<jupyter_start><jupyter_text>The plot provides an overview as to where we can focus our resources, One can invest more in uttar pradesh as the population clearly is an advantage<jupyter_code>plt.figure(figsize=(12,15))
sns.barplot(df['SCHTOT'], df['STATNAME'],alpha=0.8)
plt.xticks(rotation='vertical')
plt.xlabel('Number of Schools', fontsize=15)
plt.ylabel('States in India', fontsize=15)
plt.title("Number of schools in states of India", fontsize=16)
plt.show()<jupyter_output><empty_output><jupyter_text>We see a relation i.e population increases the number of schools also increases<jupyter_code>plt.figure(figsize=(25,17))
for i in range(1,len(data)):
    plt.subplot(4,9,i)
    plt.title(df['STATNAME'][i])
    top = ['Gov','pri']
    uttar = data.loc[df['STATNAME'] == df['STATNAME'][i],:]
    value =[float(uttar['SCHTOTG']/uttar['SCHTOT'])*100,float(uttar['SCHTOTPR']/uttar['SCHTOT'])*100]
    plt.pie(value, labels=top, autopct='%1.1f%%',startangle=140)
    plt.axis('equal')
plt.show()<jupyter_output><empty_output><jupyter_text>Ratio of government vs private schools with respect to every state in India1) The number of governments schools is high in almost all states of India (except kerala)2) 60% of the schools in kerala is private schools, which is unlike any other state ( it is an anamoly)¶3) Incidentally kerala also has the highest literacy rate among the Indian states4) In Bihar, which has one of the lowest literacy rates only 3% of the schools are private Does this mean that a states literacy depends on the number of private educations institutions, We can safely assume that there is a relation (as little as it may seem)<jupyter_code>plt.figure(figsize=(10,12))
sns.barplot(data['OVERALL_LI'], data['STATNAME'],alpha=0.8)
plt.xticks(rotation='vertical')
plt.xlabel("Literacy rate", fontsize=16)
plt.title('Literacy rate with respect to state')
plt.show()<jupyter_output><empty_output><jupyter_text>We can see almost all in India states have more than 50% literacy rate<jupyter_code>data['good'] = data['ROADTOT'] +data['SPLAYTOT'] + data['SWATTOT'] +data['SELETOT']
data['goodpercent'] = data['good']/data['SCHTOT']
plt.figure(figsize=(10,12))
sns.barplot(data['goodpercent'], data['STATNAME'],alpha=1)
plt.xticks(rotation='vertical')
plt.xlabel("Literacy rate", fontsize=16)
plt.title('Literacy rate with respect to state')
plt.show()<jupyter_output><empty_output>
StarcoderdataJupyterStructuredCleanDedup