├── README.md ├── app.py ├── requirements.txt ├── sbm_ODF_Status_district.csv ├── static ├── css │ └── style.css └── images │ ├── blocks_comparison.png │ ├── boxplots.png │ ├── odf_ratio_hist.png │ └── scatter_outliers.png └── templates └── index.html /README.md: -------------------------------------------------------------------------------- 1 | # python-project -------------------------------------------------------------------------------- /app.py: -------------------------------------------------------------------------------- 1 | import os 2 | import re 3 | from flask import Flask, render_template, url_for 4 | import pandas as pd 5 | import numpy as np 6 | from scipy import stats 7 | 8 | # Force Matplotlib to use a non‐interactive backend (no GUI) 9 | import matplotlib 10 | matplotlib.use('Agg') 11 | import matplotlib.pyplot as plt 12 | import seaborn as sns 13 | 14 | # === CONFIG === 15 | # Point this at your CSV file: 16 | DATA_PATH = r"D:\python-project ca 2\sbm_ODF_Status_district.csv" 17 | 18 | app = Flask(__name__) 19 | # Save images under your project's static folder 20 | app.config['STATIC_IMAGE_PATH'] = os.path.join(app.root_path, 'static', 'images') 21 | 22 | 23 | def load_data(): 24 | # 1. Load 25 | df = pd.read_csv(DATA_PATH) 26 | 27 | # 2. Clean column names 28 | df.columns = [col.strip() for col in df.columns] 29 | std_cols = {col: col.lower().replace(' ', '_') for col in df.columns} 30 | df.rename(columns=std_cols, inplace=True) 31 | 32 | # 3. Auto‐detect key columns 33 | cols = df.columns.tolist() 34 | # District 35 | district_cols = [c for c in cols if 'district' in c] 36 | if not district_cols: 37 | raise KeyError("No 'district' column found in your CSV") 38 | district_col = district_cols[0] 39 | # Total Blocks 40 | total_cols = [c for c in cols if re.search(r'total.*block', c)] 41 | if not total_cols: 42 | raise KeyError("No 'total blocks' column found in your CSV") 43 | total_col = next((c for c in total_cols if c == 'total_blocks'), total_cols[0]) 44 | # ODF Blocks 45 | odf_cols = [c for c in cols if re.search(r'odf.*block', c)] 46 | if not odf_cols: 47 | raise KeyError("No 'ODF blocks' column found in your CSV") 48 | odf_col = next((c for c in odf_cols if c == 'odf_blocks'), odf_cols[0]) 49 | 50 | # 4. Rename to standard 51 | df.rename(columns={ 52 | district_col: 'district', 53 | total_col: 'total_blocks', 54 | odf_col: 'odf_blocks' 55 | }, inplace=True) 56 | 57 | # 5. Dedupe & forward‐fill 58 | df.drop_duplicates(inplace=True) 59 | df.ffill(inplace=True) 60 | 61 | return df 62 | 63 | 64 | def preprocess(df): 65 | # Ensure numeric types 66 | df['total_blocks'] = pd.to_numeric(df['total_blocks'], errors='coerce') 67 | df['odf_blocks'] = pd.to_numeric(df['odf_blocks'], errors='coerce') 68 | df.dropna(subset=['total_blocks', 'odf_blocks'], inplace=True) 69 | 70 | # Compute ratio 71 | df['odf_ratio'] = df['odf_blocks'] / df['total_blocks'] 72 | return df 73 | 74 | 75 | def compute_stats(df): 76 | # Ensure the DataFrame is not empty to avoid errors/NaN results 77 | if df.empty: 78 | return { 79 | 'mean_total_blocks': 0, 'median_total_blocks': 0, 'std_total_blocks': 0, 80 | 'mean_odf_blocks': 0, 'median_odf_blocks': 0, 'std_odf_blocks': 0, 81 | 'mean_odf_ratio': 0, 'median_odf_ratio': 0, 'std_odf_ratio': 0, 82 | } 83 | return { 84 | 'mean_total_blocks': df['total_blocks'].mean(), 85 | 'median_total_blocks': df['total_blocks'].median(), 86 | 'std_total_blocks': df['total_blocks'].std(), 87 | 'mean_odf_blocks': df['odf_blocks'].mean(), 88 | 'median_odf_blocks': df['odf_blocks'].median(), 89 | 'std_odf_blocks': df['odf_blocks'].std(), 90 | 'mean_odf_ratio': df['odf_ratio'].mean(), 91 | 'median_odf_ratio': df['odf_ratio'].median(), 92 | 'std_odf_ratio': df['odf_ratio'].std(), 93 | } 94 | 95 | 96 | def detect_outliers_zscore(df, threshold=3): 97 | cols_to_check = ['total_blocks', 'odf_blocks'] 98 | # Handle empty DataFrame or missing columns gracefully 99 | if df.empty or not all(col in df.columns for col in cols_to_check): 100 | # Return an empty DataFrame with expected columns for consistency 101 | return pd.DataFrame(columns=df.columns.tolist() + ['zscore_total', 'zscore_odf']) 102 | 103 | numerical_data = df[cols_to_check] 104 | 105 | # Handle case where selected columns result in an empty frame (e.g., all NaNs previously) 106 | if numerical_data.empty: 107 | return pd.DataFrame(columns=df.columns.tolist() + ['zscore_total', 'zscore_odf']) 108 | 109 | # Calculate z-scores. This assumes no NaNs in these columns due to prior preprocessing. 