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Education in INDIA
Commit 3d12d831 authored by Manas Gabani's avatar Manas Gabani
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logical error in get_distribution_for_teachers_by_educational_qualification... 

logical error in get_distribution_for_teachers_by_educational_qualification route handled and created utils.py for all utility functions
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app.py
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from flask import Flask, flash, request, redirect, url_for, render_template
import urllib.request
from werkzeug.utils import secure_filename
from fileinput import filename
import pandas as pd
import numpy as np
import os
import glob
from utils import *
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
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_population']=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_population']]
return final_df
final_df=read_all_csv()
latest_year = max(final_df['Year'])
dict_summary={"top3":[],"bottom3":[]}
form_value_to_sub_dimension_mapping = {
'govt':'Government',
......@@ -82,196 +57,8 @@ default_state_code = 27 # for Maharashtra
fig_size_w, fig_size_h = 8, 6
fig_size_tr_w, fig_size_tr_h = 8,6
def statewise_distribution(output_filename, input_df, year, main_dimension, sub_dimension=''):
if sub_dimension:
df_filtered = input_df.loc[(input_df['Main_Dimension'] == main_dimension) & ((input_df['Sub_Dimension'] == sub_dimension))]
else:
df_filtered = input_df.loc[(input_df['Main_Dimension'] == main_dimension)]
df_filtered = df_filtered.loc[df_filtered['Year'] == year]
df_filtered = df_filtered[['Year','State_Name','total','total_by_population']]
df_sum = df_filtered.groupby(['State_Name','Year'],as_index = False).sum().sort_values("total_by_population",ascending=False)
fig = plt.figure(figsize=(fig_size_w,fig_size_h))
plt.xticks(rotation=90, ha="right")
plt.bar(df_sum['State_Name'],df_sum['total_by_population'], align='center')
plt.ylabel('Enrollements/Population')
list_states=list(df_sum['State_Name'])
dict_summary['top3']=[list_states[0],list_states[1],list_states[2]]
dict_summary['bottom3']=[list_states[-1],list_states[-2],list_states[-3]]
plt.savefig(output_filename)
plt.close(fig)
return dict_summary
def total_enrolment_by_category(output_filename, input_df, main_dimension, state_code, ylabel):
fig = plt.figure(figsize=(fig_size_tr_w,fig_size_tr_h))
df_fil = input_df.loc[input_df['Main_Dimension'] == main_dimension]
df_fil = df_fil.loc[(df_fil['State_Code'] == state_code)]
min_year, max_year = min(df_fil['Year']), max(df_fil['Year'])
df_sum = df_fil.groupby(['Sub_Dimension','State_Code','Year'],as_index = False).sum()
df_sum = df_sum[['Sub_Dimension','State_Code','Year','total']]
df_govt=df_sum.loc[(df_sum['Sub_Dimension'] == "Government")]
df_pvt=df_sum.loc[(df_sum['Sub_Dimension'] == "Private")]
plt.plot(df_govt['Year'],df_govt['total'],color='r', label='Goverment')
plt.plot(df_pvt['Year'],df_pvt['total'],color='g', label='Private')
if not 'Rural' in main_dimension:
df_madr=df_sum.loc[(df_sum['Sub_Dimension'] == "Madrasas & Unrecognised")]
plt.plot(df_madr['Year'],df_madr['total'],color='b', label='Madrasas')
plt.legend()
plt.xticks(np.arange(min_year, max_year+1, 1.0))
