pstatstools 0.0.5

Statistical tools package

pstatstools

A Python package providing a convenient Sample class for easy statistical analysis and visualization of numerical data.

Overview

pstatstools simplifies common statistical tasks by wrapping numerical data (like lists or NumPy arrays) into a Sample object. This object exposes numerous methods for:

• Calculating descriptive statistics.
• Computing confidence intervals.
• Performing hypothesis tests (t-tests, ANOVA).
• Analyzing correlation and performing linear regression.
• Utilizing bootstrap resampling techniques.
• Conducting power analysis.
• Handling data (outlier detection, transformations).
• Generating various statistical plots using Matplotlib.

Installation

You can install pstatstools using pip (once published). Make sure you have the required dependencies installed.

pip install pstatstools

Alternatively, if you have the source code locally, you can install it directly:

pip install .

This will also install the necessary dependencies listed below.

Dependencies

• Python >= 3.6
• NumPy
• SciPy (scipy.stats)
• Matplotlib
• Statsmodels (statsmodels.api, statsmodels.formula.api, statsmodels.stats.power)
• Pandas (used internally for some methods like correlation_matrix and describe formatting)

Basic Usage

Import the sample factory function (assuming it's exposed in your package's __init__.py or directly from the module file) and create a Sample object:

import numpy as np
from pstatstools import sample # Adjust import based on your package structure
import matplotlib.pyplot as plt # Needed for showing plots

# Sample data
data1 = np.random.normal(loc=10, scale=2, size=100)
data2 = np.random.normal(loc=11, scale=2.5, size=120)

# Create Sample objects
s1 = sample(data1)
s2 = sample(data2)

# --- Descriptive Statistics ---
print(f"Mean of s1: {s1.mean():.4f}")
print(f"Standard Deviation of s1: {s1.std():.4f}")
print(f"Median of s1: {s1.median():.4f}")

# Get a formatted summary (prints a nice table, especially in Jupyter)
s1.describe()

# --- Confidence Intervals ---
ci_mean = s1.ci(confidence=0.99)
print(f"\n99% CI for Mean (s1): ({ci_mean[0]:.4f}, {ci_mean[1]:.4f})")

ci_std = s1.ci_std()
print(f"95% CI for Std Dev (s1): ({ci_std[0]:.4f}, {ci_std[1]:.4f})")

# --- Hypothesis Testing ---
# One-sample t-test against a hypothesized mean of 9.5
ttest_result = s1.t_test(popmean=9.5, tail="two")
print("\nOne-Sample T-Test Result (s1 vs 9.5):")
for key, value in ttest_result.items():
     print(f"  {key}: {value}")

# Welch's t-test to compare means of s1 and s2
welch_result = s1.welch_test(s2, tail="two")
print("\nWelch's T-Test Result (s1 vs s2):")
for key, value in welch_result.items():
     print(f"  {key}: {value}")

# Normality Test
norm_test = s1.normality_test(test='shapiro')
print(f"\nNormality Test (s1): {norm_test['conclusion']} (p={norm_test['p_value']:.4f})")


# --- Visualization ---
# Note: Plotting methods return the matplotlib pyplot object (`plt`)
# Call plt.show() after generating plots to display them.

# Histogram
s1.histogram(bins=12, title="Histogram of Sample 1")
plt.show()

# Box Plot
s1.boxplot(title="Box Plot of Sample 1")
plt.show()

# Q-Q Plot
s1.qq_plot()
plt.show()

# Scatter Plot (requires data of the same length)
s1_short = sample(data1[:50])
s2_short = sample(data2[:50])
s1_short.scatter(s2_short, xlabel="Sample 1 Values", ylabel="Sample 2 Values", title="Scatter Plot")
plt.show()

# Regression Plot
s1_short.regression_plot(s2_short, xlabel="Predictor (s1)", ylabel="Response (s2)")
plt.show()

Detailed Usage Examples

Descriptive Statistics

import numpy as np
from pstatstools import sample
import matplotlib.pyplot as plt

# Create a sample with some data
data = [23, 25, 21, 24, 29, 22, 20, 25, 24, 27, 28, 23, 24, 25, 26]
s = sample(data)

# Basic statistics
print(f"Mean: {s.mean()}")
print(f"Median: {s.median()}")
print(f"Mode: {s.mode()}")
print(f"Standard Deviation: {s.std()}")
print(f"Variance: {s.var()}")
print(f"Standard Error of the Mean: {s.sem()}")
print(f"Min: {s.min()}")
print(f"Max: {s.max()}")
print(f"Range: {s.range()}")

