Alpha Vantage API 數據是什么樣的 JSON 結構，如何將其轉換為 pandas dataframe？

Question

我有一個在 Google Colab 筆記本上工作的 Alpha Vantage API 演示，我正在嘗試將該數據響應轉換為 pandas dataframe。

但是，我不清楚 JSON 數據結構是什么，這使得很難找到正確的 python 代碼。

這是有效的演示代碼：

import requests
import pandas as pd
import json

# replace the "demo" apikey below with your own key from https://www.alphavantage.co/support/#api-key
url = 'https://www.alphavantage.co/query?function=CRYPTO_INTRADAY&symbol=ETH&market=USD&interval=15min&apikey=demo'
r = requests.get(url)
data = r.json()

print(data)

這是它返回的數據示例：

{'Meta Data': {'1. Information': 'Crypto Intraday (15min) Time Series', '2. Digital Currency Code': 'ETH', '3. Digital Currency Name': 'Ethereum', '4. Market Code': 'USD', '5. Market Name': 'United States Dollar', '6. Last Refreshed': '2022-02-10 04:00:00', '7. Interval': '15min', '8. Output Size': 'Compact', '9. Time Zone': 'UTC'}, 'Time Series Crypto (15min)': {'2022-02-10 04:00:00': {'1. open': '3179.59000', '2. high': '3180.47000', '3. low': '3173.82000', '4. close': '3175.86000', '5. volume': 1031}, '2022-02-10 03:45:00': {'1. open': '3180.39000', '2. high': '3183.52000', '3. low': '3176.24000', '4. close': '3179.60000', '5. volume': 1924}, '2022-02-10 03:30:00': {'1. open': '3185.17000', '2. high': '3188.40000', '3. low': '3179.04000', '4. close': '3180.40000', '5. volume': 2018}, '2022-02-10 03:15:00': {'1. open': '3177.58000', '2. high': '3186.24000', '3. low': '3175.01000', '4. close': '3185.17000', '5. volume': 2376}, '2022-02-10 03:00:00': {'1. open': '3186.58000', '2. high': '3189.99000', '3. low': '3170.87000', '4. close': '3177.58000', '5. volume': 6907}, '2022-02-10 02:45:00': {'1. open': '3221.54000', '2. high': '3224.80000', '3. low': '3184.01000', '4. close': '3186.58000', '5. volume': 8794}, '2022-02-10 02:30:00': {'1. open': '3228.30000', '2. high': '3229.00000', '3. low': '3220.00000', '4. close': '3221.53000', '5. volume': 1552}, '2022-02-10 02:15:00': {'1. open': '3225.79000', '2. high': '3228.58000', '3. low': '3220.86000', '4. close': '3228.29000', '5. volume': 1852}, '2022-02-10 02:00:00': {'1. open': '3224.28000', '2. high': '3229.26000', '3. low': '3215.67000', '4. close': '3225.80000', '5. volume': 2698}, '2022-02-10 01:45:00': {'1. open': '3225.74000', '2. high': '3228.92000', '3. low': '3219.29000', '4. close': '3224.27000', '5. volume': 1529}, '2022-02-10 01:30:00': {'1. open': '3214.24000', '2. high': '3227.95000', '3. low': '3207.18000', '4. close': '3225.73000', '5. volume': 2239}, '2022-02-10 01:15:00': {'1. open': '3225.78000', '2. high': '3227.94000', '3. low': '3213.00000', '4. close': '3214.23000', '5. volume': 1930}, '2022-02-10 01:00:00': {'1. open': '3220.08000', '2. high': '3225.99000', '3. low': '3217.40000', '4. close': '3225.78000', '5. volume': 2333}, '2022-02-10 00:45:00': {'1. open': '3221.87000', '2. high': '3224.36000', '3. low': '3213.58000', '4. close': '3220.09000', '5. volume': 1912}, '2022-02-10 00:30:00': {'1. open': '3224.74000', '2. high': '3229.21000', '3. low': '3220.03000', '4. close': '3221.87000', '5. volume': 