{"id":1164546,"date":"2025-01-15T15:08:56","date_gmt":"2025-01-15T07:08:56","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1164546.html"},"modified":"2025-01-15T15:08:59","modified_gmt":"2025-01-15T07:08:59","slug":"python%e5%a6%82%e4%bd%95%e5%88%b6%e8%a1%a8%e5%92%8c%e6%8f%90%e5%8f%96%e6%95%b0%e6%8d%ae","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1164546.html","title":{"rendered":"python\u5982\u4f55\u5236\u8868\u548c\u63d0\u53d6\u6570\u636e"},"content":{"rendered":"

\"python\u5982\u4f55\u5236\u8868\u548c\u63d0\u53d6\u6570\u636e\"<\/p>\n

Python \u5236\u8868\u548c\u63d0\u53d6\u6570\u636e\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528pandas\u5e93\u3001csv\u5e93\u548copenpyxl\u5e93\u3002<\/strong>\u5176\u4e2d\uff0cpandas\u5e93<\/strong>\u662f\u6700\u5e38\u7528\u7684\uff0c\u56e0\u4e3a\u5b83\u529f\u80fd\u5f3a\u5927\u4e14\u6613\u4e8e\u4f7f\u7528\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u63cf\u8ff0\u5982\u4f55\u4f7f\u7528pandas\u5e93\u8fdb\u884c\u5236\u8868\u548c\u63d0\u53d6\u6570\u636e\u3002<\/p>\n<\/p>\n

\u4f7f\u7528pandas\u5e93\u8fdb\u884c\u6570\u636e\u5904\u7406<\/strong><\/p>\n<\/p>\n

Pandas\u662fPython\u4e2d\u6700\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5e93\u4e4b\u4e00\u3002\u5b83\u63d0\u4f9b\u4e86\u6570\u636e\u7ed3\u6784\u548c\u6570\u636e\u5206\u6790\u5de5\u5177\uff0c\u7279\u522b\u9002\u7528\u4e8e\u5904\u7406\u7ed3\u6784\u5316\u6570\u636e\uff0c\u5982\u8868\u683c\u3002Pandas\u7684\u4e3b\u8981\u6570\u636e\u7ed3\u6784\u662fDataFrame\u548cSeries\u3002DataFrame\u53ef\u4ee5\u770b\u4f5c\u662f\u4e00\u4e2a\u4e8c\u7ef4\u7684\u8868\u683c\uff0c\u800cSeries\u5219\u662f\u4e00\u7ef4\u7684\u6570\u7ec4\u3002<\/p>\n<\/p>\n

\u4e00\u3001Pandas\u5e93\u7684\u5b89\u88c5\u548c\u5bfc\u5165<\/h3>\n<\/p>\n

\u5728\u5f00\u59cb\u4f7f\u7528pandas\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u5148\u5b89\u88c5\u5b83\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u901a\u8fc7pip\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\u5728Python\u811a\u672c\u6216\u4ea4\u4e92\u5f0f\u73af\u5883\u4e2d\u5bfc\u5165pandas\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u8bfb\u53d6\u6570\u636e<\/h3>\n<\/p>\n
Pandas\u53ef\u4ee5\u8bfb\u53d6\u591a\u79cd\u683c\u5f0f\u7684\u6570\u636e\u6587\u4ef6\uff0c\u5305\u62ecCSV\u3001Excel\u3001SQL\u6570\u636e\u5e93\u7b49\u3002\u6700\u5e38\u89c1\u7684\u8bfb\u53d6\u65b9\u6cd5\u662f\u8bfb\u53d6CSV\u6587\u4ef6\u548cExcel\u6587\u4ef6\u3002<\/p>\n<\/p>\n
1\u3001\u8bfb\u53d6CSV\u6587\u4ef6<\/h4>\n<\/p>\n
\u8bfb\u53d6CSV\u6587\u4ef6\u975e\u5e38\u7b80\u5355\uff0c\u53ea\u9700\u8981\u4f7f\u7528read_csv<\/code>\u51fd\u6570\uff1a<\/p>\n<\/p>\n
df = pd.read_csv('data.csv')<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u8bfb\u53d6Excel\u6587\u4ef6<\/h4>\n<\/p>\n
\u8bfb\u53d6Excel\u6587\u4ef6\u9700\u8981\u4f7f\u7528read_excel<\/code>\u51fd\u6570\uff1a<\/p>\n<\/p>\n
df = pd.read_excel('data.xlsx', sheet_name='Sheet1')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u6570\u636e\u63a2\u7d22<\/h3>\n<\/p>\n
\u5728\u8bfb\u53d6\u6570\u636e\u540e\uff0c\u901a\u5e38\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u521d\u6b65\u7684\u63a2\u7d22\u548c\u67e5\u770b\u3002Pandas\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u5e2e\u52a9\u6211\u4eec\u7406\u89e3\u6570\u636e\u3002<\/p>\n<\/p>\n