# Detailed description with all statistics
stats = s.describe(print_table=True, round_to=3)

# Just want quartiles?
q1, q2, q3 = s.quartiles()
print(f"Q1: {q1}, Q2 (median): {q2}, Q3: {q3}")
print(f"Interquartile Range (IQR): {s.iqr()}")

# Distribution shape
print(f"Skewness: {s.skewness()}")
print(f"Kurtosis: {s.kurtosis()}")
print(f"Coefficient of Variation: {s.coef_variation()}")

Confidence Intervals

# Continuing with the same sample
# Confidence interval for the mean (default 95%)
lower, upper = s.ci()
print(f"95% CI for Mean: ({lower:.2f}, {upper:.2f})")

# 99% confidence interval for the mean
lower, upper = s.ci(confidence=0.99)
print(f"99% CI for Mean: ({lower:.2f}, {upper:.2f})")

# Confidence interval for the standard deviation
lower, upper = s.ci_std()
print(f"95% CI for Standard Deviation: ({lower:.2f}, {upper:.2f})")

# Confidence interval for the variance
lower, upper = s.ci_var()
print(f"95% CI for Variance: ({lower:.2f}, {upper:.2f})")

# Confidence interval for the median
lower, upper = s.ci_median()
print(f"95% CI for Median: ({lower:.2f}, {upper:.2f})")

Hypothesis Testing

# One-sample t-test (testing if mean equals 20)
result = s.t_test(popmean=20)
print("\nOne-sample t-test (H0: mean = 20):")
print(f"t-statistic: {result['t_statistic']:.4f}")
print(f"p-value: {result['p_value']:.4f}")
print(f"Conclusion: {result['conclusion']}")

# Two-sample test (comparing with another sample)
data2 = [19, 18, 22, 20, 23, 21, 20, 19, 21, 20]
s2 = sample(data2)

# Welch's t-test (does not assume equal variances)
result = s.welch_test(s2)
print("\nWelch's t-test (comparing two samples):")
print(f"t-statistic: {result['t_statistic']:.4f}")
print(f"p-value: {result['p_value']:.4f}")
print(f"Conclusion: {result['conclusion']}")
print(f"Mean Difference: {result['mean_difference']:.4f}")
print(f"95% CI for Mean Difference: ({result['ci_lower']:.4f}, {result['ci_upper']:.4f})")

# One-way ANOVA with multiple samples
data3 = [24, 25, 26, 27, 25, 24, 26, 25]
data4 = [20, 21, 22, 23, 20, 21, 22]
s3 = sample(data3)
s4 = sample(data4)

result = s.anova(s2, s3, s4)
print("\nOne-way ANOVA (comparing four samples):")
print(f"F-statistic: {result['f_statistic']:.4f}")
print(f"p-value: {result['p_value']:.4f}")
print(f"Conclusion: {result['conclusion']}")

# Normality test
result = s.normality_test(test='shapiro')
print("\nShapiro-Wilk Normality Test:")
print(f"Test Statistic: {result['statistic']:.4f}")
print(f"p-value: {result['p_value']:.4f}")
print(f"Conclusion: {result['conclusion']}")

Correlation and Regression

# Assuming we have two related samples of the same length
x_data = [5, 7, 8, 7, 2, 17, 2, 9, 4, 11, 12, 9, 6]
y_data = [99, 86, 87, 88, 111, 86, 103, 87, 94, 78, 77, 85, 86]

x = sample(x_data)
y = sample(y_data)

# Calculate Pearson correlation coefficient
corr = x.correlation(y)
print(f"Pearson correlation: {corr['r']:.4f}")
print(f"R-squared: {corr['r_squared']:.4f}")
print(f"p-value: {corr['p_value']:.4f}")
print(f"Conclusion: {corr['conclusion']}")

# Calculate covariance
cov = x.covariance(y)
print(f"Covariance: {cov:.4f}")

# Simple linear regression with x as predictor and y as response
reg = x.linear_regression(y)
model = reg['model']
print("\nLinear Regression Summary:")
print(f"Intercept: {reg['intercept']:.4f}")
print(f"Slope: {reg['slope']:.4f}")
print(f"Model: y = {reg['intercept']:.4f} + {reg['slope']:.4f}x")
print(f"R-squared: {reg['r_squared']:.4f}")
print(f"F-statistic: {reg['f_statistic']:.4f}")
print(f"p-value: {reg['p_value']:.4f}")

