Link to the previous post : https://statinfer.com/104-1-4-conditional-operators-in-python/

Packages

A package is collection of python functions. A properly structured and complied code. A package may contain many sub packages.
Many python functions are only available via “packages” that must be imported.
For example to find value of log(10) we need to first import match package that has the log function in it

In [113]:

log(10)
exp(5)
sqrt(256)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-113-b0bfd28c096c> in <module>()
----> 1 log(10)
      2 exp(5)
      3 sqrt(256)

NameError: name 'log' is not defined

In [115]:

import math
math.log(10)

Out[115]:

2.302585092994046

In [116]:

math.exp(5)

Out[116]:

148.4131591025766

In [117]:

math.sqrt(256)

Out[117]:

16.0

To be a good data scientist on python, on need to be very comfortable with below packages
- Numpy
- Scipy
- Pandas
- Scikit-Learn
- Matplotlib

Important Packages- NumPy

NumPy is for fast operations on vectors and matrices, including mathematical, logical, shape manipulation, sorting, selecting.
It is the foundation on which all higher level tools for scientific Python are built

In [118]:

import numpy as np

income = np.array([9000, 8500, 9800, 12000, 7900, 6700, 10000])
print(income) 
print(income[0])

[ 9000  8500  9800 12000  7900  6700 10000]
9000

In [119]:

expenses=income*0.65
print(expenses)

[ 5850.  5525.  6370.  7800.  5135.  4355.  6500.]

In [120]:

savings=income-expenses
print(savings)

[ 3150.  2975.  3430.  4200.  2765.  2345.  3500.]

Important Packages- Pandas

Data frames and data handling
Pandas has Data structures and operations for manipulating numerical tables and time series.

In [121]:

import pandas as pd
buyer_profile = pd.read_csv('datasets\\Buyers Profiles\\Train_data.csv')

print(buyer_profile)

    Age  Gender Bought
0    29    Male    Yes
1    34    Male    Yes
2    13  Female    Yes
3    27  Female     No
4    10  Female     No
5    68    Male    Yes
6    15    Male    Yes
7    53    Male    Yes
8    51    Male     No
9    48  Female     No
10   63  Female     No
11   43    Male    Yes
12    8  Female     No
13   47  Female     No

In [126]:

buyer_profile.Age

Out[126]:

0     29
1     34
2     13
3     27
4     10
5     68
6     15
7     53
8     51
9     48
10    63
11    43
12     8
13    47
Name: Age, dtype: int64

In [122]:

buyer_profile.Gender

Out[122]:

0       Male
1       Male
2     Female
3     Female
4     Female
5       Male
6       Male
7       Male
8       Male
9     Female
10    Female
11      Male
12    Female
13    Female
Name: Gender, dtype: object

In [124]:

buyer_profile.Age[0]

Out[124]:

In [125]:

buyer_profile.Age[0:10]

Out[125]:

0    29
1    34
2    13
3    27
4    10
5    68
6    15
7    53
8    51
9    48
Name: Age, dtype: int64

Important Packages- Scikit-Learn

Machine learning algorithms made easy

In [127]:

import sklearn as sk
import pandas as pd

air_passengers = pd.read_csv('datasets\\AirPassengers\\AirPassengers.csv')
air_passengers

Out[127]:

