Add new functions

vizbeauty/common.py

@@ -1,7 +1,122 @@
 """The common module contains common functions and classes used by the other modules.
 """
 
-def hello_world():
-    """Prints "Hello World!" to the console.
+import numpy as np
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+from scipy.stats import pearsonr
+
+def print_statistic(title, variable):
+    """
+    Prints descriptive statistics for a given variable.
+
+    Parameters:
+    - title (str): Title or label for the variable.
+    - variable (Series): Pandas Series containing the variable data.
+
+    Returns:
+    - None
+    """
+    print(f"Statistics for {title}:")
+    print("++++++++++++++++++++++++++++++++++++++++++++++++++++++")
+    print(f"Mean: {round(variable.mean(), 2)}")
+    print(f"Median: {round(variable.median(), 2)}")
+    print(f"Standard Deviation: {round(variable.std(), 2)}")
+    print(f"Minimum: {round(variable.min(), 2)}")
+    print(f"Maximum: {round(variable.max(), 2)}")
+    print(f"25th Percentile (Q1): {round(np.percentile(variable, 25), 2)}")
+    print(f"75th Percentile (Q3): {round(np.percentile(variable, 75), 2)}")
+    print(f"Skewness: {round(variable.skew(), 2)}")
+    print(f"Kurtosis: {round(variable.kurtosis(), 2)}")
+    print(f"Count of Missing Values: {variable.isnull().sum()}")
+    print("++++++++++++++++++++++++++++++++++++++++++++++++++++++")
+    print()
+
+def beautybar(x, y, data, data_avg, color="skyblue", ax=None):
+    """
+    Create a bar plot with annotations for each bar and a horizontal line indicating the general average value
+    of the variable plotted on the y-axis.
+
+    Parameters:
+        x (str): The name of the variable to be plotted on the x-axis.
+        y (str): The name of the variable to be plotted on the y-axis.
+        data (DataFrame): The dataframe containing the data to be plotted.
+        data_avg (DataFrame): The dataframe containing the average values for the y variable.
+        color (str, optional): The color of the bars. Defaults to "skyblue".
+        ax (matplotlib.axes.Axes, optional): The axes to plot on. If not provided, a new figure will be created.
+    """
+    if ax is None:
+        ax = plt.gca()
+
+    sns.barplot(x=x, y=y, data=data, color=color, ax=ax)
+
+    for index, value in enumerate(data[y]):
+        ax.text(index, value + 0.2, str(round(value, 2)), ha='center', va='bottom')
+
+    avg_variable = data_avg[y].mean()
+
+    ax.axhline(y=avg_variable, color='r', linestyle='--')
+    ax.text(9.5, avg_variable + 0.2, f'Average: {round(avg_variable, 2)}', color='red')
+
+    ax.set_xlabel(x)
+    ax.set_ylabel(y)
+    ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')
+    ax.grid(axis='y', linestyle='--')
+    plt.tight_layout()
+
+def pearson_correlation(x, y):
+    """
+    Compute the Pearson correlation coefficient for two variables and determine if there is a statistically significant correlation.
+
+    Parameters:
+    - x (Series): First variable for correlation.
+    - y (Series): Second variable for correlation.
+
+    Returns:
+    - None
+    """
+    correlation_coefficient, p_value = pearsonr(x, y)
+    alpha = 0.05
+
+    print("Pearson Correlation Coefficient:", correlation_coefficient)
+    print("P-value:", p_value)
+
+    if p_value < alpha:
+        print(f"There is a statistically significant correlation between {x.name} and {y.name}.")
+    else:
+        print(f"There is no statistically significant correlation between {x.name} and {y.name}.")
+
+def reg_scatter(x, y, data, hue=None, legend="auto", size=None, sizes=None, ax=None):
+    """
+    Create a scatter plot with a regression line.
+
+    Parameters:
+        x (str): The name of the variable to be plotted on the x-axis.
+        y (str): The name of the variable to be plotted on the y-axis.
+        data (DataFrame): The dataframe containing the data to be plotted.
+        hue (str, optional): Variable in data to map plot aspects to different colors.
+        legend ({"auto", "brief", "full"}, optional): How to draw the legend. Defaults to "auto".
+        size (str, optional): Grouping variable that will produce points with different sizes.
+        sizes (tuple, optional): Tuple of minimum and maximum size values to scale the size variable.
+        ax (matplotlib axes, optional): Axes object to draw the plot onto.
+    """
+    sns.scatterplot(x=x, y=y, data=data, ax=ax, hue=hue, legend=legend, size=size, sizes=sizes)
+    sns.regplot(x=x, y=y, data=data, ax=ax, scatter=False, ci=None, line_kws={"color": "black"})
+
+def visualize_hyperparameter(param_name, param_values, scores):
+    """
+    Visualize the effect of a hyperparameter on model performance.
+
+    Parameters:
+        param_name (str): Name of the hyperparameter.
+        param_values (list): List of values for the hyperparameter.
+        scores (list): List of mean squared error scores corresponding to each hyperparameter value.
     """
-    print("Hello World!")
+    plt.figure(figsize=(10, 6))
+    plt.scatter(param_values, scores, marker='o')
+    plt.title(f"Effect of {param_name} on Model Performance")
+    plt.xlabel(param_name)
+    plt.ylabel("Mean Squared Error")
+    plt.grid(True)
+    plt.show()