plt.ylabel(ylabel)
plt.xlabel('Year')
plt.savefig(output_filename)
plt.close(fig)
def total_teachers_by_main_dimension(output_filename, input_df, main_dimension, state_code):
fig = plt.figure(figsize=(fig_size_tr_w,fig_size_tr_h))
df_fil = input_df.loc[input_df['Main_Dimension'] == main_dimension]
df_fil = df_fil.loc[(df_fil['State_Code'] == state_code)]
min_year, max_year = min(df_fil['Year']), max(df_fil['Year'])
df_sum = df_fil.groupby(['Sub_Dimension','State_Code','Year'],as_index = False).sum()
df_sum = df_sum[['Sub_Dimension','State_Code','Year','total']]
if 'Category' in main_dimension:
df_govt = df_sum.loc[(df_sum['Sub_Dimension'] == "Government")]
df_pvt = df_sum.loc[(df_sum['Sub_Dimension'] == "Private")]
df_madr = df_sum.loc[(df_sum['Sub_Dimension'] == "Madrasas & Unrecognised")]
plt.plot(df_govt['Year'],df_govt['total'],color='r', label='Goverment')
plt.plot(df_pvt['Year'],df_pvt['total'],color='g', label='Private')
plt.plot(df_madr['Year'],df_madr['total'],color='b', label='Madrasas')
else:
df_bel_sec = df_sum.loc[(df_sum['Sub_Dimension'] == "Below Secondary")]
df_sec = df_sum.loc[(df_sum['Sub_Dimension'] == "Secondary")]
df_hs = df_sum.loc[(df_sum['Sub_Dimension'] == "Higher Secondary")]
df_grad = df_sum.loc[(df_sum['Sub_Dimension'] == "Graduate")]
df_pg = df_sum.loc[(df_sum['Sub_Dimension'] == "Post Graduate")]
df_phd = df_sum.loc[(df_sum['Sub_Dimension'] == "M.Phil / Ph.D")]
plt.plot(df_bel_sec['Year'],df_bel_sec['total'],color='r', label='Below Secondary')
plt.plot(df_sec['Year'],df_sec['total'],color='g', label='Secondary')
plt.plot(df_hs['Year'],df_hs['total'],color='b', label='Higher Secondary')
plt.plot(df_grad['Year'],df_grad['total'],color='y', label='Graduate')
plt.plot(df_pg['Year'],df_pg['total'],color='c', label='Post Graduate')
plt.plot(df_phd['Year'],df_phd['total'],color='m', label='M.Phil / Ph.D')
plt.legend()
plt.xticks(np.arange(min_year, max_year+1, 1.0))
plt.ylabel('Number of Teachers')
plt.xlabel('Year')
plt.savefig(output_filename)
plt.close(fig)
def total_classrooms_by_trend(output_filename, input_df, state_code):
fig = plt.figure(figsize=(fig_size_tr_w,fig_size_tr_h))
df_fil=input_df.loc[input_df['Main_Dimension'] == "Total Classrooms"]
df_fil=df_fil.loc[(df_fil['State_Code'] == state_code)]
min_year, max_year = min(df_fil['Year']), max(df_fil['Year'])
df_sum = df_fil.groupby(['Sub_Dimension','State_Code','Year'],as_index = False).sum()
df_sum = df_sum[['Sub_Dimension','State_Code','Year','total']]
df_good_cond = df_sum.loc[(df_sum['Sub_Dimension'] == "Good Condition")]
df_minor_repair = df_sum.loc[(df_sum['Sub_Dimension'] == "Need Minor Repair")]
df_major_repair = df_sum.loc[(df_sum['Sub_Dimension'] == "Need Major Repair")]
plt.plot(df_good_cond['Year'],df_good_cond['total'],color='r', label='Good Condition')
plt.plot(df_minor_repair['Year'],df_minor_repair['total'],color='g', label='Need Minor Repair')
plt.plot(df_major_repair['Year'],df_major_repair['total'],color='b', label='Need Major Repair')
plt.legend()
plt.xticks(np.arange(min_year, max_year+1, 1.0))
plt.ylabel('Number of classrooms')
plt.xlabel('Year')
plt.savefig(output_filename)
plt.close(fig)
def total_schools_for_facilities(output_filename, input_df, state_code):
