{"id":1174477,"date":"2025-01-15T17:18:34","date_gmt":"2025-01-15T09:18:34","guid":{"rendered":""},"modified":"2025-01-15T17:18:39","modified_gmt":"2025-01-15T09:18:39","slug":"python%e5%a6%82%e4%bd%95%e5%af%bc%e5%85%a5excel%e6%95%b0%e6%8d%ae%e9%9b%86","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1174477.html","title":{"rendered":"python\u5982\u4f55\u5bfc\u5165excel\u6570\u636e\u96c6"},"content":{"rendered":"

\"python\u5982\u4f55\u5bfc\u5165excel\u6570\u636e\u96c6\"<\/p>\n

Python \u5bfc\u5165 Excel \u6570\u636e\u96c6\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5e38\u89c1\u7684\u65b9\u5f0f\u5305\u62ec\uff1a\u4f7f\u7528 pandas \u5e93\u3001\u4f7f\u7528 openpyxl \u5e93\u3001\u4f7f\u7528 xlrd \u5e93\u3002<\/strong>\u5176\u4e2d\uff0cpandas \u5e93<\/strong>\u662f\u6700\u5e38\u7528\u7684\u65b9\u6cd5\u4e4b\u4e00\uff0c\u56e0\u4e3a\u5b83\u529f\u80fd\u5f3a\u5927\u4e14\u4f7f\u7528\u7b80\u5355\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528 pandas \u5e93\u5bfc\u5165 Excel \u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528 Pandas \u5e93<\/h3>\n<\/p>\n

1\u3001\u5b89\u88c5 Pandas \u5e93<\/h4>\n<\/p>\n

\u9996\u5148\uff0c\u786e\u4fdd\u60a8\u7684 Python \u73af\u5883\u4e2d\u5df2\u7ecf\u5b89\u88c5\u4e86 pandas \u5e93\u3002\u5982\u679c\u6ca1\u6709\u5b89\u88c5\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
2\u3001\u5bfc\u5165 Pandas \u5e93<\/h4>\n<\/p>\n
\u5728\u60a8\u7684 Python \u811a\u672c\u4e2d\u5bfc\u5165 pandas \u5e93\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u8bfb\u53d6 Excel \u6587\u4ef6<\/h4>\n<\/p>\n
\u4f7f\u7528 pandas.read_excel()<\/code> \u51fd\u6570\u8bfb\u53d6 Excel \u6587\u4ef6\uff0c\u5e76\u5c06\u5176\u5b58\u50a8\u5728\u4e00\u4e2a DataFrame \u4e2d\uff1a<\/p>\n<\/p>\n
df = pd.read_excel('path_to_file.xlsx')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5176\u4e2d\uff0cpath_to_file.xlsx<\/code> \u662f\u60a8\u7684 Excel \u6587\u4ef6\u7684\u8def\u5f84\u3002\u60a8\u53ef\u4ee5\u6307\u5b9a\u6587\u4ef6\u7684\u5b8c\u6574\u8def\u5f84\u6216\u76f8\u5bf9\u8def\u5f84\u3002<\/p>\n<\/p>\n
4\u3001\u6307\u5b9a\u5de5\u4f5c\u8868\u540d\u79f0<\/h4>\n<\/p>\n
\u5982\u679c\u60a8\u7684 Excel \u6587\u4ef6\u4e2d\u5305\u542b\u591a\u4e2a\u5de5\u4f5c\u8868\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528 sheet_name<\/code> \u53c2\u6570\u6307\u5b9a\u8981\u8bfb\u53d6\u7684\u5de5\u4f5c\u8868\u540d\u79f0\uff1a<\/p>\n<\/p>\n
df = pd.read_excel('path_to_file.xlsx', sheet_name='Sheet1')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528 openpyxl \u5e93<\/h3>\n<\/p>\n
1\u3001\u5b89\u88c5 openpyxl \u5e93<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u5b89\u88c5 openpyxl \u5e93\uff1a<\/p>\n<\/p>\n
pip install openpyxl<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5bfc\u5165 openpyxl \u5e93<\/h4>\n<\/p>\n
\u5728\u60a8\u7684 Python \u811a\u672c\u4e2d\u5bfc\u5165 openpyxl \u5e93\uff1a<\/p>\n<\/p>\n
