Numpy and PandasThis provides an introduction into the usage of Numpy and Pandas. Objectives
ContentSeries, Arrays and Matriceslogin = [10,51,33,44] print ("Login: ",login) print ("login[1]: ",login[1]) login = [[10,51,33,44], [6,3,5,11]] print ("login: ",login) print ("login [0]: ",login[0]) print ("login [1]: ",login[1]) print ("login [1][1]: ",login[1][1]) login = ['192.168.0.1',15 , 5] print ("login: ",login) print ("login [0]: ",login[0]) login.append(10) print ("login: ",login) login = {'IP': ['192.168.0.1', '192.168.0.2', '192.168.0.3', '192.168.0.11'], 'Successful': [10,51,33,44], 'Unsuccessful': [6,3,5,11]} print ("Login: ",login) print ("Login['IP']: ",login['IP']) login = np.array([[10,51,33,44],[6,3,5,11]]) print ("login: ",login) login = np.append(login,[[1,13,13,41],[3,10,11,40]]) print ("Added row for login: ",login) login = login.transpose() print ("Transpose login: ",login) import pandas as pd login = { 'IP': ['192.168.0.1', '192.168.0.2', '192.168.0.3', '192.168.0.11'], 'Successful': [10,51,33,44], 'Unsuccessful': [6,3,5,11]} df = pd.DataFrame(login) print ("Login:\n",df) print ("Login['IP']:\n",df['IP']) import numpy as np a = np.array([5, 19, 32,20,10]) print (a) print (np.arange( 10, 30, 3 )) # start, end, increment print (np.linspace(1,10,num=10)) # Return evenly spaced numbers over a specified interval. import numpy as np print (np.random.rand(2,3)) import numpy as np coeffs = np.array([[5, 3], [8, -2]]) depvars = np.array([18, 22]) solution = np.linalg.solve(coeffs, depvars) print (solution) a = np.array([5, 9, 7, 1, 8, 3, 2, 6, 10, 9, 4, 2]) a[1:3] = 0 print (a) a = np.array([5, 9, 7, 1, 8, 3, 2, 6, 10, 9, 4, 2]) a = a.reshape(3, 4) print (a) a = np.array([5, 9, 7, 1, 8, 3, 2, 6, 10, 9, 4, 2]) val1 = np.sum(a) val2 = np.average(a) val3 = np.std(a) val4 = np.log(10) val5 = np.sin(np.pi/4) val6 = np.sort(a) 8 val7 = np.ceil(9.5) 9 val8 = np.floor(9.5) 10 print ("Sum: ",val1) 11 print ("Average: ",val2) 12 print ("Std Dev: ",val3) 13 print ("Log(10)=",val4) 14 print ("Sine(10)=",val5) 15 print ("Sort: ",val6) 16 print ("Ceil(9.5)=",val7) 17 print ("Floor(9.5)=",val8) Arraysa = np.array([5, 9, 7, 1, 8, 3, 2, 6, 10, 9, 4, 2]) 2 print ("2*a=",2*a) 3 print ("3+a=",3+a) 4 print ("a-4=",a-4) 5 print ("a/2=",a/2) import numpy as np 2 3 g=[5,7,8] 4 h=[[6,2,3],[1,3,5],[5,3,8]] 5 6 def multi(m, g): 7 en = np.multiply(m, g) 8 return en 9 10 def dot(m, g): 11 en = np.dot(m, g) 12 return en 14 print ("Input:\n",g) 15 print ("Input:\n",h) 16 17 print ("-----------") 18 19 res=multi(g,h) 20 print ("Multiply:") 21 print (res) 22 23 res=dot(g,h) 24 print ("Dot:") 25 print (res) Bitwiseimport numpy as np 2 a = 11 3 b = 30 4 5 print("binary representation of a:",bin(a)) 6 print("binary representation of b:",bin(b)) 7 8 print("Bitwise a:\t\t\t\t",np.binary_repr(a,width=8 )) 9 print("Bitwise b:\t\t\t\t",np.binary_repr(b,width=8 )) 10 print("Bitwise Invert of a:\t",np.binary_repr(np.invert(a),width=8 )) 11 print("Bitwise Invert of b:\t",np.binary_repr(np.invert(b),width=8 )) 12 215 13 print("Bitwise AND of a and b:\t",np.binary_repr(np.bitwise_and(a,b), width=8 ) ) 14 print("Bitwise OR of a and b:\t",np.binary_repr(np.bitwise_or(a,b),width =8 )) 15 print("Bitwise XOR of a and b:\t",np.binary_repr(np.bitwise_xor(a,b), width=8 )) 16 print("Bitwise left shift of a by 2:\t",np.binary_repr(np.left_shift(a,2) ,width=8 )) 17 print("Bitwise right shift PandasReading data into data framesimport pandas as pd ver=pd.read_csv("test.csv") print (ver.head(3)) print (ver.tail(3)) print (ver.dtypes) import pandas as pd ver=pd.read_csv("test.csv") print ("Length of dataframe: ",len(ver)) print ("Columns: ",ver.columns) print (ver.dtypes) print (ver['Good login']) import pandas as pd ver=pd.read_csv("test.csv") print (ver['Good login']) import pandas as pd ver=pd.read_csv("test.csv") ver2= ver[['Good login','CPU Average','Location']] print (ver2) import pandas as pd ver=pd.read_csv("test.csv") print (ver.describe()) Sortingimport pandas as pd ver=pd.read_csv("test.csv") print (ver.sort_values(['Location']).head(3)) print (ver.sort_values(['Location'],ascending=False).head(3)) import pandas as pd ver=pd.read_csv("test.csv") print (ver.corr()) print (ver[['Good login','Bad login']].corr()) import pandas as pd ver=pd.read_csv("test.csv") ver2=ver['Good login']> 12 print (ver2) ver2=ver[ver['Good login']> 12] print (ver2) attacks=['DDoS','Malware','Insider','Data Loss'] print (attacks[1]) attacks[1]='Credential theft' print (attacks[1]) attacks=('DDoS','Malware','Insider','Data Loss') print (attacks[1]) attacks[1]='Credential theft' print (attacks[1]) attacks=('DDoS','Malware','Insider','Data Loss') temp = list(attacks) temp[1] = 'Credential theft' attacks = tuple(temp) print (attacks) |