	Week_num	Passengers	Promotion_Budget	Service_Quality_Score	Holiday_week	Delayed_Cancelled_flight_ind	Inter_metro_flight_ratio	Bad_Weather_Ind	Technical_issues_ind
0	1	37824	517356	4.00000	NO	NO	0.70	YES	YES
1	2	43936	646086	2.67466	NO	YES	0.80	YES	YES
2	3	42896	638330	3.29473	NO	NO	0.90	NO	NO
3	4	35792	506492	3.85684	NO	NO	0.40	NO	NO
4	5	38624	609658	3.90757	NO	NO	0.87	NO	YES
5	6	35744	476084	3.83710	NO	YES	0.66	YES	NO
6	7	40752	635978	3.60259	NO	YES	0.74	YES	NO
7	8	34592	495152	3.60086	NO	YES	0.39	NO	NO
8	9	35136	429800	3.62776	NO	NO	0.61	NO	YES
9	10	43328	613326	2.98305	NO	NO	0.66	NO	NO
10	11	34960	492758	3.60089	NO	NO	0.77	NO	NO
11	12	44464	600726	2.56064	NO	YES	0.74	YES	NO
12	13	36464	456960	3.89655	NO	YES	0.39	YES	NO
13	14	44464	586096	2.47713	NO	YES	0.79	YES	NO
14	15	51888	704802	1.77422	YES	YES	0.72	YES	YES
15	16	36800	536970	3.92254	NO	NO	0.43	NO	YES
16	17	48688	742308	1.93589	NO	NO	0.90	NO	YES
17	18	37456	500234	3.99060	NO	NO	0.46	NO	NO
18	19	44800	570682	2.43241	NO	YES	0.79	YES	YES
19	20	56032	826420	1.41139	YES	YES	0.80	YES	NO
20	21	58800	761040	1.24488	YES	NO	0.69	NO	NO
21	22	57440	753466	1.36091	YES	NO	0.60	NO	NO
22	23	32752	502712	3.37428	NO	YES	0.45	YES	YES
23	24	43424	653856	2.88878	NO	YES	0.89	YES	YES
24	25	45968	706748	2.31898	NO	YES	0.62	YES	NO
25	26	38816	532602	3.85307	NO	NO	0.75	NO	YES
26	27	35168	518070	3.70671	NO	YES	0.47	YES	YES
27	28	34496	539378	3.48455	NO	YES	0.78	YES	YES
28	29	34208	414120	3.48166	NO	YES	0.38	YES	NO
29	30	44320	653338	2.58325	NO	NO	0.71	NO	YES
…	…	…	…	…	…	…	…	…	…
50	51	43728	590492	2.77882	NO	YES	0.47	YES	NO
51	52	47040	694568	2.06989	NO	YES	0.55	YES	NO
52	53	34512	493444	3.57125	NO	NO	0.74	NO	YES
53	54	57600	781718	1.35511	YES	NO	0.67	NO	YES
54	55	36064	526162	3.87218	NO	YES	0.73	NO	YES
55	56	49392	707070	1.91865	NO	NO	0.75	NO	NO
56	57	42378	545510	3.46630	NO	NO	0.62	NO	YES
57	58	38584	555170	3.99116	NO	NO	0.77	NO	NO
58	59	28700	405916	3.07021	NO	NO	0.72	NO	NO
59	60	55160	738794	1.48667	YES	YES	0.71	YES	NO
60	61	52472	666778	1.58686	YES	YES	0.90	YES	NO
61	62	54474	715498	1.52341	YES	YES	0.55	YES	NO
62	63	54222	754418	1.58647	YES	NO	0.78	YES	NO
63	64	73444	1012130	0.91298	YES	YES	0.90	YES	NO
64	65	67130	1003002	0.98050	YES	NO	0.79	NO	YES
65	66	39984	589526	3.77575	NO	NO	0.81	NO	NO
66	67	41972	550872	3.49699	NO	YES	0.68	YES	YES
67	68	43722	652680	2.84565	NO	YES	0.69	YES	NO
68	69	76972	1041796	0.87470	YES	YES	0.90	YES	NO
69	70	58156	881818	1.33013	YES	NO	0.82	NO	NO
70	71	52304	679938	1.68678	YES	NO	0.63	NO	YES
71	72	76524	1024450	0.87933	YES	YES	0.90	YES	NO
72	73	60620	844578	1.15504	YES	NO	0.90	NO	YES
73	74	32018	445424	3.23666	NO	YES	0.64	YES	YES
74	75	51814	669144	1.87321	YES	NO	0.88	NO	YES
75	76	66934	927696	1.07138	YES	YES	0.84	NO	NO
76	77	81228	1108254	0.85536	YES	YES	0.90	YES	NO
77	78	43288	638162	3.08191	NO	NO	0.62	NO	NO
78	79	43834	636636	2.75382	NO	YES	0.79	YES	YES
79	80	40852	575008	3.52768	NO	YES	0.54	YES	YES

80 rows × 9 columns

In [130]:

x=air_passengers['Promotion_Budget']
x=x.reshape(-1,1)
y=air_passengers['Passengers']
y=y.reshape(-1,1)

In [132]:

from sklearn import linear_model
reg = linear_model.LinearRegression()
reg.fit(x, y)

Out[132]:

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

In [133]:

print('Coefficients: \n', reg.coef_)

Coefficients: 
 [[ 0.06952969]]

Important Packages- Matplotlib¶

Plotting library similar to MATLAB plots

In [3]:

import numpy as np
import matplotlib as mp
import matplotlib.pyplot

#to print the plot in the notebook:
%matplotlib inline

X = np.random.normal(0,1,1000)
Y = np.random.normal(0,1,1000)

mp.pyplot.scatter(X,Y)

Out[3]:

<matplotlib.collections.PathCollection at 0x65ae2b0>

20th June 2017

104.1.5 Python Packages

Using python packages.

Packages

Important Packages- NumPy

Important Packages- Pandas

Important Packages- Scikit-Learn

Important Packages- Matplotlib¶