![\"python\u505a\u5206\u6790\u5982\u4f55\u8f93\u51fa\u56fe\"](\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25130054\/611f7f03-e947-4d0c-807c-8793551d5f0a.webp\")
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Python\u505a\u5206\u6790\u5982\u4f55\u8f93\u51fa\u56fe \u4f7f\u7528Matplotlib\u3001Seaborn\u3001Plotly\u7b49\u5e93\uff0c\u5229\u7528\u5176\u5f3a\u5927\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u7ed3\u5408\u6570\u636e\u5206\u6790\u9700\u6c42\uff0c\u80fd\u591f\u9ad8\u6548\u5730\u8f93\u51fa\u5404\u79cd\u56fe\u8868<\/strong>\u3002\u5176\u4e2d\uff0cMatplotlib<\/strong> \u662f\u6700\u57fa\u7840\u4e5f\u662f\u6700\u5e7f\u6cdb\u4f7f\u7528\u7684\u7ed8\u56fe\u5e93\uff0c\u5b83\u53ef\u4ee5\u521b\u5efa\u9759\u6001\u7684\u3001\u4ea4\u4e92\u5f0f\u7684\u548c\u52a8\u753b\u7684\u56fe\u8868\uff0cSeaborn<\/strong> \u5219\u5728Matplotlib\u7684\u57fa\u7840\u4e0a\u8fdb\u884c\u4e86\u66f4\u9ad8\u5c42\u6b21\u7684\u5c01\u88c5\uff0c\u7b80\u5316\u4e86\u5f88\u591a\u5e38\u89c1\u7684\u7edf\u8ba1\u56fe\u8868\u7684\u7ed8\u5236\uff0c\u800c Plotly<\/strong> \u63d0\u4f9b\u4e86\u4ea4\u4e92\u5f0f\u56fe\u8868\u529f\u80fd\uff0c\u53ef\u4ee5\u7528\u4e8e\u521b\u5efa\u66f4\u52a0\u590d\u6742\u548c\u52a8\u6001\u7684\u53ef\u89c6\u5316\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u5e93\u6765\u5b9e\u73b0\u6570\u636e\u5206\u6790\u4e2d\u7684\u56fe\u8868\u8f93\u51fa\u3002<\/p>\n<\/p>\n Matplotlib \u662fPython\u4e2d\u6700\u8457\u540d\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u5b83\u63d0\u4f9b\u4e86\u7ed8\u5236\u5404\u79cd\u56fe\u8868\u7684\u529f\u80fd\uff0c\u5305\u62ec\u6298\u7ebf\u56fe\u3001\u6563\u70b9\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u997c\u56fe\u3001\u76f4\u65b9\u56fe\u7b49\u3002Matplotlib\u7684\u6838\u5fc3\u662fpyplot\u6a21\u5757\uff0c\u901a\u8fc7pyplot\u6a21\u5757\u53ef\u4ee5\u5f88\u65b9\u4fbf\u5730\u751f\u6210\u5404\u79cd\u56fe\u8868\u3002<\/p>\n<\/p>\n \u8981\u4f7f\u7528Matplotlib\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5\u8be5\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n <\/code><\/pre>\n<\/p>\n \u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u8fdb\u884c\u57fa\u672c\u7684\u7ed8\u56fe\u64cd\u4f5c\uff1a<\/p>\n<\/p>\n x = [1, 2, 3, 4, 5]<\/p>\n y = [2, 3, 5, 7, 11]<\/p>\n plt.plot(x, y)<\/p>\n plt.title("Basic Line Plot")<\/p>\n plt.xlabel("X-axis")<\/p>\n plt.ylabel("Y-axis")<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u8fd9\u6bb5\u4ee3\u7801\u5c06\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u6298\u7ebf\u56fe\u3002\u6211\u4eec\u9996\u5148\u5bfc\u5165\u4e86matplotlib.pyplot\u6a21\u5757\uff0c\u7136\u540e\u521b\u5efa\u4e86\u6570\u636e\uff0c\u5e76\u4f7f\u7528 \u6298\u7ebf\u56fe\u7528\u4e8e\u663e\u793a\u6570\u636e\u7684\u53d8\u5316\u8d8b\u52bf\u3002\u53ef\u4ee5\u901a\u8fc7 x = [1, 2, 3, 4, 5]<\/p>\n y = [2, 3, 5, 7, 11]<\/p>\n plt.plot(x, y, marker='o', linestyle='-', color='b', label='Line 1')<\/p>\n plt.title("Line Plot")<\/p>\n plt.xlabel("X-axis")<\/p>\n plt.ylabel("Y-axis")<\/p>\n