1\u3001\u67e5\u770b\u6570\u636e\u7684\u524d\u51e0\u884c<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528head<\/code>\u65b9\u6cd5\u67e5\u770b\u6570\u636e\u7684\u524d\u51e0\u884c\uff1a<\/p>\n<\/p>\n
print(df.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u67e5\u770b\u6570\u636e\u7684\u57fa\u672c\u4fe1\u606f<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528info<\/code>\u65b9\u6cd5\u67e5\u770b\u6570\u636e\u7684\u57fa\u672c\u4fe1\u606f\uff0c\u5305\u62ec\u6570\u636e\u7c7b\u578b\u548c\u7f3a\u5931\u503c\uff1a<\/p>\n<\/p>\n
print(df.info())<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u63cf\u8ff0\u6027\u7edf\u8ba1<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528describe<\/code>\u65b9\u6cd5\u67e5\u770b\u6570\u636e\u7684\u63cf\u8ff0\u6027\u7edf\u8ba1\u4fe1\u606f\uff1a<\/p>\n<\/p>\n
print(df.describe())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u6570\u636e\u6e05\u6d17\u548c\u9884\u5904\u7406<\/h3>\n<\/p>\n
\u6570\u636e\u6e05\u6d17\u662f\u6570\u636e\u5206\u6790\u4e2d\u975e\u5e38\u91cd\u8981\u7684\u4e00\u6b65\u3002Pandas\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u51fd\u6570\u6765\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u548c\u9884\u5904\u7406\u3002<\/p>\n<\/p>\n
1\u3001\u5904\u7406\u7f3a\u5931\u503c<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528dropna<\/code>\u65b9\u6cd5\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\uff0c\u6216\u8005\u4f7f\u7528fillna<\/code>\u65b9\u6cd5\u586b\u5145\u7f3a\u5931\u503c\uff1a<\/p>\n<\/p>\n
df = df.dropna()<\/p>\n
\u6216\u8005<\/strong><\/h2>\n
df = df.fillna(0)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6570\u636e\u7c7b\u578b\u8f6c\u6362<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528astype<\/code>\u65b9\u6cd5\u8f6c\u6362\u6570\u636e\u7c7b\u578b\uff1a<\/p>\n<\/p>\n
df['column_name'] = df['column_name'].astype('int')<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u91cd\u547d\u540d\u5217<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528rename<\/code>\u65b9\u6cd5\u91cd\u547d\u540d\u5217\uff1a<\/p>\n<\/p>\n
df = df.rename(columns={'old_name': 'new_name'})<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u6570\u636e\u63d0\u53d6<\/h3>\n<\/p>\n
\u63d0\u53d6\u6570\u636e\u662f\u6570\u636e\u5206\u6790\u4e2d\u5e38\u89c1\u7684\u64cd\u4f5c\u3002Pandas\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u63d0\u53d6\u6570\u636e\u3002<\/p>\n<\/p>\n
1\u3001\u57fa\u4e8e\u5217\u540d\u63d0\u53d6\u6570\u636e<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528\u5217\u540d\u63d0\u53d6\u6570\u636e\uff1a<\/p>\n<\/p>\n
column_data = df['column_name']<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u57fa\u4e8e\u6761\u4ef6\u63d0\u53d6\u6570\u636e<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528\u5e03\u5c14\u7d22\u5f15\u63d0\u53d6\u6ee1\u8db3\u6761\u4ef6\u7684\u6570\u636e\uff1a<\/p>\n<\/p>\n
filtered_data = df[df['column_name'] > 10]<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u57fa\u4e8e\u4f4d\u7f6e\u63d0\u53d6\u6570\u636e<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528iloc<\/code>\u65b9\u6cd5\u57fa\u4e8e\u4f4d\u7f6e\u63d0\u53d6\u6570\u636e\uff1a<\/p>\n<\/p>\n
row_data = df.iloc[0]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u6570\u636e\u5206\u7ec4\u548c\u805a\u5408<\/h3>\n<\/p>\n
