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Exercises

Timothy Helton


NOTE:
This notebook uses code found in the k2datascience.olympics module. To execute all the cells do one of the following items:

  • Install the k2datascience package to the active Python interpreter.
  • Add k2datascience/k2datascience to the PYTHON_PATH system variable.
  • Create a link to the olympics.py file in the same directory as this notebook.

Imports





In [1]:



    


from k2datascience import olympics

from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
%matplotlib inline

Data Prep





In [2]:



    


oly = olympics.Medals()

print(f'{"#" * 30}\nAthletes Data\n\n')
print(f'Data Types:\n{oly.athletes.dtypes}\n\n')
print(f'Data Shape:\n{oly.athletes.shape}\n\n')
print(f'Missing Data:\n{oly.athletes.isnull().sum()}\n\n')
oly.athletes.head()
oly.athletes.tail()
oly.athletes.describe()

print(f'\n\n\n{"#" * 30}\nCountries Data\n\n')
print(f'Data Types:\n{oly.countries.dtypes}\n\n')
print(f'Data Shape:\n{oly.countries.shape}\n\n')
print(f'Missing Data:\n{oly.countries.isnull().sum()}\n\n')
oly.countries.head()
oly.countries.tail()
oly.countries.describe()



    


















    






##############################
Athletes Data


Data Types:
id                  int64
name               object
country            object
sex                object
dob        datetime64[ns]
height            float64
weight            float64
sport              object
gold                int64
silver              int64
bronze              int64
dtype: object


Data Shape:
(11538, 11)


Missing Data:
id           0
name         0
country      0
sex          0
dob          1
height     330
weight     659
sport        0
gold         0
silver       0
bronze       0
dtype: int64












    
Out[2]:











  
    

      
      id
      name
      country
      sex
      dob
      height
      weight
      sport
      gold
      silver
      bronze
    

  
  
    

      0
      736041664
      A Jesus Garcia
      ESP
      male
      1969-10-17
      1.72
      64.0
      athletics
      0
      0
      0
    

    

      1
      532037425
      A Lam Shin
      KOR
      female
      1986-09-23
      1.68
      56.0
      fencing
      0
      0
      0
    

    

      2
      435962603
      Aaron Brown
      CAN
      male
      1992-05-27
      1.98
      79.0
      athletics
      0
      0
      1
    

    

      3
      521041435
      Aaron Cook
      MDA
      male
      1991-01-02
      1.83
      80.0
      taekwondo
      0
      0
      0
    

    

      4
      33922579
      Aaron Gate
      NZL
      male
      1990-11-26
      1.81
      71.0
      cycling
      0
      0
      0
    

  













    
Out[2]:











  
    

      
      id
      name
      country
      sex
      dob
      height
      weight
      sport
      gold
      silver
      bronze
    

  
  
    

      11533
      265605954
      Zurian Hechavarria
      CUB
      female
      1995-08-10
      1.64
      58.0
      athletics
      0
      0
      0
    

    

      11534
      214461847
      Zuzana Hejnova
      CZE
      female
      1986-12-19
      1.73
      63.0
      athletics
      0
      0
      0
    

    

      11535
      88361042
      di Xiao
      CHN
      male
      1991-05-14
      1.85
      100.0
      wrestling
      0
      0
      0
    

    

      11536
      900065925
      le Quoc Toan Tran
      VIE
      male
      1989-04-05
      1.60
      56.0
      weightlifting
      0
      0
      0
    

    

      11537
      711404576
      le Roux Hamman
      RSA
      male
      1992-01-06
      1.85
      70.0
      athletics
      0
      0
      0
    

  













    
Out[2]:











  
    

      
      id
      height
      weight
      gold
      silver
      bronze
    

  
  
    

      count
      1.153800e+04
      11208.000000
      10879.000000
      11538.000000
      11538.000000
      11538.000000
    

    

      mean
      4.999885e+08
      1.766282
      72.068205
      0.057722
      0.056769
      0.061016
    

    

      std
      2.908648e+08
      0.112719
      16.177334
      0.255910
      0.239147
      0.243320
    