plt.legend()<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86 \u6563\u70b9\u56fe\u7528\u4e8e\u663e\u793a\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u53ef\u4ee5\u901a\u8fc7 x = [1, 2, 3, 4, 5]<\/p>\n y = [2, 3, 5, 7, 11]<\/p>\n plt.scatter(x, y, color='r', label='Scatter Plot')<\/p>\n plt.title("Scatter Plot")<\/p>\n plt.xlabel("X-axis")<\/p>\n plt.ylabel("Y-axis")<\/p>\n plt.legend()<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u67f1\u72b6\u56fe\u7528\u4e8e\u663e\u793a\u4e0d\u540c\u7c7b\u522b\u7684\u6570\u636e\u5bf9\u6bd4\u3002\u53ef\u4ee5\u901a\u8fc7 categories = ['A', 'B', 'C', 'D']<\/p>\n values = [4, 7, 1, 8]<\/p>\n plt.bar(categories, values, color='g')<\/p>\n plt.title("Bar Chart")<\/p>\n plt.xlabel("Category")<\/p>\n plt.ylabel("Value")<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u997c\u56fe\u7528\u4e8e\u663e\u793a\u6570\u636e\u7684\u7ec4\u6210\u6bd4\u4f8b\u3002\u53ef\u4ee5\u901a\u8fc7 labels = ['A', 'B', 'C', 'D']<\/p>\n sizes = [15, 30, 45, 10]<\/p>\n colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue']<\/p>\n plt.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', startangle=140)<\/p>\n plt.title("Pie Chart")<\/p>\n plt.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n Matplotlib\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u81ea\u5b9a\u4e49\u529f\u80fd\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u5404\u79cd\u53c2\u6570\u6765\u8c03\u6574\u56fe\u8868\u7684\u6837\u5f0f\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u901a\u8fc7 x = [1, 2, 3, 4, 5]<\/p>\n y = [2, 3, 5, 7, 11]<\/p>\n plt.figure(figsize=(10, 6), dpi=80) # \u8bbe\u7f6e\u56fe\u8868\u5927\u5c0f\u548c\u5206\u8fa8\u7387<\/p>\n plt.plot(x, y, marker='o', linestyle='-', color='b', label='Line 1')<\/p>\n plt.title("Customized Line Plot")<\/p>\n plt.xlabel("X-axis")<\/p>\n plt.ylabel("Y-axis")<\/p>\n plt.legend()<\/p>\n plt.grid(True) # \u6dfb\u52a0\u7f51\u683c\u7ebf<\/p>\n plt.savefig('line_plot.png') # \u4fdd\u5b58\u56fe\u8868<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u901a\u8fc7\u8fd9\u4e9b\u8bbe\u7f6e\uff0c\u53ef\u4ee5\u521b\u5efa\u66f4\u52a0\u7f8e\u89c2\u548c\u4e13\u4e1a\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u52a0\u7b80\u6d01\u548c\u7f8e\u89c2\u7684\u7ed8\u56fe\u63a5\u53e3\u3002Seaborn\u4e13\u6ce8\u4e8e\u7edf\u8ba1\u56fe\u8868\u7684\u7ed8\u5236\uff0c\u9002\u5408\u8fdb\u884c\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n \u8981\u4f7f\u7528Seaborn\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5\u8be5\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n <\/code><\/pre>\n<\/p>\n \u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u8fdb\u884c\u57fa\u672c\u7684\u7ed8\u56fe\u64cd\u4f5c\uff1a<\/p>\n<\/p>\n import matplotlib.pyplot as plt<\/p>\n tips = sns.load_dataset("tips")<\/p>\n sns.scatterplot(x="total_bill", y="tip", data=tips)<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u8fd9\u6bb5\u4ee3\u7801\u5c06\u521b\u5efa\u4e00\u4e2a\u6563\u70b9\u56fe\u3002\u6211\u4eec\u9996\u5148\u5bfc\u5165\u4e86seaborn\u548cmatplotlib.pyplot\u6a21\u5757\uff0c\u7136\u540e\u52a0\u8f7d\u4e86\u4e00\u4e2a\u793a\u4f8b\u6570\u636e\u96c6\uff0c\u5e76\u4f7f\u7528 \u6563\u70b9\u56fe\u7528\u4e8e\u663e\u793a\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u53ef\u4ee5\u901a\u8fc7 import matplotlib.pyplot as plt<\/p>\n tips = sns.load_dataset("tips")<\/p>\n sns.scatterplot(x="total_bill", y="tip", data=tips, hue="time", style="time", size="size")<\/p>\n plt.title("Scatter Plot with Seaborn")<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86 \u7ebf\u6027\u56de\u5f52\u56fe\u7528\u4e8e\u663e\u793a\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u7ebf\u6027\u5173\u7cfb\u3002\u53ef\u4ee5\u901a\u8fc7 import matplotlib.pyplot as plt<\/p>\n tips = sns.load_dataset("tips")<\/p>\n sns.lmplot(x="total_bill", y="tip", data=tips, hue="time")<\/p>\n plt.title("Linear Regression Plot with Seaborn")<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u7bb1\u7ebf\u56fe\u7528\u4e8e\u663e\u793a\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002\u53ef\u4ee5\u901a\u8fc7 import matplotlib.pyplot as plt<\/p>\n tips = sns.load_dataset("tips")<\/p>\n sns.boxplot(x="day", y="total_bill", data=tips)<\/p>\n plt.title("Box Plot with Seaborn")<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u70ed\u529b\u56fe\u7528\u4e8e\u663e\u793a\u77e9\u9635\u6570\u636e\u7684\u70ed\u5ea6\u5206\u5e03\u3002\u53ef\u4ee5\u901a\u8fc7 import matplotlib.pyplot as plt<\/p>\n flights = sns.load_dataset("flights")<\/p>\n flights = flights.pivot("month", "year", "passengers")<\/p>\n sns.heatmap(flights, annot=True, fmt="d", cmap="YlGnBu")<\/p>\n plt.title("Heatmap with Seaborn")<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n Seaborn\u540c\u6837\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u81ea\u5b9a\u4e49\u529f\u80fd\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u5404\u79cd\u53c2\u6570\u6765\u8c03\u6574\u56fe\u8868\u7684\u6837\u5f0f\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u901a\u8fc7 import matplotlib.pyplot as plt<\/p>\n tips = sns.load_dataset("tips")<\/p>\n sns.set(style="whitegrid", palette="pastel")<\/p>\n sns.boxplot(x="day", y="total_bill", data=tips)<\/p>\n sns.despine(left=True) # \u53bb\u9664\u5de6\u8fb9\u6846<\/p>\n plt.title("Customized Box Plot with Seaborn")<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u901a\u8fc7\u8fd9\u4e9b\u8bbe\u7f6e\uff0c\u53ef\u4ee5\u521b\u5efa\u66f4\u52a0\u7f8e\u89c2\u548c\u4e13\u4e1a\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n Plotly\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u7ed8\u56fe\u5e93\uff0c\u652f\u6301\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\u3002Plotly\u9002\u5408\u8fdb\u884c\u590d\u6742\u548c\u52a8\u6001\u7684\u53ef\u89c6\u5316\uff0c\u5e38\u7528\u4e8e\u6570\u636e\u5206\u6790\u548c\u5c55\u793a\u3002<\/p>\n<\/p>\n \u8981\u4f7f\u7528Plotly\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5\u8be5\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n <\/code><\/pre>\n<\/p>\n \u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u8fdb\u884c\u57fa\u672c\u7684\u7ed8\u56fe\u64cd\u4f5c\uff1a<\/p>\n<\/p>\n df = px.data.iris()<\/p>\n fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species")<\/p>\n fig.