In this section we will examine some of the factors that influence population

# In this section we will examine some of the factors

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In this section, we will examine some of the factors that influence population growth and how they are changing around the world. The first table we will consider is the total population of each country over time. Run the cell below. In [4]: population = Table . read_table( ' population.csv ' ) population . show( 3 ) <IPython.core.display.HTML object> Note: The population csv file can also be found here . The data for this project was downloaded in February 2017. 0.2.1 Bangladesh In the population table, the geo column contains three-letter codes established by the Interna- tional Organization for Standardization (ISO) in the Alpha-3 standard. We will begin by taking a close look at Bangladesh. Inspect the standard to find the 3-letter code for Bangladesh. Question 1. Create a table called b_pop that has two columns labeled time and population_total . The first column should contain the years from 1970 through 2015 (includ- ing both 1970 and 2015) and the second should contain the population of Bangladesh in each of those years. In [5]: b_pop = population . where( ' geo ' , are . containing( ' bgd ' )) . drop( ' geo ' ) . where( ' time ' , are . bet b_pop Out[5]: time | population_total 1970 | 65048701 1971 | 66417450 1972 | 67578486 3
1973 | 68658472 1974 | 69837960 1975 | 71247153 1976 | 72930206 1977 | 74848466 1978 | 76948378 1979 | 79141947 ... (36 rows omitted) In [6]: _ = ok . grade( ' q1_1 ' ) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Running tests --------------------------------------------------------------------- Test summary Passed: 3 Failed: 0 [ooooooooook] 100.0% passed Run the following cell to create a table called b_five that has the population of Bangladesh every five years. At a glance, it appears that the population of Bangladesh has been growing quickly indeed! In [7]: b_pop . set_format( ' population_total ' , NumberFormatter) fives = np . arange( 1970 , 2016 , 5 ) # 1970, 1975, 1980, ... b_five = b_pop . sort( ' time ' ) . where( ' time ' , are . contained_in(fives)) b_five Out[7]: time | population_total 1970 | 65,048,701 1975 | 71,247,153 1980 | 81,364,176 1985 | 93,015,182 1990 | 105,983,136 1995 | 118,427,768 2000 | 131,280,739 2005 | 142,929,979 2010 | 151,616,777 2015 | 160,995,642 Question 2. Assign b_1970_through_2010 to a table that has the same columns as b_five and has one row for every five years from 1970 through 2010 (but not 2015). Then, use that table to assign initial to an array that contains the population for every five year interval from 1970 to 2010. Finally, assign changed to an array that contains the population for every five year interval from 1975 to 2015. Hint : You may find the exclude method to be helpful ( Docs ). 4
In [8]: b_1970_through_2010 = b_five . exclude( 9 ) b_1970_through_2010 Out[8]: time | population_total 1970 | 65,048,701 1975 | 71,247,153 1980 | 81,364,176 1985 | 93,015,182 1990 | 105,983,136 1995 | 118,427,768 2000 | 131,280,739 2005 | 142,929,979 2010 | 151,616,777 In [9]: b_1970_through_2010 = b_five . exclude( 9 ) initial = b_1970_through_2010 . column( ' population_total ' ) changed = b_five . exclude( 0 ) . column( ' population_total ' ) We have provided the code below that uses b_1970_through_2010 , initial , and changed in order to add a column to the table called annual_growth . Don’t worry about the calculation of the growth rates; run the test below to test your solution. If you are interested in how we came up with the formula for growth rates, consult the growth rates section of the textbook. In [10]: b_five_growth = b_1970_through_2010 . with_column( ' annual_growth ' , (changed / initial) **0.

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