    

      min
      1.834700e+04
      1.210000
      31.000000
      0.000000
      0.000000
      0.000000
    

    

      25%
      2.450997e+08
      1.690000
      60.000000
      0.000000
      0.000000
      0.000000
    

    

      50%
      5.002011e+08
      1.760000
      70.000000
      0.000000
      0.000000
      0.000000
    

    

      75%
      7.539874e+08
      1.840000
      81.000000
      0.000000
      0.000000
      0.000000
    

    

      max
      9.999878e+08
      2.210000
      170.000000
      5.000000
      2.000000
      2.000000
    

  













    








##############################
Countries Data


Data Types:
country    object
code       object
dtype: object


Data Shape:
(201, 2)


Missing Data:
country    0
code       0
dtype: int64












    
Out[2]:











  
    

      
      country
      code
    

  
  
    

      0
      Afghanistan
      AFG
    

    

      1
      Albania
      ALB
    

    

      2
      Algeria
      ALG
    

    

      3
      American Samoa*
      ASA
    

    

      4
      Andorra
      AND
    

  













    
Out[2]:











  
    

      
      country
      code
    

  
  
    

      196
      Vietnam
      VIE
    

    

      197
      Virgin Islands*
      ISV
    

    

      198
      Yemen
      YEM
    

    

      199
      Zambia
      ZAM
    

    

      200
      Zimbabwe
      ZIM
    

  













    
Out[2]:











  
    

      
      country
      code
    

  
  
    

      count
      201
      201
    

    

      unique
      201
      201
    

    

      top
      Haiti
      ANT
    

    

      freq
      1
      1

1. What was the average age in male and female athletes?





In [3]:



    


oly.calc_age_means()



    


















    
Out[3]:











  
    

      
      years
      months
      days
    

    

      sex
      
      
      
    

  
  
    

      female
      25.0
      5.0
      15.0
    

    

      male
      26.0
      5.0
      15.0

2. What are the most common Dates of Birth?

To clarify - day, month, year





In [4]:



    


oly.common_full_birthday()



    


















    






Most Common Athlete Birthdays

9 Athletes were born on the following dates:
1988-03-05
1990-12-20
1993-02-18

3. How about the most common birthdays?

  • Most Common Month
  • Modst Common Day




In [5]:



    


oly.common_month_day_birthday()



    


















    
Out[5]:











  
    

      
      Most Common Birthday
    

  
  
    

      month
      6.0
    

    

      day
      15.0

4. What are the Countries with more than 100 medals?





In [6]:



    


(oly.country_medals
 .query('total > 100')
 .sort_values('total', ascending=False))



    


















    
Out[6]:











  
    

      
      gold
      silver
      bronze
      total
    

    

      country
      
      
      
      
    

  
  
    

      USA
      139
      54
      71
      264
    

    

      GER
      49
      44
      67
      160
    

    

      GBR
      64
      55
      26
      145
    

    

      RUS
      52
      29
      34
      115
    

    

      CHN
      46
      30
      37
      113

5. Create a bar or pie chart for the results of the previous exercise.





In [7]:



    


oly.country_medals_plot()

6. Male weightlifting competitions are divided into 8 weight classes. Can you estimate these weight classes by looking at the data? Hint: Create a scatter plot with Body weight on the x-axis and choose height as y.





In [8]:



    


oly.weightlifting_classes()

The predicted weight classes are displayed in text boxes outlined in crimson.

7. Generate a histogram of male and female height distribution among all participants.





In [9]:



    


oly.height_histograms()

8. Using the Seaborn package create a box plot for male and female height distribution among all participants.





In [10]:



    


oly.height_boxplot()

9. Optional: What else would you try?

  1. Compare type of medal compared to height.
  2. Compare type of medal compared to weight.
  3. Compare height vs age.
    • Are younger generation people taller or are Olympic athletes in the top percentile irregardless?
  4. Which sport distributes the most medals.
  5. Calculate the Body Mass Index (BMI) of the athletes.
    • Compare the BMI by sport.
  6. Find the athlete that is closest to your height and mass.
  7. Compare male and female heights for each sport.




In [11]:



    


oly.height_sport()

Content source: TimothyHelton/k2datascience

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