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u8fd9\u6bb5\u4ee3\u7801\u5c06\u521b\u5efa\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u6563\u70b9\u56fe\u3002\u6211\u4eec\u9996\u5148\u5bfc\u5165\u4e86plotly.express\u6a21\u5757\uff0c\u7136\u540e\u52a0\u8f7d\u4e86\u4e00\u4e2a\u793a\u4f8b\u6570\u636e\u96c6\uff0c\u5e76\u4f7f\u7528 \u6563\u70b9\u56fe\u7528\u4e8e\u663e\u793a\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u53ef\u4ee5\u901a\u8fc7 df = px.data.iris()<\/p>\n fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species", symbol="species", size="petal_length")<\/p>\n fig.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86 \u6298\u7ebf\u56fe\u7528\u4e8e\u663e\u793a\u6570\u636e\u7684\u53d8\u5316\u8d8b\u52bf\u3002\u53ef\u4ee5\u901a\u8fc7 df = px.data.gapminder().query("continent == 'Oceania'")<\/p>\n fig = px.line(df, x="year", y="lifeExp", color="country")<\/p>\n fig.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u67f1\u72b6\u56fe\u7528\u4e8e\u663e\u793a\u4e0d\u540c\u7c7b\u522b\u7684\u6570\u636e\u5bf9\u6bd4\u3002\u53ef\u4ee5\u901a\u8fc7 df = px.data.tips()<\/p>\n fig = px.bar(df, x="day", y="total_bill", color="sex", barmode="group")<\/p>\n fig.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u997c\u56fe\u7528\u4e8e\u663e\u793a\u6570\u636e\u7684\u7ec4\u6210\u6bd4\u4f8b\u3002\u53ef\u4ee5\u901a\u8fc7 df = px.data.tips()<\/p>\n fig = px.pie(df, values="total_bill", names="day", color="day", hole=.3)<\/p>\n fig.show()<\/p>\n <\/code><\/pre>\n<\/p>\n Plotly\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u81ea\u5b9a\u4e49\u529f\u80fd\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u5404\u79cd\u53c2\u6570\u6765\u8c03\u6574\u56fe\u8868\u7684\u6837\u5f0f\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u901a\u8fc7 df = px.data.iris()<\/p>\n fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species", symbol="species", size="petal_length")<\/p>\n fig.update_layout(title="Customized Scatter Plot with Plotly", xaxis_title="Sepal Width", yaxis_title="Sepal Length")<\/p>\n fig.update_traces(marker=dict(line=dict(width=2, color='DarkSlateGrey')))<\/p>\n fig.write_image("scatter_plot.png") # \u4fdd\u5b58\u56fe\u8868<\/p>\n fig.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u901a\u8fc7\u8fd9\u4e9b\u8bbe\u7f6e\uff0c\u53ef\u4ee5\u521b\u5efa\u66f4\u52a0\u7f8e\u89c2\u548c\u4e13\u4e1a\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n \u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u5e38\u5e38\u9700\u8981\u7ed3\u5408\u4f7f\u7528\u591a\u4e2a\u5e93\u6765\u5b9e\u73b0\u590d\u6742\u7684\u56fe\u8868\u7ed8\u5236\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7efc\u5408\u5e94\u7528\u7684\u793a\u4f8b\uff0c\u5c55\u793a\u5982\u4f55\u7ed3\u5408\u4f7f\u7528Matplotlib\u3001Seaborn\u548cPlotly\u6765\u7ed8\u5236\u590d\u6742\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n