2299}, '2022-02-10 00:15:00': {'1. open': '3235.70000', '2. high': '3235.82000', '3. low': '3211.27000', '4. close': '3224.74000', '5. volume': 5900}, '2022-02-10 00:00:00': {'1. open': '3243.60000', '2. high': '3248.98000', '3. low': '3233.08000', '4. close': '3235.71000', '5. volume': 3388}, '2022-02-09 23:45:00': {'1. open': '3234.46000', '2. high': '3244.75000', '3. low': '3232.95000', '4. close': '3243.94000', '5. volume': 3475}, '2022-02-09 23:30:00': {'1. open': '3236.47000', '2. high': '3240.00000', '3. low': '3231.41000', '4. close': '3234.82000', '5. volume': 2475}, '2022-02-09 23:15:00': {'1. open': '3242.38000', '2. high': '3245.76000', '3. low': '3232.13000', '4. close': '3236.62000', '5. volume': 2145}, '2022-02-09 23:00:00': {'1. open': '3251.01000', '2. high': '3251.03000', '3. low': '3239.50000', '4. close': '3242.37000', '5. volume': 2438}, '2022-02-09 22:45:00': {'1. open': '3242.50000', '2. high': '3254.57000', '3. low': '3241.18000', '4. close': '3251.02000', '5. volume': 1700}, '2022-02-09 22:30:00': {'1. open': '3233.52000', '2. high': '3245.39000', '3. low': '3232.34000', '4. close': '3242.50000', '5. volume': 1597}, '2022-02-09 22:15:00': {'1. open': '3237.01000', '2. high': '3245.59000', '3. low': '3230.83000', '4. close': '3233.53000', '5. volume': 2708}, '2022-02-09 22:00:00': {'1. open': '3236.09000', '2. high': '3247.00000', '3. low': '3234.70000', '4. close': '3236.85000', '5. volume': 3090}, '2022-02-09 21:45:00': {'1. open': '3249.00000', '2. high': '3249.99000', '3. low': '3234.64000', '4. close': '3236.08000', '5. volume': 3263}, '2022-02-09 21:30:00': {'1. open': '3253.56000', '2. high': '3256.00000', '3. low': '3247.01000', '4. close': '3249.01000', '5. volume': 2060}, '2022-02-09 21:15:00': {'1. open': '3254.33000', '2. high': '3256.80000', '3. low': '3245.05000', '4. close': '3253.56000', '5. volume': 1957}, '2022-02-09 21:00:00': {'1. open': '3258.74000', '2. high': '3261.22000', '3. low': '3248.37000', '4. close': '3254.34000', '5. volume': 4320}, '2022-02-09 20:45:00': {'1. open': '3259.41000', '2. high': '3263.21000', '3. low': '3250.30000', '4. close': '3258.74000', '5. volume': 4215}, '2022-02-09 20:30:00': {'1. open': '3260.67000', '2. high': '3269.70000', '3. low': '3253.49000', '4. close': '3259.42000', '5. volume': 5505}, '2022-02-09 20:15:00': {'1. open': '3244.45000', '2. high': '3264.97000', '3. low': '3240.60000', '4. close': '3260.67000', '5. volume': 4368}, '2022-02-09 20:00:00': {'1. open': '3246.94000', '2. high': '3260.75000', '3. low': '3244.41000', '4. close': '3244.41000', '5. volume': 4653}, '2022-02-09 19:45:00': {'1. open': '3250.72000', '2. high': '3256.63000', '3. low': '3243.77000', '4. close': '3246.94000', '5. volume': 3484}, '2022-02-09 19:30:00': {'1. open': '3247.01000', '2. high': '3258.00000', '3. low': '3237.17000', '4. close': '3250.72000', '5. volume': 5654}, '2022-02-09 19:15:00': {'1. open': '3226.37000', '2. high': '3254.00000', '3. low': '3220.07000', '4. close': '3247.01000', '5. volume': 10292}, '2022-02-09 