{"id":1167532,"date":"2025-01-15T15:46:10","date_gmt":"2025-01-15T07:46:10","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1167532.html"},"modified":"2025-01-15T15:46:14","modified_gmt":"2025-01-15T07:46:14","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e7%94%bb%e6%95%b0%e6%8d%ae%e5%9b%be","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1167532.html","title":{"rendered":"\u5982\u4f55\u7528python\u753b\u6570\u636e\u56fe"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u753b\u6570\u636e\u56fe\"<\/p>\n

\u7528Python\u753b\u6570\u636e\u56fe\u7684\u65b9\u6cd5\u6709\u5f88\u591a\u79cd\uff0c\u6700\u5e38\u7528\u7684\u5305\u62ec\u4f7f\u7528Matplotlib\u3001Seaborn\u3001Plotly\u3001Pandas\u7b49\u5e93\u3002<\/strong>\u5176\u4e2d\uff0cMatplotlib\u662f\u57fa\u7840\u7684\u7ed8\u56fe\u5e93\uff0cSeaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0cPlotly\u5219\u662f\u7528\u4e8e\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\u7684\u5f3a\u5927\u5de5\u5177\u3002\u4e86\u89e3\u8fd9\u4e9b\u5de5\u5177\u7684\u57fa\u672c\u7528\u6cd5\u3001\u9009\u62e9\u5408\u9002\u7684\u5e93\u4ee5\u53ca\u638c\u63e1\u7ed8\u56fe\u7684\u6280\u5de7<\/strong>\u662f\u7528Python\u753b\u6570\u636e\u56fe\u7684\u5173\u952e\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u5e93\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n

\u4e00\u3001MATPLOTLIB<\/h3>\n<\/p>\n

Matplotlib\u662fPython\u6700\u57fa\u7840\u7684\u7ed8\u56fe\u5e93\uff0c\u51e0\u4e4e\u6240\u6709\u5176\u4ed6\u7684Python\u7ed8\u56fe\u5e93\u90fd\u4f9d\u8d56\u5b83\u3002\u5b83\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u7ed8\u56fe\u51fd\u6570\uff0c\u80fd\u591f\u6ee1\u8db3\u5927\u591a\u6570\u7684\u7ed8\u56fe\u9700\u6c42\u3002<\/p>\n<\/p>\n

1\u3001\u5b89\u88c5\u548c\u57fa\u672c\u4f7f\u7528<\/h4>\n<\/p>\n

\u9996\u5148\u9700\u8981\u5b89\u88c5Matplotlib\u5e93\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install matplotlib<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u7ed8\u5236\u4e00\u4e2a\u7b80\u5355\u7684\u6298\u7ebf\u56fe\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [2, 3, 5, 7, 11]<\/p>\n
\u521b\u5efa\u56fe\u8868<\/strong><\/h2>\n
plt.plot(x, y)<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title("Simple Line Chart")<\/p>\n
plt.xlabel("X Axis")<\/p>\n
plt.ylabel("Y Axis")<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5e38\u7528\u56fe\u8868\u7c7b\u578b<\/h4>\n<\/p>\n
\u6298\u7ebf\u56fe\uff08Line Chart\uff09\uff1a<\/strong> \u7528\u4e8e\u5c55\u793a\u6570\u636e\u7684\u53d8\u5316\u8d8b\u52bf\u3002<\/p>\n<\/p>\n
plt.plot(x, y)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u6563\u70b9\u56fe\uff08Scatter Plot\uff09\uff1a<\/strong> \u7528\u4e8e\u5c55\u793a\u6570\u636e\u70b9\u7684\u5206\u5e03\u3002<\/p>\n<\/p>\n
plt.scatter(x, y)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u67f1\u72b6\u56fe\uff08Bar Chart\uff09\uff1a<\/strong> \u7528\u4e8e\u6bd4\u8f83\u4e0d\u540c\u7c7b\u522b\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
