added master file and function for reading all raw data csv files

SRC_MasterData.csv

+State_Code	State_Name	Total_Population	Percentage_Population_Urban	Percentage_Population_0_6_Age_Group	Growth_Rate	Sex_Ratio	Overall_Literacy_Rate	Female_Literacy_Rate	Male_Literacy_Rate	Area_Sq_Km
+1	JAMMU & KASHMIR	12549	20.05	16.01	23.71	883	68.74	58.01	78.26	222236
+2	HIMACHAL PRADESH	6857	8.69	11.14	12.81	974	83.78	76.6	90.83	55673
+3	PUNJAB	27704	29.82	10.62	13.73	893	76.68	71.34	81.48	50362
+4	CHANDIGARH	1055	97.25	11.18	17.1	818	86.43	81.38	90.54	114
+5	UTTARAKHAND	10117	21.54	13.14	19.17	963	79.63	70.7	88.33	53483
+6	HARYANA	25353	24.12	13.01	19.9	877	76.64	66.77	85.38	44212
+7	DELHI	16753	77.03	11.76	20.96	866	86.34	80.93	91.03	1483
+8	RAJASTHAN	68621	19.26	15.31	21.44	926	67.06	52.66	80.51	342239
+9	UTTAR PRADESH	199581	17.31	14.9	20.09	908	69.72	59.26	79.24	240928
+10	BIHAR	103805	8.36	17.9	25.07	916	63.82	53.33	73.39	94163
+11	SIKKIM	608	9.85	10.05	12.36	889	82.2	76.43	87.29	7096
+12	ARUNACHAL PRADESH	1383	16.48	14.66	25.92	920	66.95	59.57	73.69	83743
+13	NAGALAND	1981	17.31	14.44	-0.47	931	80.11	76.69	83.29	16579
+14	MANIPUR	2722	21.16	12.98	18.65	987	79.85	73.17	86.49	22327
+15	MIZORAM	1091	40.42	15.17	22.78	975	91.58	89.4	93.72	21081
+16	TRIPURA	3671	14.87	12.1	14.75	961	87.75	83.15	92.18	10486
+17	MEGHALAYA	2964	15.32	18.75	27.82	986	75.48	73.78	77.17	22429
+18	ASSAM	31169	11.03	14.47	16.93	954	73.18	67.27	78.81	78438
+19	WEST BENGAL	991348	24.55	11.07	13.93	947	77.08	71.16	82.67	88752
+20	JHARKHAND	32966	18.18	15.89	22.34	947	67.63	56.21	78.45	79714
+21	ODISHA	41947	13.15	12	13.97	978	73.45	64.36	82.4	155707
+22	CHHATTISGARH	25540	16.39	14.03	22.59	991	71.04	60.59	81.45	135191
+23	MADHYA PRADESH	72598	21.99	14.53	20.3	930	70.63	60.02	80.53	308245
+24	GUJARAT	60384	31.35	12.41	19.17	918	79.31	70.73	87.23	196024
+25	DAMAN & DIU	243	23.61	10.65	53.54	618	87.07	79.59	91.48	112
+26	DADRA & NAGAR HAVELI	343	14.72	14.35	55.5	775	77.65	65.93	86.46	451
+27	MAHARASHTRA	112373	36.58	11.43	15.99	925	82.91	75.48	89.82	307713
+28	ANDHRA PRADESH	84666	24.58	10.21	11.1	992	67.66	59.74	75.56	275045
+29	KARNATAKA	61131	29.38	11.21	15.67	968	75.6	68.13	82.85	191791
+30	GOA	1458	46	9.57	8.17	968	87.4	81.84	92.81	3702
+31	LAKSHADWEEP	64	41.86	11	6.23	946	92.28	88.25	96.11	32
+32	KERALA	33388	24.76	9.95	44.86	1084	93.91	91.98	96.02	38863
+33	TAMIL NADU	72139	38.1	9.56	15.6	995	80.3	73.86	86.81	130058
+34	PUDUCHERRY	1244	52.12	10.25	27.72	1038	86.55	81.22	92.12	479
+35	ANDAMAN & NICOBAR ISLANDS	380	30.58	10.4	6.68	878	86.27	81.84	90.11	8249
+36	TELANGANA	352	38.67	39.2	13.58	987	66.46	57.92	74.95	114840
\ No newline at end of file

app.py

-from fileinput import filename
 from flask import Flask, flash, request, redirect, url_for, render_template
 import urllib.request
-import os
 from werkzeug.utils import secure_filename
+from fileinput import filename
 import pandas as pd
+import numpy as np
+import os
+import glob
 import matplotlib
 matplotlib.use('Agg')
 import matplotlib.pyplot as plt
-import numpy as np
+
+
+def read_all_csv(path='./rawdata_csv/', extension='csv'):
+    result = glob.glob(path+'SRC_R*.{}'.format(extension))
+    df=None
+    for file in result:
+        if(df is None):
+            df=pd.read_csv(file)
+        else:
+            df=pd.concat([df,pd.read_csv(file)], ignore_index=True)
+    df_master=pd.read_csv("SRC_MasterData.csv")
+    df_master=df_master[['State_Code','State_Name','Total_Population']]
+    final_df = pd.merge(df, df_master, on='State_Code', how='inner')
+    final_df['total']=final_df[['Primary_Only','Primary_with_Upper_Primary','Primary with_Upper_Primary_Sec_H.Sec','Upper_Primary_Only','Upper_Primary_with_Sec_H.Sec','Primary_with_Upper_Primary_Sec','Upper_Primary_with _Sec']].sum(axis=1)
+    final_df['total_by_pop']=final_df['total']/final_df['Total_Population']
+
+    final_df=final_df[["Year","State_Name","State_Code","Main_Dimension","Sub_Dimension","Total_Population",'total','total_by_pop']]
+
+    return final_df
+
+final_df=read_all_csv()
 
 app = Flask(__name__)