110 | # Using .values ensures we get a NumPy array for consistent indexing 111 | z_scores_array = np.abs(stats.zscore(numerical_data.values)) 112 | 113 | # Create a temporary DataFrame for z-scores with the same index as numerical_data 114 | z_df = pd.DataFrame(z_scores_array, index=numerical_data.index, columns=['zscore_total', 'zscore_odf']) 115 | 116 | # Join the z-scores back to the original DataFrame to keep all original data 117 | df_with_z = df.join(z_df) 118 | 119 | # Filter based on the threshold using the joined DataFrame 120 | # Handle potential NaNs in z-score columns if stats.zscore produced them unexpectedly 121 | outliers = df_with_z[ 122 | (df_with_z['zscore_total'].fillna(0) > threshold) | 123 | (df_with_z['zscore_odf'].fillna(0) > threshold) 124 | ] 125 | 126 | return outliers # Return only the outlier rows, now including z-score columns 127 | 128 | 129 | def detect_outliers_iqr(df): 130 | cols_to_check = ['total_blocks', 'odf_blocks'] 131 | # Handle empty DataFrame or missing columns gracefully 132 | if df.empty or not all(col in df.columns for col in cols_to_check): 133 | return pd.DataFrame(columns=df.columns) # Return empty df matching input columns 134 | 135 | numerical_data = df[cols_to_check] 136 | 137 | if numerical_data.empty: 138 | return pd.DataFrame(columns=df.columns) 139 | 140 | Q1 = numerical_data.quantile(0.25) 141 | Q3 = numerical_data.quantile(0.75) 142 | IQR = Q3 - Q1 143 | lower_bound = Q1 - 1.5 * IQR 144 | upper_bound = Q3 + 1.5 * IQR 145 | 146 | # Create the mask using the bounds. Handles NaNs correctly during comparison. 147 | mask = ((numerical_data < lower_bound) | (numerical_data > upper_bound)).any(axis=1) 148 | 149 | # Filter the original DataFrame using the mask 150 | return df[mask] 151 | 152 | 153 | def save_plots(df, outliers_z): # Accept pre-calculated outliers 154 | os.makedirs(app.config['STATIC_IMAGE_PATH'], exist_ok=True) 155 | 156 | # --- Bar chart: Total vs ODF Blocks --- 157 | plt.figure(figsize=(14, 7)) # Wider figure for potentially many districts 158 | if not df.empty: 159 | # Sort by total blocks for potentially better visualization 160 | df_sorted = df.sort_values('total_blocks', ascending=False) 161 | # Use distinct colors 162 | sns.barplot(x='district', y='total_blocks', data=df_sorted, color='skyblue', label='Total Blocks') 163 | sns.barplot(x='district', y='odf_blocks', data=df_sorted, color='lightgreen', alpha=0.9, label='ODF Blocks') 164 | plt.xticks(rotation=90) 165 | plt.ylabel('Number of Blocks') 166 | plt.xlabel('District') 167 | plt.title('Total Blocks vs ODF Blocks per District') 168 | plt.legend() 169 | else: 170 | plt.text(0.5, 0.5, 'No data available for Bar Chart', ha='center', va='center') 171 | plt.title('Total Blocks vs ODF Blocks per District') 172 | 173 | plt.tight_layout() 174 | plt.savefig(os.path.join(app.config['STATIC_IMAGE_PATH'], 'blocks_comparison.png')) 175 | plt.close() 176 | 177 | # --- Histogram: ODF Ratio --- 178 | plt.figure(figsize=(8, 6)) 179 | if not df.empty and 'odf_ratio' in df.columns and not df['odf_ratio'].isnull().all(): 180 | sns.histplot(df['odf_ratio'].dropna(), bins=20, kde=True) # Ensure NaNs dropped 181 | plt.xlabel('ODF Ratio (ODF Blocks / Total Blocks)') 182 | plt.title('Distribution of ODF Ratio') 183 | else: 184 | plt.text(0.5, 0.5, 'No data available for ODF Ratio Histogram', ha='center', va='center') 185 | plt.title('Distribution of ODF Ratio') 186 | 187 | plt.tight_layout() 188 | plt.savefig(os.path.join(app.config['STATIC_IMAGE_PATH'], 'odf_ratio_hist.png')) 189 | plt.close() 190 | 191 | # --- Boxplots --- 192 | plt.figure(figsize=(8, 6)) 193 | cols_for_boxplot = ['total_blocks', 'odf_blocks'] 194 | if not df.empty and all(col in df.columns for col in cols_for_boxplot): 195 | # Drop rows where *both* columns are NaN for boxplot if any survived preprocessing 196 | plot_data = df[cols_for_boxplot].dropna(how='all') 197 | if not plot_data.empty: 198 | sns.boxplot(data=plot_data) 199 | plt.title('Box Plot of Total Blocks and ODF Blocks') 200 | else: 201 | plt.text(0.5, 0.5, 'No numerical data for Box Plot', ha='center', va='center') 202 | plt.title('Box Plot of Total Blocks and ODF Blocks') 203 | else: 204 | plt.text(0.5, 0.5, 'No data available for Box Plot', ha='center', va='center') 205 | plt.title('Box Plot of Total Blocks and ODF Blocks') 206 | 207 | plt.tight_layout() 208 | plt.savefig(os.path.join(app.config['STATIC_IMAGE_PATH'], 'boxplots.png')) 209 | plt.close() 210 | 211 | # --- Scatter with highlighted outliers --- 212 | plt.figure(figsize=(8, 6)) 213 | if not df.empty and all(col in df.columns for col in ['total_blocks', 'odf_blocks']): 214 | plt.scatter(df['total_blocks'], df['odf_blocks'], label='Data', alpha=0.6) 215 | # Check if outliers_z DataFrame is not empty before plotting 216 | if not outliers_z.empty: 217 | plt.scatter(outliers_z['total_blocks'], outliers_z['odf_blocks'], 218 | edgecolor='r', facecolors='none', s=100, label='Outliers (Z-score)') 219 | plt.xlabel('Total Blocks') 220 | plt.ylabel('ODF Blocks') 221 | plt.title('Total Blocks vs ODF Blocks (Outliers Highlighted)') 222 | plt.legend() 223 | else: 224 | plt.text(0.5, 0.5, 'No data available for Scatter Plot', ha='center', va='center') 225 | plt.title('Total Blocks vs ODF Blocks (Outliers Highlighted)') 226 | 227 | plt.tight_layout() 228 | plt.savefig(os.path.join(app.config['STATIC_IMAGE_PATH'], 'scatter_outliers.png')) 229 | plt.close() 230 | 231 | 232 | @app.route('/') 233 | def index(): 234 | try: 235 | df = load_data() 236 | df = preprocess(df) # This step might result in an empty DataFrame 237 | 238 | # Handle potentially empty DataFrame after preprocessing 239 | if df.empty: 240 | # Prepare default/empty data for the template 241 | stats_summary = compute_stats(df) # Will return zeros 242 | # Use empty DataFrames for outliers 243 | outliers_z = pd.DataFrame(columns=df.columns.tolist() + ['zscore_total', 'zscore_odf']) 244 | outliers_iqr = pd.DataFrame(columns=df.columns) 245 | # Generate plots (they will show "No data" messages) 246 | save_plots(df, outliers_z) 247 | 248 | else: 249 | # Proceed with calculations only if df is not empty 250 | stats_summary = compute_stats(df) 251 | # Calculate outliers without modifying the original df 252 | outliers_z = detect_outliers_zscore(df.copy()) # Pass a copy to be safe 253 | outliers_iqr = detect_outliers_iqr(df.copy()) # Pass a copy to be safe 254 | # Generate plots using the main df and the calculated z-score outliers 255 | save_plots(df, outliers_z) 256 | 257 | # Render the template with calculated or default data 258 | return render_template( 259 | 'index.html', 260 | stats=stats_summary, 261 | # Convert potentially empty DataFrames to dicts safely 262 | outliers_z=outliers_z.to_dict(orient='records'), 263 | outliers_iqr=outliers_iqr.to_dict(orient='records'), 264 | # Pass image paths relative to static folder 265 | plot_urls={ 266 | 'blocks_comparison': url_for('static', filename='images/blocks_comparison.png'), 267 | 'odf_ratio_hist': url_for('static', filename='images/odf_ratio_hist.png'), 268 | 'boxplots': url_for('static', filename='images/boxplots.png'), 269 | 'scatter_outliers': url_for('static', filename='images/scatter_outliers.png') 270 | } 271 | ) 272 | 273 | except KeyError as e: 274 | # Handle specific errors like missing columns during loading 275 | # You might want to render an error template or flash a message 276 | error_message = f"Data loading error: {e}. Please check the CSV file format." 277 | # Log the error for debugging 278 | app.logger.error(error_message) 279 | # For now, return a simple error message (replace with error template later) 280 | return f"

