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I have set this script up to extract the key metrics I mentioned above so you can use it straight away to collect this data. Response_objectĭf_pagespeed_results.loc =\įid = response_objectĭf_pagespeed_results.loc = fid Here we will once again use a for loop to iterate through the response object file and set up a sequence of list indexes to return just the specific metrics.įor this, we will define the column name from the DataFrame, as well as the specific category of the response object we will be pulling each metric from, for each URL. Once we have the response object saved, we can now filter this and extract just the metrics we want. Step 8: Extract the Metrics From the Response Object I have also chosen to include the Speed Index and the overall category which will provide either a slow, average, or fast score.
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You can find out more about each metric, together with how to interpret the scores, on their individual landing pages which are linked above. These metrics each have different weights which are then used in the overall performance score: We simply need to add a column for each metric and name it appropriately, as so: # Create dataframe to store responsesĭf_pagespeed_results = pd.DataFrame(columns=įor the purpose of this script, I have used the Core Web Vital metrics, together with the additional loading and interactivity metrics used in the current Lighthouse version.
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We also need to create a DataFrame that will store the metrics we want to extract from the response object.Ī DataFrame is a data structure similar to a table, with columns and rows that store data. Step 7: Create a Dataframe to Store the Responses
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Indentation is also important here because, as each step is a part of the for loop, they must be indented within the command. I have also set the time to sleep here to 30 seconds, to reduce the number of API calls made consecutively.Īlternatively, you can append an API key to the end of the URL command if you wish to make quicker requests. This is also where we will use the column header variable to define the URL request parameter, before converting it to a JSON file. The response object prevents the URLs from overriding each other as you loop through and allows us to save the data for future use. We will be using x in range here, which will represent the URLs that we are running through the loop, as well as (0, len) which allows the loop to run through all the URLs in the DataFrame, no matter how many are included. # Insert returned json response into response_object
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Pagespeed_results_json = json.loads(pagespeed_results)
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Full instructions can be found here, but essentially the command will look like this: The next step is to set up the API request.
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This update is expected to roll out in 2021 and Google has confirmed that no immediate action is needed to be taken.