{"id":1132696,"date":"2025-01-08T20:59:38","date_gmt":"2025-01-08T12:59:38","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1132696.html"},"modified":"2025-01-08T20:59:40","modified_gmt":"2025-01-08T12:59:40","slug":"python%e5%a6%82%e4%bd%95%e5%8f%96%e5%9b%ba%e5%ae%9aindex%e7%9a%84%e6%9f%90%e4%b8%80%e5%88%97","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1132696.html","title":{"rendered":"python\u5982\u4f55\u53d6\u56fa\u5b9aindex\u7684\u67d0\u4e00\u5217"},"content":{"rendered":"

\"python\u5982\u4f55\u53d6\u56fa\u5b9aindex\u7684\u67d0\u4e00\u5217\"<\/p>\n

\u5728Python\u4e2d\u53d6\u56fa\u5b9aindex\u7684\u67d0\u4e00\u5217\u4e3b\u8981\u6709\u4ee5\u4e0b\u51e0\u79cd\u65b9\u6cd5\uff1a\u4f7f\u7528\u5217\u8868\u89e3\u6790\u3001NumPy\u6570\u7ec4\u3001Pandas DataFrame\u3002<\/strong> \u5176\u4e2d\uff0cPandas DataFrame<\/strong> \u662f\u6700\u5e38\u7528\u4e14\u529f\u80fd\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5de5\u5177\uff0c\u9002\u7528\u4e8e\u5404\u79cd\u6570\u636e\u64cd\u4f5c\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u5982\u4f55\u901a\u8fc7\u4e0d\u540c\u65b9\u6cd5\u5b9e\u73b0\u8fd9\u4e00\u76ee\u6807\uff0c\u5e76\u6df1\u5165\u4e86\u89e3Pandas DataFrame\u7684\u76f8\u5173\u64cd\u4f5c\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5217\u8868\u89e3\u6790<\/h2>\n<\/p>\n

\u5217\u8868\u89e3\u6790\u662fPython\u4e2d\u4e00\u4e2a\u7b80\u6d01\u800c\u5f3a\u5927\u7684\u7279\u6027\uff0c\u53ef\u4ee5\u7528\u4e8e\u4ece\u5217\u8868\u4e2d\u63d0\u53d6\u7279\u5b9a\u5143\u7d20\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n

data = [<\/p>\n
    [1, 2, 3],<\/p>\n
    [4, 5, 6],<\/p>\n
    [7, 8, 9]<\/p>\n
]<\/p>\n
\u53d6\u7b2c\u4e8c\u5217\uff08index=1\uff09<\/strong><\/h2>\n
column = [row[1] for row in data]<\/p>\n
print(column)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u5217\u8868\u89e3\u6790\u6765\u63d0\u53d6\u5217\u8868\u4e2d\u6bcf\u4e2a\u5b50\u5217\u8868\u7684\u7b2c\u4e8c\u4e2a\u5143\u7d20\u3002\u5217\u8868\u89e3\u6790\u7684\u4f18\u70b9\u662f\u4ee3\u7801\u7b80\u6d01\u3001\u6267\u884c\u901f\u5ea6\u5feb\uff0c\u4f46\u7f3a\u70b9\u662f\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\u53ef\u80fd\u4e0d\u591f\u9ad8\u6548\u3002<\/strong><\/p>\n<\/p>\n
\u4e8c\u3001NumPy\u6570\u7ec4<\/h2>\n<\/p>\n
NumPy\u662fPython\u4e2d\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u7684\u57fa\u7840\u5305\uff0c\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u591a\u7ef4\u6570\u7ec4\u5bf9\u8c61\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528NumPy\u4ece\u6570\u7ec4\u4e2d\u63d0\u53d6\u7279\u5b9a\u5217\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
data = np.array([<\/p>\n
    [1, 2, 3],<\/p>\n
    [4, 5, 6],<\/p>\n
    [7, 8, 9]<\/p>\n
])<\/p>\n
\u53d6\u7b2c\u4e8c\u5217\uff08index=1\uff09<\/strong><\/h2>\n
column = data[:, 1]<\/p>\n
print(column)<\/p>\n
<\/code><\/pre>\n<\/p>\n
