#100DaysOfCode in Python Transcripts
Chapter: Days 58-60: Twitter data analysis with Python
Lecture: Concepts: what did we learn
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Lets quickly summarize what we've learned so far
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you can use Tweepy to interface with the Twitter API
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always load API keys and secrets from your environment
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and a way to do that is to use the os.environ.
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Here's how you make a Tweepy API object, then
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we can use that object to get our way to cursor
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till you see we get all the tweets
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from your timeline, then we did a top ten
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of most popular tweets.
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First we looked over all the tweets
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and used sorted to get a written line down
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to get the most popular tweets as defined by
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an average of total likes and retweets per tweet
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and voila.
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I guess some marketing folks out there
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will be interesting to know which tweets
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are doing well, well you can and it's only
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a few lines of code.
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Next is most used hashtags and mentions.
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What are we talking about the most,
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so we define a couple of regular expressions
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and put all the tweets in a big string
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then we use find all in combination with calender
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and we saw that before in the collections
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and regex lessons.
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To get to most common hashtags,
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which were these, and similarly to get
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the most mentioned Twitter handles,
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which were those, very useful info.
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Lastly, we made a Wordcloud of all the tweets
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and we used the wordcloud module, we define
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the mask which was our pilots logo,
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added some stump words to get a better result
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and constructed the word cloud object.
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To get the Wordcloud to show in the notebook
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we used a little bit of matte lot lip
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and look at that, we got a word cloud
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in the shape PyBites logo.
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How cool is that right?
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I mean visually its beautiful but
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its also informative in that we really see
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some words standing out.
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Alright, with these practical examples
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under your belt now its your turn
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to go through, the practical exercises
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we have lined up for you in Day 2 and 3.
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Good luck.