Error

{error_message}

", 500 281 | except FileNotFoundError: 282 | error_message = f"Data file not found at {DATA_PATH}. Please ensure the file exists." 283 | app.logger.error(error_message) 284 | return f"

Error

{error_message}

", 500 285 | except Exception as e: 286 | # Catch other potential errors during processing or plotting 287 | error_message = f"An unexpected error occurred: {e}" 288 | app.logger.exception("Unhandled exception in index route:") # Logs the full traceback 289 | return f"

Error

{error_message}

", 500 290 | 291 | 292 | if __name__ == '__main__': 293 | # Add basic logging configuration 294 | import logging 295 | logging.basicConfig(level=logging.INFO) 296 | # Ensure the static image directory exists before starting the app 297 | image_dir = os.path.join(app.root_path, 'static', 'images') 298 | os.makedirs(image_dir, exist_ok=True) 299 | app.run(debug=True) # Keep debug=True for development 300 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | Flask 2 | pandas 3 | numpy 4 | matplotlib 5 | seaborn 6 | scipy -------------------------------------------------------------------------------- /sbm_ODF_Status_district.csv: -------------------------------------------------------------------------------- 1 | "StateID","StateName","DistrictID","DistrictCensusCode2011","DistrictName","TotalBlock","TotalODFBlock","Date" 2 | "26","A & N Islands","577","638","NICOBARS","1","1","10/30/2023 12:00:00 AM" 3 | "26","A & N Islands","577","638","NICOBARS","3","1","10/30/2023 12:00:00 AM" 4 | "26","A & N Islands","578","639","NORTH AND MIDDLE ANDAMAN","3","3","10/30/2023 12:00:00 AM" 5 | "26","A & N Islands","576","640","SOUTH ANDAMANS","3","3","10/30/2023 12:00:00 AM" 6 | "1","Andhra Pradesh","1366","805","ALLURI SITHARAMA RAJU","22","22","10/30/2023 12:00:00 AM" 7 | "1","Andhra Pradesh","1367","804","ANAKAPALLI","24","24","10/30/2023 12:00:00 AM" 8 | "1","Andhra Pradesh","1","553","ANANTAPUR","31","31","10/30/2023 12:00:00 AM" 9 | "1","Andhra Pradesh","1368","813","ANNAMAYYA","30","30","10/30/2023 12:00:00 AM" 10 | "1","Andhra Pradesh","1369","810","BAPATLA","25","25","10/30/2023 12:00:00 AM" 11 | "1","Andhra Pradesh","2","554","CHITTOOR","31","31","10/30/2023 12:00:00 AM" 12 | "1","Andhra Pradesh","3","551","CUDDAPAH","35","35","10/30/2023 12:00:00 AM" 13 | "1","Andhra Pradesh","4","545","EAST GODAVARI","18","18","10/30/2023 12:00:00 AM" 14 | "1","Andhra Pradesh","1370","808","ELURU","27","27","10/30/2023 12:00:00 AM" 15 | "1","Andhra Pradesh","1370","808","ELURU","28","27","10/30/2023 12:00:00 AM" 16 | "1","Andhra Pradesh","5","548","GUNTUR","15","15","10/30/2023 12:00:00 AM" 17 | "1","Andhra Pradesh","5","548","GUNTUR","16","15","10/30/2023 12:00:00 AM" 18 | "1","Andhra Pradesh","1371","806","KAKINADA","20","20","10/30/2023 12:00:00 AM" 19 | "1","Andhra Pradesh","1372","807","KONASEEMA","22","22","10/30/2023 12:00:00 AM" 20 | "1","Andhra Pradesh","6","547","KRISHNA","25","25","10/30/2023 12:00:00 AM" 21 | "1","Andhra Pradesh","7","552","KURNOOL","25","25","10/30/2023 12:00:00 AM" 22 | "1","Andhra Pradesh","1373","815","NANDYAL","28","28","10/30/2023 12:00:00 AM" 23 | "1","Andhra Pradesh","8","550","NELLORE","37","37","10/30/2023 12:00:00 AM" 24 | "1","Andhra Pradesh","1374","809","NTR","16","16","10/30/2023 12:00:00 AM" 25 | "1","Andhra Pradesh","1375","811","PALNADU","28","28","10/30/2023 12:00:00 AM" 26 | "1","Andhra Pradesh","1376","803","PARVATHIPURAM MANYAM","15","15","10/30/2023 12:00:00 AM" 27 | "1","Andhra Pradesh","9","549","PRAKASAM","38","38","10/30/2023 12:00:00 AM" 28 | "1","Andhra Pradesh","1377","814","Sri Sathya Sai","32","32","10/30/2023 12:00:00 AM" 29 | "1","Andhra Pradesh","10","542","SRIKAKULAM","30","30","10/30/2023 12:00:00 AM" 30 | "1","Andhra Pradesh","1378","812","TIRUPATI","33","33","10/30/2023 12:00:00 AM" 31 | "1","Andhra Pradesh","11","544","VISAKHAPATNAM","4","4","10/30/2023 12:00:00 AM" 32 | "1","Andhra Pradesh","12","543","VIZIANAGARAM","27","27","10/30/2023 12:00:00 AM" 33 | "1","Andhra Pradesh","13","546","WEST GODAVARI","19","20","10/30/2023 12:00:00 AM" 34 | "1","Andhra Pradesh","13","546","WEST GODAVARI","20","20","10/30/2023 12:00:00 AM" 35 | "2","Arunachal Pradesh","29","260","ANJAW","4","4","10/30/2023 12:00:00 AM" 36 | "2","Arunachal Pradesh","14","253","CHANGLANG","9","9","10/30/2023 12:00:00 AM" 37 | "2","Arunachal Pradesh","15","257","DIBANG VALLEY","3","3","10/30/2023 12:00:00 AM" 38 | "2","Arunachal Pradesh","16","247","EAST KAMENG","7","7","10/30/2023 12:00:00 AM" 39 | "2","Arunachal Pradesh","17","251","EAST SIANG","3","3","10/30/2023 12:00:00 AM" 40 | "2","Arunachal Pradesh","759","778","KAMLE","2","2","10/30/2023 12:00:00 AM" 41 | "2","Arunachal Pradesh","728","763","KRA-DAADI","6","6","10/30/2023 12:00:00 AM" 42 | "2","Arunachal Pradesh","28","256","KURUNG KUMEY","6","6","10/30/2023 12:00:00 AM" 43 | "2","Arunachal Pradesh","761","784","LAPA RADA","3","3","10/30/2023 12:00:00 AM" 44 | "2","Arunachal Pradesh","18","259","LOHIT","2","2","10/30/2023 12:00:00 AM" 45 | "2","Arunachal Pradesh","729","761","LONGDING","4","4","10/30/2023 12:00:00 AM" 46 | "2","Arunachal Pradesh","27","258","LOWER DIBANG VALLEY","3","3","10/30/2023 12:00:00 AM" 47 | "2","Arunachal Pradesh","758","779","LOWER SIANG","3","3","10/30/2023 12:00:00 AM" 48 | "2","Arunachal Pradesh","19","255","LOWER SUBANSIRI","4","4","10/30/2023 12:00:00 AM" 49 | "2","Arunachal Pradesh","730","762","NAMSAI","3","3","10/30/2023 12:00:00 AM" 50 | "2","Arunachal Pradesh","760","783","PAKKE-KESSANG","2","2","10/30/2023 12:00:00 AM" 51 | "2","Arunachal Pradesh","20","248","PAPUM PARE","6","6","10/30/2023 12:00:00 AM" 52 | "2","Arunachal Pradesh","762","785","SHI YOMI","2","2","10/30/2023 12:00:00 AM" 53 | "2","Arunachal Pradesh","731","764","SIANG","6","6","10/30/2023 