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Analyzing Stock's Fundamentals!

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META DATA

1. COMPANY: Represent name of company.

2. NSE_CODE: Represent code of company from National Stock Exchange.

3. BSE_CODE: Represent code of company from Bombay Stock Exchange.

4. SECTOR: Represent that in which field company works.

5. MARKET_CAP: Market cap or market capitalization refers to the total value of all a company's shares of stock. It is calculated by multiplying the price of a stock by its total number of outstanding shares.

6. CURRENT_PRICE: The current price is the most recent selling price of a stock, currency, commodity, or precious metal that is traded on an exchange and is the most reliable indicator of that security's present value.

7. DOWN_FROM_52w_HIGH: The 52-week high is the highest price at which a security, such as a stock, has traded during the time period that equates to one year.

8. UP_FROM_52w_LOW: The 52-week high is the lowest price at which a security, such as a stock, has traded during the time period that equates to one year.

9. DEBT: It's the sum of loan and short term debt taken by company.

10. DIV_YIELD: It measures the amount of cash dividends distributed to equity shareholders relative to the market value as per share

11. BOOK_VALUE: The book value of a stock is theoretically the amount of money that would be paid to shareholders if the company was liquidated and paid off all of its liabilities

12. PROMOTER_HOLDING: A promoter, as the name suggests, is an individual or an organization that helps promote the investment activity of a company. in most cases, promoters are paid in the company's stocks, or they directly invest in them. This gives the promoters a significant stake in the company. Such a stake is known as promoter's holding. Often, they also have voting rights, which gives them an influential say in the company's management.

13. HOLDING_PLEDGED:Pledging of shares is an arrangement in which the promoters of a company use their shares as collateral to fulfill their financial requirements.

14. SALES: Sales are the proceeds a company generates from selling goods or services to its customers: In accounting terms, sales comprise one component of a company's revenue figure. On an income statement, sales are typically referred to as gross sales.

15. PROFIT: Corporate profit is the money left over after a corporation pays all of its expenses excluding taxes.

16. ROCE: The term return on capital employed (ROCE) refers to a financial ratio that can be used to assess a company's profitability and capital efficiency. In other words, this ratio can help to understand how well a company is generating profits from its capital as it is put to use.

17. ROE: Return on equity (ROE) is the measure of a company's net income divided by its shareholders' equity. ROE is a gauge of a corporation's profitability and how efficiently it generates those profits. The higher the ROE, the better a company is at converting its equity financing into profits.

18. PE: Price to Earnings Ratio or Price to Earnings Multiple is the ratio of share price of a stock to its earnings per share (EPS). PE ratio is one of the most popular valuation metric of stocks. It provides indication whether a stock at its current market price is expensive or cheap.

19. CAGR: Compound annual growth rate (CAGR) is a metric that smoothes annual gains in revenue, returns, customers, etc., over a specified number of years as if the growth had happened steadily each year over that time period.

20. EPS: It's a company's net profit divided by the number of common shares it has outstanding.

21. ASSET_TURNOVER: The asset turnover ratio measures the efficiency of a company's assets in generating revenue or sales. It compares the dollar amount of sales (revenues) to its total assets as an annualized percentage. Thus, to calculate the asset turnover ratio, divide net sales or revenue by the average total assets.

22. QUICK_RATIO: The quick ratio measures a company's ability to quickly convert liquid assets into cash to pay for its short-term financial obligations

22. DEBT_EQUITY: The debt-to-equity ratio measures your company's total debt relative to the amount originally invested by the owners and the earnings that have been retained over time.

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REPORT CONTENT

1. Import Modules
   

2. Stock Selection & Scraping
   2.1 Data Required From Website
   

3. Scraping The Data With Selenium
   

4. Creating Data Frame with the Scarped Data
   4.1Data Frame Information

5. Data Set Cleaning
   5.1Removing Comma's From The Strings
   5.2Changing The Data Type Of Numeric Strings in Integer or Float
   5.3Checking for Null Values
   5.4Replacing the Null Values
   

6. Exploratory Data Analysis (E.D.A.)
   Getting Basic Necessary Details from Data Set
   6.1Name & Number of Sectors
   6.2Number of shares of each company
   6.3Number of Companies in Each Sector
   6.4Number of companies according to Market capitalization
   6.5Company according to their sectors
   6.6Loss making Sector & Companies Detail
   6.7Profit making Sector & Companies Detail
   