NumPy\u6570\u7ec4\u7684\u5207\u7247\u64cd\u4f5c\u975e\u5e38\u9ad8\u6548\uff0c\u9002\u7528\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u5904\u7406\u3002NumPy\u7684\u4f18\u70b9\u662f\u8ba1\u7b97\u901f\u5ea6\u5feb\u3001\u5185\u5b58\u5229\u7528\u7387\u9ad8\uff0c\u9002\u7528\u4e8e\u6570\u503c\u8fd0\u7b97\u5bc6\u96c6\u578b\u5e94\u7528\u3002<\/strong><\/p>\n<\/p>\n
\u4e09\u3001Pandas DataFrame<\/h2>\n<\/p>\n
Pandas\u662fPython\u4e2d\u7528\u4e8e\u6570\u636e\u5206\u6790\u7684\u5f3a\u5927\u5de5\u5177\u5305\uff0c\u63d0\u4f9b\u4e86\u7075\u6d3b\u7684\u6570\u636e\u7ed3\u6784\u548c\u64cd\u4f5c\u65b9\u6cd5\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528Pandas\u4eceDataFrame\u4e2d\u63d0\u53d6\u7279\u5b9a\u5217\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
data = pd.DataFrame({<\/p>\n
    'A': [1, 4, 7],<\/p>\n
    'B': [2, 5, 8],<\/p>\n
    'C': [3, 6, 9]<\/p>\n
})<\/p>\n
\u53d6\u7b2c\u4e8c\u5217<\/strong><\/h2>\n
column = data.iloc[:, 1]<\/p>\n
print(column)<\/p>\n
<\/code><\/pre>\n<\/p>\n
Pandas DataFrame\u7684iloc<\/code>\u65b9\u6cd5\u5141\u8bb8\u6211\u4eec\u901a\u8fc7\u7d22\u5f15\u4f4d\u7f6e\u63d0\u53d6\u7279\u5b9a\u5217\u3002Pandas\u7684\u4f18\u70b9\u662f\u529f\u80fd\u5f3a\u5927\u3001\u64cd\u4f5c\u7b80\u4fbf\uff0c\u9002\u7528\u4e8e\u5404\u79cd\u6570\u636e\u5206\u6790\u4efb\u52a1\u3002<\/strong><\/p>\n<\/p>\n
\u8be6\u7ec6\u63a2\u8ba8Pandas DataFrame\u7684\u76f8\u5173\u64cd\u4f5c<\/h3>\n<\/p>\n
Pandas\u4e0d\u4ec5\u53ef\u4ee5\u65b9\u4fbf\u5730\u63d0\u53d6\u56fa\u5b9aindex\u7684\u67d0\u4e00\u5217\uff0c\u8fd8\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u64cd\u4f5c\u65b9\u6cd5\uff0c\u5982\u8fc7\u6ee4\u3001\u5206\u7ec4\u3001\u805a\u5408\u7b49\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecdPandas DataFrame\u7684\u5e38\u89c1\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
1\u3001\u6570\u636e\u52a0\u8f7d\u4e0e\u521b\u5efa<\/h3>\n<\/p>\n
Pandas\u53ef\u4ee5\u4ece\u591a\u79cd\u6570\u636e\u6e90\u52a0\u8f7d\u6570\u636e\uff0c\u5982CSV\u6587\u4ef6\u3001Excel\u6587\u4ef6\u3001SQL\u6570\u636e\u5e93\u7b49\u3002\u4ee5\u4e0b\u662f\u4eceCSV\u6587\u4ef6\u52a0\u8f7d\u6570\u636e\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
data = pd.read_csv('data.csv')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u6211\u4eec\u4e5f\u53ef\u4ee5\u624b\u52a8\u521b\u5efaDataFrame\uff0c\u5982\u524d\u9762\u7684\u793a\u4f8b\u4ee3\u7801\u6240\u793a\u3002<\/p>\n<\/p>\n
2\u3001\u6570\u636e\u7b5b\u9009\u4e0e\u8fc7\u6ee4<\/h3>\n<\/p>\n
Pandas\u63d0\u4f9b\u4e86\u591a\u79cd\u6570\u636e\u7b5b\u9009\u4e0e\u8fc7\u6ee4\u65b9\u6cd5\uff0c\u5982\u5e03\u5c14\u7d22\u5f15\u3001\u6761\u4ef6\u7b5b\u9009\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
# \u5e03\u5c14\u7d22\u5f15<\/p>\n
filtered_data = data[data['A'] > 5]<\/p>\n
\u6761\u4ef6\u7b5b\u9009<\/strong><\/h2>\n
filtered_data = data[(data['A'] > 5) & (data['B'] < 10)]<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u6570\u636e\u5206\u7ec4\u4e0e\u805a\u5408<\/h3>\n<\/p>\n
