1. Use DictReader from import csv to read the CSV data file into a list of dictionaries, where each row is a dictionary.
>>> import csv
>>> f = open('london_2012_olympic_athlete_data.csv', 'r')
>>> dict_reader = csv.DictReader(f)
>>> athletes = []
>>> for row in dict_reader:
... athletes.append(row)
>>> ages = []
>>> for athlete in athletes:
... ages.append(int(athlete['Age']))
3. Create two lists named ages_female and ages_male that is a simple list of integers of the ages of female and male athletes.
>>> ages_female = []
>>> ages_male = []
>>>
>>> for athlete in athletes:
... if athlete['Sex'] == 'F':
... ages_female.append(int(athlete['Age']))
... else:
... ages_male.append(int(athlete['Age']))
4. Create three lists weights, weights_female, and weights_male, much like parts 2 and 3, that are simple lists of integers values of the weights from athletes.
>>> weights = []
>>> weights_female = []
>>> weights_male = []
>>>
>>> for athlete in athletes:
>>> weights.append(int(athlete['Weight (kg)']))
>>> if athlete['Sex'] == 'F':
>>> weights_female.append(int(athlete['Weight (kg)']))
>>> else:
>>> weights_male.append(int(athlete['Weight (kg)']))
5. Create three lists heights, heights_female, and heights_male, much like parts 2 and 3, that are simple lists of integers values of the heights from athletes.
>>> heights = []
>>> heights_female = []
>>> heights_male = []
>>>
>>> for athlete in athletes:
>>> heights.append(int(athlete['Height (cm)']))
>>> if athlete['Sex'] == 'F':
>>> heights_female.append(int(athlete['Height (cm)']))
>>> else:
>>> heights_male.append(int(athlete['Height (cm)']))
6. Create a list called bmi, which is a list of the body mass index (BMI) values for each athlete in our list. (HINT: BMI = weight {kg} / (height {meters} * height {meters}).)
>>> bmi = []
>>>
>>> for athlete in athletes:
>>> num = int(athlete['Weight (kg)'])
>>> denom = int(athlete['Height (cm)']) / 100.0
>>> bmi.append(num / (denom * denom))
7. Much like part 5, create two lists bmi_female and bmi_male, which include just the BMI values for the female and male atheletes respectively.
>>> bmi_female = []
>>> bmi_male = []
>>>
>>> for athlete in athletes:
... bmi_val = float(athlete['Weight (kg)']) / ((float(athlete['Height (cm)']) / 100.0) ** 2)
... if athlete['Sex'] == 'F':
... bmi_female.append(bmi_val)
... else:
... bmi_male.append(bmi_val)
1. Find the mean and standard deviation of: ages, ages_female, and ages_male. What do you now know about the age of Olympic athletes? Is this what you expected?
>>> scipy.mean(ages)
26.6275497031
>>> scipy.std(ages)
5.5641516181
>>> scipy.mean(ages_female)
25.9508290452
>>> scipy.std(ages_female)
5.37479087566
>>> scipy.mean(ages_male)
27.1847928437
>>> scipy.std(ages_male)
5.65489675013
Okay so Olympic athletes are 26.6 +/- 5.6 years. So... Olypmic athletes are in their 20s, mostly. That seems right. Female and male athletes have about the same age distribution. That also seems about right, intuitively.
2. Find the mean and standard deviation of: heights, heights_female, and heights_male. We probably expect the average man to be somewhat taller than the averge woman. Is that true for Olympic athletes?
>>> scipy.mean(heights)
178.139297702
>>> scipy.std(heights)
11.2368389768
>>> scipy.mean(heights_female)
171.334190966
>>> scipy.std(heights_female)
8.74347474997
>>> scipy.mean(heights_male)
183.742937853
>>> scipy.std(heights_male)
9.88711339731
Men are, on average, somewhat taller than women. Sure, that seems reasonable.
3. Find the mean and standard deviation of: weights, weights_female, and weights_male. We probably expect the average man to be somewhat heavier than the averge woman. Is that true for Olympic athletes?
>>> scipy.mean(weights)
73.2650400207
>>> scipy.std(weights)
16.2864434653
>>> scipy.mean(weights_female)
63.4554030875
>>> scipy.std(weights_female)
10.9700661228
>>> scipy.mean(weights_male)
81.3427495292
>>> scipy.std(weights_male)
15.4945692197
What isn't surprising at all here is that men tend to weight more than women. It is interesting just how wide the standard deviations are here. There is a really big range of weights among Olympic athletes. Is this, perhaps, due to the different body types needed for different sports?
4. Find he mean and standard deviation of: bmi, bmi_female, and bmi_male. What is a typical BMI for an Olympic athlete?
>>> scipy.mean(bmi)
22.8615208899
>>> scipy.std(bmi)
3.29673647767
>>> scipy.mean(bmi_female)
21.5335466351
>>> scipy.std(bmi_female)
2.77663273887
>>> scipy.mean(bmi_male)
23.9550364133
>>> scipy.std(bmi_male)
3.28962341446
Okay, so Olympic athletes have BMI values that tend to range from 19 to 26 (within one standard deviation of the mean). That is actually really intesting. So, accounting for body types and sporting differences, this seems to be a wide range of values for the healthiest 20-somethings of our species.
