# -*- coding: utf-8 -*-
"""
Created on Wed Jul 15 20:56:53 2020

@author: Deepak
"""


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

data=pd.read_csv(r".spyder-py3\Happiness_rank_2020.csv")
data.info()
data.head()

plt.figure(figsize=(10,10))
plt.bar(data["Country name"], data["Ladder score"])
plt.title("Happiness Ranking among Countries")
plt.xlabel("Country")
plt.ylabel("score")
plt.show()

plt.bar(data["Country name"], data["Logged GDP per capita"])
plt.title("Happiness Ranking among Countries")
plt.xlabel("Country")
plt.ylabel("Gdp per capita")
plt.show()

plt.figure(figsize=(10,10))
plt.bar(data["Country name"], data["Freedom to make life choices"])
plt.title("Happiness Ranking among Countries")
plt.xlabel("Country")
plt.ylabel("Freedom to make life choices")
plt.show()

plt.figure(figsize=(10,10))
plt.bar(data["Country name"], data["Generosity"])
plt.title("Happiness Ranking among Countries")
plt.xlabel("Country")
plt.ylabel("Generosity")
plt.show()

plt.figure(figsize=(10,10))
plt.bar(data["Country name"], data["Healthy life expectancy"])
plt.title("Happiness Ranking among Countries")
plt.xlabel("Country")
plt.ylabel("Health life expectation")
plt.show()

plt.figure(figsize=(10,10))
plt.bar(data["Country name"], data["Perceptions of corruption"])
plt.title("Happiness Ranking among Countries")
plt.xlabel("Country")
plt.ylabel("corruption")
plt.show()

plt.figure(figsize=(10,10))
plt.bar(data["Country name"], data["Social support"])
plt.title("Happiness Ranking among Countries")
plt.xlabel("Country")
plt.ylabel("social support")
plt.show()

4e694532fad226c3d2e7d7796ec71ed8484d0fb1
<mask token>

<mask token>
data.info()
data.head()
plt.figure(figsize=(10, 10))
plt.bar(data['Country name'], data['Ladder score'])
plt.title('Happiness Ranking among Countries')
plt.xlabel('Country')
plt.ylabel('score')
plt.show()
plt.bar(data['Country name'], data['Logged GDP per capita'])
plt.title('Happiness Ranking among Countries')
plt.xlabel('Country')
plt.ylabel('Gdp per capita')
plt.show()
plt.figure(figsize=(10, 10))
plt.bar(data['Country name'], data['Freedom to make life choices'])
plt.title('Happiness Ranking among Countries')
plt.xlabel('Country')
plt.ylabel('Freedom to make life choices')
plt.show()
plt.figure(figsize=(10, 10))
plt.bar(data['Country name'], data['Generosity'])
plt.title('Happiness Ranking among Countries')
plt.xlabel('Country')
plt.ylabel('Generosity')
plt.show()
plt.figure(figsize=(10, 10))
plt.bar(data['Country name'], data['Healthy life expectancy'])
plt.title('Happiness Ranking among Countries')
plt.xlabel('Country')
plt.ylabel('Health life expectation')
plt.show()
plt.figure(figsize=(10, 10))
plt.bar(data['Country name'], data['Perceptions of corruption'])
plt.title('Happiness Ranking among Countries')
plt.xlabel('Country')
plt.ylabel('corruption')
plt.show()
plt.figure(figsize=(10, 10))
plt.bar(data['Country name'], data['Social support'])
plt.title('Happiness Ranking among Countries')
plt.xlabel('Country')
plt.ylabel('social support')
plt.show()

