<jupyter_start><jupyter_text><jupyter_code>a = 50000
b = 10000

if a < b:
  print('yes')
elif a > b:
  print('no')
def add(a, b):
  add = a + b
print(add)

def area(x,y):
  area = x * y
  print()
area
x = 20
y = 30
def __repr__(self):
    return '<%s.%s object at %s>' % (
        self.__class__.__module__,
        self.__class__.__name__,
        hex(id(self))
    )
def area(a,b,c):
  area = a+b+c
  print(area)

area
a = 10
b = 20
area(10,20,200)
list = [1,2,3,4,5]
for i in list:
  i = i/10+i*10-1/1100000
  print(i, i+10, i+20)
list = [1,2,3,4,5]
while i in list:
  print(i)
list
list.copy()
list
def list():
  list = range(1,12)
  print(list)
list()
range(10,20)
iter
3+6
100*100*56
def area(x,y,z):
  
  area = x*y*z
  print(area)
area(10,11,12)
area(1,2,5)
a = 100
a
a = "city"
a[0]
a[1:1:1]
a[-1:0]
a[-2:1]
for i in a:

  print("5"+a)
int(2.0)
len(dir(a))
id(a)
2//33
20002 % 100
1%3
3%5
6==5
6!=1
str(int(120.54))
str(float(100))
id(1)
dir(int)
city = "HYDERABAD"
print("MY CITY IS " + city)
print("{1}, {0}, {2} ".format("A", "B", "C"))
print('{}, {}'.format('55', " , "))
list = list(range(2,12))
list
list.sort()
list
list.append(12)
list
list = tuple(list)
list
list.index(2)
dir(list)
list = {"A": 20, "B": 30, "C": {"c":22}}
list
dir(list)
list.items()
list.keys()
list.values()
list["A"] = 100
list
list.fromkeys("A", 20)

lambda x: x + 20
x+20
x
lambda x: x*20
20*x**2
x
list["C"]["c"]
list
i = 100

while i < 500:
     i += 2002
     print(i*10//14)
i
i
iter(list)
id(list)
id(int)
id(2)
list
x = lambda x: list(map(print, x))
dir(int)
12%5
name = input("what is your name ")
age = input("what is ur age ")
location = input("what is ur lcation ")
name
id(name)
def area(a, b, c):
  area = a+b+c
  return area
  
  print(area)

area(2,3,4)
area(1,1,1)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib_inline
df = pd.read_csv("/content/sample_data/california_housing_train.csv")
df
df.head(10)
df.tail(10)
df.count()
df.info()
df.describe()
df.isnull()
df.dropna()
df.columns
df.axes
df.loc[100]
df.iloc[10]
df
df.drop_duplicates()
!pip install ggplot
df.info()
df.isin(["population"])
type(df)
id(df)
dir(df)
df.housing_median_age
len(df)
df['population'] [12]
df
df['population'].unique()
df.shape
df.dropna(axis=0)
df.kurtosis
df['population'].median()

df['population'].mode()
df['population'].mean()

df['total_bedrooms'].median()
df.plot('population', 'median_income')
len(dir(df))
df.boxplot()
cor = df.corr()
cor
len(dir(df['population']))
df
!pip install GeoPandas
import geopandas
df
dir(geopandas)
import geopandas as gdp
shp = gdp.read_file()
x = 1000
y = 2040
if x > y:
  print("yess")
else:
  print('no')
def add(a,b,c):
  a = b+c
  b = c+a
  c = a+b
  return a+b+c/a*b*c
add(11,12,13)
lambda x: x+2
x+2
print(x)
def area(l,b):
  area = 1/2*l*b
  return area, l, b
area(5,6)
x = 15000
if x > 144400:
  print('yes')

else:
  print('no')
x
x > 100
def area(w,e,r,t,y):
  area = w*e*r*t*y/w+e+r+t+y
  return area/100
area(10,20,30,40,50)
area(1,2,3,4,5)
lambda x : x + 2
dir(lambda)
x+y
x = 100
y = 1000
print(x*y)
__init
list
a = list(range(1,15))
a
b = list(range(2,16))
b
a = b
a
a+b
a.mean()
list
import math
dir(math)
math.log(16)
math.asinh(101000)
!pip install Tenorflow
!pip install django
dir(django)
import django
!pip install tensorflow
import tensorflow
len(dir(tensorflow))
!pip install dash
!pip install descartes
!pip install Geopandas
import geopandas as gpd
import matplotlib.pyplot as plt
import fiona


with fiona.open("D:\Thesis Data\Landuse\LU_SRB_CLIP\LU_SRB_2000-2002_CLIP.tif") as shp:

for i in range(25):
    print(list(range((10))))
    print(i, end=', ')

a = [1,2,3,4,5]
a[2]
for n in a:
  print(n*2)
a 
while n in a:
  n += 1
  print(n)
  break 
print(n)
len(a)
len(dir(float))
i = 20
while i < 30:
  print (i)
  i = i+5
  
import random
ball = 100
while ball > 50:
  print(ball)
  print("the ball is drpping!")
  break
i = 0

while i < 15:
    print(i+2)
    i = i + 1
    
a = (1,2,3,4,5)
for i in a:
  print(i*2)
a[1:4]
a[::]
a[1:1]
a[1]
a.index(5)
names = ['a', 'b', 'c', 'd', 'e']
for i, name in enumerate(names):
  if name == 'a':
     print('yes')
for i in names:
    print(i)
    if i == "b":
      break
else:
   print("yes")
a
dir(a)
for i in a:
  print(a, i, " ")
a.count(1)
a.index(5)
print(100+900)
!pip install geemap
a
a[0]
a[1]
a[2]
for i in a:
  print(i)
print("cit")
20000/33
int(float(606.6365))
class ID(object):
  def __init__(self, name, age, height):
    self.name = "RAHUL"
    self.age = 27
    self.height = 175
ID_1 = ID(name = "RAHUL", age = 27, height = 175 )
ID_1.height
ID_1.name
class ID_2(object):
  def __init__(self, name, age, weight):
    self.name = 'GMR'
    self.age = 27
    self.weight = 90
new_id = ID_2(name = 'rahul', age = 29, weight = 90)
dir(new_id)
new_id.__getattribute__('age')
import random
dir(random)
a = random.randint(30,50)
a
a.numerator
list = list(range(20))
list
for i in list:
  print(i, 2*i)



i
list
list.insert(15, 100)
list
list.count(100)
len(list)
range(10)
set(list)
list.sort()
list
tuple(list)
list.pop(20)
list
myrange = list(range(10))

for number in list:
    if number in myrange:
        print(number, 'is between 1 and 10')
del list
list
a
b = 400
a>b
type(a>b)
dir(bool)
squares = lambda x: x*2
squares(2055)
area = lambda a, b : 0.5*a*b
area(10,5)
area_1 = lambda  r : 3.14 * r * r
area_1(727)
len(int(155049209.06000003))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1871086556169429, 'fraction_numerical': 0.07679558011049724, 'mean_word_length': 3.8883888388838885, 'pattern_counts': {'":': 5, '<': 10, '<?xml version=': 0, '>': 13, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '655621', 'license': 'Apache-2.0', 'n_tokens_mistral': 2794, 'n_tokens_neox': 2530, 'n_words': 659, 'path': 'ex_py_1.ipynb', 'repo_name': 'rahuldiamond/Blood-Donation-Analysis-', 'size': '5430'}