kipronokoech

# Python3 code to demonstrate 
# convert dictionary string to dictionary 
# using json.loads() 
import json 
  
# initializing string  
string = '{"Kiprono" : 67, "Bob" : 76, "Alice" : 88}' 
  
# printing original string  
print(string)

kipronokoech

# Python3 code to demonstrate 
# convert dictionary string to dictionary 
# using json.loads() 
import ast 
  
# initializing string  
string = '{"Kiprono" : 67, "Bob" : 76, "Alice" : 88}' 
  
# printing original string  
print(string)

kipronokoech

import pandas as pd
import numpy as np

# generate 15000 intergeter data points randomly between -1000 and 1000
col1= np.random.randint(-1000,1000,15000)

#dictionary of data
data = {
    "col1" : col1,
}

kipronokoech

# Q1 : Determine the average age per occupation.

df.groupby('occupation').age.mean()
#-----------------OUTPUT------------------------
# occupation
# other         43
# technician    24
# writer        23
# Name: age, dtype: int64
#-----------------END OF OUTPUT-----------------

kipronokoech

# Import necessary packages
from bs4 import BeautifulSoup
import requests
import pandas as pd
import re
# Site URL
url="https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nominal)_per_capita"

# Make a GET request to fetch the raw HTML content
html_content = requests.get(url).text

kipronokoech

# On site there are 3 tables with the class "wikitable"
# The following line will generate a list of HTML content for each table
gdp = soup.find_all("table", attrs={"class": "wikitable"})
print("Number of tables on site: ",len(gdp))

kipronokoech

# Lets go ahead and scrape first table with HTML code gdp[0]
table1 = gdp[0]
# the head will form our column names
body = table1.find_all("tr")
# Head values (Column names) are the first items of the body list
head = body[0] # 0th item is the header row
body_rows = body[1:] # All other items becomes the rest of the rows

# Lets now iterate through the head HTML code and make list of clean headings

kipronokoech

# Next is now to loop though the rest of the rows

#print(body_rows[0])
all_rows = [] # will be a list for list for all rows
for row_num in range(len(body_rows)): # A row at a time
    row = [] # this will old entries for one row
    for row_item in body_rows[row_num].find_all("td"): #loop through all row entries
        # row_item.text removes the tags from the entries
        # the following regex is to remove \xa0 and \n and comma from row_item.text
        # xa0 encodes the flag, \n is the newline and comma separates thousands in numbers

kipronokoech

# We can now use the data on all_rowsa and headings to make a table
# all_rows becomes our data and headings the column names
df = pd.DataFrame(data=all_rows,columns=headings)
df.head()

kipronokoech

#!/usr/bin/env python
# coding: utf-8

import numpy as np
from shapely.geometry import Polygon,Point
import matplotlib.pyplot as plt
import shapely
import cv2 as cv
import os