7. "Telecomm-Service" SECTOR ANALYSIS
   7.1Analyzing Debt and Market_Cap of companies
   7.2CAGR check
   

8. "IT - Software" SECTOR ANALYSIS
   8.1Analyzing Debt and Market_Cap of companies
   8.2CAGR check
   

9. "Stock comparison
   9.1Profit growth check
   9.2Quick ratio check
   

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1. Import Modules

REPORT CONTENT

• Used, libraries for this project selenium, pandas, matplotlib, seaborn, os

from selenium import webdriver
from selenium.webdriver.chrome.service import Service
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
import time
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import os

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2. Stock Selection & Scraping

REPORT CONTENT

• Took list of 50 stocks for analysis

stocks=['BAJFINANCE','DMART','RELIANCE','ASIANPAINT','BERGEPAINT','TATAMOTORS','TCS','Pidilite Inds.','TVS Motor',\
       'Rallis India','Nestle India','SBI Cards','HDFC Bank','Balkrishna Inds','Bharti Airtel','V-Mart Retail','Cipla',\
       'Shalimar Paints','Apollo Hospitals','AU Small Finance','L & T Infotech','Deepak Nitrite','Aarti Industries',\
       'Maruti Suzuki','P I Industries','Hind. Unilever','Muthoot Finance','MRF','CEAT','Tata Comm','Vodafone Idea',\
       'Trent','Torrent Pharma.','Sun Pharma.Inds.','Kansai Nerolac','Fortis Health.','St Bk of India','KPIT Technologi.',\
       'Fine Organic','Bajaj Auto','Meghmani Organi.','Varun Beverages','Power Fin.Corpn.','Goodyear India','M T N L',\
       'Medplus Health','Abbott India','Dr Lal Pathlabs','Mindtree','Subex']

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2.1 Data Required From Website

REPORT CONTENT

• Creating blank list of those things or data which is required for analysis of stock

COMPANY=[]
NSE_CODE=[]
BSE_CODE=[]
SECTOR=[]
MARKET_CAP=[]
CURRENT_PRICE=[]
DOWN_FROM_52w_HIGH=[]
UP_FROM_52w_LOW=[]

DEBT=[]
DIV_YIELD=[]
BOOK_VALUE=[]

PROMOTER_HOLDING=[]
HOLDING_PLEDGED=[]

SALES=[]
SALES_GROWTH_5yrs=[]
PROFIT=[]
PROFIT_GROWTH_5yrs=[]

ROCE=[]
ROCE_5yr_AVG=[]
ROE=[]
ROE_5yr_AVG=[]

PE=[]
PE_5yr_AVG=[]

CAGR_5yr=[]
CAGR_10yr=[]

EPS=[]
ASSET_TURNOVER=[]
QUICK_RATIO=[]
DEBT_EQUITY=[]

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3. Scraping The Data With Selenium

REPORT CONTENT

• scraping the data of stock from website screener.in
• In scarping, i have used XPATH method to scrap the data.

c_driver=Service('C:\devops\chromedriver')
driver = webdriver.Chrome(service=c_driver)
driver.maximize_window()
driver.get('https://www.screener.in/explore/')

time.sleep(1)

log_in=driver.find_element(By.XPATH, '/html/body/nav/div[1]/div/div[1]/div/div[3]/div[2]/a[1]')
log_in.click()

time.sleep(1)
#Log in details to get the specific Ratios & Data
email=driver.find_element(By.XPATH, '/html/body/main/div[2]/div[2]/form/div[1]/input')
email.send_keys('genesisconstructionsolution@gmail.com')
password=driver.find_element(By.XPATH, '/html/body/main/div[2]/div[2]/form/div[2]/input')
password.send_keys('GENESIS#C.E.')
password.send_keys(Keys.ENTER)
    
    
# created a loop for the stocks list to  get the data of each stock    
for i in range(len(stocks)):  
    
    stock_search=driver.find_element(By.XPATH, '/html/body/nav/div[1]/div/div[1]/div/div[3]/div[1]/div/input')
    stock_search.send_keys(stocks[i])
    time.sleep(4)
    stock_search.send_keys(Keys.ENTER)
    
    time.sleep(3)

