![\"\u5982\u4f55\u7528python\u5236\u4f5c\u62a5\u8868\"](\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25164305\/dfe66806-f06a-4ff7-999c-c5a5ac413ace.webp\")
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\u5728Python\u4e2d\u5236\u4f5c\u62a5\u8868\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u5de5\u5177\u548c\u5e93\u6765\u5b9e\u73b0\u3002\u5229\u7528Pandas\u8fdb\u884c\u6570\u636e\u5904\u7406\u3001\u901a\u8fc7Matplotlib\u6216Seaborn\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u3001\u4f7f\u7528ReportLab\u751f\u6210PDF\u3001\u91c7\u7528openpyxl\u6216xlsxwriter\u521b\u5efaExcel\u62a5\u8868<\/strong>\uff0c\u8fd9\u4e9b\u6b65\u9aa4\u6784\u6210\u4e86\u4e00\u4e2a\u5b8c\u6574\u7684\u62a5\u8868\u5236\u4f5c\u6d41\u7a0b\u3002\u5728\u8fd9\u5176\u4e2d\uff0cPandas\u662f\u6570\u636e\u5904\u7406\u7684\u6838\u5fc3\u5de5\u5177\uff0c\u800cMatplotlib\u548cSeaborn\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u53ef\u89c6\u5316\u529f\u80fd\u3002\u8be6\u7ec6\u7684\u62a5\u8868\u53ef\u4ee5\u901a\u8fc7ReportLab\u751f\u6210PDF\u683c\u5f0f\uff0c\u6216\u8005\u901a\u8fc7openpyxl\u548cxlsxwriter\u521b\u5efaExcel\u6587\u4ef6\u3002\u4ee5\u4e0b\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u5de5\u5177\u5236\u4f5c\u4e13\u4e1a\u7684Python\u62a5\u8868\u3002<\/p>\n<\/p>\n Python\u4e2d\u7684Pandas\u5e93\u662f\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u7684\u5f3a\u5927\u5de5\u5177\u3002\u5b83\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u636e\u7ed3\u6784\u548c\u6570\u636e\u5206\u6790\u529f\u80fd\uff0c\u975e\u5e38\u9002\u5408\u7528\u4e8e\u5904\u7406\u548c\u5206\u6790\u62a5\u8868\u6570\u636e\u3002<\/p>\n<\/p>\n Pandas\u7684\u6838\u5fc3\u6570\u636e\u7ed3\u6784\u662fSeries\u548cDataFrame\u3002Series\u662f\u4e00\u7ef4\u7684\u6570\u636e\u7ed3\u6784\uff0c\u53ef\u4ee5\u7406\u89e3\u4e3a\u5e26\u6807\u7b7e\u7684\u6570\u7ec4\uff1bDataFrame\u662f\u4e8c\u7ef4\u7684\u8868\u683c\u6570\u636e\u7ed3\u6784\uff0c\u7c7b\u4f3c\u4e8e\u7535\u5b50\u8868\u683c\u6216SQL\u8868\u3002<\/strong>DataFrame\u7684\u6bcf\u4e00\u5217\u90fd\u662f\u4e00\u4e2aSeries\u3002<\/p>\n<\/p>\n \u521b\u5efaDataFrame\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\uff0c\u5982\u4ece\u5b57\u5178\u3001\u5217\u8868\u3001Numpy\u6570\u7ec4\u6216\u8bfb\u53d6\u6587\u4ef6\uff08CSV\u3001Excel\u7b49\uff09\u3002\u4f8b\u5982\uff0c\u4ece\u5b57\u5178\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684DataFrame\uff1a<\/p>\n<\/p>\n data = {<\/p>\n 'Name': ['Alice', 'Bob', 'Charlie'],<\/p>\n 'Age': [25, 30, 35],<\/p>\n 'Salary': [50000, 60000, 70000]<\/p>\n }<\/p>\n df = pd.DataFrame(data)<\/p>\n <\/code><\/pre>\n<\/p>\n \u6570\u636e\u6e05\u6d17\u5305\u62ec\u5904\u7406\u7f3a\u5931\u503c\u3001\u91cd\u590d\u6570\u636e\u3001\u6570\u636e\u7c7b\u578b\u8f6c\u6362\u7b49\u3002Pandas\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u51fd\u6570\u6765\u7b80\u5316\u8fd9\u4e9b\u64cd\u4f5c\u3002<\/p>\n<\/p>\n \u4f8b\u5982\uff1a<\/p>\n<\/p>\n df.drop_duplicates(inplace=True) # \u5220\u9664\u91cd\u590d\u884c<\/p>\n df['Salary'] = df['Salary'].astype(float) # \u5c06\u85aa\u6c34\u8f6c\u6362\u4e3a\u6d6e\u70b9\u578b<\/p>\n <\/code><\/pre>\n<\/p>\n Pandas\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u529f\u80fd\uff0c\u5982\u5206\u7ec4\u3001\u805a\u5408\u3001\u900f\u89c6\u8868\u7b49\u3002<\/p>\n<\/p>\n \u5206\u7ec4\u4e0e\u805a\u5408<\/strong>\uff1a\u4f7f\u7528 <\/code><\/pre>\n<\/p>\n<\/li>\n \u900f\u89c6\u8868<\/strong>\uff1a\u4f7f\u7528 <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ul>\n \u6570\u636e\u53ef\u89c6\u5316\u662f\u62a5\u8868\u5236\u4f5c\u7684\u91cd\u8981\u90e8\u5206\u3002Python\u4e2d\u6709\u8bb8\u591a\u53ef\u89c6\u5316\u5e93\uff0c\u5982Matplotlib\u3001Seaborn\u3001Plotly\u7b49\u3002<\/p>\n<\/p>\n Matplotlib\u662fPython\u4e2d\u6700\u57fa\u7840\u7684\u7ed8\u56fe\u5e93\uff0c\u529f\u80fd\u5f3a\u5927\uff0c\u53ef\u4ee5\u521b\u5efa\u591a\u79cd\u56fe\u8868\uff0c\u5982\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u997c\u56fe\u7b49\u3002<\/p>\n<\/p>\n \u6298\u7ebf\u56fe<\/strong>\uff1a\u9002\u5408\u5c55\u793a\u6570\u636e\u7684\u53d8\u5316\u8d8b\u52bf\u3002<\/p>\n<\/p>\n plt.plot(df['Date'], df['Sales'])<\/p>\n plt.title('Sales Over Time')<\/p>\n plt.xlabel('Date')<\/p>\n plt.ylabel('Sales')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n<\/li>\n \u67f1\u72b6\u56fe<\/strong>\uff1a\u9002\u5408\u6bd4\u8f83\u4e0d\u540c\u7c7b\u522b\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n plt.title('Sales by Product')<\/p>\n plt.xlabel('Product')<\/p>\n plt.ylabel('Sales')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ul>\n Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u53ef\u89c6\u5316\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u7b80\u6d01\u7684API\u548c\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\u3002<\/p>\n<\/p>\n \u7bb1\u7ebf\u56fe<\/strong>\uff1a\u7528\u4e8e\u5c55\u793a\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002<\/p>\n<\/p>\n sns.boxplot(x='Category', y='Sales', data=df)<\/p>\n plt.title('Sales Distribution by Category')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n<\/li>\n \u70ed\u529b\u56fe<\/strong>\uff1a\u9002\u5408\u5c55\u793a\u77e9\u9635\u6570\u636e\u7684\u5173\u7cfb\u3002<\/p>\n<\/p>\n plt.title('Correlation Matrix')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ul>\n \u751f\u6210PDF\u62a5\u8868\u53ef\u4ee5\u4f7f\u7528ReportLab\u5e93\uff0c\u5b83\u662f\u4e00\u4e2a\u5f3a\u5927\u7684PDF\u751f\u6210\u5e93\uff0c\u53ef\u4ee5\u521b\u5efa\u590d\u6742\u7684PDF\u6587\u6863\u3002<\/p>\n<\/p>\n \u9996\u5148\u9700\u8981\u5b89\u88c5ReportLab\u5e93\uff1a<\/p>\n<\/p>\n <\/code><\/pre>\n<\/p>\n \u4f7f\u7528ReportLab\u521b\u5efaPDF\u6587\u6863\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7Canvas\u5bf9\u8c61\u7ed8\u5236\u6587\u672c\u3001\u56fe\u5f62\u548c\u56fe\u50cf\u3002<\/p>\n<\/p>\n \u521b\u5efaCanvas<\/strong>\uff1a\u9996\u5148\u521b\u5efa\u4e00\u4e2aCanvas\u5bf9\u8c61\uff0c\u6307\u5b9aPDF\u6587\u4ef6\u7684\u540d\u79f0\u548c\u5c3a\u5bf8\u3002<\/p>\n<\/p>\n c = canvas.Canvas("report.pdf")<\/p>\n <\/code><\/pre>\n<\/p>\n<\/li>\n \u7ed8\u5236\u6587\u672c<\/strong>\uff1a\u4f7f\u7528 <\/code><\/pre>\n<\/p>\n<\/li>\n \u7ed8\u5236\u56fe\u5f62\u548c\u56fe\u50cf<\/strong>\uff1a\u4f7f\u7528 <\/code><\/pre>\n<\/p>\n<\/li>\n \u4fdd\u5b58PDF<\/strong>\uff1a\u6700\u540e\u8c03\u7528 <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ul>\n \u53ef\u4ee5\u4f7f\u7528ReportLab\u7684Table\u7c7b\u6765\u521b\u5efaPDF\u4e2d\u7684\u8868\u683c\u3002<\/p>\n<\/p>\n from reportlab.lib.pagesizes import letter<\/p>\n from reportlab.platypus import SimpleDocTemplate, Table, TableStyle<\/p>\n document = SimpleDocTemplate("report_table.pdf", pagesize=letter)<\/p>\n data = [['Name', 'Age', 'Salary'], ['Alice', 25, 50000], ['Bob', 30, 60000]]<\/p>\n table = Table(data)<\/p>\n style = TableStyle([('BACKGROUND', (0, 0), (-1, 0), colors.grey),<\/p>\n ('TEXTCOLOR', (0, 0), (-1, 0), colors.whitesmoke),<\/p>\n ('ALIGN', (0, 0), (-1, -1), 'CENTER'),<\/p>\n ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),<\/p>\n ('BOTTOMPADDING', (0, 0), (-1, 0), 12),<\/p>\n ('BACKGROUND', (0, 1), (-1, -1), colors.beige),<\/p>\n ('GRID', (0, 0), (-1, -1), 1, colors.black)])<\/p>\n table.setStyle(style)<\/p>\n elements = [table]<\/p>\n document.build(elements)<\/p>\n <\/code><\/pre>\n<\/p>\n Python\u4e2d\u751f\u6210Excel\u62a5\u8868\u5e38\u7528\u7684\u5e93\u6709openpyxl\u548cxlsxwriter\u3002\u5b83\u4eec\u90fd\u53ef\u4ee5\u521b\u5efa\u3001\u4fee\u6539Excel\u6587\u4ef6\u3002<\/p>\n<\/p>\n openpyxl\u53ef\u4ee5\u8bfb\u5199Excel 2010 xlsx\/xlsm\/xltx\/xltm\u6587\u4ef6\u3002<\/p>\n<\/p>\n \u5b89\u88c5openpyxl<\/strong>\uff1a<\/p>\n<\/p>\n <\/code><\/pre>\n<\/p>\n<\/li>\n \u521b\u5efaExcel\u6587\u4ef6<\/strong>\uff1a<\/p>\n<\/p>\n wb = Workbook()<\/p>\n ws = wb.active<\/p>\n ws.title = "Sales Report"<\/p>\n ws.append(["Name", "Age", "Salary"])<\/p>\n ws.append(["Alice", 25, 50000])<\/p>\n ws.append(["Bob", 30, 60000])<\/p>\n wb.save("report.xlsx")<\/p>\n <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ul>\n xlsxwriter\u9002\u5408\u521b\u5efa\u590d\u6742\u683c\u5f0f\u7684Excel\u6587\u4ef6\u3002<\/p>\n<\/p>\n \u5b89\u88c5xlsxwriter<\/strong>\uff1a<\/p>\n<\/p>\n <\/code><\/pre>\n<\/p>\n<\/li>\n \u521b\u5efaExcel\u6587\u4ef6<\/strong>\uff1a<\/p>\n<\/p>\n workbook = xlsxwriter.Workbook('report.xlsx')<\/p>\n worksheet = workbook.add_worksheet()<\/p>\n worksheet.write('A1', 'Name')<\/p>\n worksheet.write('B1', 'Age')<\/p>\n worksheet.write('C1', 'Salary')<\/p>\n