\u6570\u636e\u5206\u7ec4\u548c\u805a\u5408\u662f\u6570\u636e\u5206\u6790\u4e2d\u7684\u5e38\u89c1\u64cd\u4f5c\u3002Pandas\u63d0\u4f9b\u4e86groupby<\/code>\u65b9\u6cd5\u8fdb\u884c\u5206\u7ec4\uff0c\u5e76\u63d0\u4f9b\u4e86\u591a\u79cd\u805a\u5408\u51fd\u6570\u3002<\/p>\n<\/p>\n
1\u3001\u6570\u636e\u5206\u7ec4<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528groupby<\/code>\u65b9\u6cd5\u8fdb\u884c\u6570\u636e\u5206\u7ec4\uff1a<\/p>\n<\/p>\n
grouped = df.groupby('column_name')<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6570\u636e\u805a\u5408<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528\u805a\u5408\u51fd\u6570\u8fdb\u884c\u6570\u636e\u805a\u5408\uff0c\u4f8b\u5982sum<\/code>\u3001mean<\/code>\u7b49\uff1a<\/p>\n<\/p>\n
grouped_sum = grouped.sum()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e03\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h3>\n<\/p>\n
Pandas\u4e0eMatplotlib\u5e93\u7d27\u5bc6\u96c6\u6210\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
1\u3001\u5b89\u88c5Matplotlib<\/h4>\n<\/p>\n
\u9996\u5148\u9700\u8981\u5b89\u88c5Matplotlib\u5e93\uff1a<\/p>\n<\/p>\n
pip install matplotlib<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5bfc\u5165Matplotlib\u5e76\u7ed8\u56fe<\/h4>\n<\/p>\n
\u5728Python\u811a\u672c\u6216\u4ea4\u4e92\u5f0f\u73af\u5883\u4e2d\u5bfc\u5165Matplotlib\u5e76\u7ed8\u56fe\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u7ed8\u5236\u6298\u7ebf\u56fe<\/strong><\/h2>\n
df['column_name'].plot()<\/p>\n
plt.show()<\/p>\n
\u7ed8\u5236\u67f1\u72b6\u56fe<\/strong><\/h2>\n
df['column_name'].plot(kind='bar')<\/p>\n
plt.show()<\/p>\n
\u7ed8\u5236\u6563\u70b9\u56fe<\/strong><\/h2>\n
df.plot.scatter(x='column1', y='column2')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516b\u3001\u4fdd\u5b58\u6570\u636e<\/h3>\n<\/p>\n
\u5904\u7406\u5b8c\u6570\u636e\u540e\uff0c\u901a\u5e38\u9700\u8981\u5c06\u6570\u636e\u4fdd\u5b58\u5230\u6587\u4ef6\u4e2d\u3002Pandas\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u5c06\u6570\u636e\u4fdd\u5b58\u4e3a\u4e0d\u540c\u683c\u5f0f\u7684\u6587\u4ef6\u3002<\/p>\n<\/p>\n
1\u3001\u4fdd\u5b58\u4e3aCSV\u6587\u4ef6<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528to_csv<\/code>\u65b9\u6cd5\u4fdd\u5b58\u4e3aCSV\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
df.to_csv('output.csv', index=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4fdd\u5b58\u4e3aExcel\u6587\u4ef6<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528to_excel<\/code>\u65b9\u6cd5\u4fdd\u5b58\u4e3aExcel\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
df.to_excel('output.xlsx', index=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e5d\u3001\u5176\u4ed6\u5e38\u7528\u64cd\u4f5c<\/h3>\n<\/p>\n
\u9664\u4e86\u4e0a\u9762\u4ecb\u7ecd\u7684\u57fa\u672c\u64cd\u4f5c\uff0cPandas\u8fd8\u63d0\u4f9b\u4e86\u8bb8\u591a\u5176\u4ed6\u5e38\u7528\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
1\u3001\u5408\u5e76\u6570\u636e<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528merge<\/code>\u65b9\u6cd5\u5408\u5e76\u4e24\u4e2aDataFrame\uff1a<\/p>\n<\/p>\n
merged_df = pd.merge(df1, df2, on='common_column')<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5220\u9664\u91cd\u590d\u503c<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528drop_duplicates<\/code>\u65b9\u6cd5\u5220\u9664\u91cd\u590d\u503c\uff1a<\/p>\n<\/p>\n
