使用数据框列值的 Python Pandas 图

Question

我正在尝试使用数据框绘制图形。

我正在使用“pandas_datareader”来获取数据。

所以我的代码如下：

tickers = ["AAPL","GOOG","MSFT","XOM","BRK-A","FB","JNJ","GE","AMZN","WFC"]
import pandas_datareader.data as web
import datetime as dt
end = dt.datetime.now().strftime("%Y-%m-%d")
start = (dt.datetime.now()-dt.timedelta(days=365*3)).strftime("%Y-%m-%d")
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
data = []
for ticker in tickers:
    sub_df = web.get_data_yahoo(ticker, start, end)
    sub_df["name"] = ticker
    data.append(sub_df)
data = pd.concat(data)

所以在变量data ，有 8 列 = ['Date', 'Open', 'High' ,'Low' ,'Close' 'Volume', 'Adj Close','name']

变量“数据”如下所示：

我想要做的是绘制一个图形，以“日期”值作为 x 参数，“高”作为 y 参数，多列作为“名称”列值（=[“AAPL”，“GOOG”，“MSFT” ,"XOM","BRK-A","FB","JNJ","GE","AMZN","WFC"])。

我怎样才能做到这一点？

当我执行data.plot() ，结果很好地将data作为 x 参数，但有 5 列['open','high','low','close','volume','adj close']不是7 列["AAPL","GOOG","MSFT","XOM","BRK-A","FB","JNJ","GE","AMZN","WFC"] ：我想要什么做。 结果如下：

Answer 1

您需要对数据进行整形，以便名称成为数据框的标题，因为您只想绘制High ，您可以提取High和name列，并将其转换为宽格式，然后进行绘图：

import matplotlib as mpl
mpl.rcParams['savefig.dpi'] = 120

high = data[["High", "name"]].set_index("name", append=True).High.unstack("name")

# notice here I scale down the BRK-A column so that it will be at the same scale as other columns
high['BRK-A'] = high['BRK-A']/1000
high.head()

ax = high.plot(figsize = (16, 10))

Answer 2

您应该按name对数据进行分组，然后进行绘图。 像data.groupby('name').plot ()应该会让你开始。 您可能需要输入date作为 x 值和high值作为 y。 由于我在移动设备上，目前无法自己测试。

更新

拿到电脑后，我意识到我有点不对劲。 您需要在分组之前重置索引，然后绘制并最终更新图例。 像这样：

fig, ax = plt.subplots()
names = data.name.unique()
data.reset_index().groupby('name').plot(x='Date', y='High', ax=ax)
plt.legend(names)
plt.show()

当然，如果您希望此图表有意义，您将需要对价值进行某种形式的调整，因为 BRK-A 比任何其他股票都贵得多。

Answer 3

@Psidom和@Grr已经给了你很好的答案。

我只想补充一点， pandas_datareader允许我们一步轻松地将所有数据读入 Pandas.Panel：

p = web.DataReader(tickers, 'yahoo', start, end)

现在我们可以根据需要轻松切片

# i'll intentionally exclude `BRK-A` as it spoils the whole graph
p.loc['High', :, ~p.minor_axis.isin(['BRK-A'])].plot(figsize=(10,8))

或者，您可以即时切片并仅保存High值：

df = web.DataReader(tickers, 'yahoo', start, end).loc['High']

这给了我们：

In [68]: df
Out[68]:
                  AAPL        AMZN     BRK-A          FB         GE         GOOG         JNJ       MSFT        WFC        XOM
Date
2014-03-13  539.659988  383.109985  188852.0   71.349998  26.000000  1210.502120   94.199997  38.450001  48.299999  94.570000
2014-03-14  530.890015  378.570007  186507.0   69.430000  25.379999  1190.872020   93.440002  38.139999  48.070000  94.220001
2014-03-17  529.969994  378.850006  185790.0   68.949997  25.629999  1197.072063   94.180000  38.410000  48.169998  94.529999
2014-03-18  531.969986  379.000000  185400.0   69.599998  25.730000  1211.532091   94.239998  39.900002  48.450001  95.250000
2014-03-19  536.239990  379.000000  185489.0   69.290001  25.700001  1211.992061   94.360001  39.549999  48.410000  95.300003
2014-03-20  532.669975  373.000000  186742.0   68.230003  25.370001  1209.612076   94.190002  40.650002  49.360001  94.739998
2014-03-21  533.750000  372.839996  188598.0   67.919998  25.830000  1209.632048   95.930000  40.939999  49.970001  95.989998
...                ...         ...       ...         ...        ...          ...         ...        ...        ...        ...
2017-03-02  140.279999  854.820007  266445.0  137.820007  30.230000   834.510010  124.360001  64.750000  59.790001  84.250000
2017-03-03  139.830002  851.989990  264690.0  137.330002  30.219999   831.359985  123.930000  64.279999  59.240002  83.599998
2017-03-06  139.770004  848.489990  263760.0  137.830002  30.080000   828.880005  124.430000  64.559998  58.880001  82.900002
2017-03-07  139.979996  848.460022  263560.0  138.369995  29.990000   833.409973  124.459999  64.779999  58.520000  83.290001
2017-03-08  139.800003  853.070007  263900.0  137.990005  29.940001   838.150024  124.680000  65.080002  59.130001  82.379997
2017-03-09  138.789993  856.400024  263620.0  138.570007  29.830000   842.000000  126.209999  65.199997  58.869999  81.720001
2017-03-10  139.360001  857.349976  263800.0  139.490005  30.430000   844.909973  126.489998  65.260002  59.180000  82.470001

[755 rows x 10 columns]