QuickStart
Loading the IPython extension#
Make sure to set the OPENAI_API_KEY environment variable first before using it in IPython or your preferred notebook platform of choice.
%load_ext genai
Features#
%%assistmagic command to generate code from natural language- Custom exception suggestions
Custom Exception Suggestions#
In [1]: %load_ext genai
In [2]: import pandas as pd
In [3]: df = pd.DataFrame(dict(col1=['a', 'b', 'c']), index=['first', 'second', 'third'])
In [4]: df.sort_values()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[4], line 1
----> 1 df.sort_values()
File ~/.pyenv/versions/3.9.9/lib/python3.9/site-packages/pandas/util/_decorators.py:331, in deprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper(*args, **kwargs)
325 if len(args) > num_allow_args:
326 warnings.warn(
327 msg.format(arguments=_format_argument_list(allow_args)),
328 FutureWarning,
329 stacklevel=find_stack_level(),
330 )
--> 331 return func(*args, **kwargs)
TypeError: sort_values() missing 1 required positional argument: 'by'
💡 Suggestion#
The error message is indicating that the sort_values() method of a pandas dataframe is missing a required positional argument.
The sort_values() method requires you to pass a column name or list of column names as the by argument. This is used to determine how the sorting will be performed.
Here's an example:
import pandas as pd
df = pd.DataFrame({
'Name': ['Alice', 'Bob', 'Carol', 'David', 'Eva'],
'Age': [32, 24, 28, 35, 29],
'Salary': [60000, 40000, 35000, 80000, 45000]
})
# sort by Age column:
df_sorted = df.sort_values(by='Age')
print(df_sorted)
In this example, the by argument is set to 'Age', which sorts the dataframe by age in ascending order. Note that you can also pass a list of column names if you want to sort by multiple columns.
Example#
In [1]: %load_ext genai
In [2]: %%assist
...:
...: # Pull census data
...:
'What would a data analyst do? 🤔'
In [3]: # generated with %%assist
...: # Pull census data
...: # To pull census data we can use the `requests` library to send a GET request to the appropriate API endpoint.
...: # First, import the requests module
...: import requests
...:
...: # Define the URL endpoint to the Census API
...: url = "https://api.census.gov/data/2019/pep/population"
...:
...: # Define the parameters needed for the API request, such as dataset and variables requested
...: params = {
...: "get": "POP",
...: "for": "state:*",
...: }
...:
...: # Send a GET request to the Census API endpoint with the parameters
...: response = requests.get(url, params=params)
...:
...: # Access the response content
...: content = response.content
...:
...: # The Census data is now stored in the `content` variable and can be processed or saved elsewhere. The user can modify the `params` variable to request different data or specify a different API endpoint.
In [6]: content
Out[6]: b'[["POP","state"],\n["4903185","01"],\n["731545","02"],\n["7278717","04"],\n["3017804","05"],\n["39512223","06"],\n["5758736","08"],\n["973764","10"],\n["705749","11"],\n["3565287","09"],\n["21477737","12"],\n["10617423","13"],\n["1787065","16"],\n["1415872","15"],\n["12671821","17"],\n["6732219","18"],\n["3155070","19"],\n["2913314","20"],\n["4467673","21"],\n["4648794","22"],\n["1344212","23"],\n["6045680","24"],\n["6892503","25"],\n["9986857","26"],\n["5639632","27"],\n["2976149","28"],\n["6137428","29"],\n["1068778","30"],\n["1934408","31"],\n["3080156","32"],\n["1359711","33"],\n["8882190","34"],\n["2096829","35"],\n["19453561","36"],\n["10488084","37"],\n["762062","38"],\n["11689100","39"],\n["3956971","40"],\n["4217737","41"],\n["12801989","42"],\n["1059361","44"],\n["5148714","45"],\n["884659","46"],\n["6829174","47"],\n["28995881","48"],\n["623989","50"],\n["3205958","49"],\n["8535519","51"],\n["7614893","53"],\n["1792147","54"],\n["5822434","55"],\n["578759","56"],\n["3193694","72"]]'