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SciPy

1 Sample T-Test

from scipy.stats import ttest_1samp

tstat, pval = ttest_1samp(example_distribution, expected_mean)
-> Generates two outputs
-> tstat:t-statistic
-> pval: p-value

2 Sample T-Test

from scipy.stats import ttest_ind

tstat, pval = ttest_ind(data1, data2)

ANOVA

from scipy.stats import f_oneway

fstat, pval = f_oneway(data1, data2, data3)
 

Tukey's Range Test (not SciPy)

from statsmodels.stats.multicomp import pairwise_tukeyhsd

# All Data has to be unioned to one List
movie_scores = np.concatenate([drama_scores, comedy_scores, documentary_scores])
labels = ['drama'] * len(drama_scores) + ['comedy'] * len(comedy_scores) + ['documentary'] * len(documentary_scores)

tukey_results = pairwise_tukeyhsd(movie_scores, labels, 0.05)
-> 0.05 represents the significance level

Binomial Test

from scipy.stats import binom_test

pval = binom_test(successes, n, p)

successes: Number observed successes
n: Number of Trials
p: Expected probability for success

-> if pval < 0.05 we can reject the Null Hypothesis

Chi Square Test

from scipy.stats import chi2_contingency

_, pval, _, _ = chi2_contingency(iron_contingency_table)

Point Distance Functions

Import:
from scipy.s­patial import distance
Euclidean
dist­anc­e.e­ucl­ide­an(pt1, pt2)
Manhattan
dist­anc­e.c­ity­blo­ck(pt1, pt2)
Hamming
dist­anc­e.h­amm­ing­(pt1, pt2)
Manh­attan Distan­ce: like calcul­ating how many blocks are between two points

Hamming Distan­ce: will always return a number between 0 and 1
--> The Hamming distance between [1, 2, 3] and [7, 2, -10] would be 2. In scipy‘s version, it would be 2/3
   

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