plt.bar(x, y)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u76f4\u65b9\u56fe\uff08Histogram\uff09\uff1a<\/strong> \u7528\u4e8e\u5c55\u793a\u6570\u636e\u7684\u9891\u7387\u5206\u5e03\u3002<\/p>\n<\/p>\n
plt.hist(data, bins=10)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u997c\u56fe\uff08Pie Chart\uff09\uff1a<\/strong> \u7528\u4e8e\u5c55\u793a\u4e0d\u540c\u90e8\u5206\u5728\u6574\u4f53\u4e2d\u7684\u6bd4\u4f8b\u3002<\/p>\n<\/p>\n
plt.pie(sizes, labels=labels)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u56fe\u8868\u7f8e\u5316<\/h4>\n<\/p>\n
\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u989c\u8272\u3001\u7ebf\u578b\u3001\u6807\u8bb0\u7b49\u5c5e\u6027\u6765\u7f8e\u5316\u56fe\u8868\u3002<\/p>\n<\/p>\n
plt.plot(x, y, color='red', linestyle='--', marker='o')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd8\u53ef\u4ee5\u6dfb\u52a0\u7f51\u683c\u3001\u56fe\u4f8b\u7b49\u5143\u7d20\uff1a<\/p>\n<\/p>\n
plt.grid(True)<\/p>\n
plt.legend(['Data'])<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001SEABORN<\/h3>\n<\/p>\n
Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u66f4\u7b80\u6d01\u7684API\u548c\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\uff0c\u975e\u5e38\u9002\u5408\u7ed8\u5236\u7edf\u8ba1\u56fe\u8868\u3002<\/p>\n<\/p>\n
1\u3001\u5b89\u88c5\u548c\u57fa\u672c\u4f7f\u7528<\/h4>\n<\/p>\n
\u9996\u5148\u9700\u8981\u5b89\u88c5Seaborn\u5e93\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install seaborn<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u7ed8\u5236\u4e00\u4e2a\u7b80\u5355\u7684\u6563\u70b9\u56fe\uff1a<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
tips = sns.load_dataset("tips")<\/p>\n
\u521b\u5efa\u56fe\u8868<\/strong><\/h2>\n
sns.scatterplot(x="total_bill", y="tip", data=tips)<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5e38\u7528\u56fe\u8868\u7c7b\u578b<\/h4>\n<\/p>\n
\u6563\u70b9\u56fe\uff08Scatter Plot\uff09\uff1a<\/strong> \u7528\u4e8e\u5c55\u793a\u6570\u636e\u70b9\u7684\u5206\u5e03\u3002<\/p>\n<\/p>\n
sns.scatterplot(x="total_bill", y="tip", data=tips)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7bb1\u7ebf\u56fe\uff08Box Plot\uff09\uff1a<\/strong> \u7528\u4e8e\u5c55\u793a\u6570\u636e\u7684\u5206\u5e03\u548c\u79bb\u7fa4\u503c\u3002<\/p>\n<\/p>\n
sns.boxplot(x="day", y="total_bill", data=tips)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5c0f\u63d0\u7434\u56fe\uff08Violin Plot\uff09\uff1a<\/strong> \u7c7b\u4f3c\u4e8e\u7bb1\u7ebf\u56fe\uff0c\u4f46\u66f4\u8be6\u7ec6\u5730\u5c55\u793a\u4e86\u6570\u636e\u7684\u5206\u5e03\u3002<\/p>\n<\/p>\n
sns.violinplot(x="day", y="total_bill", data=tips)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u70ed\u529b\u56fe\uff08Heatmap\uff09\uff1a<\/strong> \u7528\u4e8e\u5c55\u793a\u6570\u636e\u7684\u76f8\u5173\u6027\u3002<\/p>\n<\/p>\n
sns.heatmap(data.corr(), annot=True)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u56fe\u8868\u7f8e\u5316<\/h4>\n<\/p>\n