Pandas\u7684groupby<\/code>\u65b9\u6cd5\u5141\u8bb8\u6211\u4eec\u6309\u67d0\u4e00\u5217\u6216\u591a\u5217\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\uff0c\u5e76\u5e94\u7528\u805a\u5408\u51fd\u6570\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
grouped_data = data.groupby('A').sum()<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u6570\u636e\u5408\u5e76\u4e0e\u8fde\u63a5<\/h3>\n<\/p>\n
Pandas\u63d0\u4f9b\u4e86\u591a\u79cd\u6570\u636e\u5408\u5e76\u4e0e\u8fde\u63a5\u65b9\u6cd5\uff0c\u5982merge<\/code>\u3001concat<\/code>\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
# \u6309\u5217\u5408\u5e76<\/p>\n
merged_data = pd.merge(data1, data2, on='key')<\/p>\n
\u6309\u884c\u5408\u5e76<\/strong><\/h2>\n
concatenated_data = pd.concat([data1, data2])<\/p>\n
<\/code><\/pre>\n<\/p>\n
5\u3001\u6570\u636e\u900f\u89c6\u8868<\/h3>\n<\/p>\n
\u6570\u636e\u900f\u89c6\u8868\u662f\u6570\u636e\u5206\u6790\u4e2d\u7684\u4e00\u79cd\u5e38\u89c1\u64cd\u4f5c\uff0cPandas\u63d0\u4f9b\u4e86pivot_table<\/code>\u65b9\u6cd5\u6765\u521b\u5efa\u6570\u636e\u900f\u89c6\u8868\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
pivot_table = data.pivot_table(values='C', index='A', columns='B', aggfunc='sum')<\/p>\n
<\/code><\/pre>\n<\/p>\n
6\u3001\u5904\u7406\u7f3a\u5931\u6570\u636e<\/h3>\n<\/p>\n
Pandas\u63d0\u4f9b\u4e86\u591a\u79cd\u5904\u7406\u7f3a\u5931\u6570\u636e\u7684\u65b9\u6cd5\uff0c\u5982\u586b\u5145\u3001\u5220\u9664\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
# \u586b\u5145\u7f3a\u5931\u503c<\/p>\n
data.fillna(0, inplace=True)<\/p>\n
\u5220\u9664\u7f3a\u5931\u503c<\/strong><\/h2>\n
data.dropna(inplace=True)<\/p>\n
<\/code><\/pre>\n<\/p>\n
7\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h3>\n<\/p>\n
Pandas\u53ef\u4ee5\u4e0eMatplotlib\u7b49\u53ef\u89c6\u5316\u5e93\u7ed3\u5408\u4f7f\u7528\uff0c\u65b9\u4fbf\u5730\u521b\u5efa\u5404\u79cd\u56fe\u8868\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
data.plot(kind='bar')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
8\u3001\u6027\u80fd\u4f18\u5316<\/h3>\n<\/p>\n
Pandas\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\uff0c\u6027\u80fd\u53ef\u80fd\u6210\u4e3a\u74f6\u9888\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u6cd5\u4f18\u5316\u6027\u80fd\uff1a<\/p>\n<\/p>\n
    \n
  • \u4f7f\u7528categorical<\/code>\u6570\u636e\u7c7b\u578b\u51cf\u5c11\u5185\u5b58\u5360\u7528\u3002<\/li>\n
  • \u4f7f\u7528chunk<\/code>\u5206\u5757\u8bfb\u53d6\u5927\u6587\u4ef6\u3002<\/li>\n
  • \u4f7f\u7528numba<\/code>\u52a0\u901f\u6570\u503c\u8ba1\u7b97\u3002<\/li>\n<\/ul>\n
    # \u4f7f\u7528categorical\u6570\u636e\u7c7b\u578b<\/p>\n
    data['category'] = data['category'].astype('category')<\/p>\n
    \u5206\u5757\u8bfb\u53d6\u5927\u6587\u4ef6<\/strong><\/h2>\n
    for chunk in pd.read_csv('data.csv', chunksize=10000):<\/p>\n
        process(chunk)<\/p>\n
    \u4f7f\u7528numba\u52a0\u901f\u6570\u503c\u8ba1\u7b97<\/strong><\/h2>\n
    from numba import jit<\/p>\n
    @jit<\/p>\n
    def fast_function(data):<\/p>\n
