Alpha Vantage API 與 Django - 解析數據

Question

我正在使用 django 框架構建某種股票市場 Web 應用程序。 我從 Alpha Vantage API 獲取數據，但在解析所需數據時卡住了。

1 - 我可以成功調用 API 但在嘗試獲取所需數據時總是出錯，請查看我在views.py上使用的代碼：

def home(request):

import requests
import json
import pandas as pd
from alpha_vantage.timeseries import TimeSeries


url = "https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol=B3SA3.SA&outputsize=compact&apikey=XXX"

api_request = requests.post("GET", url)

try:
    api = api_request.content.json()

except Exception as e:
    api="Erro, tente novamente"

return render(request,'home.html', {'api': api})

在home.html ，我使用此代碼來顯示信息還是錯誤：

{% if api %}

    {% if api == "Erro, tente novamente."%}
        Houve um problema com a busca da ação, tente novamente.

    {% else %}
        {% for key,value in api.items %}
            {{key}}: {{value}}<br/>

        {%endfor%}

    {% endif %}

{% endif %}

使用此代碼，我得到以下信息，您可以看到有兩個單獨的字典Meta Data和Time Series (Daily) ：

{ '元數據' ：{'1. 信息”：“帶有拆分和股息事件的每日時間序列”，“2。 符號'：'B3SA3.SA'，'3. 最后刷新'：'2020-07-10'，'4。 Output 尺寸'：'緊湊'，'5。 時區'：'美國/東部'}， '時間序列（每日）' ：{'2020-07-10'：{'1. 打開'：'58.8000'，'2。 高'：'59.9800'，'3。 低'：'57.6000'，'4。 關閉'：'59.9500'，'5。 調整后的收盤價': '59.9500', '6. 卷'：'7989500'，'7。 分紅金額': '0.0000', '8. 分裂系數': '1.0000'}, '2020-07-09': {'1. 打開'：'60.9700'，'2。 高'：'60.9700'，'3。 低'：'58.4400'，'4。 關閉'：'58.8900'，'5。 調整后的收盤價'：'58.8900'，'6。 卷'：'13494000'，'7。 分紅金額': '0.0000', '8. 分裂系數': '1.0000'}, '2020-07-08': {'1. 打開'：'57.6100'，'2。 高'：'60.8900'，'3。 低'：'57.2300'，'4。 關閉'：'60.6500'，'5。 調整后的收盤價'：'60.6500'，'6。 卷'：'13847100'，'7。 分紅金額': '0.0000', '8. 分裂系數': '1.0000'}, '2020-07-07': {'1. 打開'：'56.5500'，'2。 高'：'57.6000'，'3。 低'：'56.2500'，'4。 關閉'：'57.1700'，'5。 調整后的收盤價'：'57.1700'，'6。 卷'：'9038800'，'7。 分紅金額': '0.0000', '8. 分裂系數'：'1.0000'}

我只是想獲取“時間序列（每日）”並將其解析為 dataframe 但在嘗試調用“時間序列（每日）”字典時總是出錯。

你們有什么線索我可能做錯了嗎？ 提前謝謝各位！

Answer 1

由於您沒有訪問“Time Series Daily()”鍵，因此導致了您的錯誤。

### This is data you would receive from your API call
api = {'Meta Data': {'1. Information': 'Daily Time Series with Splits and Dividend Events', '2. Symbol': 'B3SA3.SA', '3. Last Refreshed': '2020-07-10', '4. Output Size': 'Compact', '5. Time Zone': 'US/Eastern'}, 'Time Series (Daily)': {'2020-07-10': {'1. open': '58.8000', '2. high': '59.9800', '3. low': '57.6000', '4. close': '59.9500', '5. adjusted close': '59.9500', '6. volume': '7989500', '7. dividend amount': '0.0000', '8. split coefficient': '1.0000'}, '2020-07-09': {'1. open': '60.9700', '2. high': '60.9700', '3. low': '58.4400', '4. close': '58.8900', '5. adjusted close': '58.8900', '6. volume': '13494000', '7. dividend amount': '0.0000', '8. split coefficient': '1.0000'}, '2020-07-08': {'1. open': '57.6100', '2. high': '60.8900', '3. low': '57.2300', '4. close': '60.6500', '5. adjusted close': '60.6500', '6. volume': '13847100', '7. dividend amount': '0.0000', '8. split coefficient': '1.0000'}, '2020-07-07': {'1. open': '56.5500', '2. high': '57.6000', '3. low': '56.2500', '4. close': '57.1700', '5. adjusted close': '57.1700', '6. volume': '9038800', '7. dividend amount': '0.0000', '8. split coefficient': '1.0000'}}}

