{"id":1031857,"date":"2024-12-31T11:28:48","date_gmt":"2024-12-31T03:28:48","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1031857.html"},"modified":"2024-12-31T11:28:50","modified_gmt":"2024-12-31T03:28:50","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e7%bb%98%e5%88%b6%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e8%a1%a8","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1031857.html","title":{"rendered":"\u5982\u4f55\u7528Python\u7ed8\u5236\u6570\u636e\u5206\u6790\u8868"},"content":{"rendered":"

\"\u5982\u4f55\u7528Python\u7ed8\u5236\u6570\u636e\u5206\u6790\u8868\"<\/p>\n

\u4f7f\u7528Python\u7ed8\u5236\u6570\u636e\u5206\u6790\u8868\u7684\u51e0\u79cd\u65b9\u6cd5\u5305\u62ec\uff1aPandas\u3001Matplotlib\u3001Seaborn\u3001Plotly\u3002<\/strong> \u5176\u4e2d\uff0cPandas<\/strong> \u662f\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u7684\u5f3a\u5927\u5de5\u5177\uff0c\u5b83\u80fd\u591f\u8f7b\u677e\u521b\u5efa\u548c\u64cd\u4f5c\u6570\u636e\u8868\uff1bMatplotlib<\/strong> \u662f\u4e00\u79cd\u7075\u6d3b\u7684\u7ed8\u56fe\u5de5\u5177\uff0c\u53ef\u4ee5\u751f\u6210\u591a\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff1bSeaborn<\/strong> \u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u9002\u7528\u4e8e\u7edf\u8ba1\u56fe\u5f62\uff1bPlotly<\/strong> \u63d0\u4f9b\u4e86\u4ea4\u4e92\u5f0f\u56fe\u8868\uff0c\u9002\u5408\u4e8e\u7f51\u9875\u548c\u4eea\u8868\u677f\u5c55\u793a\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u5176\u4e2d\u7684\u51e0\u79cd\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

\u4e00\u3001Pandas<\/h3>\n<\/p>\n

1.1 \u6570\u636e\u5904\u7406\u4e0e\u57fa\u672c\u8868\u683c\u7ed8\u5236<\/h4>\n<\/p>\n

Pandas\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u6570\u636e\u5206\u6790\u5e93\u4e4b\u4e00\u3002\u5b83\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u7ed3\u6784\u548c\u6570\u636e\u5206\u6790\u5de5\u5177\uff0c\u5982DataFrame\u548cSeries\u3002DataFrame\u662f\u4e8c\u7ef4\u6807\u8bb0\u6570\u636e\u7ed3\u6784\uff0c\u7c7b\u4f3c\u4e8eExcel\u8868\u683c\u6216SQL\u8868\u3002<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684DataFrame<\/strong><\/h2>\n
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],<\/p>\n
        'Age': [25, 30, 35, 40],<\/p>\n
        'City': ['New York', 'Los Angeles', 'Chicago', 'Houston']}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.2 \u6570\u636e\u8bfb\u53d6\u4e0e\u5199\u5165<\/h4>\n<\/p>\n
Pandas\u53ef\u4ee5\u8f7b\u677e\u5730\u8bfb\u53d6\u548c\u5199\u5165\u5404\u79cd\u6587\u4ef6\u683c\u5f0f\uff0c\u5982CSV\u3001Excel\u3001SQL\u6570\u636e\u5e93\u7b49\u3002<\/p>\n<\/p>\n
# \u8bfb\u53d6CSV\u6587\u4ef6<\/p>\n
df = pd.read_csv('data.csv')<\/p>\n
\u5199\u5165CSV\u6587\u4ef6<\/strong><\/h2>\n
df.to_csv('output.csv', index=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.3 \u6570\u636e\u6e05\u6d17\u4e0e\u5904\u7406<\/h4>\n<\/p>\n
Pandas\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u5904\u7406\u529f\u80fd\uff0c\u5982\u7f3a\u5931\u503c\u5904\u7406\u3001\u6570\u636e\u8f6c\u6362\u3001\u6570\u636e\u805a\u5408\u7b49\u3002<\/p>\n<\/p>\n