from openpyxl import load_workbook<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u8bfb\u53d6 Excel \u6587\u4ef6<\/h4>\n<\/p>\n
\u4f7f\u7528 load_workbook()<\/code> \u51fd\u6570\u8bfb\u53d6 Excel \u6587\u4ef6\uff0c\u5e76\u83b7\u53d6\u5de5\u4f5c\u8868\u5bf9\u8c61\uff1a<\/p>\n<\/p>\n
wb = load_workbook('path_to_file.xlsx')<\/p>\n
sheet = wb['Sheet1']<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5176\u4e2d\uff0cpath_to_file.xlsx<\/code> \u662f\u60a8\u7684 Excel \u6587\u4ef6\u7684\u8def\u5f84\uff0cSheet1<\/code> \u662f\u5de5\u4f5c\u8868\u540d\u79f0\u3002<\/p>\n<\/p>\n
4\u3001\u904d\u5386\u5de5\u4f5c\u8868\u6570\u636e<\/h4>\n<\/p>\n
\u60a8\u53ef\u4ee5\u4f7f\u7528 iter_rows()<\/code> \u6216 iter_cols()<\/code> \u51fd\u6570\u904d\u5386\u5de5\u4f5c\u8868\u4e2d\u7684\u6570\u636e\uff1a<\/p>\n<\/p>\n
for row in sheet.iter_rows(values_only=True):<\/p>\n
    print(row)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528 xlrd \u5e93<\/h3>\n<\/p>\n
1\u3001\u5b89\u88c5 xlrd \u5e93<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u5b89\u88c5 xlrd \u5e93\uff1a<\/p>\n<\/p>\n
pip install xlrd<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5bfc\u5165 xlrd \u5e93<\/h4>\n<\/p>\n
\u5728\u60a8\u7684 Python \u811a\u672c\u4e2d\u5bfc\u5165 xlrd \u5e93\uff1a<\/p>\n<\/p>\n
import xlrd<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u8bfb\u53d6 Excel \u6587\u4ef6<\/h4>\n<\/p>\n
\u4f7f\u7528 xlrd.open_workbook()<\/code> \u51fd\u6570\u8bfb\u53d6 Excel \u6587\u4ef6\uff0c\u5e76\u83b7\u53d6\u5de5\u4f5c\u8868\u5bf9\u8c61\uff1a<\/p>\n<\/p>\n
workbook = xlrd.open_workbook('path_to_file.xlsx')<\/p>\n
sheet = workbook.sheet_by_name('Sheet1')<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u904d\u5386\u5de5\u4f5c\u8868\u6570\u636e<\/h4>\n<\/p>\n
\u60a8\u53ef\u4ee5\u4f7f\u7528 sheet.nrows<\/code> \u548c sheet.row_values()<\/code> \u51fd\u6570\u904d\u5386\u5de5\u4f5c\u8868\u4e2d\u7684\u6570\u636e\uff1a<\/p>\n<\/p>\n
for row_idx in range(sheet.nrows):<\/p>\n
    print(sheet.row_values(row_idx))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001Pandas \u5e93\u7684\u9ad8\u7ea7\u7528\u6cd5<\/h3>\n<\/p>\n
1\u3001\u8bfb\u53d6\u7279\u5b9a\u5217<\/h4>\n<\/p>\n
\u60a8\u53ef\u4ee5\u4f7f\u7528 usecols<\/code> \u53c2\u6570\u8bfb\u53d6\u7279\u5b9a\u7684\u5217\uff1a<\/p>\n<\/p>\n
df = pd.read_excel('path_to_file.xlsx', usecols=['A', 'C', 'E'])<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u8bfb\u53d6\u7279\u5b9a\u884c<\/h4>\n<\/p>\n
\u60a8\u53ef\u4ee5\u4f7f\u7528 skiprows<\/code> \u548c nrows<\/code> \u53c2\u6570\u8bfb\u53d6\u7279\u5b9a\u7684\u884c\uff1a<\/p>\n<\/p>\n
df = pd.read_excel('path_to_file.xlsx', skiprows=2, nrows=5)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u5904\u7406\u7f3a\u5931\u503c<\/h4>\n<\/p>\n
\u60a8\u53ef\u4ee5\u4f7f\u7528 na_values<\/code> \u53c2\u6570\u5904\u7406\u7f3a\u5931\u503c\uff1a<\/p>\n<\/p>\n
