lab03.pdf - lab03(autosaved Logout Control Panel Python 3 Not Trusted Lab 3 Tables Welcome to lab 3 This week we will focus on manipulating tables

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lab03 (autosaved) Logout Control Panel Python 3 Not Trusted Lab 3: Tables Welcome to lab 3! This week, we will focus on manipulating tables. Tables are described in Chapter 6 of the text. First, set up the tests and imports by running the cell below. In [ ]: import numpy as np from datascience import * # These lines load the tests. from client.api.notebook import Notebook ok = Notebook( 'lab03.ok' ) 1. Introduction For a collection of things in the world, an array is useful for describing a single attribute of each thing. For example, among the collection of US States, an array could describe the land area of each. Tables extend this idea by describing multiple attributes for each element of a collection. In most data science applications, we have data about many entities, but we also have several kinds of data about each entity. For example, in the cell below we have two arrays. The first one contains the world population in each year (as estimated by the US Census Bureau), and the second contains the years themselves (in order, so the first elements in the population and the years arrays correspond). In [ ]: population_amounts = Table.read_table( "world_population.csv" ).column( "Population" ) years = np.arange( 1950 , 2015 + 1 ) print ( "Population column:" , population_amounts) print ( "Years column:" , years) Suppose we want to answer this question: When did world population cross 6 billion? You could technically answer this question just from staring at the arrays, but it's a bit convoluted, since you would have to count the position where the population first crossed 6 billion, then find the
corresponding element in the years array. In cases like these, it might be easier to put the data into a Table , a 2-dimensional type of dataset. The expression below: creates an empty table using the expression Table() , adds two columns by calling with_columns with four arguments, assignes the result to the name population , and finally evaluates population so that we can see the table. The strings "Year" and "Population" are column labels that we have chosen. Ther names population_amounts and years were assigned above to two arrays of the same length. The function with_columns (you can find the documentation here) takes in alternating strings (to represent column labels) and arrays (representing the data in those columns), which are all separated by commas. In [ ]: population = Table().with_columns( "Population" , population_amounts, "Year" , years ) population Now the data are all together in a single table! It's much easier to parse this data--if you need to know what the population was in 1959, for example, you can tell from a single glance. We'll revisit this table later. 2. Creating Tables Question 2.1. In the cell below, we've created 2 arrays. Using the steps above, assign top_10_movies to a table that has two columns called "Rating" and "Name", which hold top_10_movie_ratings and top_10_movie_names respectively.

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