# scraping COMPANY NAME of stock         
    company_name=driver.find_element(By.XPATH, '/html/body/main/div[3]/div[1]/div/h1')
    c_name=company_name.text
    COMPANY.append(c_name)
    
# scraping NSE CODE of stock      
    nse=driver.find_element(By.XPATH, '/html/body/main/div[3]/div[2]/a[3]/span')
    nse_name=nse.text.split(': ')
    NSE_CODE.append(nse_name[1])
    
    
# scraping BSE CODE of stock        
    bse=driver.find_element(By.XPATH, '/html/body/main/div[3]/div[2]/a[2]/span')
    bse_name=bse.text.split(': ')
    BSE_CODE.append(bse_name[1])
    
# scraping SECTOR of stock     
    sector=driver.find_element(By.XPATH, '/html/body/main/section[3]/div[1]/div[1]/p/a[1]').text
    SECTOR.append(sector)
        
# scraping MARKET CAPITALIZATION of stock    
    m_cap=driver.find_element(By.XPATH, '/html/body/main/div[3]/div[3]/div[2]/ul/li[1]/span[2]/span').text
    MARKET_CAP.append(m_cap)
    
# scraping CURRENT PRICE of stock       
    price=driver.find_element(By.XPATH, '/html[1]/body[1]/main[1]/div[3]/div[3]/div[2]/ul[1]/li[2]/span[2]/span[1]').text
    CURRENT_PRICE.append(price)
    
# scraping the PERCENTAGE  of stock that it is how much lower than it's 52week (one year) high   
    high=driver.find_element(By.XPATH, '/html[1]/body[1]/main[1]/div[3]/div[3]/div[2]/ul[1]/li[27]/span[2]/span[1]').text
    DOWN_FROM_52w_HIGH.append(high)
    
# scraping the PERCENTAGE  of stock that it is how much upper than it's 52week (one year) high     
    low=driver.find_element(By.XPATH, '/html[1]/body[1]/main[1]/div[3]/div[3]/div[2]/ul[1]/li[26]/span[2]/span[1]').text
    UP_FROM_52w_LOW.append(low)
    
# scraping the LOAN(debt)  of stock
    debt= driver.find_element(By.XPATH, '/html[1]/body[1]/main[1]/div[3]/div[3]/div[2]/ul[1]/li[11]/span[2]/span[1]').text
    DEBT.append(debt)
    
# scraping the PROFIT SHARING PERCENTAGE (DIV_YIELD)  of stock        
    div= driver.find_element(By.XPATH, '/html[1]/body[1]/main[1]/div[3]/div[3]/div[2]/ul[1]/li[6]/span[2]/span[1]').text
    DIV_YIELD.append(div)
    
# scraping the PHYSICAL ASSET VALUE (BOOK_VALUE)  of stock    
    book= driver.find_element(By.XPATH, '/html[1]/body[1]/main[1]/div[3]/div[3]/div[2]/ul[1]/li[5]/span[2]/span[1]').text   
    BOOK_VALUE.append(book)
    
# scraping the PROMOTER & OWNERS PERCENTAGE (PROMOTER_HOLDING)  of stock    
    holding= driver.find_element(By.XPATH, '/html[1]/body[1]/main[1]/div[3]/div[3]/div[2]/ul[1]/li[13]/span[2]/span[1]').text   
    PROMOTER_HOLDING.append(holding) 
    
# scraping the PROMOTER & OWNERS PERCENTAGE OF PLEDGED (PROMOTER_HOLDING)  of stock    
    pledge= driver.find_element(By.XPATH, '/html[1]/body[1]/main[1]/div[3]/div[3]/div[2]/ul[1]/li[21]/span[2]/span[1]').text   
    HOLDING_PLEDGED.append(pledge)

# scraping the SALES GROWTH PERCENTAGE OF RECENT YEAR of stock
    sale= driver.find_element(By.XPATH, '/html[1]/body[1]/main[1]/div[3]/div[3]/div[2]/ul[1]/li[17]/span[2]/span[1]').text   
    SALES.append(sale)
    
# scraping the AVERAGE OF 5 YEAR SALES GROWTH PERCENTAGE  of stock    
    sales= driver.find_element(By.XPATH, '/html[1]/body[1]/main[1]/div[3]/div[3]/div[2]/ul[1]/li[10]/span[2]/span[1]').text
    SALES_GROWTH_5yrs.append(sales)
    