fig = plt.figure(figsize=(fig_size_tr_w,fig_size_tr_h))
df_fil=input_df.loc[input_df['Main_Dimension'] == "School Facilities"]
df_fil=df_fil.loc[(df_fil['State_Code'] == state_code)]
min_year, max_year = min(df_fil['Year']), max(df_fil['Year'])
df_sum = df_fil.groupby(['Sub_Dimension','State_Code','Year'],as_index = False).sum()
df_sum = df_sum[['Sub_Dimension','State_Code','Year','total']]
df_enrolment_lt_50 = df_sum.loc[(df_sum['Sub_Dimension'] == "Enrolment <= 50")]
df_drinking_water = df_sum.loc[(df_sum['Sub_Dimension'] == "Schools with Drinking Water")]
df_girls_toilet = df_sum.loc[(df_sum['Sub_Dimension'] == "Schools with Girls' Toilet")]
df_ramp = df_sum.loc[(df_sum['Sub_Dimension'] == "Schools with Ramp")]
df_single_classroom = df_sum.loc[(df_sum['Sub_Dimension'] == "Single Classroom Schools")]
df_single_teacher = df_sum.loc[(df_sum['Sub_Dimension'] == "Single Teacher Schools")]
plt.plot(df_enrolment_lt_50['Year'], df_enrolment_lt_50['total'], color='r', label='Enrolment <= 50')
plt.plot(df_drinking_water['Year'], df_drinking_water['total'], color='g', label='Schools with Drinking Water')
plt.plot(df_girls_toilet['Year'], df_girls_toilet['total'], color='b', label='Schools with Girls\' Toilet')
plt.plot(df_ramp['Year'], df_ramp['total'], color='y', label='Schools with Ramp')
plt.plot(df_single_classroom['Year'], df_single_classroom['total'], color='c', label='Single Classroom Schools')
plt.plot(df_single_teacher['Year'], df_single_teacher['total'], color='m', label='Single Teacher Schools')
plt.legend()
plt.xticks(np.arange(min_year, max_year+1, 1.0))
plt.ylabel('Number of schools')
plt.xlabel('Year')
plt.savefig(output_filename)
plt.close(fig)
def main_dimension_overall(output_filename, input_df, main_dimension, ylabel):
fig = plt.figure(figsize=(fig_size_tr_w,fig_size_tr_h))
df_fil=input_df.loc[input_df['Main_Dimension'] == main_dimension]
min_year, max_year = min(df_fil['Year']), max(df_fil['Year'])
df_sum = df_fil.groupby(['Sub_Dimension','Year'],as_index = False).sum()
df_sum = df_sum[['Sub_Dimension','Year','total']]
if 'Classrooms' not in main_dimension:
df_govt=df_sum.loc[(df_sum['Sub_Dimension'] == "Government")]
df_pvt=df_sum.loc[(df_sum['Sub_Dimension'] == "Private")]
df_madr=df_sum.loc[(df_sum['Sub_Dimension'] == "Madrasas & Unrecognised")]
plt.plot(df_govt['Year'],df_govt['total'],color='r', label='Goverment')
plt.plot(df_pvt['Year'],df_pvt['total'],color='g', label='Private')
plt.plot(df_madr['Year'],df_madr['total'],color='b', label='Madrasas')
else:
df_good_cond = df_sum.loc[(df_sum['Sub_Dimension'] == "Good Condition")]
df_minor_repair = df_sum.loc[(df_sum['Sub_Dimension'] == "Need Minor Repair")]
df_major_repair = df_sum.loc[(df_sum['Sub_Dimension'] == "Need Major Repair")]
plt.plot(df_good_cond['Year'],df_good_cond['total'],color='r', label='Good Condition')
plt.plot(df_minor_repair['Year'],df_minor_repair['total'],color='g', label='Need Minor Repair')
plt.plot(df_major_repair['Year'],df_major_repair['total'],color='b', label='Need Major Repair')
plt.legend()
plt.xticks(np.arange(min_year, max_year+1, 1.0))
plt.ylabel(ylabel)
plt.xlabel('Year')
plt.savefig(output_filename)
plt.close(fig)
def total_schools_for_facilities_overall(output_filename, input_df):
fig = plt.figure(figsize=(fig_size_tr_w,fig_size_tr_h))
