为每个DataFrame行生成一个NumPy数组

Question

我正在尝试使用mplfinance.plot()来绘制针对特定股票的股票市场交易图。我使用作为CSV文件的jstock来记录我的所有交易：

"Code","Symbol","Date","Units","Purchase Price","Current Price","Purchase Value","Current Value","Gain/Loss Price","Gain/Loss Value","Gain/Loss %","Broker","Clearing Fee","Stamp Duty","Net Purchase Value","Net Gain/Loss Value","Net Gain/Loss %","Comment"
"ASO","Academy Sports and Outdoors, Inc.","Sep 13, 2021","25.0","45.85","46.62","1146.25","1165.5","0.769999999999996","19.25","1.6793893129770994","0.0","0.0","0.0","1146.25","19.25","1.6793893129770994",""
"ASO","Academy Sports and Outdoors, Inc.","Aug 26, 2021","15.0","41.3","46.62","619.5","699.3","5.32","79.79999999999995","12.881355932203384","0.0","0.0","0.0","619.5","79.79999999999995","12.881355932203384",""
"ASO","Academy Sports and Outdoors, Inc.","Jun 3, 2021","10.0","37.48","46.62","374.79999999999995","466.2","9.14","91.40000000000003","24.386339381003214","0.0","0.0","0.0","374.79999999999995","91.40000000000003","24.386339381003214",""
"RMBS","Rambus Inc.","Nov 24, 2021","2.0","26.99","26.99","53.98","53.98","0.0","0.0","0.0","0.0","0.0","0.0","53.98","0.0","0.0",""

我可以很容易地使用以下命令获取这些数据

myportfolio = pd.read_csv(PORTFOLIO_LOCATION, parse_dates=[2])

但我需要为每个交易创建与每日股票价格匹配的单独列表：

Date,High,Low,Open,Close,Volume,Adj Close
2020-12-01,17.020000457763672,16.5,16.799999237060547,16.8799991607666,990900,16.8799991607666
2020-12-02,17.31999969482422,16.290000915527344,16.65999984741211,16.40999984741211,1200500,16.40999984741211

我有一个普通的包含这个的DataFrame。到目前为止，我得到的是：

for i in myportfolio.groupby("Code"):
    (code, j) = i
    if code == "ASO": # just testing it against one stock
        simp = pd.DataFrame(columns=["Date", "Units", "Price"],
            data=j[["Date", "Units", "Purchase Price"]].values, index=j[["Date"]])
        df = pd.read_csv("ASO-2020-12-01-2021-12-01.csv", index_col=0, parse_dates=True)
        # df.lookup(simp["Date"])
        df.insert(0, 'row_num', range(0,len(df)))
        k = df.loc[simp["Date"]]['row_num']
        trades = []
        for index, m in k.iteritems():
            t = np.zeros((df.shape[0], 1))
            t.fill(np.nan)
            t[m] = simp[index]["Price"]
            trades.append(t.to_list())

但是我收到了一个KeyError: Timestamp('2021-09-17 00:00:00')

有什么办法解决这个问题吗？

附录1：

import pandas as pd
trade_data = [['ASO', '5/5/21', 10], ['ASO', '5/6/21', 12], ['RBLX', '5/7/21', 15]]
trade_df = pd.DataFrame(trade_data, columns = ['Code', 'Date', 'Price'])
trade_df['Date'] = pd.to_datetime(trade_df['Date'])
trade_df

    Code    Date    Price
0   ASO 2021-05-05  10
1   ASO 2021-05-07  12
2   RBLX    2021-05-07  15

aso_data = [['5/5/21', 12, 5, 10, 7], ['5/6/21', 15, 7, 13, 8], ['5/7/21', 17, 10, 15, 11]]
aso_df = pd.DataFrame(aso_data, columns = ['Date', 'High', 'Low', 'Open', 'Close'])
aso_df['Date'] = pd.to_datetime(aso_df['Date'])
aso_df

    Date    High    Low Open    Close
0   2021-05-05  12  5   10  7
1   2021-05-06  15  7   13  8
2   2021-05-07  17  10  15  11

因此，我想为ASO创建两个NumPy数组(每个交易一个)和一个RBLX交易。对于ASO，我应该有两个看起来像[10, Nan, Nan]和[NaN, NaN, 12]的NumPy数组。

Answer 1

你想要一个列表列表吗？不需要循环。

df_list = df.values.tolist()

Answer 2

以防其他像我这样的新手遇到类似的问题。

for i in myportfolio.groupby(["Code"]):
    (code, j) = i
    if code == "ASO": # just testing it against one stock
        df = pd.read_csv("ASO-2020-12-01-2021-12-01.csv", index_col=0, parse_dates=True)
        df.insert(0, 'row_num', range(0,len(df)))
        k = df.loc[j["Date"]]['row_num']
        trades = []
        for index, m in j.iterrows(): 
            t = np.zeros((df.shape[0], 1))
            t.fill(np.nan)
            t[int(df.loc[m["Date"]]['row_num'])] = m["Purchase Price"]
            asplot = mpf.make_addplot(t, type="scatter", color='red', marker="D")
            trades.append(asplot)
        mpf.plot(df, type='candle', addplot=trades)

生成了一个显示我的入口点的my图。祝好运

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