Seaborn\u9ed8\u8ba4\u7684\u56fe\u8868\u6837\u5f0f\u5df2\u7ecf\u975e\u5e38\u7f8e\u89c2\uff0c\u4f46\u4ecd\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u8c03\u8272\u677f\u3001\u4e3b\u9898\u7b49\u5c5e\u6027\u8fdb\u4e00\u6b65\u7f8e\u5316\u56fe\u8868\u3002<\/p>\n<\/p>\n
sns.set_palette("husl")<\/p>\n
sns.set_style("whitegrid")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001PLOTLY<\/h3>\n<\/p>\n
Plotly\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u7ed8\u56fe\u5de5\u5177\uff0c\u7279\u522b\u9002\u7528\u4e8e\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\u3002\u5b83\u652f\u6301\u591a\u79cd\u56fe\u8868\u7c7b\u578b\uff0c\u5e76\u4e14\u53ef\u4ee5\u8f7b\u677e\u5730\u5d4c\u5165\u5230\u7f51\u9875\u4e2d\u3002<\/p>\n<\/p>\n
1\u3001\u5b89\u88c5\u548c\u57fa\u672c\u4f7f\u7528<\/h4>\n<\/p>\n
\u9996\u5148\u9700\u8981\u5b89\u88c5Plotly\u5e93\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install plotly<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u7ed8\u5236\u4e00\u4e2a\u7b80\u5355\u7684\u6298\u7ebf\u56fe\uff1a<\/p>\n<\/p>\n
import plotly.express as px<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
df = px.data.gapminder().query("country == 'Canada'")<\/p>\n
\u521b\u5efa\u56fe\u8868<\/strong><\/h2>\n
fig = px.line(df, x="year", y="gdpPercap", title="GDP per Capita in Canada")<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5e38\u7528\u56fe\u8868\u7c7b\u578b<\/h4>\n<\/p>\n
\u6298\u7ebf\u56fe\uff08Line Chart\uff09\uff1a<\/strong> \u7528\u4e8e\u5c55\u793a\u6570\u636e\u7684\u53d8\u5316\u8d8b\u52bf\u3002<\/p>\n<\/p>\n
fig = px.line(df, x="year", y="gdpPercap")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u6563\u70b9\u56fe\uff08Scatter Plot\uff09\uff1a<\/strong> \u7528\u4e8e\u5c55\u793a\u6570\u636e\u70b9\u7684\u5206\u5e03\u3002<\/p>\n<\/p>\n
fig = px.scatter(df, x="gdpPercap", y="lifeExp", color="continent")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u67f1\u72b6\u56fe\uff08Bar Chart\uff09\uff1a<\/strong> \u7528\u4e8e\u6bd4\u8f83\u4e0d\u540c\u7c7b\u522b\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
fig = px.bar(df, x="continent", y="pop")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u6c14\u6ce1\u56fe\uff08Bubble Chart\uff09\uff1a<\/strong> \u7c7b\u4f3c\u4e8e\u6563\u70b9\u56fe\uff0c\u4f46\u53ef\u4ee5\u901a\u8fc7\u6c14\u6ce1\u7684\u5927\u5c0f\u5c55\u793a\u7b2c\u4e09\u7ef4\u6570\u636e\u3002<\/p>\n<\/p>\n
fig = px.scatter(df, x="gdpPercap", y="lifeExp", size="pop", color="continent")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5730\u56fe\uff08Map\uff09\uff1a<\/strong> \u7528\u4e8e\u5c55\u793a\u5730\u7406\u6570\u636e\u3002<\/p>\n<\/p>\n
fig = px.choropleth(df, locations="iso_alpha", color="lifeExp", hover_name="country")<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u56fe\u8868\u7f8e\u5316<\/h4>\n<\/p>\n
Plotly\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u914d\u7f6e\u9009\u9879\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u989c\u8272\u3001\u6807\u8bb0\u3001\u5e03\u5c40\u7b49\u5c5e\u6027\u6765\u7f8e\u5316\u56fe\u8868\u3002<\/p>\n<\/p>\n