df = pd.read_excel('path_to_file.xlsx', na_values=['NA', 'N\/A', ''])<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u8bbe\u7f6e\u7d22\u5f15\u5217<\/h4>\n<\/p>\n
\u60a8\u53ef\u4ee5\u4f7f\u7528 index_col<\/code> \u53c2\u6570\u8bbe\u7f6e\u7d22\u5f15\u5217\uff1a<\/p>\n<\/p>\n
df = pd.read_excel('path_to_file.xlsx', index_col=0)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u65b9\u6cd5\uff0c\u60a8\u53ef\u4ee5\u8f7b\u677e\u5730\u5728 Python \u4e2d\u5bfc\u5165 Excel \u6570\u636e\u96c6\u3002pandas \u5e93<\/strong>\u662f\u6700\u5e38\u7528\u7684\u65b9\u6cd5\uff0c\u56e0\u4e3a\u5b83\u529f\u80fd\u5f3a\u5927\u4e14\u4f7f\u7528\u7b80\u5355\u3002openpyxl \u5e93<\/strong>\u9002\u7528\u4e8e\u5904\u7406\u8f83\u65b0\u7684 Excel \u6587\u4ef6\uff08.xlsx \u683c\u5f0f\uff09\uff0c\u800cxlrd \u5e93<\/strong>\u9002\u7528\u4e8e\u5904\u7406\u8f83\u65e7\u7684 Excel \u6587\u4ef6\uff08.xls \u683c\u5f0f\uff09\u3002\u6839\u636e\u60a8\u7684\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\uff0c\u60a8\u5c06\u80fd\u591f\u9ad8\u6548\u5730\u5904\u7406 Excel \u6570\u636e\u3002<\/p>\n<\/p>\n
\u516d\u3001\u4f7f\u7528 Pandas \u5e93\u7684\u66f4\u591a\u529f\u80fd<\/h3>\n<\/p>\n
1\u3001\u8bfb\u53d6\u591a\u4e2a\u5de5\u4f5c\u8868<\/h4>\n<\/p>\n
\u5982\u679c\u60a8\u7684 Excel \u6587\u4ef6\u5305\u542b\u591a\u4e2a\u5de5\u4f5c\u8868\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528 sheet_name<\/code> \u53c2\u6570\u8bfb\u53d6\u6240\u6709\u5de5\u4f5c\u8868\uff0c\u5e76\u5c06\u7ed3\u679c\u5b58\u50a8\u5728\u4e00\u4e2a\u5b57\u5178\u4e2d\uff1a<\/p>\n<\/p>\n
dfs = pd.read_excel('path_to_file.xlsx', sheet_name=None)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b57\u5178\u7684\u952e\u662f\u5de5\u4f5c\u8868\u540d\u79f0\uff0c\u503c\u662f\u5bf9\u5e94\u7684 DataFrame\u3002<\/p>\n<\/p>\n
2\u3001\u5408\u5e76\u591a\u4e2a\u5de5\u4f5c\u8868<\/h4>\n<\/p>\n
\u5982\u679c\u60a8\u9700\u8981\u5c06\u591a\u4e2a\u5de5\u4f5c\u8868\u7684\u6570\u636e\u5408\u5e76\u5230\u4e00\u4e2a DataFrame \u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528 pd.concat()<\/code> \u51fd\u6570\uff1a<\/p>\n<\/p>\n
dfs = pd.read_excel('path_to_file.xlsx', sheet_name=None)<\/p>\n
df_combined = pd.concat(dfs.values(), ignore_index=True)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u5199\u5165 Excel \u6587\u4ef6<\/h4>\n<\/p>\n
\u9664\u4e86\u8bfb\u53d6 Excel \u6587\u4ef6\uff0cpandas \u8fd8\u652f\u6301\u5c06 DataFrame \u5199\u5165 Excel \u6587\u4ef6\u3002\u60a8\u53ef\u4ee5\u4f7f\u7528 to_excel()<\/code> \u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
df.to_excel('output_file.xlsx', index=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u6307\u5b9a\u5199\u5165\u5de5\u4f5c\u8868<\/h4>\n<\/p>\n
\u5982\u679c\u60a8\u9700\u8981\u5c06 DataFrame \u5199\u5165\u7279\u5b9a\u7684\u5de5\u4f5c\u8868\uff0c\u53ef\u4ee5\u4f7f\u7528 sheet_name<\/code> \u53c2\u6570\uff1a<\/p>\n<\/p>\n
