#02 | Load Data from APIs to a Pandas DataFrame in Python

Learn how to load data from APIs and convert them into ready-to-use DataFrames.

© Jesús López 2022

Ask him any doubt on Twitter or LinkedIn


The following image is pretty self-explanatory to understand how APIs work:

  1. The API is the waiter who
  2. Takes the request from the clients
  3. And take them to the kitchen
  4. To later serve the "cooked" response back to the clients

The Uniform Resource Locator (URL)

The URL is an address we use to locate files on the Internet:

  • Documents: pdf, ppt, docx,...
  • Multimedia: mp4, mp3, mov, png, jpeg,...
  • Data Files: csv, json, db,...

Check out the following gif where we inspect the resources we download when locating economist.com.

URL - Watch Video


An Application Program Interface (API) is a communications tool between the client and the server to carry out information through an URL.

The API defines the rules by which the URL will work. Like Python, the API contains:

  • Functions
  • Parameters
  • Accepted Values

The only extra knowledge we need to consider is the use of tokens.

A token is a code you use in the request to validate your identity, as most platforms charge money to use their API.

Get a token from AlphaVantage and store it into a Python variable.


Look for an API Call Example

In the website documentation.


The API's Response

Every time you make a call to an API requesting some information, you later receive a response.

Check this JSON, a type of file that stores structured data returned by the API.

If you want to know more about the JSON file, see article.

The pattern:

  • Base API: https://www.alphavantage.co/query?
  • Parameters:
    • symbol=IBM
    • interval=5min
    • apikey=demo

API's Data Response to Python

Could you request the file from Python?

import requests

api_call = 'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol=IBM&interval=5min&apikey=demo'
>>> <Response [200]>
res = requests.get(url=api_call)

The function returns an object containing all the information related to the API request and response.

>>> 'ascii'
>>> {'Date': 'Mon, 18 Jul 2022 18:01:19 GMT', 'Content-Type': 'application/json', 'Transfer-Encoding': 'chunked', 'Connection': 'keep-alive', 'Vary': 'Cookie', 'X-Frame-Options': 'SAMEORIGIN', 'Allow': 'GET, HEAD, OPTIONS', 'Via': '1.1 vegur', 'CF-Cache-Status': 'DYNAMIC', 'Expect-CT': 'max-age=604800, report-uri="https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct"', 'Server': 'cloudflare', 'CF-RAY': '72cd1f3959323851-MAD', 'Content-Encoding': 'gzip'}
>>> []

To place the response object into a Python interpretable object, we need to use the function .json() to get a dictionary with the data.

>>> {'Meta Data': {'1. Information': 'Intraday (5min) open, high, low, close prices and volume',
  '2. Symbol': 'IBM',
  '3. Last Refreshed': '2022-06-29 19:25:00',
  '4. Interval': '5min',
  '5. Output Size': 'Compact',
  '6. Time Zone': 'US/Eastern'},
 'Time Series (5min)': {'2022-06-29 19:25:00': {'1. open': '140.7100',
   '2. high': '140.7100',
   '3. low': '140.7100',
   '4. close': '140.7100',
   '5. volume': '531'},
  '2022-06-28 17:25:00': {'1. open': '142.1500',
   '2. high': '142.1500',
   '3. low': '142.1500',
   '4. close': '142.1500',
   '5. volume': '100'}}}
data = res.json()

The data in the dictionary represents the symbol IBM in intervals of 5min for the TIME_SERIES_INTRADAY.

Check the dictionary above to confirm.

>>> '/query?function=TIME_SERIES_INTRADAY&symbol=IBM&interval=5min&apikey=demo'

What can we change to get the information about the Apple Stock (AAPL)?

We need to change the value of the parameter symbol within the URL we use to call the API:

stock = 'AAPL'
api_call = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol={stock}&interval=5min&apikey=demo'
res = requests.get(url=api_call)
>>> {'Information': 'The **demo** API key is for demo purposes only. Please claim your free API key at (https://www.alphavantage.co/support/#api-key) to explore our full API offerings. It takes fewer than 20 seconds.'}

Why is not displaying the information of the Apple Stock? How can you solve the problem?