# We access the Time Series dictionary from the api call.
time_series = api["Time Series (Daily)"]

# If you want to print all columns
for time, prices in time_series.items():
    print(f"{time}: {prices}")


# If you want to print a specific column i.e. close prices.
for time, prices in time_series.items():
    print(f"{time}: {prices['4. close']}")

現在，如果您想將此數據解析為 pandas，您可以使用 DataFrame class 中的 from_dict 方法。 請看下面的例子。

import pandas as pd

api = {'Meta Data': {'1. Information': 'Daily Time Series with Splits and Dividend Events', '2. Symbol': 'B3SA3.SA', '3. Last Refreshed': '2020-07-10', '4. Output Size': 'Compact', '5. Time Zone': 'US/Eastern'}, 'Time Series (Daily)': {'2020-07-10': {'1. open': '58.8000', '2. high': '59.9800', '3. low': '57.6000', '4. close': '59.9500', '5. adjusted close': '59.9500', '6. volume': '7989500', '7. dividend amount': '0.0000', '8. split coefficient': '1.0000'}, '2020-07-09': {'1. open': '60.9700', '2. high': '60.9700', '3. low': '58.4400', '4. close': '58.8900', '5. adjusted close': '58.8900', '6. volume': '13494000', '7. dividend amount': '0.0000', '8. split coefficient': '1.0000'}, '2020-07-08': {'1. open': '57.6100', '2. high': '60.8900', '3. low': '57.2300', '4. close': '60.6500', '5. adjusted close': '60.6500', '6. volume': '13847100', '7. dividend amount': '0.0000', '8. split coefficient': '1.0000'}, '2020-07-07': {'1. open': '56.5500', '2. high': '57.6000', '3. low': '56.2500', '4. close': '57.1700', '5. adjusted close': '57.1700', '6. volume': '9038800', '7. dividend amount': '0.0000', '8. split coefficient': '1.0000'}}}

time_series = api["Time Series (Daily)"]

# this will create a dataframe with the Dates and close prices.
# it first sets the date as the index then resets the index so that the date becomes its own column
df = pd.DataFrame.from_dict(time_series, orient="index", columns=["4. close"]).reset_index()
renamed_headers = {"index": "Date", "4. close": "Close Price"}
df = df.rename(columns=renamed_headers)

# this makes sure that your close prices are numeric.
df["Close Price"] = pd.to_numeric(df["Close Price"])
print(df)

編輯您的問題的解決方案如下：

DJANGO

# Its good practice to have imports at the top of script.
import requests
import json
import pandas as pd
from alpha_vantage.timeseries import TimeSeries

# We will create an object and store data from alpha vantage inside this object
from collections import namedtuple 



def home(request):    
    url = "https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol=B3SA3.SA&outputsize=compact&apikey=XXX"

    api_request = requests.post("GET", url)

    # this is our object that will contain the date and close price data
    Security_Data = namedtuple("SecurityData", ["Date", "ClosePrice"])

    # this is a list of Security_Data objects.
    all_data = []

    try:
        api = api_request.content.json()
    except Exception as e:  # It's bad practice to capture a bare exception
        api = None

    if api is not None:
        time_series = api["Time Series (Daily)"]
        for time, prices in time_series.items():
            data = Security_Data(time, prices["4. close"])
            all_data.append(data)

return render(request, 'home.html', {'all_data': all_data})

在國內。html

{% if len(all_data) == 0 %}
    Houve um problema com a busca da ação, tente novamente.

{% else %}
    {% for data in all_data %}
        {{data.Date}}: {{data.ClosePrice}}<br/>

    {%endfor%}

{% endif %}