{"id":2045,"date":"2023-07-23T23:03:35","date_gmt":"2023-07-23T23:03:35","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e7%86%8a%e7%8c%ab%e6%8f%8f%e8%bf%b0\/"},"modified":"2023-07-23T23:03:35","modified_gmt":"2023-07-23T23:03:35","slug":"%e7%86%8a%e7%8c%ab%e6%8f%8f%e8%bf%b0","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e7%86%8a%e7%8c%ab%e6%8f%8f%e8%bf%b0\/","title":{"rendered":"\u5982\u4f55\u5728pandas\u4e2d\u4f7f\u7528describe()\u51fd\u6570\uff08\u9644\u793a\u4f8b\uff09"},"content":{"rendered":"

<\/p>\n

\n

\u60a8\u53ef\u4ee5\u4f7f\u7528describe()<\/strong>\u51fd\u6570\u4e3apandas DataFrame\u751f\u6210\u63cf\u8ff0\u6027\u7edf\u8ba1\u6570\u636e\u3002<\/span><\/p>\n

\u8be5\u51fd\u6570\u4f7f\u7528\u4ee5\u4e0b\u57fa\u672c\u8bed\u6cd5\uff1a<\/span><\/p>\n

 df. describe<\/span> ()\n<\/strong><\/span><\/pre>\n
\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u8df5\u4e2d\u901a\u8fc7\u4ee5\u4e0b pandas DataFrame \u4f7f\u7528\u6b64\u8bed\u6cd5\uff1a<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'B', 'B', 'B', 'C', 'C', 'C'],\n                   ' points<\/span> ': [25, 12, 15, 14, 19, 23, 25, 29],\n                   ' assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12]})\n\n#view DataFrame\n<\/span>df\n\n\tteam points assists rebounds\n0 to 25 5 11\n1 to 12 7 8\n2 B 15 7 10\n3 B 14 9 6\n4 B 19 12 6\n5 C 23 9 5\n6 C 25 9 9\n7 C 29 4 12\n<\/strong><\/span><\/pre>\n
\u793a\u4f8b 1\uff1a\u63cf\u8ff0\u6240\u6709\u6570\u5b57\u5217<\/strong><\/span><\/h3>\n
\u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0c describe()<\/strong>\u51fd\u6570\u4ec5\u751f\u6210 pandas DataFrame \u7684\u6570\u5b57\u5217\u7684\u63cf\u8ff0\u6027\u7edf\u8ba1\u4fe1\u606f\uff1a<\/span><\/p>\n
 #generate descriptive statistics for all numeric columns\n<\/span>df. describe<\/span> ()\n\n\tpoints assists rebounds\ncount 8.000000 8.00000 8.000000\nmean 20.250000 7.75000 8.375000\nstd 6.158618 2.54951 2.559994\nmin 12.000000 4.00000 5.000000\n25% 14.750000 6.50000 6.000000\n50% 21.000000 8.00000 8.500000\n75% 25,000000 9,00000 10,250000\nmax 29.000000 12.00000 12.000000\n<\/strong><\/pre>\n
\u663e\u793a DataFrame \u7684\u4e09\u4e2a\u6570\u5b57\u5217\u7684\u63cf\u8ff0\u6027\u7edf\u8ba1\u4fe1\u606f\u3002<\/span><\/p>\n
\u6ce8\u610f\uff1a<\/strong>\u5982\u679c\u6709\u4efb\u4f55\u5217\u7f3a\u5c11\u503c\uff0cpandas \u5728\u8ba1\u7b97\u63cf\u8ff0\u6027\u7edf\u8ba1\u65f6\u4f1a\u81ea\u52a8\u6392\u9664\u8fd9\u4e9b\u503c\u3002<\/span><\/p>\n
\u793a\u4f8b 2\uff1a\u63cf\u8ff0\u6240\u6709\u5217<\/strong><\/span><\/h3>\n
\u8981\u8ba1\u7b97 DataFrame \u6bcf\u4e00\u5217\u7684\u63cf\u8ff0\u6027\u7edf\u8ba1\u6570\u636e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528include=’all’<\/strong>\u53c2\u6570\uff1a<\/span><\/p>\n
 #generate descriptive statistics for all columns\n<\/span>df. describe<\/span> (include=' all<\/span> ')\n<\/span>\n\tteam points assists rebounds\ncount 8 8.000000 8.00000 8.000000\nsingle 3 NaN NaN NaN\ntop B NaN NaN NaN\nfreq 3 NaN NaN NaN\nmean NaN 20.250000 7.75000 8.375000\nstd NaN 6.158618 2.54951 2.559994\nmin NaN 12.000000 4.00000 5.000000\n25% NaN 14.750000 6.50000 6.000000\n50% NaN 21.000000 8.00000 8.500000\n75% NaN 25.000000 9.00000 10.250000\nmax NaN 29.000000 12.00000 12.000000<\/strong><\/pre>\n
\u793a\u4f8b 3\uff1a\u63cf\u8ff0\u7279\u5b9a\u5217<\/strong><\/h3>\n
\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u4e86\u5982\u4f55\u8ba1\u7b97 pandas DataFrame \u7684\u7279\u5b9a\u5217\u7684\u63cf\u8ff0\u6027\u7edf\u8ba1\u6570\u636e\uff1a<\/span><\/p>\n
 #calculate descriptive statistics for 'points' column only\ndf[' points<\/span> ']. describe<\/span> ()\n\ncount 8.000000\nmean 20.250000\nstd 6.158618\nmin 12.000000\n25% 14.750000\n50% 21,000000\n75% 25,000000\nmax 29.000000\nName: points, dtype: float64<\/span>\n<\/span><\/strong><\/pre>\n
\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u4e86\u5982\u4f55\u8ba1\u7b97\u51e0\u4e2a\u7279\u5b9a\u5217\u7684\u63cf\u8ff0\u6027\u7edf\u8ba1\u6570\u636e\uff1a<\/span><\/p>\n
 #calculate descriptive statistics for 'points' and 'assists' columns only\ndf[[' points<\/span> ', ' assists<\/span> ']]. describe<\/span> ()\n\n\tassist points\ncount 8.000000 8.00000\nmean 20.250000 7.75000\nstd 6.158618 2.54951\nmin 12.000000 4.00000\n25% 14.750000 6.50000\n50% 21,000000 8,00000\n75% 25.000000 9.00000\nmax 29.000000 12.00000\n<\/span><\/span><\/strong><\/pre>\n
\u60a8\u53ef\u4ee5\u5728\u6b64\u5904<\/a>\u627e\u5230describe()<\/strong>\u51fd\u6570\u7684\u5b8c\u6574\u6587\u6863\u3002<\/span><\/p>\n
\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n
\u4ee5\u4e0b\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 pandas \u4e2d\u6267\u884c\u5176\u4ed6\u5e38\u89c1\u529f\u80fd\uff1a<\/span><\/p>\n
 Pandas\uff1a\u5982\u4f55\u67e5\u627e\u5217\u4e2d\u7684\u552f\u4e00\u503c<\/a>
Pandas\uff1a\u5982\u4f55\u627e\u5230\u4e24\u6761\u7ebf\u4e4b\u95f4\u7684\u5dee\u5f02<\/a>
Pandas\uff1a\u5982\u4f55\u8ba1\u7b97DataFrame\u4e2d\u7684\u7f3a\u5931\u503c<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
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