# \u5904\u7406\u7f3a\u5931\u503c<\/p>\n
df.fillna(0, inplace=True)<\/p>\n
\u6570\u636e\u8f6c\u6362<\/strong><\/h2>\n
df['Age'] = df['Age'].astype(int)<\/p>\n
\u6570\u636e\u805a\u5408<\/strong><\/h2>\n
grouped_df = df.groupby('City').mean()<\/p>\n
print(grouped_df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001Matplotlib<\/h3>\n<\/p>\n
2.1 \u57fa\u672c\u7ed8\u56fe<\/h4>\n<\/p>\n
Matplotlib\u662fPython\u4e2d\u6700\u57fa\u7840\u7684\u7ed8\u56fe\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u53ef\u4ee5\u751f\u6210\u5404\u79cd\u9759\u6001\u3001\u52a8\u6001\u548c\u4ea4\u4e92\u5f0f\u56fe\u8868\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u7b80\u5355\u6298\u7ebf\u56fe<\/strong><\/h2>\n
plt.plot(df['Name'], df['Age'])<\/p>\n
plt.xlabel('Name')<\/p>\n
plt.ylabel('Age')<\/p>\n
plt.title('Age of People')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.2 \u5b50\u56fe\u4e0e\u5e03\u5c40<\/h4>\n<\/p>\n
Matplotlib\u8fd8\u53ef\u4ee5\u521b\u5efa\u5b50\u56fe\u548c\u590d\u6742\u7684\u56fe\u8868\u5e03\u5c40\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u5b50\u56fe<\/p>\n
fig, axes = plt.subplots(2, 2, figsize=(10, 10))<\/p>\n
\u7ed8\u5236\u4e0d\u540c\u7c7b\u578b\u7684\u56fe\u8868<\/strong><\/h2>\n
axes[0, 0].plot(df['Name'], df['Age'], 'r')<\/p>\n
axes[0, 0].set_title('Line Plot')<\/p>\n
axes[0, 1].bar(df['Name'], df['Age'])<\/p>\n
axes[0, 1].set_title('Bar Plot')<\/p>\n
axes[1, 0].scatter(df['Age'], df['City'])<\/p>\n
axes[1, 0].set_title('Scatter Plot')<\/p>\n
axes[1, 1].hist(df['Age'])<\/p>\n
axes[1, 1].set_title('Histogram')<\/p>\n
plt.tight_layout()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001Seaborn<\/h3>\n<\/p>\n
3.1 \u9ad8\u7ea7\u7edf\u8ba1\u56fe\u5f62<\/h4>\n<\/p>\n
Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u5b83\u7684\u4e3b\u8981\u4f18\u52bf\u5728\u4e8e\u80fd\u591f\u8f7b\u677e\u521b\u5efa\u6f02\u4eae\u7684\u7edf\u8ba1\u56fe\u5f62\u3002<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
\u7b80\u5355\u7684\u5206\u7c7b\u56fe<\/strong><\/h2>\n
sns.catplot(x='Name', y='Age', data=df)<\/p>\n
plt.title('Age of People by Name')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3.2 \u70ed\u529b\u56fe\u4e0e\u76f8\u5173\u77e9\u9635<\/h4>\n<\/p>\n
Seaborn\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u70ed\u529b\u56fe\u548c\u76f8\u5173\u77e9\u9635\uff0c\u9002\u5408\u7528\u4e8e\u63a2\u7d22\u6570\u636e\u4e4b\u95f4\u7684\u5173\u7cfb\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u76f8\u5173\u77e9\u9635<\/p>\n
corr = df.corr()<\/p>\n
\u7ed8\u5236\u70ed\u529b\u56fe<\/strong><\/h2>\n
sns.heatmap(corr, annot=True, cmap='coolwarm')<\/p>\n
plt.title('Correlation Matrix')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001Plotly<\/h3>\n<\/p>\n
4.1 \u4ea4\u4e92\u5f0f\u56fe\u8868<\/h4>\n<\/p>\n
