import keyword
    keyword.kwlist
    # Total of 33

    a=10
    print(type(a))
    #type lets you know the type of datatype a variable is associated with
    
    id(a)
    #id lets you know the memory location of 10(not a).

    int
    a = 10
    hex(a) # 0xa -> begins with 0x
    bin(a) # 0b1010 -> begins with 0b
    oct(a) # 0o12 -> beginswith 0o
    # all belong to <class 'str'> after conversion
    
    float
    a = 10.5
    #can't covert to oct,dec, hex
    
    str
    a="Ram bro"
    len(a) => 7
    a.split(" ") => ['Ram', 'bro']
    # Splicing of string
    a[:] #=> ['Ram', 'bro']
    a[1:3] #=> 'am'
    a[::-1] #=> 'orb maR' str reversal
    a[-5:-1] #=> 'm br' reverse string index
    a[1:6:3]#=> 'ab' it takes each value after 3 steps
    
    bool
    print(True+True) # 2
    print(True+False) # 1
    print(False + False) # 0  
    print(True) # 1
    print(False) # 0
    
    complex (a+bj)
    a=10 + 20j
    
    list [a,b,c]  #Mutable
    a= [10,20,30]
    a.append(40) # appends 40 to a
    
    
    tuple (a,b,c) #Immutable
    
    set {a,b,c} # doesn't preserve order in the session
    a={10,20,30}
    a.add(40) # adds element
    
    dict {key:value} # key must be unique
    a={'a':1,'b':2.5,'c':'char'}
    a.keys() #=> dict_keys(['a', 'b'])
    a.values() #=> dict_values([1, 2])
    
    frozenset #immutable version of a set
    vowels = {'a', 'e', 'i', 'o', 'u'}
    fSet = frozenset(vowels)
    fSet #=> frozenset({'a', 'e', 'i', 'o', 'u'})
    
    range # (start, stop[, step])
    a= range(10) # 0,1,2,3,4,5,6,7,8,9
    type(a) # range(0,9)

    a = input("Give input: ")
    # a gets the input as str
    
    a = eval(input("give int: "))
    # eval() evaluates the input to their correct types
    # i/p: 23 type(a): int
    b = eval(input("give float: "))
    # i/p: 3.57 type(b): float
    c = eval(input("give str:: "))
    # i/p: "hello" type(c): str
    d = eval(input("give tuple "))
    # i/p: 1,2 or (1,2) type(d): tuple
    e = eval(input("give list "))
    # i/p: [1,2] type(e): list

    '''
    Arithmetic Operators
    Relational/Comparison Operators
    Logical Operators
    Bitwise operators
    Assignment Operators
    Special Operators
    '''
    
    a=2
    b=5
    print(a+b)
    print(a-b)
    print(a*b)
    print(a/b)
    print(a**b) # power
    
    a="hello"
    b=5
    a*b # 'hellohellohellohellohello'
    
    # Relational
    a=2
    b=5
    a > b # False
    a < b # True
    a == b # False
    a != b # True
    a <= b # True
    a >= b # False
    
    a="rat"
    b="rama"
    a > b # True, coz t > m, it compares char from L to R
    a < b # False, same for others

    class Person:
      def __init__(self, name, age):
        self.name = name
        self.age = age
    
      def myfunc(self):
        print("Hello my name is " + self.name)
    
    p1 = Person("John", 36)
    p1.myfunc()
    
    '''
    The self parameter is a reference to the current instance of the class, 
    and is used to access variables that belongs to the class.
    It does not have to be named self , you can call it whatever you like, 
    but it has to be the first parameter of any function in the class:
    
    __init__ is like a constructor:
    '''
    
    class Person:
      def __init__(mysillyobject, name, age):
        mysillyobject.name = name
        mysillyobject.age = age
    
      def myfunc(abc):
        print("Hello my name is " + abc.name)
    
    p1 = Person("John", 36)
    p1.myfunc()

    class Robot(object):
       def __init__(self):
          self.a = 123 # Public
          self._b = 123 # Protected
          self.__c = 123 # Private
    
    obj = Robot()
    print(obj.a)
    print(obj._b)
    print(obj.__c) # error occurs as it's private

    import warnings
    warnings.filterwarnings("ignore")

    import pandas as pd
    
    file = pd.read_csv("file.csv")
    file.head(10) # shows first n rows
    
    file.shape # no. of rows and columns of the file
    file.describe() # does all statistics operations on the file
    file.isnull() # detects missing values in the file
    file.isnull().sum() # summarizes all the missing data
    file.dtypes # returns all the datatypes in the file 
    file.<column name> # shows the column mentioned
    
    #Opening excel file
    data = pd.read_excel('Dataset.xlsx',Sheet_name = "sheet1")
    
    #Check the total number of rows with incomplete data
    file.isnull().sum()
    
    #Remove row with incomplete data
    file.dropna(inplace=True) # inplace makes the operaion permanent on file
    