df = df.drop_duplicates()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u6570\u636e\u900f\u89c6\u8868<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528pivot_table<\/code>\u65b9\u6cd5\u521b\u5efa\u6570\u636e\u900f\u89c6\u8868\uff1a<\/p>\n<\/p>\n
pivot_table = df.pivot_table(values='value_column', index='index_column', columns='columns_column', aggfunc='mean')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u3001\u7efc\u5408\u5b9e\u4f8b<\/h3>\n<\/p>\n
\u4e0b\u9762\u901a\u8fc7\u4e00\u4e2a\u7efc\u5408\u5b9e\u4f8b\u6765\u5c55\u793a\u5982\u4f55\u4f7f\u7528pandas\u8fdb\u884c\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u3002<\/p>\n<\/p>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5305\u542b\u9500\u552e\u6570\u636e\u7684CSV\u6587\u4ef6sales_data.csv<\/code>\uff0c\u6587\u4ef6\u5185\u5bb9\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
date,product,quantity,price<\/p>\n
2023-01-01,Product A,10,100<\/p>\n
2023-01-01,Product B,20,200<\/p>\n
2023-01-02,Product A,15,100<\/p>\n
2023-01-02,Product B,25,200<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u6211\u4eec\u9700\u8981\u5b8c\u6210\u4ee5\u4e0b\u4efb\u52a1\uff1a<\/p>\n<\/p>\n
    \n
  1. \u8bfb\u53d6\u6570\u636e<\/li>\n
  2. \u67e5\u770b\u6570\u636e\u57fa\u672c\u4fe1\u606f<\/li>\n
  3. \u8ba1\u7b97\u603b\u9500\u552e\u989d<\/li>\n
  4. \u6309\u4ea7\u54c1\u5206\u7ec4\u8ba1\u7b97\u603b\u9500\u552e\u91cf<\/li>\n
  5. \u7ed8\u5236\u9500\u552e\u91cf\u6298\u7ebf\u56fe<\/li>\n<\/ol>\n
    1\u3001\u8bfb\u53d6\u6570\u636e<\/h4>\n<\/p>\n
    \u9996\u5148\uff0c\u4f7f\u7528read_csv<\/code>\u51fd\u6570\u8bfb\u53d6\u6570\u636e\uff1a<\/p>\n<\/p>\n
    import pandas as pd<\/p>\n
    df = pd.read_csv('sales_data.csv')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2\u3001\u67e5\u770b\u6570\u636e\u57fa\u672c\u4fe1\u606f<\/h4>\n<\/p>\n
    \u4f7f\u7528info<\/code>\u548chead<\/code>\u65b9\u6cd5\u67e5\u770b\u6570\u636e\u57fa\u672c\u4fe1\u606f\uff1a<\/p>\n<\/p>\n
    print(df.info())<\/p>\n
    print(df.head())<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    3\u3001\u8ba1\u7b97\u603b\u9500\u552e\u989d<\/h4>\n<\/p>\n
    \u53ef\u4ee5\u901a\u8fc7\u8ba1\u7b97quantity<\/code>\u548cprice<\/code>\u7684\u4e58\u79ef\uff0c\u7136\u540e\u6c42\u548c\u6765\u8ba1\u7b97\u603b\u9500\u552e\u989d\uff1a<\/p>\n<\/p>\n
    df['sales'] = df['quantity'] * df['price']<\/p>\n
    total_sales = df['sales'].sum()<\/p>\n
    print(f'Total Sales: {total_sales}')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    4\u3001\u6309\u4ea7\u54c1\u5206\u7ec4\u8ba1\u7b97\u603b\u9500\u552e\u91cf<\/h4>\n<\/p>\n
    \u4f7f\u7528groupby<\/code>\u548csum<\/code>\u65b9\u6cd5\u6309\u4ea7\u54c1\u5206\u7ec4\u8ba1\u7b97\u603b\u9500\u552e\u91cf\uff1a<\/p>\n<\/p>\n
    product_sales = df.groupby('product')['quantity'].sum()<\/p>\n
    print(product_sales)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    5\u3001\u7ed8\u5236\u9500\u552e\u91cf\u6298\u7ebf\u56fe<\/h4>\n<\/p>\n
    \u4f7f\u7528Matplotlib\u7ed8\u5236\u9500\u552e\u91cf\u6298\u7ebf\u56fe\uff1a<\/p>\n<\/p>\n
    import matplotlib.pyplot as plt<\/p>\n
    df.groupby('date')['quantity'].sum().plot()<\/p>\n
    plt.title('Sales Quantity Over Time')<\/p>\n
    plt.xlabel('Date')<\/p>\n