# scraping the PROFIT GROWTH PERCENTAGE OF RECENT YEAR of stock    
    profit=driver.find_element(By.XPATH, '/html[1]/body[1]/main[1]/div[3]/div[3]/div[2]/ul[1]/li[16]/span[2]/span[1]').text
    PROFIT.append(profit)
# scraping the AVERAGE OF 5 YEAR PROFIT GROWTH PERCENTAGE  of stock    
    profits= driver.find_element(By.XPATH, '/html[1]/body[1]/main[1]/div[3]/div[3]/div[2]/ul[1]/li[14]/span[2]/span[1]').text
    PROFIT_GROWTH_5yrs.append(profits)

# scraping the RETURN ON CAPITAL EMPLOYED of stock 
    roce= driver.find_element(By.XPATH, '/html[1]/body[1]/main[1]/div[3]/div[3]/div[2]/ul[1]/li[7]/span[2]/span[1]').text   
    ROCE.append(roce) 
    
# scraping the AVERAGE OF 5 YEAR RETURN ON CAPITAL EMPLOYED of stock 
    roce_5yr= driver.find_element(By.XPATH, '/html[1]/body[1]/main[1]/div[3]/div[3]/div[2]/ul[1]/li[22]/span[2]/span[1]').text   
    ROCE_5yr_AVG.append(roce_5yr)

# scraping the RETURN ON EQUITY of stock
    roe= driver.find_element(By.XPATH, '/html[1]/body[1]/main[1]/div[3]/div[3]/div[2]/ul[1]/li[8]/span[2]/span[1]').text   
    ROE.append(roe)
    
# scraping the AVERAGE OF 5 YEAR RETURN ON EQUITY of stock     
    roe_5yr= driver.find_element(By.XPATH, '/html[1]/body[1]/main[1]/div[3]/div[3]/div[2]/ul[1]/li[18]/span[2]/span[1]').text   
    ROE_5yr_AVG.append(roe_5yr)
    
# scraping the RATIO OF PRICE TO EARNING PER SHARE of stock
    pe= driver.find_element(By.XPATH, '/html[1]/body[1]/main[1]/div[3]/div[3]/div[2]/ul[1]/li[4]/span[2]/span[1]').text   
    PE.append(pe)
    
# scraping the AVERAGE OF 5 YEAR RATIO OF PRICE TO EARNING PER SHARE of stock 
    pe_5yr= driver.find_element(By.XPATH, '/html[1]/body[1]/main[1]/div[3]/div[3]/div[2]/ul[1]/li[12]/span[2]/span[1]').text   
    PE_5yr_AVG.append(pe_5yr)
    
# scraping the AVERAGE OF 5 YEAR OF COMPOUND ANUAL GROWTH of stock 
    cagr_5=driver.find_element(By.XPATH, '/html[1]/body[1]/main[1]/div[3]/div[3]/div[2]/ul[1]/li[25]/span[2]/span[1]').text
    CAGR_5yr.append(cagr_5)

# scraping the AVERAGE OF 10 YEAR OF COMPOUND ANUAL GROWTH of stock 
    cagr_10=driver.find_element(By.XPATH, '/html[1]/body[1]/main[1]/div[3]/div[3]/div[2]/ul[1]/li[24]/span[2]/span[1]').text
    CAGR_10yr.append(cagr_10)
    
# scraping the EARNING PER SHARE of stock    
    eps= driver.find_element(By.XPATH, '/html[1]/body[1]/main[1]/div[3]/div[3]/div[2]/ul[1]/li[15]/span[2]/span[1]').text   
    EPS.append(eps)

# scraping the ASSET TURNOVER of stock  
    asset= driver.find_element(By.XPATH, '/html[1]/body[1]/main[1]/div[3]/div[3]/div[2]/ul[1]/li[19]/span[2]/span[1]').text   
    ASSET_TURNOVER.append(asset)

# scraping the CAPABILITY OF REPAYMENT RATIO(OUICK_RATIO) of stock 
    q= driver.find_element(By.XPATH, '/html[1]/body[1]/main[1]/div[3]/div[3]/div[2]/ul[1]/li[20]/span[2]/span[1]').text   
    QUICK_RATIO.append(q)
    
