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.

This exercise is part of the course

Introduction to AWS Boto in Python

View Course

Exercise instructions

Write CSV and HTML versions of agg_df and name them 'feb_final_report.csv' and 'feb_final_report.html' respectively.
Upload both versions of agg_df to the gid-reports bucket and set them to public read.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# 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': '____', 
                 '____': '____'})

Edit and Run Code