    # Remove column
    file.drop(columns="[<Column_Name1>,<cn2>,<cn3>]", inplace = True, axis =1)
    
    # Filling missing data using mean and mode
    file['<column1>'].fillna(file['<column1>'].mode(),inplace=True)
    file['<column2>'].fillna(file['<column2>'].mode(),inplace=True) # both mean and mode is done for float columns
    file['<column2>'].fillna(file['<column2>'].mean(),inplace=True) # both mean and mode is done for float columns
    file.isnull().sum() # will show all columns are filled and 0 null columns
    
    # plotting a graph
    #bar graph
    graph = file['<column name>'].value_counts().plot(kind = 'bar',figsize = (14,8), title = "title of graph")
    graph.set_xlabel("X Label")
    graph.set_ylabel("Y Label")
    
    # Other method to plot graph
    spenders.groupby('Level')['Level'].agg('count').plot.bar()

    np.ones()
    np.zeros(10) #>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
    # creaes null vector of size 10
    
    np.ones(10) #array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
    # creaes vector with 1's of size 10
    
    np.arange(5,20) #>array([ 5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19])
    #creagtes a vector from 5 to 19
    
    np.arange(9).reshape(3,3) # reshapes into 3X3 matrix
    '''
    array([[0, 1, 2],
           [3, 4, 5],
           [6, 7, 8]])
    '''
    np.nonzero([0,1,0,0,5,7,4,0,0]) #>(array([1, 4, 5, 6], dtype=int64),)
    # gives indices of non-zero values in array
    
    np.eye(n) # creates n X n identity matrix 
    np.eye(3) # array([[1., 0., 0.], [0., 1., 0.],[0., 0., 1.]])
    
    np.random.random((3,3))
    
    a = np.random.random((10,10))
    amin,amax = a.min(),a.max() # gives the max and min of a
    amean = a.mean() # mean
    
    #Create a 2D matrix with only 1 as borders
    a = np.ones((10,10))
    a[1:-1,1:-1]=0
    '''
    array([[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
           [1., 0., 0., 0., 0., 0., 0., 0., 0., 1.],
           [1., 0., 0., 0., 0., 0., 0., 0., 0., 1.],
           [1., 0., 0., 0., 0., 0., 0., 0., 0., 1.],
           [1., 0., 0., 0., 0., 0., 0., 0., 0., 1.],
           [1., 0., 0., 0., 0., 0., 0., 0., 0., 1.],
           [1., 0., 0., 0., 0., 0., 0., 0., 0., 1.],
           [1., 0., 0., 0., 0., 0., 0., 0., 0., 1.],
           [1., 0., 0., 0., 0., 0., 0., 0., 0., 1.],
           [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]])
    '''
    
    #Undestanding NaN
    print(0*np.nan) # nan
    print(np.nan == np.nan) # False
    print(np.inf > np.nan) # False
    print(np.nan - np.nan) # nan
    print(0.3 == 3*0.1) # False
    
    #Diagonal elements
    a=np.deiag(np.arang(4))
    a = np.diag(1+np.arange(4),k=0) 
    # k decides from which column the first diagonal is taken
    
    # create a 8*8 matrix, implement a chess-board pattern
    c = np.zeros((8,8),dtype=int)
    c[1:2,::2] = 1
    c[::2,1::2] = 1
    
    #Making an array immutable
    a = np.zeros(10)
    a.flags.writeable =False # makes a immutable

    import matplotlib.pyplot as plt
    
    x = np.arange(0,4*np.pi,0.1) # 0.1 is the step
    y = np.tan(x)
    plt.plot(x,y)
    plt.show()
    
    # Scatter data
    plt.scatter(dataX,dataY)
    
    # To set title and labels
    plt.title(' Title Name ')
    plt.xlabel('X Axis Label')
    plt.ylabel('Y Axis Label')
    plt.legend('<char>') # character that tell you what the point is
    
    #using pandas to plot
    file.plot(kind = 'scatter', x='<column1>', y= '<column2>')
    plt.show()
    
    # visualizing using Seaborn
    import seaborn as sns
    sns.FacetGrid(data=file, hue ='<columnName>',height = 5).map(plt.scatter, '<column1>','<column2>').add_legend()
    plt.show()
    
    #boxplot
    sns.boxplot(x='<column1>', y ='<column2>', data = file)
    plt.show() # the points not in the box region are outliers
    
    #violin plot : denser regions are fatter & sparser regions are thinner
    sns.violinplot(x='<column1>', y='<column2>', data = file)
    plt.show() # this plot indicates the median value( the white middle dot )
    
    #kernel density plot : utlizes and creates an estimate of kernel density of the underlying features.
    sns.FacetGrid(data = file, hue = 'Species', height = 5).map(sns.kdeplot,'PetalLengthCm').add_legend()
    plt.show()
    
    #pair plot : bivariate relation between each pair of features
    sns.pairplot(file.drop("<column1>",axis =1),hue='<column2>',height=3)
    plt.show()