import seaborn as sns<\/p>\n import plotly.express as px<\/p>\n df = sns.load_dataset("iris")<\/p>\n plt.figure(figsize=(10, 6))<\/p>\n plt.plot(df["sepal_length"], df["sepal_width"], marker='o', linestyle='-', color='b', label='Sepal')<\/p>\n plt.plot(df["petal_length"], df["petal_width"], marker='x', linestyle='--', color='r', label='Petal')<\/p>\n plt.title("Line Plot with Matplotlib")<\/p>\n plt.xlabel("Length")<\/p>\n plt.ylabel("Width")<\/p>\n plt.legend()<\/p>\n plt.grid(True)<\/p>\n plt.savefig('line_plot.png')<\/p>\n plt.show()<\/p>\n sns.set(style="whitegrid", palette="pastel")<\/p>\n plt.figure(figsize=(10, 6))<\/p>\n sns.boxplot(x="species", y="sepal_length", data=df)<\/p>\n sns.despine(left=True)<\/p>\n plt.title("Box Plot with Seaborn")<\/p>\n plt.savefig('box_plot.png')<\/p>\n plt.show()<\/p>\n fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species", symbol="species", size="petal_length")<\/p>\n fig.update_layout(title="Scatter Plot with Plotly", xaxis_title="Sepal Width", yaxis_title="Sepal Length")<\/p>\n fig.write_image("scatter_plot.png")<\/p>\n fig.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u8fd9\u4e2a\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u7ed3\u5408\u4f7f\u7528\u4e0d\u540c\u7684\u7ed8\u56fe\u5e93\u6765\u5b9e\u73b0\u590d\u6742\u7684\u56fe\u8868\u7ed8\u5236\u3002\u901a\u8fc7\u7ed3\u5408\u4f7f\u7528Matplotlib\u3001Seaborn\u548cPlotly\uff0c\u53ef\u4ee5\u5145\u5206\u53d1\u6325\u5404\u81ea\u7684\u4f18\u52bf\uff0c\u521b\u5efa\u66f4\u52a0\u7f8e\u89c2\u548c\u4e13\u4e1a\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n \u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u6211\u4eec\u4e86\u89e3\u4e86\u5982\u4f55\u4f7f\u7528Matplotlib\u3001Seaborn\u548cPlotly\u6765\u8fdb\u884c\u6570\u636e\u5206\u6790\u4e2d\u7684\u56fe\u8868\u8f93\u51fa\u3002Matplotlib\u63d0\u4f9b\u4e86\u57fa\u7840\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u9002\u5408\u521b\u5efa\u9759\u6001\u56fe\u8868\uff1bSeaborn\u5728Matplotlib\u7684\u57fa\u7840\u4e0a\u8fdb\u884c\u4e86\u66f4\u9ad8\u5c42\u6b21\u7684\u5c01\u88c5\uff0c\u7b80\u5316\u4e86\u5e38\u89c1\u7edf\u8ba1\u56fe\u8868\u7684\u7ed8\u5236\uff1bPlotly\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u4ea4\u4e92\u5f0f\u56fe\u8868\u529f\u80fd\uff0c\u9002\u5408\u521b\u5efa\u590d\u6742\u548c\u52a8\u6001\u7684\u53ef\u89c6\u5316\u3002\u901a\u8fc7\u7ed3\u5408\u4f7f\u7528\u8fd9\u4e9b\u5e93\uff0c\u53ef\u4ee5\u9ad8\u6548\u5730\u5b9e\u73b0\u5404\u79cd\u6570\u636e\u5206\u6790\u56fe\u8868\u7684\u7ed8\u5236\u3002\u5e0c\u671b\u672c\u6587\u80fd\u5bf9\u60a8\u5728\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u5de5\u4f5c\u4e2d\u6709\u6240\u5e2e\u52a9\u3002<\/p>\n<\/p>\n \u5982\u4f55\u5728Python\u4e2d\u751f\u6210\u56fe\u8868\uff1f<\/strong> Python\u4e2d\u6709\u54ea\u4e9b\u5e93\u9002\u5408\u6570\u636e\u53ef\u89c6\u5316\uff1f<\/strong> \u5982\u4f55\u81ea\u5b9a\u4e49Python\u56fe\u8868\u7684\u6837\u5f0f\u548c\u989c\u8272\uff1f<\/strong>\u4e00\u3001Matplotlib<\/strong><\/h2>\n
