SciPy — Statistics

Descriptive statistics

from scipy import stats
import numpy as np

data = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])

print(f"Mean: {stats.tmean(data):.2f}")
print(f"Variance: {stats.tvar(data):.2f}")
print(f"Skew: {stats.skew(data):.2f}")
print(f"Kurtosis: {stats.kurtosis(data):.2f}")

Distributions

from scipy import stats

# Normal distribution
normal = stats.norm(loc=0, scale=1)
print(f"PDF at 0: {normal.pdf(0):.4f}")
print(f"CDF at 1: {normal.cdf(1):.4f}")

# Generate random samples
samples = normal.rvs(size=1000)

Hypothesis testing

from scipy import stats
import numpy as np

# T-test
group1 = np.random.normal(0, 1, 100)
group2 = np.random.normal(0.5, 1, 100)

t_stat, p_value = stats.ttest_ind(group1, group2)
print(f"T-statistic: {t_stat:.4f}")
print(f"P-value: {p_value:.4f}")

Chi-square test

observed = [10, 20, 30, 40]
expected = [25, 25, 25, 25]

chi2, p_value = stats.chisquare(observed, expected)
print(f"Chi-square: {chi2:.4f}")
print(f"P-value: {p_value:.4f}")

Correlation

from scipy import stats
import numpy as np

x = np.array([1, 2, 3, 4, 5])
y = np.array([2, 4, 5, 4, 5])

# Pearson correlation
r, p_value = stats.pearsonr(x, y)
print(f"Pearson r: {r:.4f}")

# Spearman correlation
rho, p_value = stats.spearmanr(x, y)
print(f"Spearman rho: {rho:.4f}")

Linear regression

from scipy import stats
import numpy as np

x = np.array([1, 2, 3, 4, 5])
y = np.array([2, 4, 5, 4, 5])

slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
print(f"Slope: {slope:.4f}")
print(f"Intercept: {intercept:.4f}")

Mini Practice

1. Calculate descriptive statistics
2. Fit a distribution to data
3. Perform hypothesis testing
4. Compute correlation

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Continue with Spatial Data - Spatial algorithms.