datalib-genta 0.1.1

A Python library for data manipulation, statistical analysis, and machine learning.

Here’s how we can enhance the README for the DataLib project with some usage examples for each feature, so users can easily understand how to use the library's functions.

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DataLib

DataLib is a Python library for data manipulation, statistical analysis, and machine learning.

Features

• Data preprocessing (normalization, handling missing values)
• Statistical calculations (mean, median, mode)
• Data visualization (histograms, scatter plots)
• Regression and classification analysis (linear regression, k-means clustering, PCA)

Installation

To install DataLib, use the following command:

pip install datalib_genta

Usage

1. Data Preprocessing

Normalize Data:

Normalize your data to scale features to a range.

from datalib.preprocessing.transformations import normalize_data
import numpy as np

# Sample data
X = np.array([[1, 2], [3, 4], [5, 6]])

# Normalize the data
X_normalized = normalize_data(X)
print(X_normalized)

Handle Missing Data:

Fill missing values in the dataset.

from datalib.preprocessing.transformations import handle_missing_values
import numpy as np

# Sample data with missing values
X = np.array([[1, 2], [np.nan, 4], [5, np.nan]])

# Fill missing values with column mean
X_filled = handle_missing_values(X, method='mean')
print(X_filled)

2. Statistical Calculations

Calculate Mean, Median, and Mode:

Calculate basic statistics of a dataset.

from datalib.statistics.basic_stats import calculate_mean, calculate_median, calculate_mode
import numpy as np

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

# Mean
mean = calculate_mean(data)
print(f"Mean: {mean}")

# Median
median = calculate_median(data)
print(f"Median: {median}")

# Mode
mode = calculate_mode(data)
print(f"Mode: {mode}")

Statistical Tests:

Perform basic statistical tests like t-tests and chi-square tests.

from datalib.statistics.statistical_tests import t_test
import numpy as np

# Sample data
group1 = np.array([1, 2, 3, 4])
group2 = np.array([5, 6, 7, 8])

# Perform a t-test
t_stat, p_value = t_test(group1, group2)
print(f"T-statistic: {t_stat}, P-value: {p_value}")

3. Data Visualization

Plot Histograms:

Visualize the distribution of data using histograms.

from datalib.visualization.charts import plot_histogram
import numpy as np

# Sample data
data = np.array([1, 2, 2, 3, 3, 3, 4, 5])

# Plot histogram
plot_histogram(data, bins=5)

Plot Scatter Plots:

Create scatter plots to visualize relationships between variables.

from datalib.visualization.charts import plot_scatter
import numpy as np

# Sample data
X = np.array([1, 2, 3, 4])
y = np.array([10, 20, 30, 40])

# Plot scatter plot
plot_scatter(X, y)

4. Regression and Classification

K-Means Clustering:

Perform K-Means clustering on a dataset.

from datalib.analysis.clustering import k_means_clustering
import numpy as np

# Sample data
X = np.array([[0.1, 0.2], [0.3, 0.4], [0.5, 0.6], [0.7, 0.8], [0.9, 1.]])

# Perform K-Means clustering
model = k_means_clustering(X, 2)

# Cluster centers
print("Cluster centers:", model.cluster_centers_)

Principal Component Analysis (PCA):

Reduce the dimensionality of data using PCA.

from datalib.analysis.clustering import pca_analysis
import numpy as np

# Sample data
X = np.array([[0.1, 0.2], [0.3, 0.4], [0.5, 0.6], [0.7, 0.8], [0.9, 1.]])

# Perform PCA
X_pca, variance_ratio = pca_analysis(X, 1)

# Results
print("Reduced Data:", X_pca)
print("Variance Ratio:", variance_ratio)

Linear Regression:

Perform linear regression on a dataset.

from datalib.analysis.clustering import linear_regression
import numpy as np

# Sample data
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([1, 2, 3, 4, 5])

# Perform linear regression
model = linear_regression(X, y)

# Predictions
predictions = model.predict(X)
print(f"Predictions: {predictions}")

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This updated README provides clear examples on how to use various functions within the DataLib library, helping users get started quickly. You can adapt this further based on your users' needs.