12:00:00 AM" 54 | "2","Arunachal Pradesh","21","245","TAWANG","6","6","10/30/2023 12:00:00 AM" 55 | "2","Arunachal Pradesh","22","254","TIRAP","5","5","10/30/2023 12:00:00 AM" 56 | "2","Arunachal Pradesh","23","252","UPPER SIANG","6","6","10/30/2023 12:00:00 AM" 57 | "2","Arunachal Pradesh","24","249","UPPER SUBANSIRI","10","10","10/30/2023 12:00:00 AM" 58 | "2","Arunachal Pradesh","25","246","WEST KAMENG","5","5","10/30/2023 12:00:00 AM" 59 | "2","Arunachal Pradesh","26","250","WEST SIANG","4","4","10/30/2023 12:00:00 AM" 60 | "3","Assam","1384","799","BAJALI","2","2","10/30/2023 12:00:00 AM" 61 | "3","Assam","53","324","BAKSA","5","9","10/30/2023 12:00:00 AM" 62 | "3","Assam","53","324","BAKSA","9","9","10/30/2023 12:00:00 AM" 63 | "3","Assam","30","303","BARPETA","9","9","10/30/2023 12:00:00 AM" 64 | "3","Assam","739","756","BISWANATH","7","7","10/30/2023 12:00:00 AM" 65 | "3","Assam","31","319","BONGAIGAON","5","5","10/30/2023 12:00:00 AM" 66 | "3","Assam","32","316","CACHAR","15","15","10/30/2023 12:00:00 AM" 67 | "3","Assam","740","755","CHARAIDEO","5","5","10/30/2023 12:00:00 AM" 68 | "3","Assam","54","320","CHIRANG","3","4","10/30/2023 12:00:00 AM" 69 | "3","Assam","54","320","CHIRANG","4","4","10/30/2023 12:00:00 AM" 70 | "3","Assam","33","325","DARRANG","6","6","10/30/2023 12:00:00 AM" 71 | "3","Assam","34","308","DHEMAJI","5","5","10/30/2023 12:00:00 AM" 72 | "3","Assam","35","301","DHUBRI","13","13","10/30/2023 12:00:00 AM" 73 | "3","Assam","36","310","DIBRUGARH","7","7","10/30/2023 12:00:00 AM" 74 | "3","Assam","47","315","DIMA HASAO","5","5","10/30/2023 12:00:00 AM" 75 | "3","Assam","37","302","GOALPARA","8","8","10/30/2023 12:00:00 AM" 76 | "3","Assam","38","313","GOLAGHAT","8","8","10/30/2023 12:00:00 AM" 77 | "3","Assam","39","318","HAILAKANDI","5","5","10/30/2023 12:00:00 AM" 78 | "3","Assam","741","757","HOJAI","5","5","10/30/2023 12:00:00 AM" 79 | "3","Assam","40","312","JORHAT","6","6","10/30/2023 12:00:00 AM" 80 | "3","Assam","41","321","KAMRUP","14","14","10/30/2023 12:00:00 AM" 81 | "3","Assam","56","322","KAMRUP METROPOLITAN","4","4","10/30/2023 12:00:00 AM" 82 | "3","Assam","42","314","KARBI ANGLONG","7","7","10/30/2023 12:00:00 AM" 83 | "3","Assam","43","317","KARIMGANJ","7","7","10/30/2023 12:00:00 AM" 84 | "3","Assam","44","300","KOKRAJHAR","9","9","10/30/2023 12:00:00 AM" 85 | "3","Assam","45","307","LAKHIMPUR","9","9","10/30/2023 12:00:00 AM" 86 | "3","Assam","742","760","MAJULI","2","2","10/30/2023 12:00:00 AM" 87 | "3","Assam","46","304","MORIGAON","7","7","10/30/2023 12:00:00 AM" 88 | "3","Assam","48","305","NAGAON","15","15","10/30/2023 12:00:00 AM" 89 | "3","Assam","49","323","NALBARI","7","7","10/30/2023 12:00:00 AM" 90 | "3","Assam","50","311","SIVASAGAR","5","5","10/30/2023 12:00:00 AM" 91 | "3","Assam","51","306","SONITPUR","7","7","10/30/2023 12:00:00 AM" 92 | "3","Assam","743","758","SOUTH