# Weekly Challenge

Solve the challenge, share your solution and summit the ranks of our Community!
###### IDEAS WANTED

We're actively looking for ideas on how to improve Weekly Challenges and would love to hear what you think!

Submit Feedback
We've recently made an accessibility improvement to the community and therefore posts without any content are no longer allowed. Please use the spoiler feature or add a short message in the message body in order to submit your weekly challenge.

## Challenge #205: Taynalysis

Highlighted
6 - Meteoroid
Spoiler

Highlighted
16 - Nebula
Spoiler

Highlighted
Alteryx

Getting some slight differences. I checked some of the other solutions and they seem to be seeing the same discrepancies.

Highlighted
14 - Magnetar
Spoiler

Highlighted
11 - Bolide

Slight differences in my answer to the provided example

Spoiler
Highlighted
8 - Asteroid

Here is my solution. I couldn't figure out how the numbers were calculated so some of the numbers are off.

Highlighted
8 - Asteroid
Spoiler
Some slight differences in expected vs. actual
Highlighted
6 - Meteoroid

Hi All ,

This is my very first weekly challenge response

I am excited to share the news that my paper "Workday Data Migration : How we saved over 2000 hours of manual effort" was chosen for the Excellence Award !!!!

For this weekly challenge , I used the summarise function and the count function on the lyric field to return counts , count distinct of lines per album. Using the data I arrived at the duplicate records. The data matched for some records but was off by 1 number for a few. Attached is my workflow.

Regards

Sambit

Highlighted
11 - Bolide
Spoiler
``````#################################
# List all non-standard packages to be imported by your
# script here (only missing packages will be installed)
from ayx import Package
#Package.installPackages(['pandas','numpy'])

#################################
from ayx import Alteryx
from collections import Counter

import re

#################################
# Create a simple list of stopwords
stopwords = [w[0] for w in dfStopwords.values.tolist()]

#################################
dfTop10 = dfLyrics.groupby(['year','album'])['lyric'].apply(" ".join).reset_index()

def get_top_10_words(word_list):
word_list = re.sub(r"[^a-zA-Z0-9\s\']", r'', word_list)
list_ = word_list.split()
not_stop = [word for word in list_ if word.lower() not in stopwords]
counter = Counter(not_stop)
return " ".join([word for (word, count) in counter.most_common(10)])

dfTop10['lyric'] = dfTop10['lyric'].apply(get_top_10_words)

#################################
dfLines = dfLyrics.groupby(['year','album'])['lyric'].agg(['nunique','count']).reset_index()

dfLines['dups'] = dfLines['count'] - dfLines['nunique']
dfLines['percent'] = dfLines['dups'] * 100 / dfLines['count']

dfLines = dfLines[['year', 'album', 'nunique', 'dups', 'count', 'percent']]

#################################
dfOutput = dfTop10.merge(dfLines).rename(columns=
{"year": "Album Year",
"album": "Album Name",
"lyric": "Top_10_Lyrics",
"nunique": "Unique_Lines_Per_Album",
"dups": "Duplicate_Lines_Per_Album",
"count": "Total_Lines_Per_Album",
"percent": "Repetativeness_Percentage"
}
)

#################################
Alteryx.write(dfOutput, 1)``````

I wanted to practice my data frames with this one, so I used the python tool. Like others have mentioned, my results are very close to the expected output counts.

On a whim I also tried training an RNN (not attached) to generate new T-Swift songs, but after 30 epochs it was over-fitting. Reducing the number of epochs produced incoherent lyrics. At approximately 33k words, there wasn't enough data to satisfy the network. We'll have to wait for more Taylor albums!

Highlighted
Alteryx Certified Partner

Thanks for the challenge! Here's my solution