worksheet.write('A2', 'Alice')<\/p>\n worksheet.write('B2', 25)<\/p>\n worksheet.write('C2', 50000)<\/p>\n worksheet.write('A3', 'Bob')<\/p>\n worksheet.write('B3', 30)<\/p>\n worksheet.write('C3', 60000)<\/p>\n workbook.close()<\/p>\n <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ul>\n \u901a\u8fc7\u8bbe\u7f6e\u5355\u5143\u683c\u683c\u5f0f\u53ef\u4ee5\u8ba9Excel\u6587\u4ef6\u66f4\u7f8e\u89c2\u3002<\/p>\n<\/p>\n bold = workbook.add_format({'bold': True})<\/p>\n money = workbook.add_format({'num_format': '$#,##0'})<\/p>\n worksheet.write('A1', 'Name', bold)<\/p>\n worksheet.write('B1', 'Age', bold)<\/p>\n worksheet.write('C1', 'Salary', bold)<\/p>\n worksheet.write('C2', 50000, money)<\/p>\n worksheet.write('C3', 60000, money)<\/p>\n <\/code><\/pre>\n<\/p>\n \u901a\u8fc7Python\u5236\u4f5c\u62a5\u8868\u7684\u8fc7\u7a0b\u6d89\u53ca\u591a\u4e2a\u6b65\u9aa4\u548c\u5de5\u5177\u3002\u9996\u5148\u4f7f\u7528Pandas\u8fdb\u884c\u6570\u636e\u5904\u7406\u548c\u5206\u6790\uff0c\u7136\u540e\u901a\u8fc7Matplotlib\u6216Seaborn\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\uff0c\u6700\u540e\u751f\u6210PDF\u6216Excel\u683c\u5f0f\u7684\u62a5\u8868<\/strong>\u3002\u8fd9\u4e9b\u5de5\u5177\u548c\u5e93\u7684\u7ed3\u5408\u4f7f\u7528\uff0c\u53ef\u4ee5\u5b9e\u73b0\u4ece\u6570\u636e\u83b7\u53d6\u3001\u5904\u7406\u3001\u5206\u6790\u5230\u6700\u7ec8\u62a5\u8868\u751f\u6210\u7684\u5b8c\u6574\u6d41\u7a0b\u3002Python\u7684\u7075\u6d3b\u6027\u548c\u5f3a\u5927\u7684\u751f\u6001\u7cfb\u7edf\u4f7f\u5176\u6210\u4e3a\u62a5\u8868\u5236\u4f5c\u7684\u7406\u60f3\u9009\u62e9\u3002\u65e0\u8bba\u662f\u7b80\u5355\u7684\u8868\u683c\u8fd8\u662f\u590d\u6742\u7684\u53ef\u89c6\u5316\u56fe\u8868\uff0cPython\u90fd\u80fd\u4ee5\u9ad8\u6548\u4e14\u4e13\u4e1a\u7684\u65b9\u5f0f\u5b8c\u6210\u3002<\/p>\n<\/p>\n \u5982\u4f55\u9009\u62e9\u5408\u9002\u7684Python\u5e93\u6765\u5236\u4f5c\u62a5\u8868\uff1f<\/strong> \u4f7f\u7528Python\u5236\u4f5c\u62a5\u8868\u65f6\uff0c\u5982\u4f55\u5904\u7406\u6570\u636e\u7684\u6e05\u6d17\u4e0e\u8f6c\u6362\uff1f<\/strong> \u5982\u4f55\u5c06\u751f\u6210\u7684Python\u62a5\u8868\u5bfc\u51fa\u4e3a\u4e0d\u540c\u683c\u5f0f\uff1f<\/strong>\u4e00\u3001\u6570\u636e\u5904\u7406\u4e0e\u5206\u6790<\/h3>\n<\/p>\n
1.1 Pandas\u6570\u636e\u7ed3\u6784<\/h4>\n<\/p>\n
import pandas as pd<\/p>\n1.2 \u6570\u636e\u6e05\u6d17\u4e0e\u5904\u7406<\/h4>\n<\/p>\n
\n
df.dropna()<\/code>\u5220\u9664\u7f3a\u5931\u503c\uff0c\u6216\u4f7f\u7528df.fillna(value)<\/code>\u586b\u5145\u7f3a\u5931\u503c\u3002<\/li>\ndf.drop_duplicates()<\/code>\u53ef\u4ee5\u5220\u9664\u91cd\u590d\u884c\u3002<\/li>\ndf.astype()<\/code>\u53ef\u4ee5\u8f6c\u6362\u6570\u636e\u7c7b\u578b\u3002<\/li>\n<\/ul>\ndf['Age'] = df['Age'].fillna(df['Age'].mean()) # \u586b\u5145\u7f3a\u5931\u7684\u5e74\u9f84\u503c<\/p>\n1.3 \u6570\u636e\u5206\u6790<\/h4>\n<\/p>\n
\n
