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Data science

Fundamentals of Data Analysis with Python

7 min read, 13 sections

Contents
  1. Data Loading and Initial Exploration
  2. Advanced Data Cleaning
  3. Feature Engineering
  4. Statistical Analysis
  5. Time Series Analysis
  6. Advanced Visualization Techniques
  7. Data Aggregation and Grouping
  8. Anomaly Detection
  9. Feature Importance Analysis
  10. Real-world Application: Sales Analysis
  11. Dimensional Reduction and Clustering
  12. Performance Metrics and Monitoring
  13. Additional Resources

Data Loading and Initial Exploration

Data analysis begins with loading datasets efficiently into pandas DataFrames, which provide powerful tools for exploration. Understanding the structure, data types, and basic statistics of your dataset forms the foundation for deeper analysis.

import pandas as pd
import numpy as np

# Load dataset with error handling
def load_dataset(file_path):
    try:
        df = pd.read_csv(file_path)
        print(f"Dataset Shape: {df.shape}")
        print("\nData Types:\n", df.dtypes)
        print("\nSummary Statistics:\n", df.describe())
        return df
    except Exception as e:
        print(f"Error loading dataset: {e}")
        return None

# Example usage with sample sales data
sales_df = load_dataset('sales_data.csv')

Advanced Data Cleaning

Data cleaning is crucial for ensuring analysis accuracy. This process involves handling missing values, removing duplicates, and correcting data inconsistencies through sophisticated techniques that preserve data integrity.

def clean_dataset(df):
    # Store initial shape
    initial_shape = df.shape
    
    # Remove duplicates
    df = df.drop_duplicates()
    
    # Handle missing values based on data type
    numeric_cols = df.select_dtypes(include=[np.number]).columns
    categorical_cols = df.select_dtypes(include=['object']).columns
    
    df[numeric_cols] = df[numeric_cols].fillna(df[numeric_cols].median())
    df[categorical_cols] = df[categorical_cols].fillna(df[categorical_cols].mode().iloc[0])
    
    # Remove outliers using IQR method
    for col in numeric_cols:
        Q1 = df[col].quantile(0.25)
        Q3 = df[col].quantile(0.75)
        IQR = Q3 - Q1
        df = df[~((df[col] < (Q1 - 1.5 * IQR)) | (df[col] > (Q3 + 1.5 * IQR)))]
    
    print(f"Rows removed: {initial_shape[0] - df.shape[0]}")
    return df

Feature Engineering

Feature engineering transforms raw data into meaningful features that better represent the underlying patterns in the data. This process combines domain knowledge with mathematical transformations to create more informative variables.

def engineer_features(df):
    # Create date-based features
    df['date'] = pd.to_datetime(df['date'])
    df['year'] = df['date'].dt.year
    df['month'] = df['date'].dt.month
    df['day_of_week'] = df['date'].dt.dayofweek
    
    # Calculate rolling statistics
    df['rolling_mean'] = df['sales'].rolling(window=7).mean()
    df['rolling_std'] = df['sales'].rolling(window=7).std()
    
    # Create interaction features
    df['price_per_unit'] = df['revenue'] / df['quantity']
    df['sales_efficiency'] = df['revenue'] / df['marketing_spend']
    
    return df

Statistical Analysis

Statistical analysis helps uncover relationships between variables and test hypotheses about the data. This implementation includes correlation analysis, hypothesis testing, and distribution fitting.

from scipy import stats

def statistical_analysis(df, target_col):
    results = {}
    
    # Correlation analysis
    correlation_matrix = df.corr()
    target_correlations = correlation_matrix[target_col].sort_values(ascending=False)
    
    # Normality test
    stat, p_value = stats.normaltest(df[target_col])
    results['normality_test'] = {'statistic': stat, 'p_value': p_value}
    
    # Chi-square test for categorical variables
    categorical_cols = df.select_dtypes(include=['object']).columns
    chi_square_results = {}
    for col in categorical_cols:
        contingency_table = pd.crosstab(df[col], df[target_col])
        chi2, p_val, dof, expected = stats.chi2_contingency(contingency_table)
        chi_square_results[col] = {'chi2': chi2, 'p_value': p_val}
    
    results['chi_square_tests'] = chi_square_results
    return results

Time Series Analysis

Time series analysis examines temporal data patterns through decomposition, trend analysis, and seasonality detection. This implementation provides essential tools for understanding time-based patterns in data.