df_fil=input_df.loc[input_df['Main_Dimension'] == "School Facilities"]
min_year, max_year = min(df_fil['Year']), max(df_fil['Year'])
df_sum = df_fil.groupby(['Sub_Dimension','Year'],as_index = False).sum()
df_sum = df_sum[['Sub_Dimension','Year','total']]
df_enrolment_lt_50 = df_sum.loc[(df_sum['Sub_Dimension'] == "Enrolment <= 50")]
df_drinking_water = df_sum.loc[(df_sum['Sub_Dimension'] == "Schools with Drinking Water")]
df_girls_toilet = df_sum.loc[(df_sum['Sub_Dimension'] == "Schools with Girls' Toilet")]
df_ramp = df_sum.loc[(df_sum['Sub_Dimension'] == "Schools with Ramp")]
df_single_classroom = df_sum.loc[(df_sum['Sub_Dimension'] == "Single Classroom Schools")]
df_single_teacher = df_sum.loc[(df_sum['Sub_Dimension'] == "Single Teacher Schools")]
plt.plot(df_enrolment_lt_50['Year'], df_enrolment_lt_50['total'], color='r', label='Enrolment <= 50')
plt.plot(df_drinking_water['Year'], df_drinking_water['total'], color='g', label='Schools with Drinking Water')
plt.plot(df_girls_toilet['Year'], df_girls_toilet['total'], color='b', label='Schools with Girls\' Toilet')
plt.plot(df_ramp['Year'], df_ramp['total'], color='y', label='Schools with Ramp')
plt.plot(df_single_classroom['Year'], df_single_classroom['total'], color='c', label='Single Classroom Schools')
plt.plot(df_single_teacher['Year'], df_single_teacher['total'], color='m', label='Single Teacher Schools')
plt.legend()
plt.xticks(np.arange(min_year, max_year+1, 1.0))
plt.ylabel('Number of schools')
plt.xlabel('Year')
plt.savefig(output_filename)
plt.close(fig)
final_df=read_all_csv()
latest_year = max(final_df['Year'])
app = Flask(__name__)
print(' ------------ Server Started ------------ ')
......@@ -455,7 +242,7 @@ def get_distribution_for_teachers_by_educational_qualification():
global distribution_teachers_by_educational_qualification, trend_teachers, distribution_teachers_by_category, teachers_by_category_summary, teachers_by_educational_qualification_summary, teachers_category, teachers_qualification
teachers_qualification = form_value_to_sub_dimension_mapping[request.form['qualification']]
requested_year = int(request.form['year'])
distribution_teachers_by_educational_qualification = "img/distribution_teachers_by_educational_qualification_{}_{}.jpeg".format(request.form['category'], requested_year)
distribution_teachers_by_educational_qualification = "img/distribution_teachers_by_educational_qualification_{}_{}.jpeg".format(request.form['qualification'], requested_year)
teachers_by_educational_qualification_summary = statewise_distribution(os.path.join(app.config['UPLOAD_FOLDER'], distribution_teachers_by_educational_qualification), final_df, requested_year, main_dimension = "Teachers by Educational Qualification", sub_dimension=teachers_qualification)
return render_template('teachers.html', trend_teachers=trend_teachers, distribution_teachers_by_category=distribution_teachers_by_category, distribution_teachers_by_educational_qualification=distribution_teachers_by_educational_qualification, teachers_by_category_summary=teachers_by_category_summary, teachers_by_educational_qualification_summary=teachers_by_educational_qualification_summary, teachers_category=teachers_category, teachers_qualification=teachers_qualification)
......