fig.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')))<\/p>\n
fig.update_layout(title='Customized Plot', xaxis_title='X Axis', yaxis_title='Y Axis')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001PANDAS<\/h3>\n<\/p>\n
Pandas\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u9664\u4e86\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u529f\u80fd\u5916\uff0c\u5b83\u8fd8\u5185\u7f6e\u4e86\u4e00\u4e9b\u57fa\u672c\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u53ef\u4ee5\u76f4\u63a5\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
1\u3001\u5b89\u88c5\u548c\u57fa\u672c\u4f7f\u7528<\/h4>\n<\/p>\n
\u9996\u5148\u9700\u8981\u5b89\u88c5Pandas\u5e93\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u7ed8\u5236\u4e00\u4e2a\u7b80\u5355\u7684\u6298\u7ebf\u56fe\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
data = {'Year': [2015, 2016, 2017, 2018, 2019],<\/p>\n
        'Sales': [200, 300, 400, 500, 600]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u521b\u5efa\u56fe\u8868<\/strong><\/h2>\n
df.plot(x='Year', y='Sales', kind='line')<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5e38\u7528\u56fe\u8868\u7c7b\u578b<\/h4>\n<\/p>\n
\u6298\u7ebf\u56fe\uff08Line Chart\uff09\uff1a<\/strong> \u7528\u4e8e\u5c55\u793a\u6570\u636e\u7684\u53d8\u5316\u8d8b\u52bf\u3002<\/p>\n<\/p>\n
df.plot(x='Year', y='Sales', kind='line')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u6563\u70b9\u56fe\uff08Scatter Plot\uff09\uff1a<\/strong> \u7528\u4e8e\u5c55\u793a\u6570\u636e\u70b9\u7684\u5206\u5e03\u3002<\/p>\n<\/p>\n
df.plot(x='Year', y='Sales', kind='scatter')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u67f1\u72b6\u56fe\uff08Bar Chart\uff09\uff1a<\/strong> \u7528\u4e8e\u6bd4\u8f83\u4e0d\u540c\u7c7b\u522b\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
df.plot(x='Year', y='Sales', kind='bar')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u76f4\u65b9\u56fe\uff08Histogram\uff09\uff1a<\/strong> \u7528\u4e8e\u5c55\u793a\u6570\u636e\u7684\u9891\u7387\u5206\u5e03\u3002<\/p>\n<\/p>\n
df['Sales'].plot(kind='hist', bins=5)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u997c\u56fe\uff08Pie Chart\uff09\uff1a<\/strong> \u7528\u4e8e\u5c55\u793a\u4e0d\u540c\u90e8\u5206\u5728\u6574\u4f53\u4e2d\u7684\u6bd4\u4f8b\u3002<\/p>\n<\/p>\n
df['Sales'].plot(kind='pie', autopct='%1.1f%%')<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u56fe\u8868\u7f8e\u5316<\/h4>\n<\/p>\n
\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u989c\u8272\u3001\u7ebf\u578b\u3001\u6807\u8bb0\u7b49\u5c5e\u6027\u6765\u7f8e\u5316\u56fe\u8868\u3002<\/p>\n<\/p>\n
df.plot(x='Year', y='Sales', kind='line', color='red', linestyle='--', marker='o')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd8\u53ef\u4ee5\u6dfb\u52a0\u6807\u9898\u3001\u6807\u7b7e\u7b49\u5143\u7d20\uff1a<\/p>\n<\/p>\n
plt.title("Sales Over Years")<\/p>\n
plt.xlabel("Year")<\/p>\n
plt.ylabel("Sales")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u7efc\u5408\u5b9e\u4f8b<\/h3>\n<\/p>\n
