<\/p>\n
cmap_name = 'custom_cmap'<\/p>\n
cm = LinearSegmentedColormap.from_list(cmap_name, colors, N=n_bins)<\/p>\n
plt.imshow(data, cmap=cm)<\/p>\n
plt.colorbar()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u5b9a\u4e49\u4e86\u4e00\u4e2a\u7531\u84dd\u8272\u3001\u7eff\u8272\u3001\u9ec4\u8272\u3001\u7ea2\u8272\u6e10\u53d8\u7684\u8272\u6807\uff0c\u5e76\u5e94\u7528\u5230\u6570\u636e\u5c55\u793a\u4e2d\u3002<\/p>\n<\/p>\n
\u4e8c\u3001Seaborn\u5e93\u4e2d\u7684\u6e10\u53d8\u8272\u6807<\/h3>\n<\/p>\n
Seaborn\u662f\u57fa\u4e8eMatplotlib\u6784\u5efa\u7684\u9ad8\u7ea7\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u66f4\u591a\u7684\u7f8e\u89c2\u9ed8\u8ba4\u8bbe\u7f6e\u548c\u7b80\u6d01\u7684API\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5\u548c\u5bfc\u5165Seaborn<\/h4>\n<\/p>\n
pip install seaborn<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5bfc\u5165Seaborn\u5e93\uff1a<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u521b\u5efa\u6e10\u53d8\u8272\u6807<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528Seaborn\u7684heatmap<\/code>\u65b9\u6cd5\u76f4\u63a5\u521b\u5efa\u70ed\u529b\u56fe\u5e76\u8bbe\u7f6e\u6e10\u53d8\u8272\u6807\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
data = np.random.rand(10, 10)<\/p>\n
sns.heatmap(data, cmap='coolwarm')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0ccmap<\/code>\u53c2\u6570\u6307\u5b9a\u4e86\u989c\u8272\u6620\u5c04\u8868\uff0c\u8fd9\u91cc\u4f7f\u7528\u7684\u662fcoolwarm<\/code>\uff0c\u5b83\u662fSeaborn\u4e2d\u9884\u5b9a\u4e49\u7684\u6e10\u53d8\u8272\u6807\u4e4b\u4e00\u3002<\/p>\n<\/p>\n
3. \u81ea\u5b9a\u4e49\u6e10\u53d8\u8272\u6807<\/h4>\n<\/p>\n
\u540c\u6837\u53ef\u4ee5\u81ea\u5b9a\u4e49\u6e10\u53d8\u8272\u6807\uff0c\u4f7f\u7528Matplotlib\u7684LinearSegmentedColormap<\/code>\uff1a<\/p>\n<\/p>\n
from matplotlib.colors import LinearSegmentedColormap<\/p>\n
colors = ["blue", "purple", "pink", "orange"]<\/p>\n
n_bins = [10, 20, 30, 50]<\/p>\n
cmap_name = 'custom_cmap'<\/p>\n
cm = LinearSegmentedColormap.from_list(cmap_name, colors, N=n_bins)<\/p>\n
sns.heatmap(data, cmap=cm)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Plotly\u5e93\u8bbe\u7f6e\u6e10\u53d8\u8272\u6807<\/h3>\n<\/p>\n
Plotly\u662f\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u7ed8\u56fe\u5e93\uff0c\u5e7f\u6cdb\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\uff0c\u7279\u522b\u662f\u9700\u8981\u4ea4\u4e92\u529f\u80fd\u7684\u573a\u666f\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5\u548c\u5bfc\u5165Plotly<\/h4>\n<\/p>\n
pip install plotly<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5bfc\u5165Plotly\u5e93\uff1a<\/p>\n<\/p>\n
import plotly.graph_objects as go<\/p>\n
import numpy as np<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u521b\u5efa\u6e10\u53d8\u8272\u6807<\/h4>\n<\/p>\n
\u4f7f\u7528Plotly\u521b\u5efa\u4e00\u4e2a\u5e26\u6709\u6e10\u53d8\u8272\u6807\u7684\u70ed\u529b\u56fe\uff1a<\/p>\n<\/p>\n
