Pandas Vectorized Methods

As a refresher on lambda, lambda functions are small inline functions that are defined on-the-fly in Python.

lambda x: x >= 1 will take an input x and return x >= 1, or a boolean that equals True or False. -In this example, map() and applymap() create a new Series or DataFrame by applying the lambda function to each element. -Note that map() can only be used on a Series to return a new Series and applymap() can only be used on a DataFrame to return a new DataFrame.

import numpy
from pandas import DataFrame, Series

olympic_medal_counts = {'country_name' : Series(countries),
                        'gold' : Series(gold),
                        'silver' : Series(silver),
                        'bronze' : Series(bronze)
                       }
olympic_medal_counts_df = DataFrame(olympic_medal_counts)

avg_medal_count = olympic_medal_counts_df[['gold', 'silver', 'bronze']].apply(numpy.mean)

print(avg_medal_count)

Multiplying Matrixes using `Numpy.dot(a, b)`



In [ ]:

    
import numpy
from pandas import DataFrame, Series


def points():
    '''
    Imagine a point system in which each country is awarded 4 points for each
    gold medal,  2 points for each silver medal, and one point for each 
    bronze medal.  

    Using the numpy.dot function, create a new dataframe called 
    'olympic_points_df' that includes:
        a) a column called 'country_name' with the country name
        b) a column called 'points' with the total number of points the country
           earned at the Sochi olympics.
           
    You do not need to call the function in your code when running it in the
    browser - the grader will do that automatically when you submit or test it.
    '''

    countries = ['Russian Fed.', 'Norway', 'Canada', 'United States',
                 'Netherlands', 'Germany', 'Switzerland', 'Belarus',
                 'Austria', 'France', 'Poland', 'China', 'Korea', 
                 'Sweden', 'Czech Republic', 'Slovenia', 'Japan',
                 'Finland', 'Great Britain', 'Ukraine', 'Slovakia',
                 'Italy', 'Latvia', 'Australia', 'Croatia', 'Kazakhstan']

    gold = [13, 11, 10, 9, 8, 8, 6, 5, 4, 4, 4, 3, 3, 2, 2, 2, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0]
    silver = [11, 5, 10, 7, 7, 6, 3, 0, 8, 4, 1, 4, 3, 7, 4, 2, 4, 3, 1, 0, 0, 2, 2, 2, 1, 0]
    bronze = [9, 10, 5, 12, 9, 5, 2, 1, 5, 7, 1, 2, 2, 6, 2, 4, 3, 1, 2, 1, 0, 6, 2, 1, 0, 1]
 
    # YOUR CODE HERE
    olympic_medal_counts = {'country_name' : Series(countries),
                            'gold' : Series(gold),
                            'silver' : Series(silver),
                            'bronze' : Series(bronze)
                            }
    olympic_medal_counts_df = DataFrame(olympic_medal_counts)
    
    # Create a small data frame with only the `gold`, `silver`, `bronze`.
    medal_counts = olympic_medal_counts_df[['gold', 'silver', 'bronze']]
    
    # Create a vector store the score for each type of number.
    point_rule = [4, 2, 1]
    
    # Calculate the point of all the medal from each country by multiplying
    # two matrix `medal_counts` and `point_rule` using `numpy.dot` function.
    points = numpy.dot(medal_counts, point_rule)
    
    # Create data
    olympic_points = {'country_name' : Series(countries),
                      'points' : Series(points)
                      }
    # Create a small dataframe `olympic_points_df` as the 
    olympic_points_df = DataFrame(olympic_points)
    
    return olympic_points_df

Pandas Vectorized Methods

Multiplying Matrixes using Numpy.dot(a, b)

Multiplying Matrixes using `Numpy.dot(a, b)`