\u4e0b\u9762\u662f\u4e00\u4e2a\u7efc\u5408\u5b9e\u4f8b\uff0c\u5c55\u793a\u5982\u4f55\u4f7f\u7528\u4e0a\u8ff0\u5e93\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import seaborn as sns<\/p>\n
import plotly.express as px<\/p>\n
import pandas as pd<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
data = {'Year': [2015, 2016, 2017, 2018, 2019],<\/p>\n
        'Sales': [200, 300, 400, 500, 600]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
Matplotlib \u6298\u7ebf\u56fe<\/strong><\/h2>\n
plt.figure(figsize=(10, 5))<\/p>\n
plt.plot(df['Year'], df['Sales'], color='blue', linestyle='--', marker='o')<\/p>\n
plt.title("Sales Over Years (Matplotlib)")<\/p>\n
plt.xlabel("Year")<\/p>\n
plt.ylabel("Sales")<\/p>\n
plt.grid(True)<\/p>\n
plt.show()<\/p>\n
Seaborn \u6563\u70b9\u56fe<\/strong><\/h2>\n
plt.figure(figsize=(10, 5))<\/p>\n
sns.scatterplot(x='Year', y='Sales', data=df)<\/p>\n
plt.title("Sales Over Years (Seaborn)")<\/p>\n
plt.xlabel("Year")<\/p>\n
plt.ylabel("Sales")<\/p>\n
plt.grid(True)<\/p>\n
plt.show()<\/p>\n
Plotly \u67f1\u72b6\u56fe<\/strong><\/h2>\n
fig = px.bar(df, x='Year', y='Sales', title="Sales Over Years (Plotly)")<\/p>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u5b9e\u4f8b\uff0c\u53ef\u4ee5\u770b\u5230\u4e0d\u540c\u5e93\u7684\u7ed8\u56fe\u6548\u679c\u548c\u4f7f\u7528\u65b9\u6cd5\u3002\u9009\u62e9\u9002\u5408\u7684\u5e93\u548c\u56fe\u8868\u7c7b\u578b\uff0c\u80fd\u591f\u66f4\u597d\u5730\u5c55\u793a\u6570\u636e\u7684\u7279\u5f81\u548c\u8d8b\u52bf\u3002<\/p>\n<\/p>\n
\u516d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u7528Python\u753b\u6570\u636e\u56fe\u7684\u65b9\u6cd5\u6709\u5f88\u591a\u79cd\uff0c\u9009\u62e9\u5408\u9002\u7684\u5e93\u548c\u56fe\u8868\u7c7b\u578b\u662f\u5173\u952e\u3002Matplotlib\u3001Seaborn\u3001Plotly\u548cPandas\u662f\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\uff0c\u5404\u6709\u7279\u70b9\u3002<\/strong>Matplotlib\u529f\u80fd\u5f3a\u5927\u4f46\u7a0d\u663e\u590d\u6742\uff0cSeaborn\u7f8e\u89c2\u6613\u7528\uff0cPlotly\u9002\u5408\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\uff0cPandas\u5219\u9002\u5408\u4e0e\u6570\u636e\u5206\u6790\u7ed3\u5408\u4f7f\u7528\u3002\u638c\u63e1\u8fd9\u4e9b\u5de5\u5177\u7684\u57fa\u672c\u7528\u6cd5\u548c\u9ad8\u7ea7\u6280\u5de7\uff0c\u80fd\u591f\u6709\u6548\u5730\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\uff0c\u5e2e\u52a9\u66f4\u597d\u5730\u7406\u89e3\u548c\u5c55\u793a\u6570\u636e\u3002<\/strong><\/p>\n<\/p>\n
\u901a\u8fc7\u4e0d\u65ad\u5b9e\u8df5\u548c\u5b66\u4e60\uff0c\u53ef\u4ee5\u9010\u6b65\u63d0\u9ad8\u6570\u636e\u53ef\u89c6\u5316\u7684\u80fd\u529b\uff0c\u521b\u9020\u51fa\u66f4\u52a0\u4e13\u4e1a\u548c\u6709\u5438\u5f15\u529b\u7684\u6570\u636e\u56fe\u8868\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u9009\u62e9\u9002\u5408\u7684\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff1f<\/strong>