The API returns a JSON which implicitly says we previously used a *demo API key* to retrieve data from the symbol IBM. Nevertheless, using the same demo API key to retrieve the AAPL stock data is impossible.

We should include our token in the API call:

api_call = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol={stock}&interval=5min&apikey={token}'
res = requests.get(url=api_call)
data = res.json()
>>> {'Meta Data': {'1. Information': 'Intraday (5min) open, high, low, close prices and volume',
  '2. Symbol': 'AAPL',
  '3. Last Refreshed': '2022-07-15 20:00:00',
  '4. Interval': '5min',
  '5. Output Size': 'Compact',
  '6. Time Zone': 'US/Eastern'},
 'Time Series (5min)': {'2022-06-29 19:25:00': {'1. open': '140.7100',
   '2. high': '140.7100',
   '3. low': '140.7100',
   '4. close': '140.7100',
   '5. volume': '531'},
  '2022-06-28 17:25:00': {'1. open': '142.1500',
   '2. high': '142.1500',
   '3. low': '142.1500',
   '4. close': '142.1500',
   '5. volume': '100'}}}

Can we make plots and mathematical operations with the object data? Why?

data contains a dictionary, which it's a very simple Python object.


AttributeError                            Traceback (most recent call last)

Input In [46], in <cell line: 1>()
----> 1 data.sum()

AttributeError: 'dict' object has no attribute 'sum'

API's Data Response to a DataFrame

We need to create a DataFrame out of this dictionary to have a powerful object we could use to apply many functions.

import dataframe_image as dfi
import pandas as pd



Filter the Information in the Response

We'd like to have the open, high, close,... variables as the columns. Not Meta Data and Time Series (5min). Why is this happening?

  • Meta Data and Time Series (5min) are the keys of the dictionary data.
  • The value of the key Time Series (5min) key is the information we want in the DataFrame.
data['Time Series (5min)']
>>> {'2022-07-15 20:00:00': {'1. open': '150.0300',
  '2. high': '150.0700',
  '3. low': '150.0300',
  '4. close': '150.0300',
  '5. volume': '4752'},
 '2022-06-28 17:25:00': {'1. open': '142.1500',
  '2. high': '142.1500',
  '3. low': '142.1500',
  '4. close': '142.1500',
  '5. volume': '100'}
pd.DataFrame(data['Time Series (5min)'])


df_apple = pd.DataFrame(data['Time Series (5min)'])

Preprocess the DataFrame

The DataFrame is not represented as we'd like because the Dates are in the columns and the variables are in the index. So which function can we use to transpose the DataFrame?



df_apple = df_apple.transpose()

Let's get the average value from the close price:

df_apple['4. close']
>>> 2022-07-15 20:00:00    150.0300
    2022-07-15 19:55:00    150.0700
    2022-07-15 11:45:00    149.1500
    2022-07-15 11:40:00    149.1100
    Name: 4. close, Length: 100, dtype: object
df_apple['4. close'].mean()

ValueError                                Traceback (most recent call last)

File ~/miniforge3/lib/python3.9/site-packages/pandas/core/nanops.py:1622, in _ensure_numeric(x)
   1621 try:
-> 1622     x = float(x)
   1623 except (TypeError, ValueError):
   1624     # e.g. "1+1j" or "foo"

