human
I am working on the file "Report.ipynb".
The first task is:
Data Challenge
Files are stored in an S3 bucket. The purpose here is to fully analyze the data and make some predictions.
This workbook was exported to a Python script and the resulting code was checked for PEP8 problems. The problems found were with formatting and order of imports. We can't fix the latter due to the way the script is exported from the notebook.
Any known or potential performance problems are flagged for further work.
Some of the information here is based on posts from Stackoverflow. I haven't kept up with it, so I'll just indicate by flagging with SO.
Prepare an AWS Instance
We're using a tiny instance here since the data and processing requirements are minimal.

Install Anaconda for Python 3.6.
Install the boto3 and seaborn packages.
Install and configure the AWS command-line tools.
Use jupyter notebook --generate-config to generate a config file in ~/.jupyter. An example is enclosed in the GitHub repository.
Run conda install -c conda-forge jupyter_contrib_nbextensions.
Configure Jupyter on the remote side to run without a browser and to require a password. Make sure you put the config file in the proper place.
On the client side, run ssh -N -L 8888:localhost:8888 ubuntu@ec2-34-230-78-129.compute-1.amazonaws.com (the hostname may vary). This sets up the ssh tunnel and maps to port 8888 locally.
ssh into the instance using ssh ubuntu@ec2-34-230-78-129.compute-1.amazonaws.com.
Start jupyter notebook.

Note: You can start the tmux terminal multiplexer in order to use notebooks when you are logged out.
Note: This doesn't start X windows tunnelling, which is a separate configuration and is not presented here.
Get the Data from S3
Instead of processing the data with the boto3.s3 class, we chose to
bash
aws s3 cp --recursive s3://my_bucket_name local_folder
This is not scalable, so we can use code such as
```python
s3 = boto3.resource('s3')
bucket_name = "postie-testing-assets"
test = s3.Bucket(bucket_name)
s3.meta.client.head_bucket(Bucket=bucket_name)
{'ResponseMetadata': {'HTTPHeaders':...
Can you write Python program for it?

Give me a Python program to fix the latter due to the way the script is exported from the notebook?
assistant
#Here is Python program to fix the latter due to the way the script is exported from the notebook.

def convert_list(query_string):
    """Parse the query string of the url into a dictionary.
    
    Handle special cases:
    - There is a single query "error=True" which is rewritten to 1 if True, else 0.
    - Parsing the query returns a dictionary of key-value pairs. The value is a list.
      We must get the list value as an int.
      
    Note: This function may be a bottleneck when processing larger data files.
    """
    def handle_error(z, col):
        """
        Called in the dictionary comprehension below to handle the "error" key.
        """
        if "error" in col:
            return 1 if "True" in z else 0
        return z
    dd = parse_qs(query_string)
    return {k: int(handle_error(dd[k][0], k)) for k in dd}