data = np.random.rand(10, 10)<\/p>\n
fig = go.Figure(data=go.Heatmap(<\/p>\n
                   z=data,<\/p>\n
                   colorscale='Viridis'))<\/p>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0ccolorscale<\/code>\u53c2\u6570\u6307\u5b9a\u4e86\u989c\u8272\u6620\u5c04\u8868\uff0c\u8fd9\u91cc\u4f7f\u7528\u7684\u662fViridis<\/code>\uff0c\u5b83\u662fPlotly\u4e2d\u9884\u5b9a\u4e49\u7684\u6e10\u53d8\u8272\u6807\u4e4b\u4e00\u3002<\/p>\n<\/p>\n
3. \u81ea\u5b9a\u4e49\u6e10\u53d8\u8272\u6807<\/h4>\n<\/p>\n
\u5982\u679c\u9700\u8981\u81ea\u5b9a\u4e49\u6e10\u53d8\u8272\u6807\uff0c\u53ef\u4ee5\u4f7f\u7528\u5982\u4e0b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
colorscale = [[0, 'blue'], [0.5, 'yellow'], [1, 'red']]  # \u6e10\u53d8\u8272\u6807\u7684\u5b9a\u4e49<\/p>\n
fig = go.Figure(data=go.Heatmap(<\/p>\n
                   z=data,<\/p>\n
                   colorscale=colorscale))<\/p>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u5b9a\u4e49\u4e86\u4e00\u4e2a\u7531\u84dd\u8272\u5230\u9ec4\u8272\u5230\u7ea2\u8272\u6e10\u53d8\u7684\u8272\u6807\uff0c\u5e76\u5e94\u7528\u5230\u6570\u636e\u5c55\u793a\u4e2d\u3002<\/p>\n<\/p>\n
\u56db\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u8bbe\u7f6ePython\u4e2d\u7684\u6e10\u53d8\u8272\u6807\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u65b9\u6cd5\uff0c\u6bcf\u79cd\u65b9\u6cd5\u90fd\u6709\u5176\u72ec\u7279\u7684\u4f18\u52bf\u548c\u9002\u7528\u573a\u666f\u3002Matplotlib\u5e93<\/strong>\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\u548c\u9ad8\u5ea6\u7684\u81ea\u5b9a\u4e49\u6027\uff0c\u975e\u5e38\u9002\u5408\u9700\u8981\u7cbe\u7ec6\u63a7\u5236\u7684\u573a\u666f\uff1bSeaborn\u5e93<\/strong>\u5728\u9ed8\u8ba4\u7f8e\u89c2\u8bbe\u7f6e\u548c\u7b80\u6d01\u7684API\u65b9\u9762\u8868\u73b0\u4f18\u79c0\uff0c\u9002\u5408\u5feb\u901f\u7f8e\u89c2\u7684\u53ef\u89c6\u5316\u9700\u6c42\uff1bPlotly\u5e93<\/strong>\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u4ea4\u4e92\u529f\u80fd\uff0c\u975e\u5e38\u9002\u5408\u9700\u8981\u4ea4\u4e92\u5f0f\u6570\u636e\u5c55\u793a\u7684\u573a\u666f\u3002<\/p>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u548c\u5e93\uff0c\u8fbe\u5230\u6700\u4f73\u7684\u53ef\u89c6\u5316\u6548\u679c\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u521b\u5efa\u6e10\u53d8\u8272\u6807\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u8f7b\u677e\u521b\u5efa\u6e10\u53d8\u8272\u6807\u3002\u9996\u5148\uff0c\u786e\u4fdd\u5b89\u88c5\u4e86Matplotlib\u3002\u7136\u540e\uff0c\u4f7f\u7528LinearSegmentedColormap<\/code>\u51fd\u6570\u5b9a\u4e49\u6e10\u53d8\u8272\u6807\uff0c\u6700\u540e\u5c06\u5176\u5e94\u7528\u4e8e\u7ed8\u56fe\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff0c\u5c55\u793a\u4e86\u5982\u4f55\u5b9e\u73b0\u8fd9\u4e00\u70b9\uff1a<\/p>\n