df.to_excel('output_file.xlsx', sheet_name='Sheet1', index=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
5\u3001\u5904\u7406\u590d\u6742\u7684 Excel \u6587\u4ef6<\/h4>\n<\/p>\n
\u5bf9\u4e8e\u5305\u542b\u590d\u6742\u683c\u5f0f\u6216\u5d4c\u5165\u56fe\u8868\u7684 Excel \u6587\u4ef6\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528 ExcelWriter<\/code> \u7c7b\u8fdb\u884c\u66f4\u7075\u6d3b\u7684\u64cd\u4f5c\uff1a<\/p>\n<\/p>\n
with pd.ExcelWriter('output_file.xlsx', engine='xlsxwriter') as writer:<\/p>\n
    df.to_excel(writer, sheet_name='Sheet1', index=False)<\/p>\n
    # \u6dfb\u52a0\u66f4\u591a\u5de5\u4f5c\u8868\u6216\u8fdb\u884c\u5176\u4ed6\u64cd\u4f5c<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e03\u3001\u6848\u4f8b\u5206\u6790<\/h3>\n<\/p>\n
\u4ee5\u4e0b\u662f\u4e00\u4e2a\u5b8c\u6574\u7684\u6848\u4f8b\u5206\u6790\uff0c\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528 pandas \u5e93\u8bfb\u53d6\u3001\u5904\u7406\u548c\u5199\u5165 Excel \u6570\u636e\uff1a<\/p>\n<\/p>\n
1\u3001\u8bfb\u53d6 Excel \u6587\u4ef6<\/h4>\n<\/p>\n
import pandas as pd<\/p>\n
\u8bfb\u53d6 Excel \u6587\u4ef6<\/strong><\/h2>\n
df = pd.read_excel('sales_data.xlsx')<\/p>\n
\u67e5\u770b\u524d\u51e0\u884c\u6570\u636e<\/strong><\/h2>\n
print(df.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6570\u636e\u6e05\u6d17\u548c\u5904\u7406<\/h4>\n<\/p>\n
# \u5904\u7406\u7f3a\u5931\u503c<\/p>\n
df.fillna(0, inplace=True)<\/p>\n
\u8f6c\u6362\u6570\u636e\u7c7b\u578b<\/strong><\/h2>\n
df['Sales'] = df['Sales'].astype(float)<\/p>\n
\u6dfb\u52a0\u65b0\u5217<\/strong><\/h2>\n
df['Revenue'] = df['Sales'] * df['Price']<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u6570\u636e\u5206\u6790<\/h4>\n<\/p>\n
# \u6309\u4ea7\u54c1\u7c7b\u522b\u6c47\u603b\u9500\u552e\u6570\u636e<\/p>\n
sales_summary = df.groupby('Category')['Sales'].sum()<\/p>\n
\u6309\u6708\u4efd\u6c47\u603b\u6536\u5165\u6570\u636e<\/strong><\/h2>\n
df['Month'] = pd.to_datetime(df['Date']).dt.month<\/p>\n
revenue_summary = df.groupby('Month')['Revenue'].sum()<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u5199\u5165 Excel \u6587\u4ef6<\/h4>\n<\/p>\n
with pd.ExcelWriter('processed_data.xlsx', engine='xlsxwriter') as writer:<\/p>\n
    df.to_excel(writer, sheet_name='SalesData', index=False)<\/p>\n
    sales_summary.to_excel(writer, sheet_name='SalesSummary')<\/p>\n
    revenue_summary.to_excel(writer, sheet_name='RevenueSummary')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516b\u3001\u5904\u7406\u5927\u6570\u636e\u96c6<\/h3>\n<\/p>\n
1\u3001\u5206\u5757\u8bfb\u53d6 Excel \u6587\u4ef6<\/h4>\n<\/p>\n
\u5bf9\u4e8e\u975e\u5e38\u5927\u7684 Excel \u6587\u4ef6\uff0c\u76f4\u63a5\u8bfb\u53d6\u6574\u4e2a\u6587\u4ef6\u53ef\u80fd\u4f1a\u5bfc\u81f4\u5185\u5b58\u4e0d\u8db3\u3002\u60a8\u53ef\u4ee5\u4f7f\u7528 chunksize<\/code> \u53c2\u6570\u5206\u5757\u8bfb\u53d6\uff1a<\/p>\n<\/p>\n
