Quick Start

Getting started with DataSafari is straightforward.

Install it using pip in your terminal:

pip install datasafari

Or install it using Poetry:

poetry add datasafari

Import DataSafari in your Python script:

import datasafari as ds

For detailed installation options, including installing from the source, see the Installation Guide.


Hypothesis Testing? One line.

from datasafari.predictor import predict_hypothesis
import pandas as pd
import numpy as np

# Create a sample DataFrame
df_hypothesis = pd.DataFrame({
    'Group': np.random.choice(['Control', 'Treatment'], size=100),
    'Score': np.random.normal(0, 1, 100)
})

# Perform hypothesis testing
results = predict_hypothesis(df_hypothesis, 'Group', 'Score')

How DataSafari Streamlines Hypothesis Testing:

  • Automatic Test Selection: Depending on the data types, predict_hypothesis() automatically selects the appropriate test. It uses Chi-square, Fisher’s exact test or other exact tests for categorical pairs, and T-tests, ANOVA and others for categorical and numerical combinations, adapting based on group counts, sample size and data distribution.

  • Assumption Verification: Essential assumptions for the chosen tests are automatically checked.

    • Normality: Normality is verified using tests like Shapiro-Wilk or Anderson-Darling, essential for parametric tests.

    • Variance Homogeneity: Tests such as Levene’s or Bartlett’s are used to confirm equal variances, informing the choice between ANOVA types.

  • Comprehensive Output:

    • Justifications: Provides comprehensive reasoning on all test choices.

    • Test Statistics: Key quantitative results from the hypothesis test.

    • P-values: Indicators of the statistical significance of the findings.

    • Conclusions: Clear textual interpretations of whether the results support or reject the hypothesis.


Machine Learning? You guessed it.

from datasafari.predictor import predict_ml
import pandas as pd
import numpy as np

# Create another sample DataFrame for ML
df_ml = pd.DataFrame({
    'Age': np.random.randint(20, 60, size=100),
    'Salary': np.random.normal(50000, 15000, size=100),
    'Experience': np.random.randint(1, 20, size=100)
})
x_cols = ['Age', 'Experience']  # Feature columns
y_col = 'Salary'  # Target column

# Find the best models for your data
best_models = predict_ml(df_ml, x_cols, y_col)

How DataSafari Simplifies Machine Learning Model Selection:

  • Tailored Data Preprocessing: The function automatically processes various types of data (numerical, categorical, text, datetime), preparing them optimally for machine learning.

    • Numerical data might be scaled or normalized.

    • Categorical data can be encoded.

    • Text data might be vectorized using techniques suitable for the analysis.

  • Intelligent Model Evaluation: The function evaluates a variety of models using a composite score that synthesizes performance across multiple metrics, taking into account the multidimensional aspects of model performance.

    • Composite Score Calculation: Scores for each metric are weighted according to specified priorities by the user, with lower weights assigned to non-priority metrics (e.g. RMSE over MAE). This composite score serves as a holistic measure of model performance, ensuring that the models recommended are not just good in one aspect but are robust across multiple criteria.

  • Automated Hyperparameter Tuning: Once the top models are identified based on the composite score, the pipeline employs techniques like grid search, random search, or Bayesian optimization to fine-tune the models.

    • Output of Tuned Models: The best configurations for the models are output, along with their performance metrics, allowing users to make informed decisions about which models to deploy based on robust, empirically derived data.

  • Customization Options & Sensible Defaults: Users can define custom hyperparameter grids, select specific tuning algorithms, prioritize models, tailor data preprocessing, and prioritize metrics.

    • Accessibility: Every part of the process is in the hands of the user, but sensible defaults are provided for ultimate simplicity of use, which is the approach for datasafari in general.