Module 4 Assignment¶

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import numpy as np
from nose.tools import assert_equal, assert_almost_equal, assert_true
import pandas as pd

Problem 1: A function that creates an array¶

Write a function that takes in a list of numbers and outputs a numpy array

def array_maker(my_list):
    """
    Input
    ------
    
    my_list: a list of numbers
    
    
    Output
    -------
    
    my_array: a numpy array created from my_list
    
    """
    
    ### YOUR CODE HERE
    
    return my_array

assert_equal(type(array_maker([1,2,3])), np.ndarray)
assert_equal(array_maker([1,2,3])[0], 1)
assert_equal(array_maker([1,2,3])[1], 2)
assert_equal(array_maker([1,2,3])[2], 3)

Problem 2: Summarizing random data¶

Write a function which takes in an integer $n$, generates $n$ uniform[0,1) random numbers, and then outputs their average.

def average_calculator(n):
    """
    Input
    ------
    
    n: an integer
    
    
    Output
    -------
    
    avg: the average of n uniform[0,1) random numbers
    
    """ 
    
    ### YOUR CODE HERE
    
    return avg

assert_almost_equal(average_calculator(10000), .5, places=1)

Problem 3: Reading data¶

Write a function which reads in a comma delimited datafile with only 1 row and saves it as an array called "data".

Tip: In this example, the "usecols" argument should not be used.

def array_reader(file_name):
    """
    Input
    ------
    
    file_name: a string, the name of the file to be read
    
    
    Output
    -------
    
    saved_array: a numpy array
    
    """
    
    ### YOUR CODE HERE
    
    return data

assert_equal(type(array_reader('data_file.txt')), np.ndarray)
assert_equal(array_reader('data_file.txt')[9], 10)

For the next few cells we will use the iris dataset, focusing only on the setosa species.

iris_data = pd.read_csv('iris.csv')
iris_data.head()

setosa_data = iris_data.loc[iris_data['class'] == 'Iris-setosa']

Problem 4:Finding the median¶

Write a function that takes in a column name from the setosa_data dataset and outputs the median value of that column.

def median_finder(column_name):
    """
    Input
    ------
    
    column_name: a string, the name of the column
    
    
    Output
    -------
    
    median: the median value of the column
    
    """
    
    ### YOUR CODE HERE
    
    return median

assert_equal(median_finder('sepal length (in cm)'), 5.0)
assert_equal(median_finder('sepal width (in cm)'), 3.4)
assert_equal(median_finder('petal length (in cm)'), 1.5)
assert_equal(median_finder('petal width (in cm)'), 0.2)

Problem 5: Comparing medians¶

Write a function that takes in two column names from the setosa_data dataset, compares their medians, and returns the name of the column with the larger median.

def median_comparer(column_name_one,column_name_two):
    """
    Input
    ------
    
    column_name_one: a string, the name of the first column
    column_name_two: a string, the name of the second column
    
    Output
    -------
    
    greater_mean: a string, the name of the column with the greater mean
    
    """
    
    ### YOUR CODE HERE
    
    return greater_median

assert_equal(median_comparer('sepal width (in cm)','sepal length (in cm)'), 'sepal length (in cm)')
assert_equal(median_comparer('petal width (in cm)','sepal length (in cm)'), 'sepal length (in cm)')
assert_equal(median_comparer('petal width (in cm)','petal length (in cm)'), 'petal length (in cm)')
assert_equal(median_comparer('petal width (in cm)','sepal width (in cm)'), 'sepal width (in cm)')

df = pd.read_csv('./airports.csv', index_col='iata')
df.head()

Problem 6: Finding all Airports in a state.¶

For this problem you will find all airports in a given state from the airports dataset.

Finish writing the find_state function in the cell below: The state parameter indicates which state to find all of airports for. The dataframe is passed in as df.

def find_state(df, state='IL'):
    '''
    df - airports.csv loaded into a dataframe
    state - state to find all data for
    returns dataframe containing data from a particular state
    '''
    
    ### YOUR CODE HERE
    
    return df_state

Finding all airports in IL.¶

Your dataframe should look similar to this:

IL df

from helper import fs, srt
assert_true(fs(df, state='IL').equals(find_state(df, state='IL')))

# Checking a few more states
assert_true(fs(df, state='IL').equals(find_state(df, state='IL')))
assert_true(fs(df, state='NC').equals(find_state(df, state='NC')))
assert_true(fs(df, state='CA').equals(find_state(df, state='CA')))

Problem 7: Sorting dataframes¶

Finish writing the sort_lat_asc function. It should sort dataframes passed into it by latitude in ascending order.

def sort_lat_asc(df):
    '''
    df - airports.csv loaded into a dataframe
    returns sorted dataframe by latitiude in ascending order
    '''
    ### YOUR CODE HERE
    
    return sorted_df

nc_df = find_state(df, state='NC')
assert_true(srt(nc_df).equals(sort_lat_asc(nc_df)))

md_df = find_state(df, state='MD')
assert_true(srt(md_df).equals(sort_lat_asc(md_df)))

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