Matplotlib\u7b80\u4ecb<\/h2>\n<\/p>\n
\u57fa\u672c\u7528\u6cd5<\/h2>\n<\/p>\n
pip install matplotlib<\/p>\nimport matplotlib.pyplot as plt<\/p>\n\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
\u521b\u5efa\u56fe\u8868<\/strong><\/h2>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.plot<\/code>\u51fd\u6570\u7ed8\u5236\u4e86\u6298\u7ebf\u56fe\u3002\u901a\u8fc7plt.title<\/code>\u3001plt.xlabel<\/code>\u548cplt.ylabel<\/code>\u51fd\u6570\u6dfb\u52a0\u4e86\u6807\u9898\u548c\u8f74\u6807\u7b7e\uff0c\u6700\u540e\u4f7f\u7528plt.show<\/code>\u51fd\u6570\u663e\u793a\u56fe\u8868\u3002<\/p>\n<\/p>\n\u5e38\u7528\u56fe\u8868<\/h2>\n<\/p>\n
1\u3001\u6298\u7ebf\u56fe<\/h3>\n<\/p>\n
plt.plot<\/code>\u51fd\u6570\u521b\u5efa\u6298\u7ebf\u56fe\u3002<\/p>\n<\/p>\nimport matplotlib.pyplot as plt<\/p>\nmarker<\/code>\u53c2\u6570\u6dfb\u52a0\u6570\u636e\u70b9\u6807\u8bb0\uff0clinestyle<\/code>\u53c2\u6570\u8bbe\u7f6e\u7ebf\u578b\uff0ccolor<\/code>\u53c2\u6570\u8bbe\u7f6e\u989c\u8272\uff0clabel<\/code>\u53c2\u6570\u6dfb\u52a0\u56fe\u4f8b\u3002<\/p>\n<\/p>\n2\u3001\u6563\u70b9\u56fe<\/h3>\n<\/p>\n
plt.scatter<\/code>\u51fd\u6570\u521b\u5efa\u6563\u70b9\u56fe\u3002<\/p>\n<\/p>\nimport matplotlib.pyplot as plt<\/p>\n3\u3001\u67f1\u72b6\u56fe<\/h3>\n<\/p>\n
plt.bar<\/code>\u51fd\u6570\u521b\u5efa\u67f1\u72b6\u56fe\u3002<\/p>\n<\/p>\nimport matplotlib.pyplot as plt<\/p>\n4\u3001\u997c\u56fe<\/h3>\n<\/p>\n
plt.pie<\/code>\u51fd\u6570\u521b\u5efa\u997c\u56fe\u3002<\/p>\n<\/p>\nimport matplotlib.pyplot as plt<\/p>\n\u81ea\u5b9a\u4e49\u56fe\u8868<\/h2>\n<\/p>\n
plt.figure<\/code>\u51fd\u6570\u8bbe\u7f6e\u56fe\u8868\u7684\u5927\u5c0f\u548c\u5206\u8fa8\u7387\uff0c\u901a\u8fc7plt.grid<\/code>\u51fd\u6570\u6dfb\u52a0\u7f51\u683c\u7ebf\uff0c\u901a\u8fc7plt.savefig<\/code>\u51fd\u6570\u4fdd\u5b58\u56fe\u8868\u3002<\/p>\n<\/p>\nimport matplotlib.pyplot as plt<\/p>\n\u4e8c\u3001Seaborn<\/strong><\/h2>\n
Seaborn\u7b80\u4ecb<\/h2>\n<\/p>\n
\u5b89\u88c5\u548c\u57fa\u672c\u7528\u6cd5<\/h2>\n<\/p>\n
pip install seaborn<\/p>\nimport seaborn as sns<\/p>\n\u52a0\u8f7d\u793a\u4f8b\u6570\u636e\u96c6<\/strong><\/h2>\n
\u521b\u5efa\u6563\u70b9\u56fe<\/strong><\/h2>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
sns.scatterplot<\/code>\u51fd\u6570\u7ed8\u5236\u4e86\u6563\u70b9\u56fe\u3002\u6700\u540e\u4f7f\u7528plt.show<\/code>\u51fd\u6570\u663e\u793a\u56fe\u8868\u3002<\/p>\n<\/p>\n\u5e38\u7528\u56fe\u8868<\/h2>\n<\/p>\n
1\u3001\u6563\u70b9\u56fe<\/h3>\n<\/p>\n
sns.scatterplot<\/code>\u51fd\u6570\u521b\u5efa\u6563\u70b9\u56fe\u3002<\/p>\n<\/p>\nimport seaborn as sns<\/p>\nhue<\/code>\u3001style<\/code>\u548csize<\/code>\u53c2\u6570\u5206\u522b\u8bbe\u7f6e\u989c\u8272\u3001\u6837\u5f0f\u548c\u5927\u5c0f\u3002<\/p>\n<\/p>\n2\u3001\u7ebf\u6027\u56de\u5f52\u56fe<\/h3>\n<\/p>\n
sns.lmplot<\/code>\u51fd\u6570\u521b\u5efa\u7ebf\u6027\u56de\u5f52\u56fe\u3002<\/p>\n<\/p>\nimport seaborn as sns<\/p>\n3\u3001\u7bb1\u7ebf\u56fe<\/h3>\n<\/p>\n