static/.DS_Store
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utils.py 0 → 100644
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import pandas as pd
import numpy as np
import os
import glob
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
dict_summary={"top3":[],"bottom3":[]}
fig_size_w, fig_size_h = 8, 6
fig_size_tr_w, fig_size_tr_h = 8,6
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_population']=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_population']]
return final_df
def statewise_distribution(output_filename, input_df, year, main_dimension, sub_dimension=''):
if sub_dimension:
df_filtered = input_df.loc[(input_df['Main_Dimension'] == main_dimension) & ((input_df['Sub_Dimension'] == sub_dimension))]
else:
df_filtered = input_df.loc[(input_df['Main_Dimension'] == main_dimension)]
df_filtered = df_filtered.loc[df_filtered['Year'] == year]
df_filtered = df_filtered[['Year','State_Name','total','total_by_population']]
df_sum = df_filtered.groupby(['State_Name','Year'],as_index = False).sum().sort_values("total_by_population",ascending=False)
fig = plt.figure(figsize=(fig_size_w,fig_size_h))
plt.xticks(rotation=90, ha="right")
plt.bar(df_sum['State_Name'],df_sum['total_by_population'], align='center')
plt.ylabel('Enrollements/Population')
list_states=list(df_sum['State_Name'])
dict_summary['top3']=[list_states[0],list_states[1],list_states[2]]
dict_summary['bottom3']=[list_states[-1],list_states[-2],list_states[-3]]
plt.savefig(output_filename)
plt.close(fig)
return dict_summary
def total_enrolment_by_category(output_filename, input_df, main_dimension, state_code, ylabel):
fig = plt.figure(figsize=(fig_size_tr_w,fig_size_tr_h))
df_fil = input_df.loc[input_df['Main_Dimension'] == main_dimension]
df_fil = df_fil.loc[(df_fil['State_Code'] == state_code)]
min_year, max_year = min(df_fil['Year']), max(df_fil['Year'])
df_sum = df_fil.groupby(['Sub_Dimension','State_Code','Year'],as_index = False).sum()
df_sum = df_sum[['Sub_Dimension','State_Code','Year','total']]
df_govt=df_sum.loc[(df_sum['Sub_Dimension'] == "Government")]
df_pvt=df_sum.loc[(df_sum['Sub_Dimension'] == "Private")]
plt.plot(df_govt['Year'],df_govt['total'],color='r', label='Goverment')
plt.plot(df_pvt['Year'],df_pvt['total'],color='g', label='Private')
if not 'Rural' in main_dimension:
df_madr=df_sum.loc[(df_sum['Sub_Dimension'] == "Madrasas & Unrecognised")]
plt.plot(df_madr['Year'],df_madr['total'],color='b', label='Madrasas')
plt.legend()
plt.xticks(np.arange(min_year, max_year+1, 1.0))
plt.ylabel(ylabel)
plt.xlabel('Year')
plt.savefig(output_filename)
plt.close(fig)
def total_teachers_by_main_dimension(output_filename, input_df, main_dimension, state_code):
fig = plt.figure(figsize=(fig_size_tr_w,fig_size_tr_h))
df_fil = input_df.loc[input_df['Main_Dimension'] == main_dimension]
df_fil = df_fil.loc[(df_fil['State_Code'] == state_code)]
min_year, max_year = min(df_fil['Year']), max(df_fil['Year'])
df_sum = df_fil.groupby(['Sub_Dimension','State_Code','Year'],as_index = False).sum()
df_sum = df_sum[['Sub_Dimension','State_Code','Year','total']]
if 'Category' in main_dimension:
df_govt = df_sum.loc[(df_sum['Sub_Dimension'] == "Government")]
df_pvt = df_sum.loc[(df_sum['Sub_Dimension'] == "Private")]
df_madr = df_sum.loc[(df_sum['Sub_Dimension'] == "Madrasas & Unrecognised")]
plt.plot(df_govt['Year'],df_govt['total'],color='r', label='Goverment')
plt.plot(df_pvt['Year'],df_pvt['total'],color='g', label='Private')
plt.plot(df_madr['Year'],df_madr['total'],color='b', label='Madrasas')
else:
