试图绘制股票数据 - 获得一个奇怪的图表

Question

我试图在十年内绘制股票数据。我希望y轴的价格从0到32不等，x轴的年份从2002年到2012年。

import csv, urllib.request, shutil, pandas as pd
import numpy as np, pylab as pl

url = "http://real-chart.finance.yahoo.com/table.csv?s=YHOO&a=03&b=12&c=2006&d=01&e=9&f=2016&g=d&ignore=.csv"

with urllib.request.urlopen(url) as response, open("C:/Examples/data.csv", 'wb') as out_file:
  shutil.copyfileobj(response, out_file)

with open("C:/Examples/data.csv", 'rb') as csvfile:
  df = pd.read_csv(csvfile)
  dates = df.Date;
  prices = df['Adj Close']
  x = [int(i[:4]) for i in dates] # int(i[:4]) to get the year part
  y = [p for p in prices]
  pl.xlim(2002, 2012)
  pl.ylim(0, 32.)
  pl.plot(x, y)
  pl.show()

当我打印出情节时，这就是我得到的。

我不知道这是否是正确的输出。我期待从情节的一侧开始到另一侧的线条有点平滑。这是stock csv文件：

    Date    Open    High    Low     Close   Avg Vol Adj Close
Nov 1, 2012 16.90   19.16   16.86   18.77   27,813,200  18.77
Oct 1, 2012 16.00   16.89   15.65   16.84   23,074,600  16.84
Sep 4, 2012 14.64   16.20   14.59   15.98   21,900,500  15.98
Aug 1, 2012 15.86   16.37   14.59   14.65   17,527,600  14.65
Jul 2, 2012 15.80   16.17   15.23   15.84   15,348,400  15.84
Jun 1, 2012 15.04   15.83   14.81   15.83   14,102,700  15.83
May 1, 2012 15.51   16.00   14.80   15.24   19,935,500  15.24
Apr 2, 2012 15.19   15.70   14.73   15.54   16,640,200  15.54
Mar 1, 2012 14.89   15.61   14.35   15.22   15,408,200  15.22
Feb 1, 2012 15.57   16.31   14.37   14.83   22,020,900  14.83
Jan 3, 2012 16.27   16.39   15.35   15.47   18,167,300  15.47
Dec 1, 2011 16.42   16.46   14.57   16.13   21,943,000  16.13
Nov 1, 2011 14.95   16.50   14.75   15.71   24,382,900  15.71
Oct 3, 2011 13.70   16.79   13.37   15.64   36,438,000  15.64
Sep 1, 2011 13.67   15.40   12.45   13.17   42,743,300  13.17
Aug 1, 2011 13.24   13.98   11.09   13.61   33,473,700  13.61
Jul 1, 2011 15.08   15.95   13.04   13.10   29,550,000  13.10
Jun 1, 2011 16.34   16.43   14.50   15.04   25,772,600  15.04
May 2, 2011 17.79   18.84   15.63   16.55   41,113,700  16.55
Apr 1, 2011 16.83   17.77   16.06   17.70   20,728,700  17.70
Mar 1, 2011 16.46   17.70   15.81   16.68   25,018,300  16.68
Feb 1, 2011 16.33   17.84   16.04   16.40   24,578,700  16.40
Jan 3, 2011 16.81   17.34   15.41   16.12   23,201,900  16.12
Dec 1, 2010 16.00   17.22   16.00   16.63   12,970,200  16.63
Nov 1, 2010 16.50   17.60   15.77   15.82   21,166,800  15.82
Oct 1, 2010 14.19   16.76   14.13   16.49   29,726,500  16.49
Sep 1, 2010 13.20   14.53   13.13   14.17   27,527,400  14.17
Aug 2, 2010 14.01   14.52   12.94   13.11   13,811,800  13.11
Jul 1, 2010 13.99   15.60   13.52   13.88   24,186,600  13.88
Jun 1, 2010 15.31   15.84   13.79   13.84   21,523,300  13.84
May 3, 2010 16.68   16.99   14.63   15.34   26,797,300  15.34
Apr 1, 2010 16.58   19.12   16.22   16.53   33,650,200  16.53
Mar 1, 2010 15.43   16.94   15.40   16.53   21,831,600  16.53
Feb 1, 2010 15.14   15.71   14.48   15.31   21,501,000  15.31
Jan 4, 2010 16.94   17.30   14.90   15.01   24,600,000  15.01
Dec 1, 2009 15.03   16.99   14.85   16.78   18,305,700  16.78
Nov 2, 2009 15.75   16.36   14.80   14.97   20,043,500  14.97
Oct 1, 2009 17.65   18.02   15.74   15.90   25,250,700  15.90
Sep 1, 2009 14.50   17.94   13.97   17.81   36,378,300  17.81
Aug 3, 2009 14.56   15.21   14.30   14.61   28,097,800  14.61
Jul 1, 2009 15.49   17.68   14.22   14.32   36,381,400  14.32
Jun 1, 2009 16.17   16.99   14.55   15.66   19,810,700  15.66
May 1, 2009 14.50   15.84   13.91   15.84   24,369,700  15.84
Apr 1, 2009 12.70   15.39   12.60   14.29   24,663,000  14.29
Mar 2, 2009 12.85   14.14   12.31   12.81   20,620,800  12.81
Feb 2, 2009 11.50   14.00   11.49   13.23   18,837,100  13.23
Jan 2, 2009 12.17   13.56   10.81   11.73   21,408,600  11.73
Dec 1, 2008 11.82   13.57   10.50   12.20   20,089,400  12.20
Nov 3, 2008 12.74   14.89   8.94    11.51   31,084,900  11.51
Oct 1, 2008 17.17   17.31   11.25   12.82   32,771,500  12.82
Sep 2, 2008 19.63   20.82   16.88   17.30   24,333,600  17.30
Aug 1, 2008 20.09   20.82   18.87   19.38   13,981,900  19.38
Jul 1, 2008 20.48   24.80   19.59   19.89   30,010,200  19.89

我这样做是正确的吗？

Answer 1

最好使用pandas的内置日期绘图：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

url = "http://real-chart.finance.yahoo.com/table.csv?s=YHOO&a=03&b=12&c=2006&d=01&e=9&f=2016&g=d&ignore=.csv"

df = pd.read_csv(url)
df.index = df["Date"]
df.sort_index(inplace=True)

df['Adj Close'].plot()
plt.show()