chunks = pd.read_excel('large_file.xlsx', chunksize=10000)<\/p>\n
for chunk in chunks:<\/p>\n
    # \u5904\u7406\u6bcf\u4e2a\u6570\u636e\u5757<\/p>\n
    print(chunk.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f18\u5316\u5185\u5b58\u4f7f\u7528<\/h4>\n<\/p>\n
\u60a8\u53ef\u4ee5\u901a\u8fc7\u4f18\u5316\u6570\u636e\u7c7b\u578b\u6765\u51cf\u5c11\u5185\u5b58\u4f7f\u7528\uff1a<\/p>\n<\/p>\n
df = pd.read_excel('large_file.xlsx', dtype={'Sales': 'float32', 'Price': 'float32'})<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e5d\u3001\u4e0e\u6570\u636e\u5e93\u96c6\u6210<\/h3>\n<\/p>\n
1\u3001\u8bfb\u53d6\u6570\u636e\u5e93\u6570\u636e<\/h4>\n<\/p>\n
\u60a8\u53ef\u4ee5\u4f7f\u7528 pandas \u8bfb\u53d6 SQL \u6570\u636e\u5e93\u4e2d\u7684\u6570\u636e\uff0c\u5e76\u5c06\u5176\u5199\u5165 Excel \u6587\u4ef6\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
import sqlite3<\/p>\n
\u8fde\u63a5\u5230\u6570\u636e\u5e93<\/strong><\/h2>\n
conn = sqlite3.connect('database.db')<\/p>\n
\u67e5\u8be2\u6570\u636e<\/strong><\/h2>\n
df = pd.read_sql_query('SELECT * FROM sales', conn)<\/p>\n
\u5199\u5165 Excel \u6587\u4ef6<\/strong><\/h2>\n
df.to_excel('database_data.xlsx', index=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5199\u5165\u6570\u636e\u5e93<\/h4>\n<\/p>\n
\u540c\u6837\uff0c\u60a8\u4e5f\u53ef\u4ee5\u5c06 Excel \u6570\u636e\u5199\u5165\u6570\u636e\u5e93\uff1a<\/p>\n<\/p>\n
# \u8bfb\u53d6 Excel \u6587\u4ef6<\/p>\n
df = pd.read_excel('sales_data.xlsx')<\/p>\n
\u5199\u5165\u6570\u636e\u5e93<\/strong><\/h2>\n
df.to_sql('sales', conn, if_exists='replace', index=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u65b9\u6cd5\uff0c\u60a8\u53ef\u4ee5\u5728 Python \u4e2d\u9ad8\u6548\u5730\u5bfc\u5165\u548c\u5904\u7406 Excel \u6570\u636e\u96c6\u3002pandas \u5e93<\/strong>\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\uff0c\u9002\u7528\u4e8e\u5927\u591a\u6570\u573a\u666f\uff1bopenpyxl \u5e93<\/strong>\u548cxlrd \u5e93<\/strong>\u5219\u9002\u7528\u4e8e\u7279\u5b9a\u9700\u6c42\u3002\u638c\u63e1\u8fd9\u4e9b\u5de5\u5177\uff0c\u60a8\u5c06\u80fd\u591f\u8f7b\u677e\u5e94\u5bf9\u5404\u79cd\u6570\u636e\u5904\u7406\u4efb\u52a1\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u8bfb\u53d6Excel\u6587\u4ef6\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u4e2a\u5e93\u6765\u8bfb\u53d6Excel\u6587\u4ef6\uff0c\u5176\u4e2d\u6700\u5e38\u7528\u7684\u662fpandas\u548copenpyxl\u3002\u4f7f\u7528pandas\u5e93\uff0c\u53ef\u4ee5\u901a\u8fc7pandas.read_excel()<\/code>\u51fd\u6570\u8f7b\u677e\u5bfc\u5165Excel\u6570\u636e\u96c6\u3002\u5728\u4f7f\u7528\u4e4b\u524d\uff0c\u786e\u4fdd\u5df2\u5b89\u88c5pandas\u5e93\u548copenpyxl\u5e93\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u4ee3\u7801\uff1a  <\/p>\n
import pandas as pd\n\n# \u5bfc\u5165Excel\u6587\u4ef6\ndata = pd.read_excel('\u6587\u4ef6\u8def\u5f84.xlsx', sheet_name='Sheet1')\nprint(data.head())\n<\/code><\/pre>\n