import matplotlib.pyplot as plt\nimport numpy as np\nfrom matplotlib.colors import LinearSegmentedColormap\n\n# \u5b9a\u4e49\u6e10\u53d8\u8272\u6807\ncolors = ["blue", "green", "yellow", "red"]\ncmap = LinearSegmentedColormap.from_list("my_gradient", colors)\n\n# \u751f\u6210\u6570\u636e\nx = np.linspace(0, 10, 100)\ny = np.sin(x)\n\n# \u7ed8\u5236\u66f2\u7ebf\u5e76\u5e94\u7528\u6e10\u53d8\u8272\u6807\nplt.scatter(x, y, c=y, cmap=cmap)\nplt.colorbar()  # \u6dfb\u52a0\u8272\u6807\u6761\nplt.show()\n<\/code><\/pre>\n
\u53ef\u4ee5\u4f7f\u7528\u54ea\u4e9b\u5e93\u6765\u5b9e\u73b0\u6e10\u53d8\u8272\u6807\uff1f<\/strong>
\u9664\u4e86Matplotlib\uff0c\u5176\u4ed6\u5e93\u5982Seaborn\u3001Plotly\u548cBokeh\u7b49\u4e5f\u652f\u6301\u6e10\u53d8\u8272\u6807\u3002Seaborn\u5728\u6570\u636e\u53ef\u89c6\u5316\u65b9\u9762\u975e\u5e38\u5f3a\u5927\uff0c\u80fd\u591f\u65b9\u4fbf\u5730\u751f\u6210\u6e10\u53d8\u8272\u56fe\u3002Plotly\u5219\u9002\u5408\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\uff0c\u800cBokeh\u540c\u6837\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u53ef\u89c6\u5316\u9009\u9879\u3002\u6839\u636e\u9700\u6c42\u9009\u62e9\u9002\u5408\u7684\u5e93\uff0c\u53ef\u4ee5\u83b7\u5f97\u66f4\u597d\u7684\u6548\u679c\u3002<\/p>\n
\u5982\u4f55\u81ea\u5b9a\u4e49\u6e10\u53d8\u8272\u6807\u7684\u989c\u8272\uff1f<\/strong>
\u81ea\u5b9a\u4e49\u6e10\u53d8\u8272\u6807\u7684\u989c\u8272\u975e\u5e38\u7b80\u5355\u3002\u4f60\u53ea\u9700\u5728\u5b9a\u4e49\u8272\u6807\u65f6\u66f4\u6539\u989c\u8272\u5217\u8868\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u9009\u62e9\u4efb\u610f\u989c\u8272\u7ec4\u5408\uff0c\u5305\u62ecRGB\u503c\u6216HTML\u989c\u8272\u4ee3\u7801\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a<\/p>\n
colors = ["#FF0000", "#00FF00", "#0000FF"]  # \u7ea2\u3001\u7eff\u3001\u84dd\ncmap = LinearSegmentedColormap.from_list("custom_gradient", colors)\n<\/code><\/pre>\n
\u8fd9\u6837\uff0c\u4f60\u5c31\u53ef\u4ee5\u6839\u636e\u81ea\u5df1\u7684\u9700\u6c42\u521b\u5efa\u72ec\u7279\u7684\u6e10\u53d8\u6548\u679c\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u8bbe\u7f6e\u8272\u6807\u4e3a\u6e10\u53d8\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528Matplotlib\u5e93\u3001Seaborn\u5e93\u4ee5\u53caPlotly\u5e93\u7b49\u3002\u6700\u5e38 […]","protected":false},"author":3,"featured_media":1052437,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1052432"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1052432"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1052432\/revisions"}],"predecessor-version":[{"id":1052438,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1052432\/revisions\/1052438"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1052437"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1052432"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1052432"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1052432"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}