ValueError: could not convert string to float: '150.0300150.0700150.0400150.0100150.0300150.0500149.9900149.9900149.9800149.9900150.0000149.9900150.0000149.9900150.0000149.9800150.0000150.0100150.0500150.0100150.0100150.0000150.0200150.0100150.0100150.0098150.0100150.0000150.0200150.0000150.0007150.0100150.0100150.0200150.0325150.0200150.0300150.0200150.0000150.0300150.0001150.0000150.0000150.0100150.0560150.0500150.0900150.1700149.8900149.4410149.5300149.2700149.2160149.2094149.2000149.3450149.3778149.5450149.3600149.3500149.4700149.5400149.3993149.2150149.3015149.4100149.2916149.2650149.1200149.0400148.9800149.1350148.8800149.1850149.3924149.4600149.3496149.3250149.0874149.0600149.0000149.0101148.9350148.9100148.8620149.0050148.8100148.6340148.5500148.7600148.6950148.6800148.5488148.3500148.7351148.7910148.9305149.2000149.1500149.1100'

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)

File ~/miniforge3/lib/python3.9/site-packages/pandas/core/nanops.py:1626, in _ensure_numeric(x)
   1625 try:
-> 1626     x = complex(x)
   1627 except ValueError as err:
   1628     # e.g. "foo"

ValueError: complex() arg is a malformed string

The above exception was the direct cause of the following exception:

TypeError                                 Traceback (most recent call last)

Input In [38], in <cell line: 1>()
----> 1 df_apple['4. close'].mean()

File ~/miniforge3/lib/python3.9/site-packages/pandas/core/generic.py:11117, in NDFrame._add_numeric_operations.<locals>.mean(self, axis, skipna, level, numeric_only, **kwargs)
  11099 @doc(
  11100     _num_doc,
  11101     desc="Return the mean of the values over the requested axis.",
  11115     **kwargs,
  11116 ):
> 11117     return NDFrame.mean(self, axis, skipna, level, numeric_only, **kwargs)

File ~/miniforge3/lib/python3.9/site-packages/pandas/core/generic.py:10687, in NDFrame.mean(self, axis, skipna, level, numeric_only, **kwargs)
  10679 def mean(
  10680     self,
  10681     axis: Axis | None | lib.NoDefault = lib.no_default,
  10685     **kwargs,
  10686 ) -> Series | float:
> 10687     return self._stat_function(
  10688         "mean", nanops.nanmean, axis, skipna, level, numeric_only, **kwargs
  10689     )

File ~/miniforge3/lib/python3.9/site-packages/pandas/core/generic.py:10639, in NDFrame._stat_function(self, name, func, axis, skipna, level, numeric_only, **kwargs)
  10629     warnings.warn(
  10630         "Using the level keyword in DataFrame and Series aggregations is "
  10631         "deprecated and will be removed in a future version. Use groupby "
  10634         stacklevel=find_stack_level(),
  10635     )
  10636     return self._agg_by_level(
  10637         name, axis=axis, level=level, skipna=skipna, numeric_only=numeric_only
  10638     )
> 10639 return self._reduce(
  10640     func, name=name, axis=axis, skipna=skipna, numeric_only=numeric_only
  10641 )

File ~/miniforge3/lib/python3.9/site-packages/pandas/core/series.py:4471, in Series._reduce(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)
   4467     raise NotImplementedError(
   4468         f"Series.{name} does not implement {kwd_name}."
   4469     )
   4470 with np.errstate(all="ignore"):
-> 4471     return op(delegate, skipna=skipna, **kwds)

File ~/miniforge3/lib/python3.9/site-packages/pandas/core/nanops.py:93, in disallow.__call__.<locals>._f(*args, **kwargs)
     91 try:
     92     with np.errstate(invalid="ignore"):
---> 93         return f(*args, **kwargs)
     94 except ValueError as e:
     95     # we want to transform an object array
     96     # ValueError message to the more typical TypeError
     97     # e.g. this is normally a disallowed function on
     98     # object arrays that contain strings
     99     if is_object_dtype(args[0]):

File ~/miniforge3/lib/python3.9/site-packages/pandas/core/nanops.py:155, in bottleneck_switch.__call__.<locals>.f(values, axis, skipna, **kwds)
    153         result = alt(values, axis=axis, skipna=skipna, **kwds)
    154 else:
--> 155     result = alt(values, axis=axis, skipna=skipna, **kwds)
    157 return result