df_bel_sec = df_sum.loc[(df_sum['Sub_Dimension'] == "Below Secondary")]
df_sec = df_sum.loc[(df_sum['Sub_Dimension'] == "Secondary")]
df_hs = df_sum.loc[(df_sum['Sub_Dimension'] == "Higher Secondary")]
df_grad = df_sum.loc[(df_sum['Sub_Dimension'] == "Graduate")]
df_pg = df_sum.loc[(df_sum['Sub_Dimension'] == "Post Graduate")]
df_phd = df_sum.loc[(df_sum['Sub_Dimension'] == "M.Phil / Ph.D")]
plt.plot(df_bel_sec['Year'],df_bel_sec['total'],color='r', label='Below Secondary')
plt.plot(df_sec['Year'],df_sec['total'],color='g', label='Secondary')
plt.plot(df_hs['Year'],df_hs['total'],color='b', label='Higher Secondary')
plt.plot(df_grad['Year'],df_grad['total'],color='y', label='Graduate')
plt.plot(df_pg['Year'],df_pg['total'],color='c', label='Post Graduate')
plt.plot(df_phd['Year'],df_phd['total'],color='m', label='M.Phil / Ph.D')
plt.legend()
plt.xticks(np.arange(min_year, max_year+1, 1.0))
plt.ylabel('Number of Teachers')
plt.xlabel('Year')
plt.savefig(output_filename)
plt.close(fig)
def total_classrooms_by_trend(output_filename, input_df, state_code):
fig = plt.figure(figsize=(fig_size_tr_w,fig_size_tr_h))
df_fil=input_df.loc[input_df['Main_Dimension'] == "Total Classrooms"]
df_fil=df_fil.loc[(df_fil['State_Code'] == state_code)]
min_year, max_year = min(df_fil['Year']), max(df_fil['Year'])
df_sum = df_fil.groupby(['Sub_Dimension','State_Code','Year'],as_index = False).sum()
df_sum = df_sum[['Sub_Dimension','State_Code','Year','total']]
df_good_cond = df_sum.loc[(df_sum['Sub_Dimension'] == "Good Condition")]
df_minor_repair = df_sum.loc[(df_sum['Sub_Dimension'] == "Need Minor Repair")]
df_major_repair = df_sum.loc[(df_sum['Sub_Dimension'] == "Need Major Repair")]
plt.plot(df_good_cond['Year'],df_good_cond['total'],color='r', label='Good Condition')
plt.plot(df_minor_repair['Year'],df_minor_repair['total'],color='g', label='Need Minor Repair')
plt.plot(df_major_repair['Year'],df_major_repair['total'],color='b', label='Need Major Repair')
plt.legend()
plt.xticks(np.arange(min_year, max_year+1, 1.0))
plt.ylabel('Number of classrooms')
plt.xlabel('Year')
plt.savefig(output_filename)
plt.close(fig)
def total_schools_for_facilities(output_filename, input_df, state_code):
fig = plt.figure(figsize=(fig_size_tr_w,fig_size_tr_h))
df_fil=input_df.loc[input_df['Main_Dimension'] == "School Facilities"]
df_fil=df_fil.loc[(df_fil['State_Code'] == state_code)]
min_year, max_year = min(df_fil['Year']), max(df_fil['Year'])
df_sum = df_fil.groupby(['Sub_Dimension','State_Code','Year'],as_index = False).sum()
df_sum = df_sum[['Sub_Dimension','State_Code','Year','total']]
df_enrolment_lt_50 = df_sum.loc[(df_sum['Sub_Dimension'] == "Enrolment <= 50")]
df_drinking_water = df_sum.loc[(df_sum['Sub_Dimension'] == "Schools with Drinking Water")]
df_girls_toilet = df_sum.loc[(df_sum['Sub_Dimension'] == "Schools with Girls' Toilet")]
df_ramp = df_sum.loc[(df_sum['Sub_Dimension'] == "Schools with Ramp")]
df_single_classroom = df_sum.loc[(df_sum['Sub_Dimension'] == "Single Classroom Schools")]
df_single_teacher = df_sum.loc[(df_sum['Sub_Dimension'] == "Single Teacher Schools")]
plt.plot(df_enrolment_lt_50['Year'], df_enrolment_lt_50['total'], color='r', label='Enrolment <= 50')
plt.plot(df_drinking_water['Year'], df_drinking_water['total'], color='g', label='Schools with Drinking Water')
plt.plot(df_girls_toilet['Year'], df_girls_toilet['total'], color='b', label='Schools with Girls\' Toilet')
plt.plot(df_ramp['Year'], df_ramp['total'], color='y', label='Schools with Ramp')