    plt.ylabel('Quantity')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u6211\u4eec\u5b8c\u6210\u4e86\u6570\u636e\u7684\u8bfb\u53d6\u3001\u57fa\u672c\u4fe1\u606f\u67e5\u770b\u3001\u6570\u636e\u8ba1\u7b97\u3001\u5206\u7ec4\u805a\u5408\u548c\u53ef\u89c6\u5316\u3002Pandas\u5e93\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u529f\u80fd\uff0c\u4f7f\u5f97\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u53d8\u5f97\u7b80\u5355\u9ad8\u6548\u3002<\/p>\n<\/p>\n
    \u603b\u7ed3\uff1a<\/p>\n<\/p>\n
    \u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u6211\u4eec\u4e86\u89e3\u4e86\u5982\u4f55\u4f7f\u7528Pandas\u5e93\u8fdb\u884c\u6570\u636e\u5236\u8868\u548c\u63d0\u53d6\u3002Pandas\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u51fd\u6570\u548c\u65b9\u6cd5\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u8bfb\u53d6\u3001\u6e05\u6d17\u3001\u5904\u7406\u3001\u63d0\u53d6\u3001\u5206\u7ec4\u3001\u805a\u5408\u548c\u53ef\u89c6\u5316\u3002\u540c\u65f6\uff0c\u6211\u4eec\u901a\u8fc7\u4e00\u4e2a\u7efc\u5408\u5b9e\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528Pandas\u8fdb\u884c\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u3002\u5e0c\u671b\u672c\u6587\u80fd\u5e2e\u52a9\u8bfb\u8005\u66f4\u597d\u5730\u7406\u89e3\u548c\u4f7f\u7528Pandas\u5e93\u8fdb\u884c\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u3002<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     \u5982\u4f55\u5728Python\u4e2d\u521b\u5efa\u548c\u683c\u5f0f\u5316\u8868\u683c\uff1f<\/strong>
    \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u4e2a\u5e93\u6765\u521b\u5efa\u548c\u683c\u5f0f\u5316\u8868\u683c\u3002\u5e38\u7528\u7684\u5e93\u5305\u62ecPandas\u3001PrettyTable\u548cTabulate\u3002Pandas\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u7ed3\u6784\u548c\u6570\u636e\u5206\u6790\u5de5\u5177\uff0c\u9002\u5408\u7528\u4e8e\u5904\u7406\u590d\u6742\u7684\u6570\u636e\u96c6\uff1bPrettyTable\u5219\u9002\u5408\u7528\u4e8e\u5feb\u901f\u751f\u6210\u7b80\u5355\u7684\u6587\u672c\u8868\u683c\uff1bTabulate\u53ef\u4ee5\u8f93\u51fa\u591a\u79cd\u683c\u5f0f\u7684\u8868\u683c\uff0c\u5982HTML\u3001Markdown\u7b49\u3002\u901a\u8fc7\u8fd9\u4e9b\u5e93\uff0c\u4f60\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u3001\u683c\u5f0f\u5316\u548c\u5c55\u793a\u6570\u636e\u8868\u683c\u3002<\/p>\n
    \u5982\u4f55\u4ece\u6570\u636e\u6587\u4ef6\u4e2d\u63d0\u53d6\u6570\u636e\u5230Python\uff1f<\/strong>
    \u63d0\u53d6\u6570\u636e\u7684\u65b9\u5f0f\u591a\u79cd\u591a\u6837\uff0c\u5177\u4f53\u53d6\u51b3\u4e8e\u6570\u636e\u6e90\u7684\u683c\u5f0f\u3002\u5bf9\u4e8eCSV\u6587\u4ef6\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u7684read_csv()<\/code>\u51fd\u6570\u5feb\u901f\u52a0\u8f7d\u6570\u636e\uff1b\u5bf9\u4e8eExcel\u6587\u4ef6\uff0c\u53ef\u4ee5\u4f7f\u7528read_excel()<\/code>\u51fd\u6570\uff1b\u5982\u679c\u6570\u636e\u5b58\u50a8\u5728\u6570\u636e\u5e93\u4e2d\uff0c\u5219\u53ef\u4ee5\u4f7f\u7528SQLAlchemy\u6216SQLite\u5e93\u8fdb\u884c\u6570\u636e\u63d0\u53d6\u3002\u4ee5\u4e0a\u65b9\u6cd5\u90fd\u80fd\u6709\u6548\u5730\u5c06\u6570\u636e\u5bfc\u5165\u5230Python\u4e2d\uff0c\u65b9\u4fbf\u540e\u7eed\u7684\u5904\u7406\u548c\u5206\u6790\u3002<\/p>\n
    \u5982\u4f55\u5904\u7406\u63d0\u53d6\u7684\u6570\u636e\u4ee5\u4fbf\u4e8e\u5206\u6790\uff1f<\/strong>
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