19:00:00': {'1. open': '3210.13000', '2. high': '3238.50000', '3. low': '3207.01000', '4. close': '3226.38000', '5. volume': 10794}, '2022-02-09 18:45:00': {'1. open': '3204.34000', '2. high': '3210.99000', '3. low': '3197.89000', '4. close': '3210.13000', '5. volume': 2054}, '2022-02-09 18:30:00': {'1. open': '3193.03000', '2. high': '3205.00000', '3. low': '3187.13000', '4. close': '3204.35000', '5. volume': 2349}, '2022-02-09 18:15:00': {'1. open': '3193.37000', '2. high': '3200.56000', '3. low': '3183.02000', '4. close': '3193.00000', '5. volume': 2203}, '2022-02-09 18:00:00': {'1. open': '3199.76000', '2. high': '3204.07000', '3. low': '3191.63000', '4. close': '3193.76000', '5. volume': 1864}, '2022-02-09 17:45:00': {'1. open': '3203.52000', '2. high': '3207.57000', '3. low': '3192.66000', '4. close': '3199.76000', '5. volume': 2315}, '2022-02-09 17:30:00': {'1. open': '3206.02000', '2. high': '3211.36000', '3. low': '3196.97000', '4. close': '3203.52000', '5. volume': 3534}, '2022-02-09 17:15:00': {'1. open': '3205.73000', '2. high': '3217.69000', '3. low': '3205.73000', '4. close': '3206.01000', '5. volume': 3901}, '2022-02-09 17:00:00': {'1. open': '3202.95000', '2. high': '3208.80000', '3. low': '3198.49000', '4. close': '3205.74000', '5. volume': 3619}, '2022-02-09 16:45:00': {'1. open': '3193.45000', '2. high': '3212.28000', '3. low': '3193.06000', '4. close': '3202.95000', '5. volume': 6311}, '2022-02-09 16:30:00': {'1. open': '3183.83000', '2. high': '3194.75000', '3. low': '3179.19000', '4. close': '3193.46000', '5. volume': 3741}, '2022-02-09 16:15:00': {'1. open': '3172.45000', '2. high': '3189.38000', '3. low': '3169.50000', '4. close': '3183.83000', '5. volume': 2725}, '2022-02-09 16:00:00': {'1. open': '3178.44000', '2. high': '3195.00000', '3. low': '3163.02000', '4. close': '3172.45000', '5. volume': 10011}, '2022-02-09 15:45:00': {'1. open': '3166.00000', '2. high': '3179.80000', '3. low': '3165.78000', '4. close': '3178.45000', '5. volume': 2626}, '2022-02-09 15:30:00': {'1. open': '3177.69000', '2. high': '3184.61000', '3. low': '3152.44000', '4. close': '3166.00000', '5. volume': 7079}, '2022-02-09 15:15:00': {'1. open': '3181.27000', '2. high': '3181.50000', '3. low': '3166.54000', '4. close': '3177.68000', '5. volume': 4888}, '2022-02-09 15:00:00': {'1. open': '3188.49000', '2. high': '3200.91000', '3. low': '3179.47000', '4. close': '3181.27000', '5. volume': 3970}, '2022-02-09 14:45:00': {'1. open': '3202.56000', '2. high': '3209.75000', '3. low': '3188.00000', '4. close': '3188.50000', '5. volume': 5743}, '2022-02-09 14:30:00': {'1. open': '3186.82000', '2. high': '3216.99000', '3. low': '3182.91000', '4. close': '3202.61000', '5. volume': 6893}, '2022-02-09 14:15:00': {'1. open': '3179.02000', '2. high': '3190.91000', '3. low': '3171.48000', '4. close': '3186.83000', '5. volume': 2638}, '2022-02-09 14:00:00': {'1. open': '3180.69000', '2. high': '3186.70000', '3. low': '3161.62000', '4. close': '3179.01000', '5. volume': 5528}, '2022-02-09 