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  3. Python Data Science Toolbox (Part 2)

Exercise

List comprehensions for time-stamped data

You will now make use of what you've learned from this chapter to solve a simple data extraction problem. You will also be introduced to a data structure, the pandas Series, in this exercise. We won't elaborate on it much here, but what you should know is that it is a data structure that you will be working with a lot of times when analyzing data from pandas DataFrames. You can think of DataFrame columns as single-dimension arrays called Series.

In this exercise, you will be using a list comprehension to extract the time from time-stamped Twitter data. The pandas package has been imported as pd and the file 'tweets.csv' has been imported as the df DataFrame for your use.

Instructions

100 XP
  • Extract the column 'created_at' from df and assign the result to tweet_time. Fun fact: the extracted column in tweet_time here is a Series data structure!
  • Create a list comprehension that extracts the time from each row in tweet_time. Each row is a string that represents a timestamp, and you will access the 12th to 19th characters in the string to extract the time. Use entry as the iterator variable and assign the result to tweet_clock_time. Remember that Python uses 0-based indexing!