【Python交易程式開發現場54】接收商品報價(2) | 將訂閱商品資訊存入記憶體

結論:

1. 用dict建dataframe時, 要設index

df = pd.DataFrame(dictRepStock, index=[0])

2. 於pandas加一筆data要用concat, 

df_RepStock = pd.concat([df_RepStock, df], axis=0) 

1.檢查接收到的商品報價之資料型態: 結果為Tuple

def twTuple_2_Str(twTuple):

    strTuple=[str(t) for t in twTuple]

    return ','.join(strTuple)

2. tuple要先轉成list

1. ref: Pandas常見基本操作: 

   https://www.learncodewithmike.com/2020/11/python-pandas-dataframe-tutorial.html?m=1&fbclid=IwAR2RBnzUtL5PHngo08qXo-kfhNWq4zJjNWWSOhCju0EgyqFcVZxaLbrOp0M

Create Empty dataframe:

https://www.geeksforgeeks.org/how-to-create-an-empty-dataframe-and-append-rows-columns-to-it-in-pandas/

# import pandas library as pd import pandas as pd   df = pd.DataFrame()   print(df)

1. 3. 客製化Pandas DataFrame的資料索引及欄位名稱，可以分別利用index及columns屬性(Attribute)來達成，如下範例：

   import pandas as pd
    
    
   grades = {
       "name": ["Mike", "Sherry", "Cindy", "John"],
       "math": [80, 75, 93, 86],
       "chinese": [63, 90, 85, 70]
   }
    
   df = pd.DataFrame(grades)
   df.index = ["s1", "s2", "s3", "s4"]  #自訂索引值
   df.columns = ["student_name", "math_score", "chinese_score"]  #自訂欄位名稱
   print(df)

2. 
   
3. 4. append()：新增一筆或一列的資料，透過傳入字典(Dictionary)來指定各欄位的值，並且會回傳一個新的Pandas DataFrame資料集，如下範例：
4. 
   
5. new_df = df.append({
6. "name": "Henry",
7. "math": 60,
8. "chinese": 62
9. }, ignore_index=True)

    def OnNotifyQuoteLONG(self, sMarketNo, nStockidx):    #ctw # 3-【查詢】回報    

        pStock = sk.SKSTOCKLONG()

        m_nCode = skQ.SKQuoteLib_GetStockByIndexLONG(sMarketNo, nStockidx, pStock)

        strMsg = '代碼:',pStock.bstrStockNo,'--名稱:',pStock.bstrStockName,'--開盤價:',str(pStock.nOpen/math.pow(10,pStock.sDecimal)),'--最高:',\

            str(pStock.nHigh/math.pow(10,pStock.sDecimal)),'--最低:',\

            str(pStock.nLow/math.pow(10,pStock.sDecimal)),'--成交價:',str(pStock.nClose/math.pow(10,pStock.sDecimal))\

            ,'--總量:',str(pStock.nTQty)

               

        twStrMsg=twTuple_2_Str(strMsg)

        send.run(twStrMsg)  #發射Signal訊號

        dictRepStock = {'name':pStock.bstrStockName, 'close': str(pStock.nClose/math.pow(10,pStock.sDecimal))}

        import pandas as pd

        global df_RepStock

        df_RepStock = df_RepStock.append(dictRepStock)

d:\tw-skcom-python-43\all\call_twMain.py:519: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
df_RepStock = df_RepStock.append(dictRepStock)


# 使用 concat 合併 axis=0 為直向合併 
res = pd.concat([df1,df2,df3],axis=0)
http://violin-tao.blogspot.com/2017/06/pandas-2-concat-merge.html

1. 
   

2.     def OnNotifyQuoteLONG(self, sMarketNo, nStockidx):    #ctw # 3-【查詢】回報    

           pStock = sk.SKSTOCKLONG()

           m_nCode = skQ.SKQuoteLib_GetStockByIndexLONG(sMarketNo, nStockidx, pStock)

           strMsg = '代碼:',pStock.bstrStockNo,'--名稱:',pStock.bstrStockName,'--開盤價:',str(pStock.nOpen/math.pow(10,pStock.sDecimal)),'--最高:',\

               str(pStock.nHigh/math.pow(10,pStock.sDecimal)),'--最低:',\

               str(pStock.nLow/math.pow(10,pStock.sDecimal)),'--成交價:',str(pStock.nClose/math.pow(10,pStock.sDecimal))\

               ,'--總量:',str(pStock.nTQty)

                  

           twStrMsg=twTuple_2_Str(strMsg)

           send.run(twStrMsg)  #發射Signal訊號

   
   

           dictRepStock = {'name':pStock.bstrStockName, 'close': str(pStock.nClose/math.pow(10,pStock.sDecimal))}

   
   

           global df_RepStock

           df_RepStock = pd.concat([df_RepStock, dictRepStock], axis=0)

