Employee_attrition.py

# Define a dictionary for the target mapping
target_map = {'Yes':1, 'No':0}
# Use the pandas apply method to numerically encode our attrition target variable
attrition["Attrition_numerical"] = attrition["Attrition"].apply(lambda x: target_map[x])

# creating a list of only numerical values
numerical = [u'Age', u'DailyRate', u'DistanceFromHome', 
             u'Education', u'EmployeeNumber', u'EnvironmentSatisfaction',
             u'HourlyRate', u'JobInvolvement', u'JobLevel', u'JobSatisfaction',
             u'MonthlyIncome', u'MonthlyRate', u'NumCompaniesWorked',
             u'PercentSalaryHike', u'PerformanceRating', u'RelationshipSatisfaction',
             u'StockOptionLevel', u'TotalWorkingYears',
             u'TrainingTimesLastYear', u'WorkLifeBalance', u'YearsAtCompany',
             u'YearsInCurrentRole', u'YearsSinceLastPromotion',u'YearsWithCurrManager']
data = [
    go.Heatmap(
        z= attrition[numerical].astype(float).corr().values, # Generating the Pearson correlation
        x=attrition[numerical].columns.values,
        y=attrition[numerical].columns.values,
        colorscale='Viridis',
        reversescale = False,
#         text = True ,
        opacity = 1.0
        
    )
]


layout = go.Layout(
    title='Pearson Correlation of numerical features',
    xaxis = dict(ticks='', nticks=36),
    yaxis = dict(ticks='' ),
    width = 900, height = 700,
    
)


fig = go.Figure(data=data, layout=layout)
py.iplot(fig, filename='labelled-heatmap')