plt.plot(df_single_classroom['Year'], df_single_classroom['total'], color='c', label='Single Classroom Schools')
plt.plot(df_single_teacher['Year'], df_single_teacher['total'], color='m', label='Single Teacher Schools')
plt.legend()
plt.xticks(np.arange(min_year, max_year+1, 1.0))
plt.ylabel('Number of schools')
plt.xlabel('Year')
plt.savefig(output_filename)
plt.close(fig)
def main_dimension_overall(output_filename, input_df, main_dimension, ylabel):
fig = plt.figure(figsize=(fig_size_tr_w,fig_size_tr_h))
df_fil=input_df.loc[input_df['Main_Dimension'] == main_dimension]
min_year, max_year = min(df_fil['Year']), max(df_fil['Year'])
df_sum = df_fil.groupby(['Sub_Dimension','Year'],as_index = False).sum()
df_sum = df_sum[['Sub_Dimension','Year','total']]
if 'Classrooms' not in main_dimension:
df_govt=df_sum.loc[(df_sum['Sub_Dimension'] == "Government")]
df_pvt=df_sum.loc[(df_sum['Sub_Dimension'] == "Private")]
df_madr=df_sum.loc[(df_sum['Sub_Dimension'] == "Madrasas & Unrecognised")]
plt.plot(df_govt['Year'],df_govt['total'],color='r', label='Goverment')
plt.plot(df_pvt['Year'],df_pvt['total'],color='g', label='Private')
plt.plot(df_madr['Year'],df_madr['total'],color='b', label='Madrasas')
else:
df_good_cond = df_sum.loc[(df_sum['Sub_Dimension'] == "Good Condition")]
df_minor_repair = df_sum.loc[(df_sum['Sub_Dimension'] == "Need Minor Repair")]
df_major_repair = df_sum.loc[(df_sum['Sub_Dimension'] == "Need Major Repair")]
plt.plot(df_good_cond['Year'],df_good_cond['total'],color='r', label='Good Condition')
plt.plot(df_minor_repair['Year'],df_minor_repair['total'],color='g', label='Need Minor Repair')
plt.plot(df_major_repair['Year'],df_major_repair['total'],color='b', label='Need Major Repair')
plt.legend()
plt.xticks(np.arange(min_year, max_year+1, 1.0))
plt.ylabel(ylabel)
plt.xlabel('Year')
plt.savefig(output_filename)
plt.close(fig)
def total_schools_for_facilities_overall(output_filename, input_df):
fig = plt.figure(figsize=(fig_size_tr_w,fig_size_tr_h))
df_fil=input_df.loc[input_df['Main_Dimension'] == "School Facilities"]
min_year, max_year = min(df_fil['Year']), max(df_fil['Year'])
df_sum = df_fil.groupby(['Sub_Dimension','Year'],as_index = False).sum()
df_sum = df_sum[['Sub_Dimension','Year','total']]
df_enrolment_lt_50 = df_sum.loc[(df_sum['Sub_Dimension'] == "Enrolment <= 50")]
df_drinking_water = df_sum.loc[(df_sum['Sub_Dimension'] == "Schools with Drinking Water")]
df_girls_toilet = df_sum.loc[(df_sum['Sub_Dimension'] == "Schools with Girls' Toilet")]
df_ramp = df_sum.loc[(df_sum['Sub_Dimension'] == "Schools with Ramp")]
df_single_classroom = df_sum.loc[(df_sum['Sub_Dimension'] == "Single Classroom Schools")]
df_single_teacher = df_sum.loc[(df_sum['Sub_Dimension'] == "Single Teacher Schools")]
plt.plot(df_enrolment_lt_50['Year'], df_enrolment_lt_50['total'], color='r', label='Enrolment <= 50')
plt.plot(df_drinking_water['Year'], df_drinking_water['total'], color='g', label='Schools with Drinking Water')
plt.plot(df_girls_toilet['Year'], df_girls_toilet['total'], color='b', label='Schools with Girls\' Toilet')
plt.plot(df_ramp['Year'], df_ramp['total'], color='y', label='Schools with Ramp')
plt.plot(df_single_classroom['Year'], df_single_classroom['total'], color='c', label='Single Classroom Schools')
plt.plot(df_single_teacher['Year'], df_single_teacher['total'], color='m', label='Single Teacher Schools')
plt.legend()
plt.xticks(np.arange(min_year, max_year+1, 1.0))
plt.ylabel('Number of schools')
plt.xlabel('Year')
plt.savefig(output_filename)
plt.close(fig)
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