\u5728\u4f7f\u7528Python\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u65f6\uff0c\u6709\u591a\u4e2a\u5e93\u53ef\u4f9b\u9009\u62e9\u3002\u5e38\u89c1\u7684\u5e93\u5305\u62ecMatplotlib\u3001Seaborn\u548cPlotly\u7b49\u3002Matplotlib\u662f\u57fa\u7840\u5e93\uff0c\u9002\u5408\u7b80\u5355\u56fe\u8868\u7684\u7ed8\u5236\uff1bSeaborn\u57fa\u4e8eMatplotlib\uff0c\u63d0\u4f9b\u66f4\u7f8e\u89c2\u7684\u7edf\u8ba1\u56fe\u8868\uff1b\u800cPlotly\u5219\u9002\u5408\u4ea4\u4e92\u5f0f\u56fe\u8868\u7684\u5236\u4f5c\u3002\u6839\u636e\u4f60\u7684\u9700\u6c42\u548c\u6570\u636e\u7c7b\u578b\uff0c\u53ef\u4ee5\u9009\u62e9\u6700\u9002\u5408\u7684\u5e93\u3002<\/p>\n
\u5728Python\u4e2d\u5982\u4f55\u8bfb\u53d6\u6570\u636e\u4ee5\u4fbf\u8fdb\u884c\u53ef\u89c6\u5316\uff1f<\/strong>
\u4e3a\u4e86\u5728Python\u4e2d\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\uff0c\u9996\u5148\u9700\u8981\u8bfb\u53d6\u6570\u636e\u3002\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528Pandas\u5e93\u6765\u8bfb\u53d6CSV\u6587\u4ef6\u6216Excel\u6587\u4ef6\u3002\u53ef\u4ee5\u4f7f\u7528pd.read_csv('file.csv')<\/code>\u6216pd.read_excel('file.xlsx')<\/code>\u6765\u5bfc\u5165\u6570\u636e\u3002\u786e\u4fdd\u5728\u5bfc\u5165\u540e\u68c0\u67e5\u6570\u636e\u7684\u7ed3\u6784\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u8fdb\u884c\u540e\u7eed\u7684\u7ed8\u56fe\u64cd\u4f5c\u3002<\/p>\n
\u5982\u4f55\u81ea\u5b9a\u4e49\u56fe\u8868\u7684\u6837\u5f0f\u548c\u989c\u8272\uff1f<\/strong>
\u5728Python\u4e2d\u7ed8\u5236\u56fe\u8868\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u53c2\u6570\u6765\u81ea\u5b9a\u4e49\u56fe\u8868\u7684\u6837\u5f0f\u548c\u989c\u8272\u3002\u4f8b\u5982\uff0c\u5728Matplotlib\u4e2d\u53ef\u4ee5\u4f7f\u7528plt.plot()<\/code>\u51fd\u6570\u7684color<\/code>\u548clinestyle<\/code>\u53c2\u6570\u6765\u6539\u53d8\u7ebf\u6761\u7684\u989c\u8272\u548c\u6837\u5f0f\u3002Seaborn\u5219\u63d0\u4f9b\u4e3b\u9898\u8bbe\u7f6e\u529f\u80fd\uff0c\u53ef\u4ee5\u901a\u8fc7sns.set_style()<\/code>\u6765\u66f4\u6539\u56fe\u8868\u7684\u6574\u4f53\u5916\u89c2\u3002\u5229\u7528\u8fd9\u4e9b\u81ea\u5b9a\u4e49\u9009\u9879\uff0c\u53ef\u4ee5\u4f7f\u4f60\u7684\u56fe\u8868\u66f4\u5177\u5438\u5f15\u529b\u548c\u8868\u8fbe\u529b\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u7528Python\u753b\u6570\u636e\u56fe\u7684\u65b9\u6cd5\u6709\u5f88\u591a\u79cd\uff0c\u6700\u5e38\u7528\u7684\u5305\u62ec\u4f7f\u7528Matplotlib\u3001Seaborn\u3001Plotly\u3001Pa […]","protected":false},"author":3,"featured_media":1167542,"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\/1167532"}],"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=1167532"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1167532\/revisions"}],"predecessor-version":[{"id":1167544,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1167532\/revisions\/1167544"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1167542"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1167532"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1167532"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1167532"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}