sns.boxplot<\/code>\u51fd\u6570\u521b\u5efa\u7bb1\u7ebf\u56fe\u3002<\/p>\n<\/p>\nimport seaborn as sns<\/p>\n4\u3001\u70ed\u529b\u56fe<\/h3>\n<\/p>\n
sns.heatmap<\/code>\u51fd\u6570\u521b\u5efa\u70ed\u529b\u56fe\u3002<\/p>\n<\/p>\nimport seaborn as sns<\/p>\n\u81ea\u5b9a\u4e49\u56fe\u8868<\/h2>\n<\/p>\n
sns.set<\/code>\u51fd\u6570\u8bbe\u7f6e\u56fe\u8868\u7684\u4e3b\u9898\uff0c\u901a\u8fc7sns.despine<\/code>\u51fd\u6570\u53bb\u9664\u56fe\u8868\u7684\u8fb9\u6846\uff0c\u901a\u8fc7sns.set_palette<\/code>\u51fd\u6570\u8bbe\u7f6e\u8c03\u8272\u677f\u3002<\/p>\n<\/p>\nimport seaborn as sns<\/p>\n\u521b\u5efa\u7bb1\u7ebf\u56fe<\/strong><\/h2>\n
\u4e09\u3001Plotly<\/strong><\/h2>\n
Plotly\u7b80\u4ecb<\/h2>\n<\/p>\n
\u5b89\u88c5\u548c\u57fa\u672c\u7528\u6cd5<\/h2>\n<\/p>\n
pip install plotly<\/p>\nimport plotly.express as px<\/p>\n\u52a0\u8f7d\u793a\u4f8b\u6570\u636e\u96c6<\/strong><\/h2>\n
\u521b\u5efa\u6563\u70b9\u56fe<\/strong><\/h2>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
px.scatter<\/code>\u51fd\u6570\u7ed8\u5236\u4e86\u6563\u70b9\u56fe\u3002\u6700\u540e\u4f7f\u7528fig.show<\/code>\u51fd\u6570\u663e\u793a\u56fe\u8868\u3002<\/p>\n<\/p>\n\u5e38\u7528\u56fe\u8868<\/h2>\n<\/p>\n
1\u3001\u6563\u70b9\u56fe<\/h3>\n<\/p>\n
px.scatter<\/code>\u51fd\u6570\u521b\u5efa\u6563\u70b9\u56fe\u3002<\/p>\n<\/p>\nimport plotly.express as px<\/p>\ncolor<\/code>\u3001symbol<\/code>\u548csize<\/code>\u53c2\u6570\u5206\u522b\u8bbe\u7f6e\u989c\u8272\u3001\u6837\u5f0f\u548c\u5927\u5c0f\u3002<\/p>\n<\/p>\n2\u3001\u6298\u7ebf\u56fe<\/h3>\n<\/p>\n
px.line<\/code>\u51fd\u6570\u521b\u5efa\u6298\u7ebf\u56fe\u3002<\/p>\n<\/p>\nimport plotly.express as px<\/p>\n3\u3001\u67f1\u72b6\u56fe<\/h3>\n<\/p>\n
px.bar<\/code>\u51fd\u6570\u521b\u5efa\u67f1\u72b6\u56fe\u3002<\/p>\n<\/p>\nimport plotly.express as px<\/p>\n4\u3001\u997c\u56fe<\/h3>\n<\/p>\n
px.pie<\/code>\u51fd\u6570\u521b\u5efa\u997c\u56fe\u3002<\/p>\n<\/p>\nimport plotly.express as px<\/p>\n\u81ea\u5b9a\u4e49\u56fe\u8868<\/h2>\n<\/p>\n
fig.update_layout<\/code>\u51fd\u6570\u8bbe\u7f6e\u56fe\u8868\u7684\u5e03\u5c40\uff0c\u901a\u8fc7fig.update_traces<\/code>\u51fd\u6570\u8bbe\u7f6e\u56fe\u8868\u7684\u8ffd\u8e2a\u5668\uff0c\u901a\u8fc7fig.write_image<\/code>\u51fd\u6570\u4fdd\u5b58\u56fe\u8868\u3002<\/p>\n<\/p>\nimport plotly.express as px<\/p>\n\u56db\u3001\u7efc\u5408\u5e94\u7528<\/strong><\/h2>\n
import matplotlib.pyplot as plt<\/p>\n\u52a0\u8f7d\u793a\u4f8b\u6570\u636e\u96c6<\/strong><\/h2>\n
\u4f7f\u7528Matplotlib\u7ed8\u5236\u6298\u7ebf\u56fe<\/strong><\/h2>\n
\u4f7f\u7528Seaborn\u7ed8\u5236\u7bb1\u7ebf\u56fe<\/strong><\/h2>\n
\u4f7f\u7528Plotly\u7ed8\u5236\u6563\u70b9\u56fe<\/strong><\/h2>\n
\u4e94\u3001\u603b\u7ed3<\/strong><\/h2>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
