Upload aggregated reports for February
In the last exercise, Sam downloaded the files for the month from the raw data bucket.
Then she combined them all into one DataFrame that showcases all of the month's requests and requests type.
She stored this DataFrame in the variable all_reqs and used pandas's groupby functionality to count requests by service name, generating a new DataFrame agg_df:
| service_name | count | |
|---|---|---|
| 0 | 72 Hour Violation | 2910 |
| 1 | Chain Link Fence Repair | 90 |
| 2 | Collections Truck Spill | 30 |
| 3 | Container Left Out | 120 |
| 4 | Dead Animal | 360 |
She has already created the boto3 S3 client in the s3 variable.
Help her publish this month's request statistics.
Write agg_df to CSV and HTML files, and upload them to S3 as public files.
Diese Übung ist Teil des Kurses
Introduction to AWS Boto in Python
Anleitung zur Übung
- Write CSV and HTML versions of
agg_dfand name them'feb_final_report.csv'and'feb_final_report.html'respectively. - Upload both versions of
agg_dfto thegid-reportsbucket and set them to public read.
Interaktive Übung
Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.
# Write agg_df to a CSV and HTML file with no border
agg_df.____('./____')
agg_df.____('./____', border=0)
# Upload the generated CSV to the gid-reports bucket
s3.____(Filename='./feb_final_report.csv',
Key='2019/feb/final_report.html', Bucket='gid-reports',
____ = {'ACL': '____'})
# Upload the generated HTML to the gid-reports bucket
s3.upload_file(Filename='./feb_final_report.html',
Key='2019/feb/final_report.html', Bucket='gid-reports',
____ = {'ContentType': '____',
'____': '____'})