groupby()<\/code>\u51fd\u6570\u53ef\u4ee5\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\u5e76\u805a\u5408\u3002<\/p>\n<\/p>\ngrouped = df.groupby('Department').agg({'Salary': 'mean', 'Age': 'max'})<\/p>\npivot_table()<\/code>\u53ef\u4ee5\u521b\u5efa\u7c7b\u4f3cExcel\u7684\u900f\u89c6\u8868\u3002<\/p>\n<\/p>\npivot = df.pivot_table(index='Department', values='Salary', aggfunc='mean')<\/p>\n\u4e8c\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h3>\n<\/p>\n
2.1 Matplotlib<\/h4>\n<\/p>\n
\n
import matplotlib.pyplot as plt<\/p>\nplt.bar(df['Product'], df['Sales'])<\/p>\n2.2 Seaborn<\/h4>\n<\/p>\n
\n
import seaborn as sns<\/p>\nsns.heatmap(df.corr(), annot=True, cmap='coolwarm')<\/p>\n\u4e09\u3001\u751f\u6210PDF\u62a5\u8868<\/h3>\n<\/p>\n
3.1 \u5b89\u88c5ReportLab<\/h4>\n<\/p>\n
pip install reportlab<\/p>\n3.2 \u521b\u5efaPDF\u6587\u6863<\/h4>\n<\/p>\n
\n
from reportlab.pdfgen import canvas<\/p>\ndrawString()<\/code>\u65b9\u6cd5\u53ef\u4ee5\u5728PDF\u4e0a\u7ed8\u5236\u6587\u672c\u3002<\/p>\n<\/p>\nc.drawString(100, 750, "Sales Report")<\/p>\ndrawImage()<\/code>\u53ef\u4ee5\u5728PDF\u4e2d\u63d2\u5165\u56fe\u50cf\u3002<\/p>\n<\/p>\nc.drawImage("chart.png", 100, 500, width=200, height=100)<\/p>\nsave()<\/code>\u65b9\u6cd5\u4fdd\u5b58PDF\u6587\u4ef6\u3002<\/p>\n<\/p>\nc.save()<\/p>\n3.3 \u6dfb\u52a0\u8868\u683c<\/h4>\n<\/p>\n
from reportlab.lib import colors<\/p>\n\u56db\u3001\u751f\u6210Excel\u62a5\u8868<\/h3>\n<\/p>\n
4.1 \u4f7f\u7528openpyxl<\/h4>\n<\/p>\n
\n
pip install openpyxl<\/p>\nfrom openpyxl import Workbook<\/p>\n\u6dfb\u52a0\u6570\u636e<\/strong><\/h2>\n
\u4fdd\u5b58\u6587\u4ef6<\/strong><\/h2>\n
4.2 \u4f7f\u7528xlsxwriter<\/h4>\n<\/p>\n
\n
pip install XlsxWriter<\/p>\nimport xlsxwriter<\/p>\n\u6dfb\u52a0\u6570\u636e<\/strong><\/h2>\n
\u4fdd\u5b58\u6587\u4ef6<\/strong><\/h2>\n
4.3 \u683c\u5f0f\u5316Excel\u6587\u4ef6<\/h4>\n<\/p>\n
# \u521b\u5efa\u683c\u5f0f<\/p>\n\u5e94\u7528\u683c\u5f0f<\/strong><\/h2>\n
\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
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\u6570\u636e\u6e05\u6d17\u662f\u5236\u4f5c\u9ad8\u8d28\u91cf\u62a5\u8868\u7684\u91cd\u8981\u4e00\u6b65\u3002\u4f7f\u7528Pandas\u5e93\uff0c\u4f60\u53ef\u4ee5\u8f7b\u677e\u5730\u5904\u7406\u7f3a\u5931\u503c\u3001\u91cd\u590d\u6570\u636e\u548c\u6570\u636e\u7c7b\u578b\u8f6c\u6362\u7b49\u95ee\u9898\u3002\u901a\u8fc7\u65b9\u6cd5\u5982dropna()<\/code>\u3001fillna()<\/code>\u3001astype()<\/code>\u7b49\uff0c\u53ef\u4ee5\u786e\u4fdd\u6570\u636e\u7684\u51c6\u786e\u6027\u548c\u4e00\u81f4\u6027\uff0c\u4e3a\u540e\u7eed\u7684\u62a5\u8868\u751f\u6210\u6253\u4e0b\u575a\u5b9e\u7684\u57fa\u7840\u3002\u638c\u63e1\u6570\u636e\u6e05\u6d17\u6280\u5de7\uff0c\u5c06\u4f7f\u5f97\u62a5\u8868\u66f4\u52a0\u53ef\u9760\u548c\u4e13\u4e1a\u3002<\/p>\n
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