\u8fd9\u4e2a\u4ee3\u7801\u5c06\u5bfc\u5165\u6307\u5b9a\u8def\u5f84\u7684Excel\u6587\u4ef6\uff0c\u5e76\u663e\u793a\u524d\u51e0\u884c\u6570\u636e\u3002<\/p>\n
\u5982\u4f55\u5904\u7406Excel\u6570\u636e\u4e2d\u7684\u7f3a\u5931\u503c\uff1f<\/strong>
\u5728\u5bfc\u5165Excel\u6570\u636e\u540e\uff0c\u53ef\u80fd\u4f1a\u9047\u5230\u7f3a\u5931\u503c\u95ee\u9898\u3002pandas\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u5904\u7406\u7f3a\u5931\u503c\uff0c\u6bd4\u5982\u4f7f\u7528dropna()<\/code>\u65b9\u6cd5\u5220\u9664\u542b\u6709\u7f3a\u5931\u503c\u7684\u884c\uff0c\u6216\u8005\u4f7f\u7528fillna()<\/code>\u65b9\u6cd5\u7528\u7279\u5b9a\u503c\u586b\u5145\u7f3a\u5931\u503c\u3002\u793a\u4f8b\u5982\u4e0b\uff1a  <\/p>\n
# \u5220\u9664\u7f3a\u5931\u503c\ndata_cleaned = data.dropna()\n\n# \u75280\u586b\u5145\u7f3a\u5931\u503c\ndata_filled = data.fillna(0)\n<\/code><\/pre>\n
\u8fd9\u4e9b\u65b9\u6cd5\u53ef\u4ee5\u5e2e\u52a9\u4fdd\u6301\u6570\u636e\u7684\u5b8c\u6574\u6027\uff0c\u4ee5\u4fbf\u8fdb\u884c\u8fdb\u4e00\u6b65\u5206\u6790\u3002<\/p>\n
\u5728Python\u4e2d\u5982\u4f55\u5c06\u5904\u7406\u540e\u7684\u6570\u636e\u5bfc\u51fa\u4e3aExcel\u6587\u4ef6\uff1f<\/strong>
\u5b8c\u6210\u6570\u636e\u5904\u7406\u540e\uff0c\u53ef\u80fd\u9700\u8981\u5c06\u6570\u636e\u5bfc\u51fa\u4e3aExcel\u6587\u4ef6\u3002\u4f7f\u7528pandas\u7684to_excel()<\/code>\u51fd\u6570\u53ef\u4ee5\u5b9e\u73b0\u8fd9\u4e00\u70b9\u3002\u53ef\u4ee5\u6307\u5b9a\u6587\u4ef6\u540d\u548c\u8981\u5199\u5165\u7684\u5de5\u4f5c\u8868\u540d\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a  <\/p>\n
# \u5bfc\u51fa\u4e3aExcel\u6587\u4ef6\ndata_cleaned.to_excel('\u6e05\u7406\u540e\u7684\u6570\u636e.xlsx', sheet_name='Sheet1', index=False)\n<\/code><\/pre>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u5904\u7406\u540e\u7684\u6570\u636e\u5c06\u88ab\u4fdd\u5b58\u4e3a\u65b0\u7684Excel\u6587\u4ef6\uff0cindex=False<\/code>\u8868\u793a\u4e0d\u5c06\u884c\u7d22\u5f15\u5199\u5165\u6587\u4ef6\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python \u5bfc\u5165 Excel \u6570\u636e\u96c6\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5e38\u89c1\u7684\u65b9\u5f0f\u5305\u62ec\uff1a\u4f7f\u7528 pandas \u5e93\u3001\u4f7f\u7528 openpy […]","protected":false},"author":3,"featured_media":1174484,"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\/1174477"}],"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=1174477"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1174477\/revisions"}],"predecessor-version":[{"id":1174485,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1174477\/revisions\/1174485"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1174484"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1174477"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1174477"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1174477"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}