SALMARA MANCACHAR","3","3","10/30/2023 12:00:00 AM" 93 | "3","Assam","52","309","TINSUKIA","7","7","10/30/2023 12:00:00 AM" 94 | "3","Assam","55","326","UDALGURI","11","11","10/30/2023 12:00:00 AM" 95 | "3","Assam","744","759","WEST KARBI ANGLONG","4","4","10/30/2023 12:00:00 AM" 96 | "4","Bihar","57","209","ARARIA","9","9","10/30/2023 12:00:00 AM" 97 | "4","Bihar","94","240","ARWAL","5","5","10/30/2023 12:00:00 AM" 98 | "4","Bihar","58","235","AURANGABAD","11","11","10/30/2023 12:00:00 AM" 99 | 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15px; 68 | border-radius: 4px; 69 | box-shadow: 0 1px 3px rgba(0,0,0,0.05); 70 | } 71 | 72 | .stats-grid strong { 73 | color: #16a085; 74 | } 75 | 76 | /* Outliers Section */ 77 | .outliers-section { 78 | margin-bottom: 30px; 79 | } 80 | 81 | .outlier-table { 82 | margin-bottom: 25px; 83 | overflow-x: auto; /* Add scroll for smaller screens */ 84 | } 85 | 86 | table { 87 | width: 100%; 88 | border-collapse: collapse; 89 | margin-top: 15px; 90 | box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1); 91 | } 92 | 93 | th, td { 94 | border: 1px solid #ddd; 95 | padding: 10px 12px; 96 | text-align: left; 97 | } 98 | 99 | th { 100 | background-color: #3498db; 101 | color: white; 102 | font-weight: bold; 103 | } 104 | 105 | tbody tr:nth-child(even) { 106 | background-color: #f2f8fc; 107 | } 108 | 109 | tbody tr:hover { 110 | background-color: #eaf2f8; 111 | } 112 | 113 | /* Plots Section */ 114 | .plots-section { 115 | margin-bottom: 30px; 116 | } 117 | 118 | .plot-grid { 119 | display: grid; 120 | 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-------------------------------------------------------------------------------- /static/images/blocks_comparison.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Gavisingh12/python-project/fe0d77ee66b5062434d08fb77cda0b31f8a88fd1/static/images/blocks_comparison.png -------------------------------------------------------------------------------- /static/images/boxplots.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Gavisingh12/python-project/fe0d77ee66b5062434d08fb77cda0b31f8a88fd1/static/images/boxplots.png -------------------------------------------------------------------------------- /static/images/odf_ratio_hist.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Gavisingh12/python-project/fe0d77ee66b5062434d08fb77cda0b31f8a88fd1/static/images/odf_ratio_hist.png -------------------------------------------------------------------------------- /static/images/scatter_outliers.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Gavisingh12/python-project/fe0d77ee66b5062434d08fb77cda0b31f8a88fd1/static/images/scatter_outliers.png -------------------------------------------------------------------------------- /templates/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | ODF Status Dashboard 7 | 8 | 9 | 10 | 11 | 12 | 13 | 18 |
19 |

