如何在 for 循环中通过 for 循环将列表附加到数据帧

Question

I have made a for loop which uses a list of stock tickers to get day closing prices. Once collected, I ask the code to store the data in a dataframe. This works fine, but I am having trouble creating a way to append the dataframe over and over again, such that I am left with one large dataframe. Can anybody help with that? Please note that the API connection allows a certain amount of calls pr. minutes and so there should be a time-extension if the call fails - I have tried to account for this. Please see code below:

C20 = ['AMBU-B.CPH', 'MAERSK-B.CPH']

df = pd.DataFrame()

def getdata(symbol_input):

for i in symbol_input:

    try:

        API_KEY = 'XXXXXXXXX' #MY API KEY

        symbol = i #søg på google efter firmanavnet og "stock price". Tickeren er den der skal bruges

        r = requests.get('https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=' + i + '&apikey=' + API_KEY)

        result = r.json()
        AllData = result['Time Series (Daily)']
        alldays = list(AllData.keys())
        alldays.sort()
        timeinterval = 10
        days = alldays[len(alldays)-timeinterval:len(alldays)]
        #print(days)

        SymbolList = []
        for i in range(timeinterval):
            SymbolList.append(symbol)
        #print(SymbolList)

        StockPriceList = []


        if (r.status_code == 200):

            for i, day in enumerate(days):
                result = r.json()
                dataForAllDays = result['Time Series (Daily)']
                dataForSingleDate = dataForAllDays[days[i]]
                #print (days[i], dataForSingleDate['4. close']) 
                StockPriceList.append(dataForSingleDate['4. close'])  


        #print(StockPriceList)

        combined_lists = list(zip(days, StockPriceList, SymbolList)) #create tuples to feed into dataframe from multiple lists

        df1 = pd.DataFrame(combined_lists, columns = ['Date', 'Price', 'Stock Ticker'])
        print(df1)

        time.sleep(10)

    except:
        print('could not get data for: ' + i)
        time.sleep(1)  # wait for 1 seconds before trying to fetch the data again
        continue

print(getdata(C20))

Answer 1

You can use pd.concat and then joining everything by using temporary dataframe into one final dataframe.

You can use this code as an example for concatenating two different dataframes into a single final dataframe.

dataset1 = pd.DataFrame([[1,2],[2,3],[3,4]],columns=['A','B'])
dataset2 = pd.DataFrame([[4,5],[5,6],[6,7]],columns=['A','B'])
full_dataset = pd.concat([dataset1,dataset2])
full_dataset
   A  B
0  1  2
1  2  3
2  3  4
0  4  5
1  5  6
2  6  7

Reference: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.concat.html

Let me know if you require anything else. Have a great day!