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my_list= [45.4, 44.2, 36.8, 35.1, 39.0, 60.0, 47.4, 41.1, 45.8, 35.6]
my_list[4]
39.0

my_list.append(55.2)
my_list
[45.4, 44.2, 36.8, 35.1, 39.0, 60.0, 47.4, 41.1, 45.8, 35.6, 55.2]

my_list.pop(5)
60.0

for i in range(0,10):
   if my_list[i]>45:
        print(my_list[i])
45.4
47.4
45.8
55.2

import numpy
numpy.array(my_list)
array([ 45.4,  44.2,  36.8,  35.1,  39. ,  47.4,  41.1,  45.8,  35.6,  55.2])

numpy.mean(my_list)
42.560000000000002

numpy.std(my_list)
5.9709630713981143

result = []
for i in range(0,10):
    result.append(my_list[i]<45)
    
result = numpy.array(result)
mylist = numpy.array(my_list)
mylist[result]
array([ 44.2,  36.8,  35.1,  39. ,  41.1,  35.6])

numpy.max(my_list)
55.200000000000003

numpy.min(my_list)
35.100000000000001

import pandas
iris = pandas.read_csv('c:/Users/sahluwalia/Downloads/Iris.csv', skipinitialspace=True,engine='python',)
iris.head()
Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species
0 1 5.1 3.5 1.4 0.2 Iris-setosa
1 2 4.9 3.0 1.4 0.2 Iris-setosa
2 3 4.7 3.2 1.3 0.2 Iris-setosa
3 4 4.6 3.1 1.5 0.2 Iris-setosa
4 5 5.0 3.6 1.4 0.2 Iris-setosa 
iris = iris.drop('Id', 1)
iris.head()
SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species
0 5.1 3.5 1.4 0.2 Iris-setosa
1 4.9 3.0 1.4 0.2 Iris-setosa
2 4.7 3.2 1.3 0.2 Iris-setosa
3 4.6 3.1 1.5 0.2 Iris-setosa
4 5.0 3.6 1.4 0.2 Iris-setosa 
iris1 = iris.query('Species == "Iris-setosa"')
iris1
SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species
0 5.1 3.5 1.4 0.2 Iris-setosa
1 4.9 3.0 1.4 0.2 Iris-setosa
2 4.7 3.2 1.3 0.2 Iris-setosa
3 4.6 3.1 1.5 0.2 Iris-setosa
4 5.0 3.6 1.4 0.2 Iris-setosa
5 5.4 3.9 1.7 0.4 Iris-setosa
6 4.6 3.4 1.4 0.3 Iris-setosa
7 5.0 3.4 1.5 0.2 Iris-setosa
8 4.4 2.9 1.4 0.2 Iris-setosa
9 4.9 3.1 1.5 0.1 Iris-setosa
10 5.4 3.7 1.5 0.2 Iris-setosa
11 4.8 3.4 1.6 0.2 Iris-setosa
12 4.8 3.0 1.4 0.1 Iris-setosa
13 4.3 3.0 1.1 0.1 Iris-setosa
14 5.8 4.0 1.2 0.2 Iris-setosa
15 5.7 4.4 1.5 0.4 Iris-setosa
16 5.4 3.9 1.3 0.4 Iris-setosa
17 5.1 3.5 1.4 0.3 Iris-setosa
18 5.7 3.8 1.7 0.3 Iris-setosa
19 5.1 3.8 1.5 0.3 Iris-setosa
20 5.4 3.4 1.7 0.2 Iris-setosa
21 5.1 3.7 1.5 0.4 Iris-setosa
22 4.6 3.6 1.0 0.2 Iris-setosa
23 5.1 3.3 1.7 0.5 Iris-setosa
24 4.8 3.4 1.9 0.2 Iris-setosa
25 5.0 3.0 1.6 0.2 Iris-setosa
26 5.0 3.4 1.6 0.4 Iris-setosa
27 5.2 3.5 1.5 0.2 Iris-setosa
28 5.2 3.4 1.4 0.2 Iris-setosa
29 4.7 3.2 1.6 0.2 Iris-setosa
30 4.8 3.1 1.6 0.2 Iris-setosa
31 5.4 3.4 1.5 0.4 Iris-setosa
32 5.2 4.1 1.5 0.1 Iris-setosa
33 5.5 4.2 1.4 0.2 Iris-setosa
34 4.9 3.1 1.5 0.1 Iris-setosa
35 5.0 3.2 1.2 0.2 Iris-setosa