Swachh Bharat Mission - ODF Status Dashboard

20 |

Analysis of Open Defecation Free (ODF) blocks per district.

21 | 22 |
23 |

Summary Statistics

24 | {% if stats %} 25 |
26 |
Mean Total Blocks: {{ "%.2f"|format(stats.mean_total_blocks) }}
27 |
Median Total Blocks: {{ "%.2f"|format(stats.median_total_blocks) }}
28 |
Std Dev Total Blocks: {{ "%.2f"|format(stats.std_total_blocks) }}
29 |
Mean ODF Blocks: {{ "%.2f"|format(stats.mean_odf_blocks) }}
30 |
Median ODF Blocks: {{ "%.2f"|format(stats.median_odf_blocks) }}
31 |
Std Dev ODF Blocks: {{ "%.2f"|format(stats.std_odf_blocks) }}
32 |
Mean ODF Ratio: {{ "%.2f"|format(stats.mean_odf_ratio) }}
33 |
Median ODF Ratio: {{ "%.2f"|format(stats.median_odf_ratio) }}
34 |
Std Dev ODF Ratio: {{ "%.2f"|format(stats.std_odf_ratio) }}
35 |
36 | {% else %} 37 |

Statistics could not be calculated.

38 | {% endif %} 39 |
40 | 41 |
42 |

Outlier Detection

43 | 44 |
45 |

Outliers (Z-score Method, Threshold=3)

46 | {% if outliers_z %} 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | {% for outlier in outliers_z %} 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | {% endfor %} 67 | 68 |
DistrictTotal BlocksODF BlocksZ-Score (Total)Z-Score (ODF)
{{ outlier.district }}{{ outlier.total_blocks }}{{ outlier.odf_blocks }}{{ "%.2f"|format(outlier.zscore_total) }}{{ "%.2f"|format(outlier.zscore_odf) }}
69 | {% else %} 70 |

No significant outliers detected using the Z-score method.

71 | {% endif %} 72 |
73 | 74 |
75 |

Outliers (IQR Method)

76 | {% if outliers_iqr %} 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | {% for outlier in outliers_iqr %} 87 | 88 | 89 | 90 | 91 | 92 | {% endfor %} 93 | 94 |
DistrictTotal BlocksODF Blocks
{{ outlier.district }}{{ outlier.total_blocks }}{{ outlier.odf_blocks }}
95 | {% else %} 96 |

No significant outliers detected using the IQR method.

97 | {% endif %} 98 |
99 |
100 | 101 |
102 |

Visualizations

103 |
104 |
105 |

Blocks Comparison per District

106 | Blocks Comparison Bar Chart 107 |

Compares the total number of blocks (blue) and ODF blocks (green) for each district.

108 |
109 |
110 |

Distribution of ODF Ratio

111 | ODF Ratio Histogram 112 |

Shows the frequency distribution of the ratio of ODF blocks to total blocks across districts.

113 |
114 |
115 |

Distribution of Block Counts

116 | Box Plots 117 |

Displays the distribution (median, quartiles, potential outliers) for total blocks and ODF blocks.

118 |
119 |
120 |

Total Blocks vs ODF Blocks (Outliers Highlighted)

121 | Scatter Plot with Outliers 122 |

Each point represents a district. Outliers identified by the Z-score method are circled in red.

123 |
124 |
125 |
126 | 127 | 130 |
131 | 132 | 133 | 134 | 135 | --------------------------------------------------------------------------------