Is there a way to plot a pandas series in ggplot?

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不知归路
不知归路 2021-02-06 15:53

I\'m experimenting with pandas and non-matplotlib plotting. Good suggestions are here. This question regards yhat\'s ggplot and I am running into two issues. Plotting a series

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  • 2021-02-06 15:58

    This is more of a workaround but you can use qplot for quick, shorthand plots using series.

    from ggplot import *
    qplot(meat.beef)
    
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  • 2021-02-06 16:03

    I run into this problem frequently in Python's ggplot when working with multiple stock prices and economic timeseries. The key to remember with ggplot is that data is best organized in long format to avoid any issues. I use a quick two step process as a workaround. First let's grab some stock data:

    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
    

    First understand that ggplot needs the datetime index to be a column in the pandas dataframe in order to plot correctly when switching from wide to long format. I wrote a function to address this particular point. It simply creates a 'Date' column of type=datetime from the pandas series index.

    def dateConvert(df):
      df['Date'] = df.index
      df.reset_index(drop=True)
      return df
    

    From there run the function on the df. Use the result as the object in pandas pd.melt using the 'Date' as the id_vars. The returned df is now ready to be plotted using the standard ggplot() format.

    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
    

    From here you can now plot your data however you want. A common plot I use is the following:

    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()
    

    FB cumRet plot using ggplot

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