exercise_solutions.md

Exercise Solutions

General polynomial function:

def calc_poly(params, data):
    x = np.c_[[data**i for i in range(len(params))]]
    return np.dot(params, x)

Microbiome exercise:

metadata = pd.read_excel('data/microbiome/metadata.xls', sheetname='Sheet1')

chunks = []
for i in range(9):
    this_file = pd.read_excel('data/microbiome/MID{0}.xls'.format(i+1), 'Sheet 1', index_col=0, header=None, names=['Taxon', 'Count'])
    this_file.columns = ['Count']
    this_file.index.name = 'Taxon'
    for m in metadata.columns:
        this_file[m] = metadata.ix[i][m]
    chunks.append(this_file)

pd.concat(chunks)

Titanic proportions:

titanic = pd.read_excel("data/titanic.xls", "titanic")

titanic.groupby('sex')['survived'].mean()

titanic.groupby(['pclass','sex'])['survived'].mean()

titanic['agecat'] = pd.cut(titanic.age, [0, 13, 20, 64, 100], labels=['child', 'adolescent', 'adult', 'senior'])
titanic.groupby(['agecat', 'pclass','sex'])['survived'].mean()

Survivor KDE plots:

surv = dict(list(titanic.groupby('survived')))
for s in surv:
    surv[s]['age'].dropna().plot(kind='kde', label=bool(s)*'survived' or 'died', grid=False)
legend()
xlim(0,100)

OBP:

baseball[['h','bb', 'hbp']].sum(axis=1).div(
        baseball[['bb', 'hbp','ab', 'sf']].sum(axis=1)
                            ).order(ascending=False)

Cervical dystonia estimation:

norm_like = lambda theta, x: -np.log(norm.pdf(x, theta[0], theta[1])).sum()

fmin(norm_like, np.array([1,2]), args=(cdystonia.twstrs[(cdystonia.obs==6) & (cdystonia.treat=='Placebo')],))
fmin(norm_like, np.array([1,2]), args=(cdystonia.twstrs[(cdystonia.obs==6) & (cdystonia.treat=='5000U')],))

Cervical dystonia bootstrapping:

x = cdystonia.twstrs[(cdystonia.obs==6) & (cdystonia.treat=='Placebo') & (cdystonia.twstrs.notnull())].values
n = len(x)
s = [x[np.random.randint(0,n,n)].mean() for i in range(R)]
placebo_mean = np.sum(s)/R

s_sorted = np.sort(s)
alpha = 0.05
s_sorted[[(R+1)*alpha/2, (R+1)*(1-alpha/2)]]