# 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)

import keyword
keyword.kwlist
# 35 in total as of now

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).

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

a = 10
b = 20
print("{0},{1}".format(a,b))

'''
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()

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

!pip install <package_name>

import warnings
warnings.filterwarnings("ignore")

try:
result = 10 / 2
except ZeroDivisionError as detail:
print("division by zeroi error")
print("error detail:",detail)
else: #only executes if try block is successful
print(result)
finally:
print("finally is executed everytime")

#Reading a file in a list
a=[]
readfile = open("text.txt", "r")
for row in readfile:
a.append(row.strip())
readfile.close()
print(a)

a=[]
with open("text.txt", "r") as readfile:
for row in readfile:
a.append(row.strip())
print(a)

a = "This is a nice string."
with open("some.txt","w") as writefile:
writefile.write(a)

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()