13:45:00': {'1. open': '3198.63000', '2. high': '3202.50000', '3. low': '3179.34000', '4. close': '3180.94000', '5. volume': 5184}, '2022-02-09 13:30:00': {'1. open': '3198.74000', '2. high': '3204.45000', '3. low': '3186.76000', '4. close': '3198.64000', '5. volume': 4447}, '2022-02-09 13:15:00': {'1. open': '3200.08000', '2. high': '3206.00000', '3. low': '3191.43000', '4. close': '3198.74000', '5. volume': 7063}, '2022-02-09 13:00:00': {'1. open': '3181.70000', '2. high': '3207.99000', '3. low': '3174.43000', '4. close': '3200.07000', '5. volume': 9626}, '2022-02-09 12:45:00': {'1. open': '3190.59000', '2. high': '3193.53000', '3. low': '3174.70000', '4. close': '3181.49000', '5. volume': 5283}, '2022-02-09 12:30:00': {'1. open': '3154.62000', '2. high': '3192.78000', '3. low': '3151.18000', '4. close': '3190.59000', '5. volume': 11267}, '2022-02-09 12:15:00': {'1. open': '3154.60000', '2. high': '3159.75000', '3. low': '3150.14000', '4. close': '3154.62000', '5. volume': 3160}, '2022-02-09 12:00:00': {'1. open': '3152.89000', '2. high': '3158.83000', '3. low': '3140.00000', '4. close': '3154.61000', '5. volume': 5264}, '2022-02-09 11:45:00': {'1. open': '3139.54000', '2. high': '3158.37000', '3. low': '3139.37000', '4. close': '3152.89000', '5. volume': 5061}, '2022-02-09 11:30:00': {'1. open': '3138.69000', '2. high': '3146.00000', '3. low': '3134.57000', '4. close': '3139.53000', '5. volume': 4378}, '2022-02-09 11:15:00': {'1. open': '3126.04000', '2. high': '3139.87000', '3. low': '3124.39000', '4. close': '3138.70000', '5. volume': 3621}, '2022-02-09 11:00:00': {'1. open': '3136.30000', '2. high': '3142.33000', '3. low': '3119.04000', '4. close': '3126.04000', '5. volume': 3827}, '2022-02-09 10:45:00': {'1. open': '3131.03000', '2. high': '3137.00000', '3. low': '3123.14000', '4. close': '3136.30000', '5. volume': 3638}, '2022-02-09 10:30:00': {'1. open': '3135.25000', '2. high': '3142.97000', '3. low': '3127.20000', '4. close': '3131.02000', '5. volume': 3903}, '2022-02-09 10:15:00': {'1. open': '3123.50000', '2. high': '3143.83000', '3. low': '3118.56000', '4. close': '3135.24000', '5. volume': 6848}, '2022-02-09 10:00:00': {'1. open': '3099.46000', '2. high': '3128.43000', '3. low': '3099.11000', '4. close': '3123.50000', '5. volume': 5342}, '2022-02-09 09:45:00': {'1. open': '3105.92000', '2. high': '3111.37000', '3. low': '3096.00000', '4. close': '3099.21000', '5. volume': 2038}, '2022-02-09 09:30:00': {'1. open': '3109.85000', '2. high': '3115.96000', '3. low': '3091.92000', '4. close': '3105.93000', '5. volume': 3369}, '2022-02-09 09:15:00': {'1. open': '3110.20000', '2. high': '3116.00000', '3. low': '3105.85000', '4. close': '3109.83000', '5. volume': 2234}, '2022-02-09 09:00:00': {'1. open': '3094.09000', '2. high': '3115.00000', '3. low': '3094.09000', '4. close': '3110.21000', '5. volume': 5781}, '2022-02-09 08:45:00': {'1. open': '3081.81000', '2. high': '3099.51000', '3. low': '3081.43000', '4. close': '3094.10000', '5. volume': 2801}, '2022-02-09 08:30:00': {'1. open': '3079.49000', '2. high': '3084.93000', '3. low': '3069.00000', '4. close': '3081.81000', '5. volume': 