<p>在使用多个股票价格和经济时间序列时,我经常在Python的ggplot中遇到这个问题。使用ggplot要记住的关键是数据最好以长格式组织,以避免出现任何问题。我用一个快速的两步流程作为解决方法。首先让我们获取一些股票数据:</p>
<pre><code>import pandas.io.data as web
import pandas as pd
import time
from ggplot import *
stocks = [ 'GOOG', 'MSFT', 'LNKD', 'YHOO', 'FB', 'GOOGL','HPQ','AMZN'] # stock list
# get stock price function #
def get_px(stock, start, end):
return web.get_data_yahoo(stock, start, end)['Adj Close']
# dataframe of equity prices
px = pd.DataFrame({n: get_px(n, '1/1/2014', date_today) for n in stocks})
px.head()
AMZN FB GOOG GOOGL HPQ LNKD MSFT YHOO
Date
2014-01-02 397.97 54.71 NaN 557.12 27.40 207.64 36.63 39.59
2014-01-03 396.44 54.56 NaN 553.05 28.07 207.42 36.38 40.12
2014-01-06 393.63 57.20 NaN 559.22 28.02 203.92 35.61 39.93
2014-01-07 398.03 57.92 NaN 570.00 27.91 209.64 35.89 40.92
2014-01-08 401.92 58.23 NaN 571.19 27.19 209.06 35.25 41.02
</code></pre>
<p>首先要明白,ggplot需要datetime索引作为pandas数据帧中的一列,以便在从宽格式切换到长格式时正确地绘制。我写了一个函数来解决这个问题。它只是从pandas系列索引中创建一个类型为datetime的“Date”列。</p>
<pre><code>def dateConvert(df):
df['Date'] = df.index
df.reset_index(drop=True)
return df
</code></pre>
<p>然后在df上运行函数。将结果用作pandas pd.melt中的对象,将“日期”用作id变量。现在可以使用标准的ggplot()格式打印返回的df。</p>
<pre><code>px_returns = px.pct_change() # common stock transformation
cumRet = (1+px_returns).cumprod() - 1 # transform daily returns to cumulative
cumRet_dateConverted = dateConvert(cumRet) # run the function here see the result below#
cumRet_dateConverted.info()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 118 entries, 2014-01-02 00:00:00 to 2014-06-20 00:00:00
Data columns (total 9 columns):
AMZN 117 non-null float64
FB 117 non-null float64
GOOG 59 non-null float64
GOOGL 117 non-null float64
HPQ 117 non-null float64
LNKD 117 non-null float64
MSFT 117 non-null float64
YHOO 117 non-null float64
Date 118 non-null datetime64[ns]
dtypes: datetime64[ns](1), float64(8)
data = pd.melt(cumRet_dateConverted, id_vars='Date').dropna() # Here is the method I use to format the data in the long format. Please note the use of 'Date' as the id_vars.
data = data.rename(columns = {'Date':'Date','variable':'Stocks','value':'Returns'}) # common to rename these columns
</code></pre>
<p>从这里开始,您可以随心所欲地绘制数据。我常用的一个图是:</p>
<pre><code>retPlot_YTD = ggplot(data, aes('Date','Returns',color='Stocks')) \
+ geom_line(size=2.) \
+ geom_hline(yintercept=0, color='black', size=1.7, linetype='-.') \
+ scale_y_continuous(labels='percent') \
+ scale_x_date(labels='%b %d %y',breaks=date_breaks('week') ) \
+ theme_seaborn(style='whitegrid') \
+ ggtitle(('%s Cumulative Daily Return vs Peers_YTD') % key_Stock)
fig = retPlot_YTD.draw()
ax = fig.axes[0]
offbox = ax.artists[0]
offbox.set_bbox_to_anchor((1, 0.5), ax.transAxes)
fig.show()
</code></pre>
<p><img src="https://i.stack.imgur.com/Mdntl.png" alt="FB cumRet plot using ggplot"/></p>