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# Import required packages 
import json
import requests
import pandas as pd
import urllib
import time
from google.colab import files
import io

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 # Convert to json format
result_json = json.loads(result)

print(result_json)

with open('result.json', 'w') as outfile:
  json.dump(result_json, outfile)

files.download('result.json')

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# Define URL  
url = 'https://www.example.co.uk'

# API request url
result = urllib.request.urlopen('https://www.googleapis.com/pagespeedonline/v5/runPagespeed?url={}/&strategy=mobile'\
.format(url)).read().decode('UTF-8')

print(result)

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uploaded = files.upload()
#if your column header is something other than 'url' please define it here 
column_header='url'

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# Get the filename from the upload so we can read it into a CSV.
for key in uploaded.keys():
  filename = key
# Read the selected file into a Pandas Dataframe
df = pd.read_csv(io.BytesIO(uploaded[filename]))

df.head()
 

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response_object = {}


# Iterate through the df
for x in range(0, len(df)):

        # Define request parameter
        url = df.iloc[x][column_header]

        # Make request

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# Create dataframe to store field data responses
df_pagespeed_results = pd.DataFrame(columns=
          ['url',
          'Overall_Category',
          'Largest_Contentful_Paint',
          'First_Input_Delay',
          'Cumulative_Layout_Shift',
          'First_Contentful_Paint',
          'Time_to_Interactive',
          'Total_Blocking_Time',

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for (url, x) in zip(
    response_object.keys(),
    range(0, len(response_object))
):

        # URLs
        df_pagespeed_results.loc[x, 'url'] =\
            response_object[url]['lighthouseResult']['finalUrl']

        # Overall Category

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for (url, x) in zip(
    response_object.keys(),
    range(0, len(response_object))
):

        # URLs
        df_pagespeed_results.loc[x, 'url'] =\
            response_object[url]['lighthouseResult']['finalUrl']

        # Overall Category

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summary = df_pagespeed_results

df_pagespeed_results.head()