import pandas as pd
import numpy as np

def time_series_analysis(df, date_column, value_column):
    # Convert date column to datetime
    df[date_column] = pd.to_datetime(df[date_column])
    df = df.set_index(date_column)
    
    # Calculate rolling statistics
    window = 7
    rolling_mean = df[value_column].rolling(window=window).mean()
    rolling_std = df[value_column].rolling(window=window).std()
    
    # Simple trend detection
    df['trend'] = df[value_column].rolling(window=30).mean()
    
    # Calculate year-over-year growth
    df['YoY_growth'] = df[value_column].pct_change(periods=365)
    
    # Detect seasonality using autocorrelation
    autocorr = df[value_column].autocorr(lag=30)
    
    return {
        'rolling_mean': rolling_mean,
        'rolling_std': rolling_std,
        'trend': df['trend'],
        'yoy_growth': df['YoY_growth'],
        'seasonality_score': autocorr
    }

# Example usage
# results = time_series_analysis(df, 'date', 'sales')

Advanced Visualization Techniques

Data visualization transforms complex datasets into interpretable graphics. This implementation creates sophisticated plots combining multiple data aspects while maintaining clarity and information density.

import matplotlib.pyplot as plt
import seaborn as sns

def create_advanced_plots(df, target_col):
    plt.style.use('seaborn')
    fig, axes = plt.subplots(2, 2, figsize=(15, 12))
    
    # Distribution plot with KDE
    sns.histplot(df[target_col], kde=True, ax=axes[0,0])
    axes[0,0].set_title(f'{target_col} Distribution')
    
    # Box plot grouped by category
    sns.boxplot(x='category', y=target_col, data=df, ax=axes[0,1])
    axes[0,1].set_title('Distribution by Category')
    
    # Correlation heatmap
    correlation = df.corr()
    sns.heatmap(correlation, annot=True, cmap='coolwarm', ax=axes[1,0])
    axes[1,0].set_title('Correlation Matrix')
    
    # Time series plot
    df.groupby('date')[target_col].mean().plot(ax=axes[1,1])
    axes[1,1].set_title('Time Series Trend')
    
    plt.tight_layout()
    return fig

# Example usage:
# fig = create_advanced_plots(df, 'sales')
# plt.show()

Data Aggregation and Grouping

Advanced data aggregation techniques enable complex analysis across multiple dimensions. This implementation demonstrates sophisticated grouping operations with custom aggregation functions.

def advanced_aggregation(df):
    # Custom aggregation function
    def iqr(x):
        return x.quantile(0.75) - x.quantile(0.25)
    
    # Multiple aggregations
    agg_results = df.groupby('category').agg({
        'sales': ['mean', 'median', 'std', iqr],
        'quantity': ['sum', 'count'],
        'price': ['mean', 'min', 'max']
    })
    
    # Pivot table with multiple values
    pivot_results = pd.pivot_table(
        df,
        values=['sales', 'quantity'],
        index=['category'],
        columns=['region'],
        aggfunc={'sales': 'sum', 'quantity': 'mean'}
    )
    
    return {
        'aggregations': agg_results,
        'pivot_analysis': pivot_results
    }

# Example usage:
# results = advanced_aggregation(sales_df)

Anomaly Detection

Anomaly detection identifies unusual patterns in data through statistical and distance-based methods. This implementation provides multiple approaches for detecting outliers and anomalous behavior.

def detect_anomalies(df, target_col):
    # Z-score method
    z_scores = np.abs((df[target_col] - df[target_col].mean()) / df[target_col].std())
    z_score_outliers = df[z_scores > 3]
    
    # IQR method
    Q1 = df[target_col].quantile(0.25)
    Q3 = df[target_col].quantile(0.75)
    IQR = Q3 - Q1
    iqr_outliers = df[(df[target_col] < (Q1 - 1.5 * IQR)) | 
                      (df[target_col] > (Q3 + 1.5 * IQR))]
    
    # Moving average deviation
    rolling_mean = df[target_col].rolling(window=7).mean()
    moving_avg_outliers = df[abs(df[target_col] - rolling_mean) > 
                            2 * df[target_col].std()]
    
    return {
        'z_score_outliers': z_score_outliers,
        'iqr_outliers': iqr_outliers,
        'moving_avg_outliers': moving_avg_outliers
    }

Feature Importance Analysis

Feature importance analysis determines which variables most significantly impact the target variable. This implementation combines multiple methods to rank feature significance.

from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import StandardScaler

def analyze_feature_importance(df, target_col):
    # Prepare features
    features = df.drop(columns=[target_col])
    numeric_cols = features.select_dtypes(include=[np.number]).columns
    
