寻找一个小项目的帮助。我正在努力学习Python，我完全迷失在一个问题上。请让我解释一下。
我有一个包含'Apple股价'的csv文件，到目前为止我可以使用csv模块导入Python，但是，我需要分析数据并生成月平均值并确定最佳和最差的6个月。我的csv列是Date，Price。
非常感谢帮助。
"Date","Open","High","Low","Close","Volume","Adj Close"
"2012-11-14",660.66,662.18,650.5,652.55,1668400,652.55
"2012-11-13",663,667.6,658.23,659.05,1594200,659.05
"2012-11-12",663.75,669.8,660.87,665.9,1405900,665.9
"2012-11-09",654.65,668.34,650.3,663.03,3114100,663.03
"2012-11-08",670.2,671.49,651.23,652.29,2597000,652.29
"2012-11-07",675,678.23,666.49,667.12,2232300,667.12
"2012-11-06",685.48,686.5,677.55,681.72,1582800,681.72
"2012-11-05",684.5,686.86,675.56,682.96,1635900,682.96
"2012-11-02",694.79,695.55,687.37,687.92,2324400,687.92
"2012-11-01",679.5,690.9,678.72,687.59,2050100,687.59
"2012-10-31",679.86,681,675,680.3,1537000,680.3
"2012-10-26",676.5,683.03,671.2,675.15,1950800,675.15
"2012-10-25",680,682,673.51,677.76,2401100,677.76
"2012-10-24",686.8,687,675.27,677.3,2496500,677.3
等...
答案 0 :(得分：2)
使用pandas，这将是
In [28]: df = pd.read_csv('my_data.csv', parse_dates=True, index_col=0, sep=',')
In [29]: df
Out[29]:
Open High Low Close Volume Adj Close
Date
2012-11-14 660.66 662.18 650.50 652.55 1668400 652.55
2012-11-13 663.00 667.60 658.23 659.05 1594200 659.05
2012-11-12 663.75 669.80 660.87 665.90 1405900 665.90
2012-11-09 654.65 668.34 650.30 663.03 3114100 663.03
2012-11-08 670.20 671.49 651.23 652.29 2597000 652.29
2012-11-07 675.00 678.23 666.49 667.12 2232300 667.12
2012-11-06 685.48 686.50 677.55 681.72 1582800 681.72
2012-11-05 684.50 686.86 675.56 682.96 1635900 682.96
2012-11-02 694.79 695.55 687.37 687.92 2324400 687.92
2012-11-01 679.50 690.90 678.72 687.59 2050100 687.59
2012-10-31 679.86 681.00 675.00 680.30 1537000 680.30
2012-10-26 676.50 683.03 671.20 675.15 1950800 675.15
2012-10-25 680.00 682.00 673.51 677.76 2401100 677.76
2012-10-24 686.80 687.00 675.27 677.30 2496500 677.30
In [30]: monthly = df.resample('1M')
In [31]: monthly
Out[30]:
Open High Low Close Volume Adj Close
Date
2012-10-31 680.790 683.2575 673.745 677.6275 2096350 677.6275
2012-11-30 673.153 677.7450 665.682 670.0130 2020510 670.0130
您可以对所需的列进行排序
In [33]: monthly.sort('Close')
Out[33]:
Open High Low Close Volume Adj Close
Date
2012-11-30 673.153 677.7450 665.682 670.0130 2020510 670.0130
2012-10-31 680.790 683.2575 673.745 677.6275 2096350 677.6275
您甚至可以从雅虎财务中获取数据：
In [37]: from pandas.io import data as pddata
In [40]: df = pddata.DataReader('AAPL', data_source='yahoo', start='2012-01-01')
In [41]: df.resample('1M').sort('Close')
Out[44]:
Open High Low Close Volume Adj Close
Date
2012-01-31 428.760000 431.008500 425.810500 428.578000 12249740.000000 424.804500
2012-02-29 494.803000 500.849000 491.437500 497.571000 20300990.000000 493.191000
2012-11-30 560.365385 566.118462 548.523846 555.789231 24861884.615385 554.970769
2012-05-31 565.785000 572.141364 558.397273 564.673182 18029781.818182 559.702273
2012-06-30 574.660952 578.889048 569.213333 574.562381 13360247.619048 569.504762
2012-03-31 576.858182 582.064545 570.245909 577.507727 25299250.000000 572.424545
2012-07-31 599.610000 604.920952 594.680476 601.068095 15152466.666667 595.776667
2012-04-30 609.607500 615.487500 598.650000 606.003000 27855340.000000 600.668500
2012-10-31 638.667143 643.650476 628.213810 634.714286 20651071.428571 631.828571
2012-08-31 641.527826 646.655217 637.138261 642.696087 12851252.173913 639.090870
2012-09-30 682.118421 687.007895 676.095263 681.568421 17291363.157895 678.470526
答案 1 :(得分：1)
在阅读完项目并在列表中保存[month，mean_price]对后，您可以对列表进行排序：
import operator
values_list.sort(key=operator.itemgetter(1))
这将按价格对值进行排序。获得前n个值：
print values_list[-n:]
或底部的n：
print values_list[:n]