2372}, '2022-02-09 08:15:00': {'1. open': '3097.48000', '2. high': '3103.96000', '3. low': '3069.61000', '4. close': '3079.48000', '5. volume': 5602}, '2022-02-09 08:00:00': {'1. open': '3105.93000', '2. high': '3113.69000', '3. low': '3094.51000', '4. close': '3097.49000', '5. volume': 2361}, '2022-02-09 07:45:00': {'1. open': '3088.19000', '2. high': '3112.61000', '3. low': '3087.68000', '4. close': '3105.93000', '5. volume': 3146}, '2022-02-09 07:30:00': {'1. open': '3087.07000', '2. high': '3091.80000', '3. low': '3079.69000', '4. close': '3088.16000', '5. volume': 2029}, '2022-02-09 07:15:00': {'1. open': '3101.27000', '2. high': '3101.34000', '3. low': '3085.87000', '4. close': '3087.07000', '5. volume': 2136}, '2022-02-09 07:00:00': {'1. open': '3099.93000', '2. high': '3106.57000', '3. low': '3092.94000', '4. close': '3101.26000', '5. volume': 2478}, '2022-02-09 06:45:00': {'1. open': '3101.43000', '2. high': '3107.29000', '3. low': '3098.00000', '4. close': '3099.93000', '5. volume': 2642}, '2022-02-09 06:30:00': {'1. open': '3091.52000', '2. high': '3105.41000', '3. low': '3089.74000', '4. close': '3101.49000', '5. volume': 2442}, '2022-02-09 06:15:00': {'1. open': '3087.91000', '2. high': '3099.99000', '3. low': '3085.49000', '4. close': '3091.52000', '5. volume': 1486}, '2022-02-09 06:00:00': {'1. open': '3089.75000', '2. high': '3096.00000', '3. low': '3084.43000', '4. close': '3087.90000', '5. volume': 2402}, '2022-02-09 05:45:00': {'1. open': '3100.22000', '2. high': '3106.40000', '3. low': '3087.84000', '4. close': '3089.75000', '5. volume': 3642}, '2022-02-09 05:30:00': {'1. open': '3072.85000', '2. high': '3101.64000', '3. low': '3071.27000', '4. close': '3100.23000', '5. volume': 3586}, '2022-02-09 05:15:00': {'1. open': '3066.72000', '2. high': '3074.87000', '3. low': '3055.00000', '4. close': '3072.85000', '5. volume': 2808}, '2022-02-09 05:00:00': {'1. open': '3080.40000', '2. high': '3084.02000', '3. low': '3057.68000', '4. close': '3066.72000', '5. volume': 4779}, '2022-02-09 04:45:00': {'1. open': '3085.53000', '2. high': '3094.37000', '3. low': '3079.70000', '4. close': '3080.41000', '5. volume': 2142}, '2022-02-09 04:30:00': {'1. open': '3083.75000', '2. high': '3088.77000', '3. low': '3078.20000', '4. close': '3085.53000', '5. volume': 1813}, '2022-02-09 04:15:00': {'1. open': '3089.00000', '2. high': '3091.52000', '3. low': '3081.56000', '4. close': '3083.67000', '5. volume': 1639}, '2022-02-09 04:00:00': {'1. open': '3076.52000', '2. high': '3090.13000', '3. low': '3075.04000', '4. close': '3089.00000', '5. volume': 2153}, '2022-02-09 03:45:00': {'1. open': '3074.50000', '2. high': '3078.82000', '3. low': '3072.34000', '4. close': '3076.52000', '5. volume': 1178}, '2022-02-09 03:30:00': {'1. open': '3079.31000', '2. high': '3084.81000', '3. low': '3073.05000', '4. close': '3074.49000', '5. volume': 1746}, '2022-02-09 03:15:00': {'1. open': '3073.70000', '2. high': '3081.01000', '3. low': '3073.70000', '4. close': '3079.32000', '5. volume': 2243}}}