# Print the full model summary from statsmodels
print("\nDetailed Regression Summary:")
print(reg['summary'])

# Use the model to predict y when x = 10
prediction = x.linear_prediction(y, x_value=10)
print(f"\nPredicted y when x = 10: {prediction['prediction']:.4f}")
print(f"95% CI for mean response: ({prediction['ci_mean_lower']:.4f}, {prediction['ci_mean_upper']:.4f})")
print(f"95% PI for new observation: ({prediction['pi_lower']:.4f}, {prediction['pi_upper']:.4f})")

# Create a correlation matrix with multiple samples
z_data = [65, 78, 81, 80, 95, 77, 93, 84, 85, 70, 69, 83, 81]
z = sample(z_data)
corr_matrix = x.correlation_matrix(y, z, labels=['Age', 'Score', 'Weight'])
print("\nCorrelation Matrix:")
print(corr_matrix)

# Multiple regression with x and z as predictors, y as response
multi_reg = x.multiple_regression(y, z)
print("\nMultiple Regression Summary:")
print(f"R-squared: {multi_reg['r_squared']:.4f}")
print(f"Adjusted R-squared: {multi_reg['adj_r_squared']:.4f}")
print(f"F-statistic: {multi_reg['f_statistic']:.4f}")
print(f"p-value: {multi_reg['p_value']:.4f}")
print(multi_reg['summary'])

Bootstrap Methods

# Using the bootstrap for robust statistics
data = [23, 25, 21, 24, 29, 22, 20, 25, 24, 27, 28, 23, 24, 25, 26, 19, 32, 36, 18, 40]
s = sample(data)

# Generate 1000 bootstrap samples and compute their means
bootstrap_means = s.bootstrap(n_samples=1000, statistic=np.mean)
print(f"Original sample mean: {s.mean():.4f}")
print(f"Bootstrap mean: {np.mean(bootstrap_means):.4f}")
print(f"Bootstrap standard error: {np.std(bootstrap_means):.4f}")

# Calculate 95% bootstrap confidence interval for the mean
lower, upper = s.bootstrap_ci(confidence=0.95, n_samples=1000, statistic=np.mean)
print(f"95% Bootstrap CI for mean: ({lower:.4f}, {upper:.4f})")

# Non-parametric bootstrap for multiple statistics
results = s.nonparametric_bootstrap(n_samples=1000)
print("\nNon-parametric bootstrap 95% CIs:")
print(f"Mean: ({results['mean_ci'][0]:.4f}, {results['mean_ci'][1]:.4f})")
print(f"Median: ({results['median_ci'][0]:.4f}, {results['median_ci'][1]:.4f})")
print(f"Std Dev: ({results['std_ci'][0]:.4f}, {results['std_ci'][1]:.4f})")

# Parametric bootstrap (assuming normal distribution)
results = s.parametric_bootstrap(distribution='normal', n_samples=1000)
print("\nParametric bootstrap (normal) 95% CIs:")
print(f"Mean: ({results['mean_ci'][0]:.4f}, {results['mean_ci'][1]:.4f})")
print(f"Median: ({results['median_ci'][0]:.4f}, {results['median_ci'][1]:.4f})")
print(f"Std Dev: ({results['std_ci'][0]:.4f}, {results['std_ci'][1]:.4f})")

Power Analysis

# Sample data
data = np.random.normal(loc=5, scale=2, size=30)
s = sample(data)

# Calculate required sample size for detecting an effect size of 0.5
# with 80% power and alpha=0.05
sample_size = s.power_analysis(effect_size=0.5, alpha=0.05, power=0.8)
print(f"Required sample size to detect effect size 0.5 with 80% power: {sample_size:.0f}")

# Calculate the power of a study with n=50 to detect an effect size of 0.4
power = s.calculate_power(n=50, effect_size=0.4)
print(f"Power to detect effect size 0.4 with n=50: {power:.4f}")

# Calculate the minimum detectable effect size with current sample
effect = s.calculate_detectable_effect(power=0.8)
print(f"Minimum detectable effect size with n={len(s)} and 80% power: {effect:.4f}")

# Sample size for two-group study with unequal allocation (ratio 2:1)
n1, n2 = s.calculate_sample_size(effect_size=0.5, power=0.8, ratio=2.0)
print(f"Required sample sizes (2:1 ratio): n1={n1:.0f}, n2={n2:.0f}")

Data Handling & Transformations

# Create a sample with outliers
data = [23, 25, 21, 24, 29, 22, 20, 25, 24, 27, 28, 50, 24, 25, 26, 19, 65]
s = sample(data)

# Calculate z-scores
z_scores = s.z_scores()
print("Z-scores for each data point:")
for i, z in enumerate(z_scores):
    print(f"Data point {i+1} ({s[i]}): z-score = {z:.2f}")

