Subhradeep Rang srang992

## Imp_func.py
import itertools


def count_df(data,data_col):
  choco_count = data[data_col].value_counts().rename_axis(data_col).reset_index(name='count')
  choco_data_with_counts = pd.merge(left=data, right=choco_count, left_on=data_col, right_on=data_col)
  return choco_data_with_counts


def number_indicator(val, title_text, row_num, col_num):

## Converty_cat.py
# converting the column's datatype to 'object'
choco_data['reference_no'] = choco_data['reference_no'].astype('object')
choco_data['review_date'] = choco_data['review_date'].astype('object')

# selecting the object(or you can say string) type columns
choco_data_cat = choco_data.select_dtypes(include=['object'])

# showing the summary statistics
choco_data_cat.describe()

## clean_cocoa.py
# removing the '%' sign from the cocoa_percent column
choco_data['cocoa_percent'] = choco_data['cocoa_percent'].str.strip('%')

# converting the datatype
choco_data['cocoa_percent'] = choco_data['cocoa_percent'].astype('float')

# showing the first 5 rows
choco_data['cocoa_percent'].head()

## Rename_columns.py
# defining a dictionary for renaming the columns
column_names = {"ref": "reference_no",
                "Company (Manufacturer)": "manufacturer",
                "Company Location": "company_loc",
                "Review Date": "review_date",
                "Country of Bean Origin": "bean_origin",
                "Specific Bean Origin or Bar Name": "bar_name",
                "Cocoa Percent": "cocoa_percent",
                "Ingredients": "ingredients",
                "Most Memorable Characteristics": "taste",

## Load_data.py
import pandas as pd
query = "SELECT * FROM chocolate_database"
choco_data = pd.read_sql(query, conn)
choco_data.head()

## Access_data.py
import pickle
import psycopg2

# loading the file containing credentials
with open("heroku_database_credentials.pickle", "rb") as cred:
  credential = pickle.load(cred)

# connecting to the postgresql database with those credentials
conn = psycopg2.connect(database=credential['Database'],
                        host=credential['Host'],

## khoj_import.py
import numpy as np
import pandas as pd

## KHOJ Description.csv

          
            Column
             Description

            
              Name of the Judge
               Containing the name of the Judge

            
              Gender
               Gender of the Judge

            
              Date of Birth
               The date in which the Judges were born

            
              Date of Appointment
               The date on which the person was elevated as a Judge of any High Court (appointment as an Additional Judge is also considered here)

            
              Date of Retirement
               The date on which the person demits office as a Judge of High Court or of the Supreme Court (if elevated to it)

            
              If appointed Chief Justice in any High Court
               Categorical column specifying if a judge is appointed as Chief Justice or not.

            
              If appointed to the Supreme Court
               Categorical column specifying if a judge is appointed to the Supreme Court or not.

            
              Foreign Degree in Law
               If the judge has a Foreign Degree in Law or not.

            
              Post-Graduate in Law
               If the judge has a PG Degree in Law or not.

## function1.jl
function minus_one!(x)
      x .= x .+ 1
end

K = [1, 2, 3, 4, 5]

modify_array!(K)

println(K)

## dict1.jl
list1 = [1, 2, 3, 4 ,5]
list2 = ["one", "two", "three", "four", "five"]

new_dict = Dict(zip(list1, list2))

#=
it will return
Dict(5 => "five", 4 => "four", 2 => "two", 3 => "three", 1 => "one")
=#
	import itertools


	def count_df(data,data_col):
	choco_count = data[data_col].value_counts().rename_axis(data_col).reset_index(name='count')
	choco_data_with_counts = pd.merge(left=data, right=choco_count, left_on=data_col, right_on=data_col)
	return choco_data_with_counts


	def number_indicator(val, title_text, row_num, col_num):
	# converting the column's datatype to 'object'
	choco_data['reference_no'] = choco_data['reference_no'].astype('object')
	choco_data['review_date'] = choco_data['review_date'].astype('object')

	# selecting the object(or you can say string) type columns
	choco_data_cat = choco_data.select_dtypes(include=['object'])

	# showing the summary statistics
	choco_data_cat.describe()
	# removing the '%' sign from the cocoa_percent column
	choco_data['cocoa_percent'] = choco_data['cocoa_percent'].str.strip('%')

	# converting the datatype
	choco_data['cocoa_percent'] = choco_data['cocoa_percent'].astype('float')

	# showing the first 5 rows
	choco_data['cocoa_percent'].head()
	# defining a dictionary for renaming the columns
	column_names = {"ref": "reference_no",
	"Company (Manufacturer)": "manufacturer",
	"Company Location": "company_loc",
	"Review Date": "review_date",
	"Country of Bean Origin": "bean_origin",
	"Specific Bean Origin or Bar Name": "bar_name",
	"Cocoa Percent": "cocoa_percent",
	"Ingredients": "ingredients",
	"Most Memorable Characteristics": "taste",
	import pandas as pd
	query = "SELECT * FROM chocolate_database"
	choco_data = pd.read_sql(query, conn)
	choco_data.head()
	import pickle
	import psycopg2

	# loading the file containing credentials
	with open("heroku_database_credentials.pickle", "rb") as cred:
	credential = pickle.load(cred)

	# connecting to the postgresql database with those credentials
	conn = psycopg2.connect(database=credential['Database'],
	host=credential['Host'],
	Column	Description
	Name of the Judge	Containing the name of the Judge
	Gender	Gender of the Judge
	Date of Birth	The date in which the Judges were born
	Date of Appointment	The date on which the person was elevated as a Judge of any High Court (appointment as an Additional Judge is also considered here)
	Date of Retirement	The date on which the person demits office as a Judge of High Court or of the Supreme Court (if elevated to it)
	If appointed Chief Justice in any High Court	Categorical column specifying if a judge is appointed as Chief Justice or not.
	If appointed to the Supreme Court	Categorical column specifying if a judge is appointed to the Supreme Court or not.
	Foreign Degree in Law	If the judge has a Foreign Degree in Law or not.
	Post-Graduate in Law	If the judge has a PG Degree in Law or not.
	function minus_one!(x)
	x .= x .+ 1
	end

	K = [1, 2, 3, 4, 5]

	modify_array!(K)

	println(K)
	list1 = [1, 2, 3, 4 ,5]
	list2 = ["one", "two", "three", "four", "five"]

	new_dict = Dict(zip(list1, list2))

	#=
	it will return
	Dict(5 => "five", 4 => "four", 2 => "two", 3 => "three", 1 => "one")
	=#