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import numpy as np
import matplotlib.pyplot as plt
import pandas as pd 
import datetime as dt
import statsmodels.api as sm
import seaborn as sns
import cufflinks as cf
import plotly.offline as pyo
import plotly.graph_objs as go
cf.go_offline()
df = pd.read_csv('OECDBLI2017.csv')
df.head()
df.set_index('Country')['Employment rate as pct'].sort_values(ascending=False).iplot(kind='bar')
df.iplot(kind='scatter',
x='Labour market insecurity as pct',
y='Employment rate as pct',
mode='markers',
         	xTitle='Labour market insecurity as pct',
yTitle='Employment rate as pct',
text='Country')
df['Water quality as pct'].iplot(kind='hist')
df.iplot(kind='bubble',
         x='Air pollution in ugm3',
         y='Water quality as pct',
         size='Household net financial wealth in usd',
        title='Zanieczyszczenie powietrza oraz jakość wody w porównaniu do zamożności gospodarstwa domowego',
        text='Country')
df2 = pd.DataFrame({'x':[1,2,3,4,5],'y':[10,20,30,20,10],'z':[5,4,3,2,1]})
df2.iplot(kind='surface',colorscale='rdylbu')
import plotly.offline as pyo
import plotly.graph_objs as go
trace0 = go.Scatter(x=df['Labour market insecurity as pct'],
                   y=df['Employment rate as pct'],
                   mode='markers',
                   marker=dict(
                           size=15,
                           color='rgb(10, 166, 106)'),
                  text=df['Country'])
data = [trace0]
layout = go.Layout(title='Labour market insecurity vs Employment rate',
                  xaxis=dict(title='Labour market insecurity'),
                  yaxis=dict(title='Employment rate'),
                  hovermode='closest')
pyo.plot(go.Figure(data= data,layout=layout),filename='labour_scatter.html')
x = np.arange(1900,2021)
y = np.random.randint(1000,2000,len(x))
y2 = np.random.randint(500,1000,len(x))



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