我運行這段代碼來嘗試將該響應放入 dataframe：

df = pd.DataFrame(data["Time Series (15min)"])
df = df.T  # Transpose Dataframe for desired results

我的理解是數據響應是一個 JSON 字典數據結構，但是產生了這個錯誤信息：

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-9-ba79690310fb> in <module>()
----> 1 df = pd.DataFrame(data["Time Series (15min)"])
      2 df = df.T  # Transpose Dataframe for desired results

KeyError: 'Time Series (15min)'

我不明白錯誤消息，但我認為它一定是來自 pandas 而不是 AlphaVantage API，盡管我還沒有找到任何關於我應該使用什么而不是"Time Series (15min)"的明確信息"Time Series (15min)"

非常感謝有關如何處理此問題的任何意見。

Answer 1

您沒有使用正確的密鑰：它不是Time Series (15min)而是Time Series Crypto (15min) ：

df = pd.DataFrame(data["Time Series Crypto (15min)"]).T
print(df)

# Output
                        1. open     2. high      3. low    4. close 5. volume
2022-02-10 04:00:00  3179.59000  3180.47000  3173.82000  3175.86000      1031
2022-02-10 03:45:00  3180.39000  3183.52000  3176.24000  3179.60000      1924
2022-02-10 03:30:00  3185.17000  3188.40000  3179.04000  3180.40000      2018
2022-02-10 03:15:00  3177.58000  3186.24000  3175.01000  3185.17000      2376
2022-02-10 03:00:00  3186.58000  3189.99000  3170.87000  3177.58000      6907
...                         ...         ...         ...         ...       ...
2022-02-09 04:15:00  3089.00000  3091.52000  3081.56000  3083.67000      1639
2022-02-09 04:00:00  3076.52000  3090.13000  3075.04000  3089.00000      2153
2022-02-09 03:45:00  3074.50000  3078.82000  3072.34000  3076.52000      1178
2022-02-09 03:30:00  3079.31000  3084.81000  3073.05000  3074.49000      1746
2022-02-09 03:15:00  3073.70000  3081.01000  3073.70000  3079.32000      2243

[100 rows x 5 columns]

獎金重命名列：

df.columns = df.columns.str.split().str[1].str.capitalize()
print(df)

# Output
                           Open        High         Low       Close Volume
2022-02-10 04:00:00  3179.59000  3180.47000  3173.82000  3175.86000   1031
2022-02-10 03:45:00  3180.39000  3183.52000  3176.24000  3179.60000   1924
2022-02-10 03:30:00  3185.17000  3188.40000  3179.04000  3180.40000   2018
2022-02-10 03:15:00  3177.58000  3186.24000  3175.01000  3185.17000   2376
2022-02-10 03:00:00  3186.58000  3189.99000  3170.87000  3177.58000   6907
...                         ...         ...         ...         ...    ...
2022-02-09 04:15:00  3089.00000  3091.52000  3081.56000  3083.67000   1639
2022-02-09 04:00:00  3076.52000  3090.13000  3075.04000  3089.00000   2153
2022-02-09 03:45:00  3074.50000  3078.82000  3072.34000  3076.52000   1178
2022-02-09 03:30:00  3079.31000  3084.81000  3073.05000  3074.49000   1746
2022-02-09 03:15:00  3073.70000  3081.01000  3073.70000  3079.32000   2243

[100 rows x 5 columns]