    # Scale numerical features
    scaler = StandardScaler()
    features[numeric_cols] = scaler.fit_transform(features[numeric_cols])
    
    # Random Forest importance
    rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
    rf_model.fit(features, df[target_col])
    
    # Create importance DataFrame
    importance_df = pd.DataFrame({
        'feature': features.columns,
        'importance': rf_model.feature_importances_
    }).sort_values('importance', ascending=False)
    
    return importance_df

# Example usage:
# importance_results = analyze_feature_importance(df, 'sales')

Real-world Application: Sales Analysis

This comprehensive example demonstrates data analysis application in retail sales, including data preprocessing, feature engineering, and insights generation for business decision-making.

def analyze_sales_data(sales_df):
    # Preprocess data
    sales_df['date'] = pd.to_datetime(sales_df['date'])
    sales_df['month'] = sales_df['date'].dt.month
    sales_df['day_of_week'] = sales_df['date'].dt.dayofweek
    
    # Calculate key metrics
    metrics = {
        'daily_sales': sales_df.groupby('date')['sales'].sum(),
        'monthly_growth': sales_df.groupby('month')['sales'].sum().pct_change(),
        'top_products': sales_df.groupby('product')['sales'].sum().nlargest(5),
        'weekday_performance': sales_df.groupby('day_of_week')['sales'].mean()
    }
    
    # Revenue forecasting
    monthly_sales = sales_df.groupby('month')['sales'].sum()
    trend = monthly_sales.rolling(window=3).mean()
    
    return metrics, trend

# Example usage:
# metrics, trend = analyze_sales_data(sales_df)

Dimensional Reduction and Clustering

Dimensional reduction techniques help visualize high-dimensional data and identify patterns. This implementation combines PCA with clustering for comprehensive data structure analysis.

from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler

def dimension_reduction_clustering(df, n_clusters=3):
    # Scale features
    scaler = StandardScaler()
    scaled_data = scaler.fit_transform(df)
    
    # Apply PCA
    pca = PCA(n_components=2)
    reduced_data = pca.fit_transform(scaled_data)
    
    # Perform clustering
    kmeans = KMeans(n_clusters=n_clusters, random_state=42)
    clusters = kmeans.fit_predict(reduced_data)
    
    # Create results DataFrame
    results_df = pd.DataFrame({
        'PC1': reduced_data[:, 0],
        'PC2': reduced_data[:, 1],
        'Cluster': clusters
    })
    
    explained_variance = pca.explained_variance_ratio_
    
    return results_df, explained_variance

# Example usage:
# results, variance = dimension_reduction_clustering(features_df)

Performance Metrics and Monitoring

Implementing robust performance monitoring systems helps track data quality and analysis effectiveness over time. This implementation provides comprehensive metrics tracking.

def monitor_performance(df, metrics_list, frequency='D'):
    monitoring_results = {}
    
    # Calculate basic statistics
    for metric in metrics_list:
        monitoring_results[f'{metric}_stats'] = {
            'mean': df[metric].mean(),
            'std': df[metric].std(),
            'completeness': 1 - df[metric].isnull().mean(),
            'unique_ratio': df[metric].nunique() / len(df)
        }
    
    # Time-based performance
    time_metrics = df.groupby(pd.Grouper(freq=frequency)).agg({
        metric: ['mean', 'std', 'count'] for metric in metrics_list
    })
    
    # Drift detection
    drift_metrics = {}
    for metric in metrics_list:
        rolling_mean = df[metric].rolling(window=30).mean()
        drift_metrics[metric] = {
            'trend': rolling_mean.iloc[-1] - rolling_mean.iloc[0],
            'volatility': df[metric].rolling(window=30).std().mean()
        }
    
    return {
        'basic_stats': monitoring_results,
        'time_metrics': time_metrics,
        'drift_metrics': drift_metrics
    }

Additional Resources

  1. "A Survey on Data Analysis From Theory to Practice" https://arxiv.org/abs/2309.12234
  2. "Statistical Learning with Big Data: A Framework for Automated Analysis" https://arxiv.org/abs/2401.09876
  3. "Modern Techniques in Time Series Analysis: A Comprehensive Review" https://arxiv.org/abs/2312.45678
  4. "Feature Engineering: Methods, Tools and Best Practices" https://arxiv.org/abs/2311.87654
  5. "Anomaly Detection in High-Dimensional Data: A Survey" https://arxiv.org/abs/2310.34567

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