# Identify outliers (using IQR method)
outliers = s.is_outlier(threshold=1.5)
print("\nOutliers in the dataset:")
for i, is_out in enumerate(outliers):
    if is_out:
        print(f"Data point {i+1}: {s[i]} (outlier)")

# Create a new sample with outliers removed
s_clean = s.remove_outliers(threshold=1.5)
print(f"\nOriginal sample size: {len(s)}")
print(f"Cleaned sample size: {len(s_clean)}")
print(f"Original mean: {s.mean():.2f}")
print(f"Cleaned mean: {s_clean.mean():.2f}")
print(f"Original standard deviation: {s.std():.2f}")
print(f"Cleaned standard deviation: {s_clean.std():.2f}")

Visualization

import matplotlib.pyplot as plt

# Create a sample
data = np.random.normal(loc=25, scale=5, size=100)
s = sample(data)

# Histogram with overlaid normal curve
s.histogram(bins=15, density=True, title="Histogram with Normal Curve", 
            show_norm=True, show_mean=True, show_median=True)
plt.show()

# Box plot
s.boxplot(title="Box Plot", vert=True, patch_artist=True)
plt.show()

# QQ plot to check normality
s.qq_plot()
plt.show()

# Wally plot - multiple QQ plots, one with real data
# Useful for visually detecting non-normality
s.wally_plot()
plt.show()

# Empirical Cumulative Distribution Function
s.ecdf(title="Empirical CDF")
plt.show()

# Create two samples for correlation/regression plots
x_data = np.random.uniform(0, 10, 50)
y_data = 2*x_data + 3 + np.random.normal(0, 2, 50)
x = sample(x_data)
y = sample(y_data)

# Simple scatter plot
x.scatter(y, title="Scatter Plot", xlabel="X Variable", ylabel="Y Variable")
plt.show()

# Regression plot with confidence and prediction intervals
x.regression_plot(y, title="Regression Analysis", xlabel="X", ylabel="Y")
plt.show()

# Create multiple samples for pairs plot
z_data = x_data * 0.5 + y_data * 0.3 + np.random.normal(0, 1, 50)
w_data = x_data * -0.3 + y_data * 0.7 + np.random.normal(0, 1.5, 50)
z = sample(z_data)
w = sample(w_data)

# Pairs plot (scatter plot matrix with KDEs on diagonal)
x.pairs_plot([y, z, w], labels=["X", "Y", "Z", "W"])
plt.show()

Features / API Reference

The Sample class provides a wide range of statistical methods, accessible directly from the Sample object instance (s = sample(data)):

1. Descriptive Statistics:

• s.mean(), s.median(), s.mode()
• s.std(ddof=1), s.var(ddof=1), s.sem() (Standard Error of Mean)
• s.quantile(q), s.quartiles(), s.iqr() (Interquartile Range)
• s.skewness(), s.kurtosis() (excess kurtosis)
• s.min(), s.max(), s.range()
• s.coef_variation(): Coefficient of Variation (std/mean).
• s.describe(print_table=True, round_to=4): Provides a comprehensive formatted summary table (text or HTML in notebooks). Returns a dictionary of stats.

2. Confidence Intervals:

• s.ci(confidence=0.95): CI for the mean (using t-distribution).
• s.ci_std(confidence=0.95): CI for the standard deviation (using Chi-squared).
• s.ci_var(confidence=0.95): CI for the variance.
• s.ci_median(confidence=0.95): CI for the median (using log transformation approximation).

3. Hypothesis Testing:

• s.t_test(popmean=0, alpha=0.05, tail="two"): One-sample t-test. Returns a dictionary with results.
• s.welch_test(other_sample, alpha=0.05, tail="two"): Two-sample Welch's t-test (unequal variances assumed). Compares s with other_sample. Returns a dictionary.
• s.anova(*other_samples): One-way ANOVA comparing s with one or more other_samples. Returns a dictionary.
• s.normality_test(test='shapiro', alpha=0.05): Shapiro-Wilk ('shapiro') or Kolmogorov-Smirnov ('ks') test for normality. Returns a dictionary.