File ~/miniforge3/lib/python3.9/site-packages/pandas/core/nanops.py:410, in _datetimelike_compat.<locals>.new_func(values, axis, skipna, mask, **kwargs)
    407 if datetimelike and mask is None:
    408     mask = isna(values)
--> 410 result = func(values, axis=axis, skipna=skipna, mask=mask, **kwargs)
    412 if datetimelike:
    413     result = _wrap_results(result, orig_values.dtype, fill_value=iNaT)

File ~/miniforge3/lib/python3.9/site-packages/pandas/core/nanops.py:698, in nanmean(values, axis, skipna, mask)
    695     dtype_count = dtype
    697 count = _get_counts(values.shape, mask, axis, dtype=dtype_count)
--> 698 the_sum = _ensure_numeric(values.sum(axis, dtype=dtype_sum))
    700 if axis is not None and getattr(the_sum, "ndim", False):
    701     count = cast(np.ndarray, count)

File ~/miniforge3/lib/python3.9/site-packages/pandas/core/nanops.py:1629, in _ensure_numeric(x)
   1626             x = complex(x)
   1627         except ValueError as err:
   1628             # e.g. "foo"
-> 1629             raise TypeError(f"Could not convert {x} to numeric") from err
   1630 return x

TypeError: Could not convert 150.0300150.0700150.0400150.0100150.0300150.0500149.9900149.9900149.9800149.9900150.0000149.9900150.0000149.9900150.0000149.9800150.0000150.0100150.0500150.0100150.0100150.0000150.0200150.0100150.0100150.0098150.0100150.0000150.0200150.0000150.0007150.0100150.0100150.0200150.0325150.0200150.0300150.0200150.0000150.0300150.0001150.0000150.0000150.0100150.0560150.0500150.0900150.1700149.8900149.4410149.5300149.2700149.2160149.2094149.2000149.3450149.3778149.5450149.3600149.3500149.4700149.5400149.3993149.2150149.3015149.4100149.2916149.2650149.1200149.0400148.9800149.1350148.8800149.1850149.3924149.4600149.3496149.3250149.0874149.0600149.0000149.0101148.9350148.9100148.8620149.0050148.8100148.6340148.5500148.7600148.6950148.6800148.5488148.3500148.7351148.7910148.9305149.2000149.1500149.1100 to numeric

Why are we getting this ugly error?

  • The values of the Series aren't numerical objects.
>>> 1. open      object
    2. high      object
    3. low       object
    4. close     object
    5. volume    object
    dtype: object

Can you change the type of the values into numerical objects?

df_apple = df_apple.apply(pd.to_numeric)

Now that we have the Series values as numerical objects:

>>> 1. open      float64
    2. high      float64
    3. low       float64
    4. close     float64
    5. volume      int64
    dtype: object

We should be able to get the average close price:

df_apple['4. close'].mean()
>>> 149.551566

What else could we do?



df_apple.hist(layout=(2,3), figsize=(15,8));



stock = 'AAPL'
api_call = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol={stock}&interval=5min&apikey={token}'

res = requests.get(url=api_call)
data = res.json()

df_apple = pd.DataFrame(data=data['Time Series (5min)'])
df_apple = df_apple.transpose()
df_apple = df_apple.apply(pd.to_numeric)

df_apple.hist(layout=(2,3), figsize=(15,8));


Other Example

info_type = 'TIME_SERIES_DAILY'
api_call = f'https://www.alphavantage.co/query?function={info_type}&symbol={stock}&outputsize={size}&apikey={token}'

res = requests.get(url=api_call)
data = res.json()

df_apple_daily = pd.DataFrame(data['Time Series (Daily)'])
df_apple_daily = df_apple_daily.transpose()
df_apple_daily = df_apple_daily.apply(pd.to_numeric)
df_apple_daily.index = pd.to_datetime(df_apple_daily.index)

df_apple_daily.plot.line(layout=(2,3), figsize=(15,8), subplots=True);


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