           #df_dictRepStock = df_dictRepStock.append(dictRepStock)

           pass

TypeError: cannot concatenate object of type '<class 'dict'>'; only Series and DataFrame objs are valid  

    def OnNotifyQuoteLONG(self, sMarketNo, nStockidx):    #ctw # 3-【查詢】回報    

        pStock = sk.SKSTOCKLONG()

        m_nCode = skQ.SKQuoteLib_GetStockByIndexLONG(sMarketNo, nStockidx, pStock)

        strMsg = '代碼:',pStock.bstrStockNo,'--名稱:',pStock.bstrStockName,'--開盤價:',str(pStock.nOpen/math.pow(10,pStock.sDecimal)),'--最高:',\

            str(pStock.nHigh/math.pow(10,pStock.sDecimal)),'--最低:',\

            str(pStock.nLow/math.pow(10,pStock.sDecimal)),'--成交價:',str(pStock.nClose/math.pow(10,pStock.sDecimal))\

            ,'--總量:',str(pStock.nTQty)

               

        twStrMsg=twTuple_2_Str(strMsg)

        send.run(twStrMsg)  #發射Signal訊號

        dictRepStock = {'name':pStock.bstrStockName, 'close': str(pStock.nClose/math.pow(10,pStock.sDecimal))}

        df = pd.DataFrame(dictRepStock)

        global df_RepStock

        df_RepStock = pd.concat([df_RepStock, df], axis=0)

        #df_dictRepStock = df_dictRepStock.append(dictRepStock)

        pass

File "d:\tw-skcom-python-43\all\.venv\lib\site-packages\pandas\core\internals\construction.py", line 119, in arrays_to_mgr
index = _extract_index(arrays)
File "d:\tw-skcom-python-43\all\.venv\lib\site-packages\pandas\core\internals\construction.py", line 657, in _extract_index
raise ValueError("If using all scalar values, you must pass an index")
ValueError: If using all scalar values, you must pass an index

https://blog.csdn.net/weixin_39750084/article/details/81429037

    def OnNotifyQuoteLONG(self, sMarketNo, nStockidx):    #ctw # 3-【查詢】回報    

        pStock = sk.SKSTOCKLONG()

        m_nCode = skQ.SKQuoteLib_GetStockByIndexLONG(sMarketNo, nStockidx, pStock)

        strMsg = '代碼:',pStock.bstrStockNo,'--名稱:',pStock.bstrStockName,'--開盤價:',str(pStock.nOpen/math.pow(10,pStock.sDecimal)),'--最高:',\

            str(pStock.nHigh/math.pow(10,pStock.sDecimal)),'--最低:',\

            str(pStock.nLow/math.pow(10,pStock.sDecimal)),'--成交價:',str(pStock.nClose/math.pow(10,pStock.sDecimal))\

            ,'--總量:',str(pStock.nTQty)

               

        twStrMsg=twTuple_2_Str(strMsg)

        send.run(twStrMsg)  #發射Signal訊號

        dictRepStock = {'name':pStock.bstrStockName, 'close': str(pStock.nClose/math.pow(10,pStock.sDecimal))}

        df = pd.DataFrame(dictRepStock, index=[0])

        global df_RepStock

        df_RepStock = pd.concat([df_RepStock, df], axis=0)

        #df_dictRepStock = df_dictRepStock.append(dictRepStock)

        pass

1. 成功解決問題

2. 5. 修改Pandas DataFrame資料

   利用Pandas DataFrame的at[]及iat[]取得所要修改的單一值後，來進行資料內容的修改，如下範例：

   import pandas as pd
    
    
   grades = {
       "name": ["Mike", "Sherry", "Cindy", "John"],
       "math": [80, 75, 93, 86],
       "chinese": [63, 90, 85, 70]
   }
    
   df = pd.DataFrame(grades)
    
   print("原來的df")
   print(df)
    
   print("=================================")
    
   df.at[1, "math"] = 100  #修改索引值為1的math欄位資料
   df.iat[1, 0] = "Larry"  #修改索引值為1的第一個欄位資料
   print("修改後的df")
   print(df)

1. 5. pandas, 用list加新資料, 新增一行(新增一資料欄)
2. 
   
3. 在 Pandas 中 [] 運算子方法新增新列
4. 在 Pandas 中 df.insert() 方法可新增新列
5. 在 Pandas 中 df.assign() 方法可新增新列
6. 在 Pandas 中 df.loc() 方法新增新列
7. 
   
https://www.delftstack.com/zh-tw/howto/python-pandas/how-to-add-new-column-to-existing-dataframe-in-python-pandas/?fbclid=IwAR2FxrW1x1ip4662M5h2fgzOXdlpUTRooEWN4NzlTkuEWLVGHbwdvNS04dc

import pandas as pd
data = [
    ['Ali',
     'Azmat',
     '30'],
    ['Sharukh',
     'Khan',
     '40'],
    ['Linus',
     'Torvalds',
     '70']
]
df = pd.DataFrame(data,columns=['First','Last','Age'])
print(df)

輸出：

     First      Last Age
0      Ali     Azmat  30
1  Sharukh      Khan  40
2    Linus  Torvalds  70

df = df.assign(city = ['Lahore','Dehli','New York'], 
               score = [20,30,40])

df.loc[:,'city'] = ['Lahore','Dehli','New York']

df.insert(3,"city",['Lahore','Dehli','New York'],True)

city = ['Lahore','Dehli','New York']
df['city'] = city

8.