Question

我正在使用django框架构建某种类型的Stock Web App。我从Alpha Vantage API获取数据，并且在分析所需数据时卡住了。

1-我可以成功调用API，但是在尝试获取需要查看views.py上使用的代码的数据时总是出现错误：

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上，我使用此代码显示信息还是错误：

{% 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 %}

使用此代码，我得到以下内容，如您所见，有两个单独的字典元数据和时间序列（每日）：

{“元数据” ：{'1。信息”：“具有拆分和股息事件的每日时间序列”，“ 2。符号”：“ B3SA3.SA”，“ 3。最后刷新''：'2020-07-10'，'4。输出大小”：“紧凑”，“ 5”。时区”：“美国/东部”}，“时间序列（每日）” ：{'2020-07-10'：{'1。打开”：“ 58.8000”，“ 2。高”：“ 59.9800”，“ 3。低”：“ 57.6000”，“ 4。关闭”：“ 59.9500”，“ 5。调整收盘价”：“ 59.9500”，“ 6。量”：“ 7989500”，“ 7。股息金额”：“ 0.0000”，“ 8。分割系数”：“ 1.0000”}，“ 2020-07-09”：{'1。打开”：“ 60.9700”，“ 2。高”：“ 60.9700”，“ 3。低”：“ 58.4400”，“ 4。关闭”：“ 58.8900”，“ 5。调整收盘价”：“ 58.8900”，“ 6。量”：“ 13494000”，“ 7。股息金额”：“ 0.0000”，“ 8。系数'：'1.0000'}，'2020-07-08'：{'1。打开”：“ 57.6100”，“ 2。高”：“ 60.8900”，“ 3。低”：“ 57.2300”，“ 4。关闭”：“ 60.6500”，“ 5。调整收盘价”：“ 60.6500”，“ 6。量”：“ 13847100”，“ 7。股息金额”：“ 0.0000”，“ 8。分割系数”：“ 1.0000”}，“ 2020-07-07”：{'1。打开”：“ 56.5500”，“ 2。高”：“ 57.6000”，“ 3。低”：“ 56.2500”，“ 4。关闭”：“ 57.1700”，“ 5。调整收盘价”：“ 57.1700”，“ 6。量”：“ 9038800”，“ 7。股息金额”：“ 0.0000”，“ 8。分割系数”：“ 1.0000”}

我只想获取“时间序列（每日）” 并将其解析为数据框，但是在尝试调用“时间序列（每日）时，我总是会出错'字典。

你们对我可能做错了什么有任何线索吗？预先感谢大家！

Answer 1

由于未访问“时间序列Daily（）”键而引起错误。

### 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']}")

现在，如果您想将此数据解析为熊猫，则可以使用DataFrame类中的from_dict方法。请参见下面的示例。

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)

编辑解决您的问题的方法如下：

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home.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 %}

带有Django的Alpha Vantage API-解析数据

1 个答案: