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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 License
Copyright 2020 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
  http
Step1:  Log Overview
Step2:  Client Latency
Step3:  Scatter Plot
Step4:  Responses
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Python Code:
from google.colab import auth
auth.authenticate_user()
project_id = '[your project id]'
Explanation: License
Copyright 2020 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
  http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Load test analysis
Initialization
End of explanation
import pandas as pd
import datetime
today = datetime.datetime.utcnow().strftime("%Y%m%d")
df = pd.io.gbq.read_gbq('''
  SELECT
  count(*) as total
FROM
  `web_instr_container.stdout_{}`
'''.format(today), project_id=project_id)
total = df.total[0]
print(f'Log records: {total}')
Explanation: Log Overview
End of explanation
import pandas as pd
import datetime
today = datetime.datetime.utcnow().strftime("%Y%m%d")
df = pd.io.gbq.read_gbq('''
SELECT
  EXTRACT(MINUTE FROM timestamp) AS Minute,
  REGEXP_EXTRACT(textPayload, r"LoadTest: latency: ([0-9]+)\.") AS Latency
FROM 
  `web_instr_container.stdout_{}`
WHERE
  EXTRACT(HOUR FROM timestamp) = 2
  AND
  EXTRACT(MINUTE FROM timestamp) > 0
  AND
  EXTRACT(MINUTE FROM timestamp) < 5
ORDER BY
  Minute
'''.format(today), project_id=project_id)
df1 = df[pd.isnull(df['Latency']) == False]
df1 = df1.astype({'Minute': 'int64', 'Latency': 'int64'})
latency = df1.groupby('Minute').median()
ax = latency.plot()
ax.set_xlabel('Time (min)')
ax.set_ylabel('Median Client Latency (ms)')
Explanation: Client Latency
End of explanation
import pandas as pd
import datetime
today = datetime.datetime.utcnow().strftime("%Y%m%d")
df = pd.io.gbq.read_gbq('''
SELECT
  EXTRACT(SECOND FROM timestamp) AS Seconds,
  REGEXP_EXTRACT(textPayload, r"LoadTest: latency: ([0-9]+)\.") AS Latency
FROM 
  `web_instr_container.stdout_{}`
WHERE
  EXTRACT(HOUR FROM timestamp) = 2
  AND
  EXTRACT(MINUTE FROM timestamp) = 3
ORDER BY
  Seconds
'''.format(today), project_id=project_id)
df1 = df[pd.isnull(df['Latency']) == False]
df1 = df1.astype({'Seconds': 'int64', 'Latency': 'int64'})
ax = df1.plot.scatter(x='Seconds', y='Latency', c='DarkBlue')
ax.set_xlabel('Time (s)')
ax.set_ylabel('Client Latency (ms)')
Explanation: Scatter Plot
End of explanation
import pandas as pd
import datetime
today = datetime.datetime.utcnow().strftime("%Y%m%d")
df = pd.io.gbq.read_gbq('''
SELECT
  EXTRACT(MINUTE FROM timestamp) as Minute,
  httpRequest.status AS Status
FROM
   `web_instr_load_balancer.requests_{}`
GROUP BY
  Minute, Status
ORDER BY Minute, Status
'''.format(today), project_id=project_id)
df.head(10)
Explanation: Responses
End of explanation
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