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Hi Yangyi, this is my review for your project 1 notebook.
I was able to run your notebook at datanotebook.org just fine.
I like your solution for problem 1 part B. Your answer is different from mine, and I never thought that wc –w could be used independently in counting the target words.
Your answer for question 3 part B also was different from mine, and you executed three python documents to remove ten common words and the top 25 words in little Women. I thought the pipeline only could execute one python document each time, but your solution proved that I could apply multiple python documents within one pipeline commend. Your answer makes me understand the usage of the grep further.
Great Job! Thanks, -Jeff
The text was updated successfully, but these errors were encountered:
Hi Yangyi, this is my review for your project 1 notebook.
I was able to run your notebook at datanotebook.org just fine.
I like your solution for problem 1 part B. Your answer is different from mine, and I never thought that wc –w could be used independently in counting the target words.
Your answer for question 3 part B also was different from mine, and you executed three python documents to remove ten common words and the top 25 words in little Women. I thought the pipeline only could execute one python document each time, but your solution proved that I could apply multiple python documents within one pipeline commend. Your answer makes me understand the usage of the grep further.
Great Job! Thanks, -Jeff
The text was updated successfully, but these errors were encountered: