<jupyter_start><jupyter_text>Big Data Assignment<jupyter_code>import numpy as np
import pandas as pd
#Practice with Basic function
no = 10 + 5 * 3 / 4.0

print(no)
#Single % sign use to get division. dividend % divisor = remainder.
remainder = 15 % 9
print(remainder)
#using strings

word= ('Hello everyone' +' '+ 'My name is Bhagyashree')
print(word)

#creating while loop
count = 1
while (count <= 15):
   print ('The count is:', count+0.25)
   count = count + 1

print ("While Loop Practice")
#Example
for letter in 'Assignment':
   print ('Letter :', letter)

Books = ['Python','Java','C++']

#Example
for Book in Books:        
   print ('Book name :', Book)

print ("For Loop Practice")
# creating if condition

name = "Pallavi"
age = 25
if name == "Pallavi" and age == 25:
    print("Your name is Pallavi, and you are also 25 years old.")

if name == "Pallavi" or name == "Bhagyashree":
    print("Your name is either Pallavi or Bhagyashree.")
# creating if and else condition

x = 10
if x == 10:
    print("x equals ten")
else:
    print("x does not equal to ten.")
    
y =  20

if y==10:
    print('y equals to tewnty')
else:
    print('y does not to equal to twenty')
# Create 2 new lists height and weight
x = [87,  17, 82, 91, 15, 51]
y = [65, 92, 25, 98, 18, 45]

# Create 2 numpy arrays from height and weight
x1 = np.array(x)
y1 = np.array(y)
print(type(x))

c = x1/y1 **2
print(c)
# reading file from system
#reading sample dataset to perform basic operations

import pandas as pd

df = pd.read_csv('Downloads/2019-plu-total-hab-data.csv')

df.head()
# This is how we read columns data
df.head(10)
#creating tail of dataset
df.tail(5)
#creating sample
df.sample()
#creating information of dataset
df.info()
#creating decribing in numerical format
df.describe()
# checking count from dataset 

#missing count values range


count= df.isnull().sum()

count

total_cells = np.product(df)

total_missing = count.sum()

print('Total Missing Values:',total_missing)

#importing the following library

import matplotlib.pyplot as plt
import seaborn as sns

#creating histogram

plt.figure()
plt.figure(figsize=(20,7))
plt.hist(df['Total Bulk and Bags Units'],color = 'blue', edgecolor = 'black', bins= 50)
plt.style.use('ggplot')
plt.title('Trend of Product')
plt.xlabel('Total Bulk and Bags Units')
plt.ylabel('TotalBagged Units')
plt.show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12930310663308145, 'fraction_numerical': 0.030646515533165407, 'mean_word_length': 4.191721132897603, 'pattern_counts': {'":': 1, '<': 6, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 2, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '597899', 'license': 'MIT', 'n_tokens_mistral': 906, 'n_tokens_neox': 821, 'n_words': 298, 'path': 'Big Data Practice.ipynb', 'repo_name': 'spu-bigdataanalytics-201/welcome-bchavan22', 'size': '2382'}