<mask token>
data = pd.read_csv('.spyder-py3\\Happiness_rank_2020.csv')
data.info()
data.head()
plt.figure(figsize=(10, 10))
plt.bar(data['Country name'], data['Ladder score'])
plt.title('Happiness Ranking among Countries')
plt.xlabel('Country')
plt.ylabel('score')
plt.show()
plt.bar(data['Country name'], data['Logged GDP per capita'])
plt.title('Happiness Ranking among Countries')
plt.xlabel('Country')
plt.ylabel('Gdp per capita')
plt.show()
plt.figure(figsize=(10, 10))
plt.bar(data['Country name'], data['Freedom to make life choices'])
plt.title('Happiness Ranking among Countries')
plt.xlabel('Country')
plt.ylabel('Freedom to make life choices')
plt.show()
plt.figure(figsize=(10, 10))
plt.bar(data['Country name'], data['Generosity'])
plt.title('Happiness Ranking among Countries')
plt.xlabel('Country')
plt.ylabel('Generosity')
plt.show()
plt.figure(figsize=(10, 10))
plt.bar(data['Country name'], data['Healthy life expectancy'])
plt.title('Happiness Ranking among Countries')
plt.xlabel('Country')
plt.ylabel('Health life expectation')
plt.show()
plt.figure(figsize=(10, 10))
plt.bar(data['Country name'], data['Perceptions of corruption'])
plt.title('Happiness Ranking among Countries')
plt.xlabel('Country')
plt.ylabel('corruption')
plt.show()
plt.figure(figsize=(10, 10))
plt.bar(data['Country name'], data['Social support'])
plt.title('Happiness Ranking among Countries')
plt.xlabel('Country')
plt.ylabel('social support')
plt.show()

<mask token>
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv('.spyder-py3\\Happiness_rank_2020.csv')
data.info()
data.head()
plt.figure(figsize=(10, 10))
plt.bar(data['Country name'], data['Ladder score'])
plt.title('Happiness Ranking among Countries')
plt.xlabel('Country')
plt.ylabel('score')
plt.show()
plt.bar(data['Country name'], data['Logged GDP per capita'])
plt.title('Happiness Ranking among Countries')
plt.xlabel('Country')
plt.ylabel('Gdp per capita')
plt.show()
plt.figure(figsize=(10, 10))
plt.bar(data['Country name'], data['Freedom to make life choices'])
plt.title('Happiness Ranking among Countries')
plt.xlabel('Country')
plt.ylabel('Freedom to make life choices')
plt.show()
plt.figure(figsize=(10, 10))
plt.bar(data['Country name'], data['Generosity'])
plt.title('Happiness Ranking among Countries')
plt.xlabel('Country')
plt.ylabel('Generosity')
plt.show()
plt.figure(figsize=(10, 10))
plt.bar(data['Country name'], data['Healthy life expectancy'])
plt.title('Happiness Ranking among Countries')
plt.xlabel('Country')
plt.ylabel('Health life expectation')
plt.show()
plt.figure(figsize=(10, 10))
plt.bar(data['Country name'], data['Perceptions of corruption'])
plt.title('Happiness Ranking among Countries')
plt.xlabel('Country')
plt.ylabel('corruption')
plt.show()
plt.figure(figsize=(10, 10))
plt.bar(data['Country name'], data['Social support'])
plt.title('Happiness Ranking among Countries')
plt.xlabel('Country')
plt.ylabel('social support')
plt.show()

# -*- coding: utf-8 -*-
"""
Created on Wed Jul 15 20:56:53 2020

@author: Deepak
"""


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

data=pd.read_csv(r".spyder-py3\Happiness_rank_2020.csv")
data.info()
data.head()

plt.figure(figsize=(10,10))
plt.bar(data["Country name"], data["Ladder score"])
plt.title("Happiness Ranking among Countries")
plt.xlabel("Country")
plt.ylabel("score")
plt.show()

plt.bar(data["Country name"], data["Logged GDP per capita"])
plt.title("Happiness Ranking among Countries")
plt.xlabel("Country")
plt.ylabel("Gdp per capita")
plt.show()

plt.figure(figsize=(10,10))
plt.bar(data["Country name"], data["Freedom to make life choices"])
plt.title("Happiness Ranking among Countries")
plt.xlabel("Country")
plt.ylabel("Freedom to make life choices")
plt.show()

plt.figure(figsize=(10,10))
plt.bar(data["Country name"], data["Generosity"])
plt.title("Happiness Ranking among Countries")
plt.xlabel("Country")
plt.ylabel("Generosity")
plt.show()

plt.figure(figsize=(10,10))
plt.bar(data["Country name"], data["Healthy life expectancy"])
plt.title("Happiness Ranking among Countries")
plt.xlabel("Country")
plt.ylabel("Health life expectation")
plt.show()

plt.figure(figsize=(10,10))
plt.bar(data["Country name"], data["Perceptions of corruption"])
plt.title("Happiness Ranking among Countries")
plt.xlabel("Country")
plt.ylabel("corruption")
plt.show()

plt.figure(figsize=(10,10))
plt.bar(data["Country name"], data["Social support"])
plt.title("Happiness Ranking among Countries")
plt.xlabel("Country")
plt.ylabel("social support")
plt.show()