>>> import scipy
>>> from scipy import stats
>>>
>>> scipy.mean(heights_female)
171.334190966
>>> stats.gmean(heights_female)
171.111899541
>>> stats.hmean(heights_female)
170.890202922
Okay, they are very close. But the Harmonic Mean is notably lower.
>>> import scipy
>>> from scipy import stats
>>>
>>> scipy.mean(weights_male)
81.3427495292
>>> stats.gmean(weights_male)
79.961280503
>>> stats.hmean(weights_male)
78.6466219327
Again, they are all pretty close, but on this scattered dataset, the Harmonic Mean is the lowest.
>>> from scipy import stats
>>> stats.histogram(bmi, 10)
(array([ 3.00000000e+00, 1.31100000e+03, 5.48500000e+03,
7.88000000e+02, 1.27000000e+02, 2.50000000e+01,
3.00000000e+00, 1.00000000e+00, 1.00000000e+00,
2.00000000e+00]), 8.552998285872599, 5.804540028960087, 0)
So, to re-write those numbers to be easier to read, the count in each bin is:
[3, 1311, 5485, 788, 127, 25, 3, 1, 1, 2]
The first bin starts at 8.55, and each bin is 5.8 wide. There are no data points left outside the historgram.
4. Build a histogram for the heights_female and heights_male lists, starting at 1 meter and going to up to 2.6 meters in 0.2 meter increments.
>>> from scipy import stats
>>>
>>> bins = range(120, 221, 10)
>>> print bins
[120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220]
>>> print stats.histogram2(heights_female, bins)
[ 0 2 7 256 1218 1369 552 89 5 0 0]
>>> print stats.histogram2(heights_male, bins)
[ 0 0 0 18 265 1106 1629 987 214 28 1]
If Angelina Jolie and Brad Pitt were in the athletes list above, here is what their lines would look like:
{'Name': 'Angelina Jolie', 'Age': '40', 'Sex': 'F', 'Weight (kg)': '56.5', 'Sport': 'Acting', 'Height (cm)': '173'}
{'Name': 'Brad Pitt', 'Age': '52', 'Sex': 'M', 'Weight (kg)': '78', 'Sport': 'Acting', 'Height (cm)': '180'}
>>> from scipy import stats
>>>
>>> stats.percentileofscore(weights_female, 56.5)
>>> 25.4716981132
>>> stats.percentileofscore(heights_male, 180)
>>> 35.8168549906
>>> stats.percentileofscore(bmi_female, 56.5 / (1.73 * 1.73))
>>> 10.5488850772
>>>
>>> stats.percentileofscore(bmi_male, 78.0 / (1.8 * 1.8))
>>> 57.827212806
4. What percentile would YOU fall into, in your respective sex height, weight, and bmi? (No judgements!)
This question is just for you. It is interesting, but unless you want to compete in a sport at the Olympic level, please don't take it too seriously.
1. Use dict and zip to make a dictionary of the first 25 athletes in your ages and bmi lists. Name your dictionary bmi_by_age.
>>> bmi_by_age = dict(zip(ages[:25], bmi[:25]))
>>>> age_keys = sorted(bmi_by_age.keys())
3. Create a list, named bmi_values, of the bmi values associate with each age in age_keys. (Use a for loop and your age_keys along with bmi_by_age.)
>>> bmi_values = []
>>> for age in age_keys:
... bmi_values.append(bmi_by_age[age])
>>> from scipy.interpolate import interp1d
>>> f_linear = interp1d(age_keys, bmi_values)
5. Create a function f_cubic that is a cubic interpolation of age_keys and bmi_values. (Use interp1d along with kind='cubic'.)
>>> f_cubic = interp1d(age_keys, bmi_values, kind='cubic')
>>> f_linear([20, 22, 24, 26, 28])
array([ 23.78121284, 22.40587695, 19.96035302, 20.84398292, 24.44180209])
>>> f_cubic([20, 22, 24, 26, 28])
array([ 23.78121284, 22.40587695, 19.29999598, 20.99535975, 24.44180209])
They actually turn out to be nearly identical fits to the data. In this case, using a cubic fit didn't really win us much. Then again, we were only using 25 data points, so we probably didn't expect much from these fits. The values they give seem a little random, you imagine if we fit more data points we would get better results.
1. Convert the following from lists to numpy.array: ages_female, ages_male, bmi_female, and bmi_male.
>>> import numpy as np
>>> ages_female = np.array(ages_female)
>>> ages_male = np.array(ages_male)
>>> bmi_female = np.array(bmi_female)
>>> bmi_male = np.array(bmi_male)
>>> def linear(x, a, b):
... return x * a + b
3. Use curve_fit and your linear function to fit the data where female athletes ages are the x-value and female athletes BMI are the y-values.
>>> from scipy.optimize import curve_fit
>>> f_female = curve_fit(linear, age_female, bmi_female)
4. Use curve_fit and your linear function to fit the data where male athletes ages are the x-value and male athletes BMI are the y-values.
>>> from scipy.optimize import curve_fit
>>> f_male = curve_fit(linear, age_male, bmi_male)