        # \u52a0\u901f\u8ba1\u7b97<\/p>\n
        return result<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u603b\u7ed3<\/h2>\n<\/p>\n
    \u901a\u8fc7\u672c\u6587\uff0c\u6211\u4eec\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u5982\u4f55\u5728Python\u4e2d\u53d6\u56fa\u5b9aindex\u7684\u67d0\u4e00\u5217\u7684\u65b9\u6cd5\uff0c\u5305\u62ec\u5217\u8868\u89e3\u6790\u3001NumPy\u6570\u7ec4\u548cPandas DataFrame\u3002Pandas DataFrame\u662f\u6700\u5e38\u7528\u4e14\u529f\u80fd\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5de5\u5177\uff0c\u9002\u7528\u4e8e\u5404\u79cd\u6570\u636e\u64cd\u4f5c<\/strong>\u3002\u6b64\u5916\uff0c\u6211\u4eec\u8fd8\u63a2\u8ba8\u4e86Pandas DataFrame\u7684\u5e38\u89c1\u64cd\u4f5c\uff0c\u5982\u6570\u636e\u52a0\u8f7d\u4e0e\u521b\u5efa\u3001\u6570\u636e\u7b5b\u9009\u4e0e\u8fc7\u6ee4\u3001\u6570\u636e\u5206\u7ec4\u4e0e\u805a\u5408\u3001\u6570\u636e\u5408\u5e76\u4e0e\u8fde\u63a5\u3001\u6570\u636e\u900f\u89c6\u8868\u3001\u5904\u7406\u7f3a\u5931\u6570\u636e\u3001\u6570\u636e\u53ef\u89c6\u5316\u4ee5\u53ca\u6027\u80fd\u4f18\u5316\u3002<\/p>\n<\/p>\n
    \u5e0c\u671b\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u60a8\u80fd\u66f4\u597d\u5730\u7406\u89e3\u548c\u5e94\u7528\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u63d0\u9ad8\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u7684\u6548\u7387\u3002<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     \u5982\u4f55\u5728Python\u4e2d\u63d0\u53d6\u7279\u5b9a\u7d22\u5f15\u7684\u5217\uff1f<\/strong>
    \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u8f7b\u677e\u63d0\u53d6\u7279\u5b9a\u7d22\u5f15\u7684\u5217\u3002\u9996\u5148\uff0c\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u5e76\u5bfc\u5165\u4e86Pandas\u5e93\u3002\u7136\u540e\uff0c\u901a\u8fc7DataFrame\u7684.iloc<\/code>\u65b9\u6cd5\u53ef\u4ee5\u8bbf\u95ee\u7279\u5b9a\u7d22\u5f15\u7684\u884c\u548c\u5217\u3002\u4f8b\u5982\uff0c\u5982\u679c\u4f60\u6709\u4e00\u4e2aDataFrame\uff0c\u60f3\u63d0\u53d6\u7b2c2\u5217\uff0c\u53ef\u4ee5\u4f7f\u7528df.iloc[:, 1]<\/code>\uff0c\u8fd9\u91cc\u76841\u4ee3\u8868\u7684\u662f\u7b2c\u4e8c\u5217\u7684\u7d22\u5f15\uff08\u6ce8\u610f\u7d22\u5f15\u4ece0\u5f00\u59cb\uff09\u3002<\/p>\n
    \u5728\u4f7f\u7528Python\u53d6\u5217\u65f6\uff0c\u6709\u54ea\u4e9b\u5e38\u89c1\u7684\u9519\u8bef\uff1f<\/strong>
    \u5728\u63d0\u53d6\u5217\u65f6\uff0c\u53ef\u80fd\u4f1a\u9047\u5230\u4e00\u4e9b\u5e38\u89c1\u95ee\u9898\uff0c\u4f8b\u5982\u7d22\u5f15\u8d85\u51fa\u8303\u56f4\u6216\u5c1d\u8bd5\u8bbf\u95ee\u4e0d\u5b58\u5728\u7684\u5217\u3002\u5982\u679c\u6307\u5b9a\u7684\u7d22\u5f15\u4e0d\u5b58\u5728\uff0cPandas\u4f1a\u5f15\u53d1IndexError<\/code>\u3002\u56e0\u6b64\uff0c\u5728\u63d0\u53d6\u5217\u4e4b\u524d\uff0c\u6700\u597d\u68c0\u67e5DataFrame\u7684\u5f62\u72b6\uff0c\u786e\u4fdd\u6240\u8bf7\u6c42\u7684\u7d22\u5f15\u5728\u6709\u6548\u8303\u56f4\u5185\u3002<\/p>\n
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