Plotly\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u7ed8\u56fe\u5e93\uff0c\u9002\u7528\u4e8e\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\u3002\u5b83\u7279\u522b\u9002\u5408\u7528\u4e8e\u7f51\u9875\u548c\u4eea\u8868\u677f\u5c55\u793a\u3002<\/p>\n<\/p>\n
import plotly.express as px<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u4ea4\u4e92\u5f0f\u6298\u7ebf\u56fe<\/strong><\/h2>\n
fig = px.line(df, x='Name', y='Age', title='Age of People')<\/p>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
4.2 \u590d\u6742\u7684\u4ea4\u4e92\u5f0f\u56fe\u8868<\/h4>\n<\/p>\n
Plotly\u8fd8\u53ef\u4ee5\u521b\u5efa\u590d\u6742\u7684\u4ea4\u4e92\u5f0f\u56fe\u8868\uff0c\u59823D\u56fe\u3001\u5730\u7406\u56fe\u3001\u65f6\u95f4\u5e8f\u5217\u56fe\u7b49\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u4e00\u4e2a\u4ea4\u4e92\u5f0f3D\u6563\u70b9\u56fe<\/p>\n
fig = px.scatter_3d(df, x='Name', y='Age', z='City', color='City', title='3D Scatter Plot')<\/p>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u6848\u4f8b\u5206\u6790\uff1a\u4ece\u6570\u636e\u8bfb\u53d6\u5230\u56fe\u8868\u7ed8\u5236<\/h3>\n<\/p>\n
\u4e3a\u4e86\u66f4\u597d\u5730\u7406\u89e3\u5982\u4f55\u7528Python\u7ed8\u5236\u6570\u636e\u5206\u6790\u8868\uff0c\u6211\u4eec\u5c06\u901a\u8fc7\u4e00\u4e2a\u5177\u4f53\u6848\u4f8b\u8fdb\u884c\u6f14\u793a\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5305\u542b\u9500\u552e\u6570\u636e\u7684CSV\u6587\u4ef6\uff0c\u6211\u4eec\u5c06\u4ece\u6570\u636e\u8bfb\u53d6\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u5904\u7406\u548c\u56fe\u8868\u7ed8\u5236\u7b49\u65b9\u9762\u8fdb\u884c\u8be6\u7ec6\u4ecb\u7ecd\u3002<\/p>\n<\/p>\n
5.1 \u6570\u636e\u8bfb\u53d6\u4e0e\u9884\u5904\u7406<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u8bfb\u53d6CSV\u6587\u4ef6\u5e76\u8fdb\u884c\u57fa\u672c\u7684\u6570\u636e\u9884\u5904\u7406\u3002<\/p>\n<\/p>\n
# \u8bfb\u53d6CSV\u6587\u4ef6<\/p>\n
sales_data = pd.read_csv('sales_data.csv')<\/p>\n
\u67e5\u770b\u6570\u636e\u57fa\u672c\u4fe1\u606f<\/strong><\/h2>\n
print(sales_data.info())<\/p>\n
\u5904\u7406\u7f3a\u5931\u503c<\/strong><\/h2>\n
sales_data.fillna(0, inplace=True)<\/p>\n
\u6570\u636e\u8f6c\u6362<\/strong><\/h2>\n
sales_data['Sales'] = sales_data['Sales'].astype(float)<\/p>\n
<\/code><\/pre>\n<\/p>\n
5.2 \u6570\u636e\u5206\u6790\u4e0e\u805a\u5408<\/h4>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u8fdb\u884c\u6570\u636e\u5206\u6790\u4e0e\u805a\u5408\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u3002<\/p>\n<\/p>\n
# \u6309\u6708\u4efd\u805a\u5408\u9500\u552e\u6570\u636e<\/p>\n
monthly_sales = sales_data.groupby('Month')['Sales'].sum().reset_index()<\/p>\n
\u6309\u4ea7\u54c1\u7c7b\u522b\u805a\u5408\u9500\u552e\u6570\u636e<\/strong><\/h2>\n
category_sales = sales_data.groupby('Category')['Sales'].sum().reset_index()<\/p>\n
<\/code><\/pre>\n<\/p>\n
5.3 \u7ed8\u5236\u6570\u636e\u5206\u6790\u8868<\/h4>\n<\/p>\n
\u6211\u4eec\u5c06\u4f7f\u7528Matplotlib\u548cSeaborn\u7ed8\u5236\u6570\u636e\u5206\u6790\u8868\uff0c\u4ee5\u4fbf\u66f4\u76f4\u89c2\u5730\u5c55\u793a\u6570\u636e\u3002<\/p>\n<\/p>\n
