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# Correlation with seaborn with better axis labels
import seaborn as sns
import pandas as pd 
import matplotlib.pyplot as plt
attrition_num = pd.read_csv('C:/Users/monik/Desktop/DA/final_project/excel_txt/vstupy/att_num_csv.csv')
attrition_num.head()
labels = ['employee number', 'age', 
        'business travel', 'monhtly income', 	'department', 	'distance from home',
        'education', 'education field', 'environment satisfation', 'gender', 
        'job involvement', 'job level',	'job role', 'job satisfaction',

MonikaPdb

import pandas as pd
import numpy as np 
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
import plotly.offline as py
py.init_notebook_mode(connected=True)
from sklearn.ensemble import RandomForestClassifier

# Data import
attrition = pd.read_csv('C:/Users/monik/Desktop/DA/final_project/excel_txt/vstupy/att_csv.csv')

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# Non-proportional venn diagram for all correctly assigned
from matplotlib import pyplot as plt
import numpy as np
from matplotlib_venn import venn3, venn3_circles, venn3_unweighted
plt.figure(figsize=(10, 10))
vd = venn3_unweighted(subsets=(61, 37, 39, 6, 41, 31, 1158))
for text in vd.subset_labels:
    text.set_weight('bold')
for text in vd.subset_labels:
    text.set_color('black')

MonikaPdb

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

header = ['monthly income','job role','marital status','overtime', 'stock option level', 'age']
dataset = [(100, 8.9, 32.0, 28.3, 42.9, 100),(0, 3.5,45.8,71.7,40.5,0), (0,17.6,22.2,0,10.7,0), (0,6.9,0,0,5.8,0), (0,9.9,0,0,0,0), (0,5.4,0,0,0,0), (0,19.9,0,0,0,0), (0,22.2,0,0,0,0), (0,5.6,0,0,0,0)]

fig = matplotlib.pyplot.gcf()
fig.set_size_inches(18.5, 10.5)
configs = dataset[0]

MonikaPdb

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

header = ['monthly income','job role','marital status','overtime','stock option level', 'age']
dataset = [(100, 0, 80, 85, 86.7, 100),(0, 3.3, 16.7, 15, 10, 0), (0, 31.7, 3.3, 0, 0, 0), (0, 0, 0, 0, 3.3, 0), (0, 0, 0, 0, 0, 0), (0, 0, 0, 0, 0, 0), (0, 20, 0, 0, 0, 0),(0, 15, 0, 0, 0, 0), (0, 30, 0, 0, 0, 0) ]

fig = matplotlib.pyplot.gcf()
fig.set_size_inches(18.5, 10.5)
configs = dataset[0]

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department

employee_number	age	attrition	attrition_num	business_travel	business_travel_num	monhtly_income

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#total working years
plt.figure(figsize = (8, 4))
N = 8
att_yes = (33, 17, 12, 11, 8, 5, 8, 13)
att_no = (67, 83, 88, 89, 92, 95, 92, 88)
ind = np.arange(N)    # the x locations for the groups
width = 0.35       # the width of the bars: can also be len(x) sequence

p1 = plt.bar(ind, att_yes, width, color='#ab1f43')
p2 = plt.bar(ind, att_no, width, color='#1f43ab', 

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number = int(input('Insert number. The program will return list of all prime numbers to the inserted number: '))
print('You inserted the number %i' % number)
listofprimenumbers = []
for i in range (0, number):
    raisingnumber = 1
    listofnumbers = []
    for i in range (0, number): 
         if number % raisingnumber == 0:
             listofnumbers.append(raisingnumber)
         raisingnumber = raisingnumber + 1