\u8981\u5728Python\u4e2d\u751f\u6210\u56fe\u8868\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u5e93\uff0c\u5176\u4e2d\u6700\u5e38\u7528\u7684\u662fMatplotlib\u548cSeaborn\u3002Matplotlib\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u7ed8\u56fe\u5e93\uff0c\u9002\u5408\u521b\u5efa\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff0c\u800cSeaborn\u5219\u63d0\u4f9b\u66f4\u9ad8\u7ea7\u7684\u63a5\u53e3\uff0c\u80fd\u591f\u8f7b\u677e\u751f\u6210\u7f8e\u89c2\u7684\u7edf\u8ba1\u56fe\u5f62\u3002\u9996\u5148\uff0c\u60a8\u9700\u8981\u5b89\u88c5\u76f8\u5173\u5e93\uff0c\u7136\u540e\u4f7f\u7528\u76f8\u5e94\u7684\u51fd\u6570\u7ed8\u5236\u56fe\u5f62\u3002\u4f8b\u5982\uff0c\u4f7f\u7528plt.plot()<\/code>\u53ef\u4ee5\u521b\u5efa\u6298\u7ebf\u56fe\uff0c\u4f7f\u7528sns.barplot()<\/code>\u53ef\u4ee5\u751f\u6210\u6761\u5f62\u56fe\u3002<\/p>\n
\u9664\u4e86Matplotlib\u548cSeaborn\uff0cPython\u4e2d\u8fd8\u6709\u8bb8\u591a\u5176\u4ed6\u5e93\u9002\u5408\u6570\u636e\u53ef\u89c6\u5316\u3002\u4f8b\u5982\uff0cPlotly\u662f\u4e00\u4e2a\u652f\u6301\u4ea4\u4e92\u5f0f\u56fe\u8868\u7684\u5e93\uff0c\u9002\u5408\u5c55\u793a\u590d\u6742\u7684\u6570\u636e\u3002Bokeh\u5219\u4e13\u6ce8\u4e8e\u521b\u5efa\u7f51\u9875\u5e94\u7528\u7684\u5b9e\u65f6\u4ea4\u4e92\u5f0f\u53ef\u89c6\u5316\u3002\u5bf9\u4e8e\u5730\u7406\u6570\u636e\u7684\u53ef\u89c6\u5316\uff0cFolium\u662f\u4e00\u4e2a\u5f88\u597d\u7684\u9009\u62e9\u3002\u6839\u636e\u60a8\u7684\u9700\u6c42\uff0c\u53ef\u4ee5\u9009\u62e9\u5408\u9002\u7684\u5e93\u6765\u5b9e\u73b0\u6570\u636e\u7684\u53ef\u89c6\u5316\u3002<\/p>\n
\u5728Python\u4e2d\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u53c2\u6570\u6765\u81ea\u5b9a\u4e49\u56fe\u8868\u7684\u6837\u5f0f\u548c\u989c\u8272\u3002\u5728Matplotlib\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528plt.style.use()<\/code>\u9009\u62e9\u4e0d\u540c\u7684\u6837\u5f0f\uff0c\u4e5f\u53ef\u4ee5\u901a\u8fc7color<\/code>\u53c2\u6570\u8bbe\u7f6e\u989c\u8272\u3002Seaborn\u5219\u63d0\u4f9b\u4e86\u591a\u79cd\u5185\u7f6e\u8c03\u8272\u677f\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7palette<\/code>\u53c2\u6570\u8f7b\u677e\u66f4\u6539\u56fe\u5f62\u7684\u989c\u8272\u65b9\u6848\u3002\u6b64\u5916\uff0c\u60a8\u8fd8\u53ef\u4ee5\u8c03\u6574\u5b57\u4f53\u5927\u5c0f\u3001\u7ebf\u6761\u7c97\u7ec6\u7b49\uff0c\u4ee5\u4f7f\u56fe\u8868\u66f4\u7b26\u5408\u60a8\u7684\u9700\u6c42\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u505a\u5206\u6790\u5982\u4f55\u8f93\u51fa\u56fe \u4f7f\u7528Matplotlib\u3001Seaborn\u3001Plotly\u7b49\u5e93\uff0c\u5229\u7528\u5176\u5f3a\u5927\u7684\u7ed8\u56fe\u529f\u80fd […]","protected":false},"author":3,"featured_media":1181904,"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\/1181899"}],"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=1181899"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1181899\/revisions"}],"predecessor-version":[{"id":1181907,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1181899\/revisions\/1181907"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1181904"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1181899"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1181899"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1181899"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}