# \u7ed8\u5236\u6708\u5ea6\u9500\u552e\u6298\u7ebf\u56fe<\/p>\n
plt.plot(monthly_sales['Month'], monthly_sales['Sales'])<\/p>\n
plt.xlabel('Month')<\/p>\n
plt.ylabel('Sales')<\/p>\n
plt.title('Monthly Sales')<\/p>\n
plt.show()<\/p>\n
\u7ed8\u5236\u4ea7\u54c1\u7c7b\u522b\u9500\u552e\u67f1\u72b6\u56fe<\/strong><\/h2>\n
sns.barplot(x='Category', y='Sales', data=category_sales)<\/p>\n
plt.xlabel('Category')<\/p>\n
plt.ylabel('Sales')<\/p>\n
plt.title('Sales by Category')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Python\u8f7b\u677e\u7ed8\u5236\u6570\u636e\u5206\u6790\u8868\u3002Pandas<\/strong> \u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u529f\u80fd\uff0cMatplotlib<\/strong> \u548c Seaborn<\/strong> \u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7ed8\u56fe\u529f\u80fd\uff0cPlotly<\/strong> \u63d0\u4f9b\u4e86\u4ea4\u4e92\u5f0f\u56fe\u8868\u7684\u80fd\u529b\u3002\u7ed3\u5408\u8fd9\u4e9b\u5de5\u5177\uff0c\u6211\u4eec\u53ef\u4ee5\u9ad8\u6548\u5730\u5b8c\u6210\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u5de5\u4f5c\u3002\u5e0c\u671b\u672c\u6587\u5bf9\u4f60\u5728\u6570\u636e\u5206\u6790\u548c\u7ed8\u56fe\u65b9\u9762\u6709\u6240\u5e2e\u52a9\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u9009\u62e9\u5408\u9002\u7684Python\u5e93\u6765\u7ed8\u5236\u6570\u636e\u5206\u6790\u8868\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u6709\u591a\u79cd\u5e93\u53ef\u7528\u4e8e\u7ed8\u5236\u6570\u636e\u5206\u6790\u8868\u3002Pandas\u662f\u5904\u7406\u6570\u636e\u7684\u9996\u9009\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u6846\u67b6\uff0c\u4fbf\u4e8e\u6570\u636e\u6e05\u6d17\u548c\u5904\u7406\u3002Matplotlib\u548cSeaborn\u5219\u662f\u89c6\u89c9\u5316\u6570\u636e\u7684\u4f18\u9009\u5e93\u3002Matplotlib\u9002\u5408\u7ed8\u5236\u57fa\u672c\u56fe\u8868\uff0c\u800cSeaborn\u5219\u63d0\u4f9b\u4e86\u66f4\u7f8e\u89c2\u7684\u7edf\u8ba1\u56fe\u8868\u3002\u6839\u636e\u4f60\u7684\u5177\u4f53\u9700\u6c42\uff0c\u53ef\u4ee5\u9009\u62e9\u9002\u5408\u7684\u5e93\u6765\u5448\u73b0\u6570\u636e\u3002<\/p>\n
\u5728Python\u4e2d\u5982\u4f55\u5904\u7406\u6570\u636e\u4ee5\u4fbf\u4e8e\u7ed8\u5236\u5206\u6790\u8868\uff1f<\/strong>
\u5728\u7ed8\u5236\u6570\u636e\u5206\u6790\u8868\u4e4b\u524d\uff0c\u901a\u5e38\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u9884\u5904\u7406\u3002\u4f7f\u7528Pandas\u8bfb\u53d6\u6570\u636e\u6587\u4ef6\uff08\u5982CSV\uff09\uff0c\u53ef\u4ee5\u901a\u8fc7\u6570\u636e\u6e05\u7406\u3001\u7f3a\u5931\u503c\u5904\u7406\u548c\u6570\u636e\u8f6c\u6362\u7b49\u6b65\u9aa4\u6765\u51c6\u5907\u6570\u636e\u3002\u6b64\u5916\uff0c\u53ef\u4ee5\u4f7f\u7528groupby\u51fd\u6570\u8fdb\u884c\u6570\u636e\u805a\u5408\uff0c\u4fbf\u4e8e\u540e\u7eed\u7684\u53ef\u89c6\u5316\u3002\u786e\u4fdd\u6570\u636e\u7684\u683c\u5f0f\u548c\u7ed3\u6784\u9002\u5408\u7ed8\u5236\u6240\u9700\u7684\u56fe\u8868\u7c7b\u578b\u3002<\/p>\n
\u5982\u4f55\u63d0\u9ad8\u6570\u636e\u5206\u6790\u8868\u7684\u53ef\u89c6\u5316\u6548\u679c\uff1f<\/strong>
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