36 5.5 3.5 1.3 0.2 Iris-setosa
37 4.9 3.1 1.5 0.1 Iris-setosa
38 4.4 3.0 1.3 0.2 Iris-setosa
39 5.1 3.4 1.5 0.2 Iris-setosa
40 5.0 3.5 1.3 0.3 Iris-setosa
41 4.5 2.3 1.3 0.3 Iris-setosa
42 4.4 3.2 1.3 0.2 Iris-setosa
43 5.0 3.5 1.6 0.6 Iris-setosa
44 5.1 3.8 1.9 0.4 Iris-setosa
45 4.8 3.0 1.4 0.3 Iris-setosa
46 5.1 3.8 1.6 0.2 Iris-setosa
47 4.6 3.2 1.4 0.2 Iris-setosa
48 5.3 3.7 1.5 0.2 Iris-setosa
49 5.0 3.3 1.4 0.2 Iris-setosa 
iris.describe()
SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm
count 150.000000 150.000000 150.000000 150.000000
mean 5.843333 3.054000 3.758667 1.198667
std 0.828066 0.433594 1.764420 0.763161
min 4.300000 2.000000 1.000000 0.100000
25% 5.100000 2.800000 1.600000 0.300000
50% 5.800000 3.000000 4.350000 1.300000
75% 6.400000 3.300000 5.100000 1.800000
max 7.900000 4.400000 6.900000 2.500000 
iris.groupby(['Species']).describe()
PetalLengthCm PetalWidthCm SepalLengthCm SepalWidthCm
Species
Iris-setosa count 50.000000 50.000000 50.000000 50.000000
mean 1.464000 0.244000 5.006000 3.418000
std 0.173511 0.107210 0.352490 0.381024
min 1.000000 0.100000 4.300000 2.300000
25% 1.400000 0.200000 4.800000 3.125000
50% 1.500000 0.200000 5.000000 3.400000
75% 1.575000 0.300000 5.200000 3.675000
max 1.900000 0.600000 5.800000 4.400000
Iris-versicolor count 50.000000 50.000000 50.000000 50.000000
mean 4.260000 1.326000 5.936000 2.770000
std 0.469911 0.197753 0.516171 0.313798
min 3.000000 1.000000 4.900000 2.000000
25% 4.000000 1.200000 5.600000 2.525000
50% 4.350000 1.300000 5.900000 2.800000
75% 4.600000 1.500000 6.300000 3.000000
max 5.100000 1.800000 7.000000 3.400000
Iris-virginica count 50.000000 50.000000 50.000000 50.000000
mean 5.552000 2.026000 6.588000 2.974000
std 0.551895 0.274650 0.635880 0.322497
min 4.500000 1.400000 4.900000 2.200000
25% 5.100000 1.800000 6.225000 2.800000
50% 5.550000 2.000000 6.500000 3.000000
75% 5.875000 2.300000 6.900000 3.175000
max 6.900000 2.500000 7.900000 3.800000 
iris.groupby('Species').boxplot()
Iris-setosa             Axes(0.1,0.559091;0.363636x0.340909)
Iris-versicolor    Axes(0.536364,0.559091;0.363636x0.340909)
Iris-virginica              Axes(0.1,0.15;0.363636x0.340909)
dtype: object

png

!pip install seaborn
Requirement already satisfied: seaborn in c:\programdata\anaconda3\lib\site-packages

import seaborn as sns
%matplotlib inline
sns.pairplot(iris,hue='Species')
<seaborn.axisgrid.PairGrid at 0xb484a90>

png



   




      


  
      



  

    

      

  
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        Assignment 12 for MSDS 6306
      



    
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