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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Basics of NUmpy & Pandas
Numpy
Numpy uses array whereas pandas used scaler <br />
Step1:  Array are similar to python list , but it all element must be of same data type, and it faster than list
Step2:  Lets see some of functionality
Step3:  Vector Operation
Step4:  vector [+-*/] Scaler
Step5:  vector & boolean vector
Step6:  num[bl],, what it will return ??
<h4>It return array of values corresponding to which elemnt in bl is True
Step7:  find all elemnt greter than 100 from num
Step8:  <h5> All element less than 50 ??
Step9:  In-place operation in numpay (Diff between += and +)
Step10:  <h5>this happen becouse a and b both point to same array and + operation create a new array and then a point to that so b remain unaffected" </h5>
Step11:  Pandas Series
<h4> Basics are same as numpy array but pandas series also contain lots of functionality and speciality
Step12:  <h6>See All basic results using describe() function
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Python Code:
import numpy as np
Explanation: Basics of NUmpy & Pandas
Numpy
Numpy uses array whereas pandas used scaler <br />
End of explanation
num = np.array([3,4,2,5,7,23,56,23,7,23,89,43,676,43])
num
Explanation: Array are similar to python list , but it all element must be of same data type, and it faster than list
End of explanation
print "Mean :",num.mean()
print "sum :",num.sum()
print "max :",num.max()
print "std :",num.std()
#slicing
num[:5]
#find index of any element let say max
print "index of max :",num.argmax()
print "data Type of array :",num.dtype
Explanation: Lets see some of functionality
End of explanation
a=np.array([5,6,15])
b=np.array([5,4,-5])
# Addition
print "{} + {} = {}".format(a,b,a+b) 
print "{} * {} = {}".format(a,b,a*b) 
print "{} / {} = {}".format(a,b,a/b) 
# If size mismatch then error occure
b=np.array([5,4,-5,5])
print "{} + {} = {}".format(a,b,a+b) 
Explanation: Vector Operation
End of explanation
print "{} + {} = {}".format(a,3,a+3) 
print "{} * {} = {}".format(a,3,a*3) 
print "{} / {} = {}".format(a,3,a/3) 
Explanation: vector [+-*/] Scaler
End of explanation
num=np.array([5,6,15,65,32,656,23,435,2,45,21])
bl=np.array([False,True,True,False,True,False,True,False,True,True,False])
num[6]
Explanation: vector & boolean vector
End of explanation
num[bl]
Explanation: num[bl],, what it will return ??
<h4>It return array of values corresponding to which elemnt in bl is True
End of explanation
num[num>100]
Explanation: find all elemnt greter than 100 from num
End of explanation
num[num<50]
Explanation: <h5> All element less than 50 ??
End of explanation
a=np.array([5,6,15])
b=a
a += 2
print b
print "this happen becouse a and b both point to same array and += is In-place operation so it maintain that"
a=np.array([5,6,15])
b=a
a = a + 2
print b
Explanation: In-place operation in numpay (Diff between += and +)
End of explanation
a=np.array([5,6,15])
b=a[:3]
b[0]=1000
print a,"Reason is similar as +="
Explanation: <h5>this happen becouse a and b both point to same array and + operation create a new array and then a point to that so b remain unaffected" </h5>
End of explanation
import pandas as pd
num = pd.Series([3,4,2,5,7,23,56,23,7,23,89,43,676,43])
num
Explanation: Pandas Series
<h4> Basics are same as numpy array but pandas series also contain lots of functionality and speciality
End of explanation
num.describe()
Explanation: <h6>See All basic results using describe() function
End of explanation
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