Ошибка возникает из-за того, что вы не получаете доступ к клавише «Временной ряд Daily ()».
### 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']}")
Теперь, если вы хотите проанализировать эти данные в pandas, вы можете использовать метод from_dict в классе DataFrame. см. пример ниже.
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)
EDIT Решение вашей проблемы будет следующим:
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})
Дома. 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 %}