USA voters' (2012) analysis¶
Data visualisations made by Janhavi Pimplikar
A student at Pimpri Chinchwad College of Engineering
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly as py
from plotly.offline import iplot
import cufflinks as cf
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py.offline.init_notebook_mode(connected=True)
import seaborn as sns
cf.go_offline()
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import plotly.express as px
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Total and citizen population in the US¶
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abc=pd.read_csv('USVoters.csv')
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abc
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ABC=abc.groupby('Abbr.').sum().reset_index()
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ABC
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fig=px.choropleth(ABC,locations='Abbr.',locationmode='USA-states',color=np.log(ABC['Total Population']),hover_name='Abbr.',color_continuous_scale=px.colors.sequential.Inferno,scope='usa',range_color=(0,20),title='US population by State')
fig.update(layout_coloraxis_showscale=True)
fig.show()
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AGE=abc.groupby('Age')['Total Population'].sum()
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AGE
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fig,ax=plt.subplots(figsize=(10,10))
AGE.plot.pie(y='Total Population',legend=True,shadow=True,colors=sns.color_palette('Set2'),ax=ax,autopct='%1.2f%%',fontsize=15)
c=plt.Circle((0,0),0.3,color='white')
plt.gca().add_artist(c)
plt.legend(loc='upper right')
plt.ylabel('Total US population by age group',size=15)
fig.show()
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fig=px.choropleth(ABC,locations='Abbr.',locationmode='USA-states',color=np.log(ABC['Citizen Population']),hover_name='Abbr.',color_continuous_scale=px.colors.sequential.Viridis,scope='usa',range_color=(0,20),title='US citizen population by State')
fig.update(layout_coloraxis_showscale=True)
fig.show()
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fig,ax=plt.subplots(figsize=(10,10))
AGE.plot.pie(y='Citizen Population',legend=True,shadow=True,colors=sns.color_palette('Pastel1'),ax=ax,autopct='%1.2f%%',fontsize=15)
c=plt.Circle((0,0),0.3,color='white')
plt.gca().add_artist(c)
plt.legend(loc='upper right')
plt.ylabel('US citizen population by age group',size=15)
fig.show()
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sns.jointplot(x='Total Population',y='Citizen Population',color='red',data=ABC,height=8)
plt.xticks(size=20)
plt.yticks(size=20)
sns.set_style('darkgrid')
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Registered voters and confirmed voters in the US¶
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ABC.iplot(x='Abbr.',y='Registered Voters',kind='bar',color='purple',xTitle='States',yTitle='Registered Voters',title='Number of Registered voters per state')
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ABC.iplot(x='Citizen Population',y='Registered Voters',mode='markers',color='lightgreen',xTitle='Citizen Population',yTitle='Registered Voters',title='Relational analysis between citizens and registered voters')
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sns.catplot(x='Age',y='Registered Voters',kind='violin',height=8,aspect=2,data=abc,palette='rainbow')
plt.xlabel('Age groups',size=20)
plt.xticks(size=20)
plt.ylabel('Number of Registered Voters',size=20)
plt.yticks(size=20)
plt.title('Frequency of Registered voters by age',size=20)
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ABC.iplot(x='Abbr.',y='Confirmed Voters',kind='bar',color='fuchsia',xTitle='States',yTitle='Confirmed Voters',title='Number of Confirmed voters per state')
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ABC.iplot(x='Citizen Population',y='Confirmed Voters',mode='markers',color='navy',xTitle='Citizen Population',yTitle='Confirmed Voters',title='Relational analysis between citizens and confirmed voters')
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sns.catplot(x='Age',y='Confirmed Voters',kind='violin',height=8,aspect=2,data=abc,palette='twilight')
plt.xlabel('Age groups',size=20)
plt.xticks(size=20)
plt.ylabel('Number of Confirmed Voters',size=20)
plt.yticks(size=20)
plt.title('Frequency of confirmed voters by age',size=20)
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Top 10 states with the highest voter turnout¶
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#voter turnout %=(confirmed voters/registered voters)*100
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ABC['Voter Turnout in %']=(ABC['Confirmed Voters']/ABC['Registered Voters'])*100
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ABC
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ABC_edited=ABC.sort_values('Voter Turnout in %',ascending=False,inplace=True)
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ABC_edited=ABC.head(10)
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ABC_edited
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fig=px.choropleth(ABC_edited,locations='Abbr.',locationmode='USA-states',color='Voter Turnout in %',hover_name='Abbr.',color_continuous_scale=px.colors.sequential.Electric,title='States with the highest turnout',scope="usa")
fig.update(layout_coloraxis_showscale=True)
fig.show()
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dataset imported from the resources from https://www.udemy.com/course/data-analysis-with-excel-pivot-tables/
attributes: Websites such as stackoverflow.com, plotlyexpress.com, geeksforgeeks.com etc.