4. Correlation and Regression:

• s.correlation(other_sample): Pearson correlation coefficient, R-squared, and p-value between s and other_sample. Requires equal lengths. Returns a dictionary.
• s.covariance(other_sample): Covariance between s and other_sample. Requires equal lengths.
• s.correlation_matrix(*other_samples, labels=None): Correlation matrix (Pandas DataFrame) for s and other_samples. Requires equal lengths.
• s.linear_regression(y_sample, alpha=0.05): Simple linear regression with s as predictor (x) and y_sample as response (y). Returns a dictionary including the fitted statsmodels model object and summary. Requires equal lengths.
• s.linear_prediction(y_sample, x_value, alpha=0.05): Make prediction at x_value based on simple linear regression model (s ~ y_sample). Returns predicted value and intervals. Requires equal lengths.
• s.multiple_regression(y_sample, *other_x_samples, alpha=0.05): Multiple linear regression with s and other_x_samples as predictors, y_sample as response. Returns a dictionary including the fitted statsmodels model. Requires equal lengths.

5. Bootstrap Methods:

• s.bootstrap(n_samples=1000, statistic=np.mean): Generate n_samples bootstrap estimates of the given statistic (default: mean). Returns an array of the bootstrap statistics.
• s.bootstrap_ci(confidence=0.95, n_samples=1000, statistic=np.mean): Calculate bootstrap confidence interval using the percentile method for the given statistic. Returns (lower_bound, upper_bound).
• s.parametric_bootstrap(distribution='normal', n_samples=1000, alpha=0.05): Parametric bootstrap CIs assuming 'normal', 'lognormal', or 'exponential' distribution. Returns a dictionary of CIs for mean, median, std.
• s.nonparametric_bootstrap(n_samples=1000, alpha=0.05): Non-parametric bootstrap CIs (resampling from data) for mean, median, std. Returns a dictionary.

6. Power Analysis (using statsmodels):

• s.power_analysis(effect_size=None, alpha=0.05, power=0.8): Calculate required sample size for a one-sample t-test to achieve desired power for a given effect_size (Cohen's d) and alpha. If effect_size is None, it's estimated from the sample mean relative to 0 and the sample std dev.
• s.calculate_power(n=None, effect_size=0.5, alpha=0.05, ratio=1.0): Calculate statistical power for a two-sample independent t-test given sample size n (for group 1), effect_size, alpha, and sample size ratio (n2/n1). Uses sample size of s if n is None.
• s.calculate_sample_size(effect_size=0.5, alpha=0.05, power=0.8, ratio=1.0): Calculate required sample size per group for a two-sample independent t-test for given effect_size, alpha, power, and sample size ratio. Returns (n1, n2).
• s.calculate_detectable_effect(n=None, alpha=0.05, power=0.8, ratio=1.0): Calculate the minimum detectable effect size (Cohen's d) for a two-sample independent t-test given sample size n (for group 1), alpha, power, and ratio. Uses sample size of s if n is None.

7. Data Handling & Transformations:

• s.z_scores(): Calculate standard scores (z-scores) for each data point. Returns a NumPy array.
• s.is_outlier(threshold=1.5): Identify outliers using the IQR method (Q3 + threshold*IQR or Q1 - threshold*IQR). Returns a boolean NumPy array.
• s.remove_outliers(threshold=1.5): Return a new Sample object with outliers (identified by is_outlier) removed.

8. Visualization (using Matplotlib & Statsmodels):

• s.histogram(bins=10, **kwargs): Plots a histogram. Includes lines for mean and median.
• s.boxplot(**kwargs): Creates a box plot.
• s.qq_plot(): Creates a Q-Q plot against a normal distribution using statsmodels.
• s.wally_plot(): Creates a Wally plot (multiple Q-Q plots, one with real data) for visually assessing normality.
• s.scatter(other_sample, inverted=False, add_line=True, **kwargs): Creates a scatter plot between s (x-axis by default) and other_sample (y-axis by default). inverted=True swaps axes. Adds a linear regression line by default. Requires equal lengths.
• s.regression_plot(y_sample, alpha=0.05, **kwargs): Creates a scatter plot with regression line, confidence interval band, and prediction interval band using statsmodels. Requires equal lengths.
• s.ecdf(**kwargs): Plots the empirical cumulative distribution function (ECDF).
• s.pairs_plot(others, labels=None): Creates a pairs plot (scatter plot matrix with KDE on diagonal) for s and a list of others samples/arrays. Requires Pandas and equal lengths.

9. Utility Methods:

• s.summary(): Prints a text-based comprehensive summary including size, mean, CI, median, std dev, range, quartiles, IQR, skewness, kurtosis, and normality test results.
• len(s): Returns the number of data points in the sample.
• s[index]: Allows accessing data points using indexing or slicing.
• print(s): Shows the underlying NumPy array representation.

Note on Plots: All plotting methods return the matplotlib.pyplot module object (plt), allowing for further customization (e.g., saving the figure with plt.savefig('filename.png')) before or after calling plt.show().

Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue on the repository (if applicable).

License

Distributed under the terms of the MIT License.