# scraping the LOAN TO VALUE OF COMPANY RATIO of stock    
    de= driver.find_element(By.XPATH, '/html[1]/body[1]/main[1]/div[3]/div[3]/div[2]/ul[1]/li[23]/span[2]/span[1]').text
    DEBT_EQUITY.append(de)
    
        
    nxt_stock=driver.find_element(By.XPATH, '/html[1]/body[1]/nav[1]/div[1]/div[1]/div[1]/div[1]/div[1]/a[1]/img[1]')
    nxt_stock.click()

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4. Creating Data Frame With The Scarped Data

REPORT CONTENT

• Creating a DataFrame for the scraped data as "df" named file

df=pd.DataFrame({'COMPANY':COMPANY, 'NSE_CODE':NSE_CODE, 'BSE_CODE':BSE_CODE, 'SECTOR':SECTOR,\
                 'MARKET_CAP(in Cr.)':MARKET_CAP,'CURRENT_PRICE':CURRENT_PRICE,\
                 'DOWN_FROM_52w_HIGH(in %)':DOWN_FROM_52w_HIGH,'UP_FROM_52w_LOW(in %)':UP_FROM_52w_LOW,\
                 'DEBT(in Cr.)':DEBT,'DIV_YIELD(in %)':DIV_YIELD,'BOOK_VALUE':BOOK_VALUE,\
                 'PROMOTER_HOLDING(in %)':PROMOTER_HOLDING, 'HOLDING_PLEDGED(in %)': HOLDING_PLEDGED,\
                 'SALES(in %)':SALES, 'SALES_GROWTH_5yrs(in %)':SALES_GROWTH_5yrs,'PROFIT(in %)':PROFIT,\
                 'PROFIT_GROWTH_5yrs(in %)': PROFIT_GROWTH_5yrs,'ROCE(in %)':ROCE,\
                 'ROCE_5yr_AVG(in %)':ROCE_5yr_AVG, 'ROE(in %)':ROE, 'ROE_5yr_AVG(in %)':ROE_5yr_AVG,\
                 'PE_RATIO':PE, 'PE_RATIO_5yr_AVG':PE_5yr_AVG,'CAGR_5yr(in %)':CAGR_5yr,\
                 'CAGR_10yr(in %)':CAGR_10yr,'EPS':EPS,'ASSET_TURNOVER':ASSET_TURNOVER,\
                 'QUICK_RATIO':QUICK_RATIO,'DEBT_EQUITY_RATIO':DEBT_EQUITY
             })

• Saving & reading the Data Frame as a CSV file

df.to_csv('C:/Users/ASUS/Downloads/stock fundamental analysis project data csv.csv',index=False)

df=pd.read_csv("C:/Users/ASUS/Downloads/stock fundamental analysis project data csv.csv")

pd.set_option('display.max_columns',29)
df

	COMPANY	NSE_CODE	BSE_CODE	SECTOR	MARKET_CAP(in Cr.)	CURRENT_PRICE	DOWN_FROM_52w_HIGH(in %)	UP_FROM_52w_LOW(in %)	DEBT(in Cr.)	DIV_YIELD(in %)	BOOK_VALUE	PROMOTER_HOLDING(in %)	HOLDING_PLEDGED(in %)	SALES(in %)	SALES_GROWTH_5yrs(in %)	PROFIT(in %)	PROFIT_GROWTH_5yrs(in %)	ROCE(in %)	ROCE_5yr_AVG(in %)	ROE(in %)	ROE_5yr_AVG(in %)	PE_RATIO	PE_RATIO_5yr_AVG	CAGR_5yr(in %)	CAGR_10yr(in %)	EPS	ASSET_TURNOVER	QUICK_RATIO	DEBT_EQUITY_RATIO
0	Bajaj Finance Ltd	BAJFINANCE	500034	Finance	422,293	6,975	13.30	33.60	183,273	0.29	794	55.9	0.00	30.10	26.00	99.4	30.90	10.30	11.00	17.50	17.70	42.60	40.70	31.90	48.50	164	0.16	1.26	3.81
1	Avenue Supermarts Ltd	DMART	540376	Retail	268,104	4,139	20.10	29.90	711	0.00	232	75.0	0.00	38.50	21.10	68.0	25.80	15.80	18.00	11.50	13.20	116.00	103.00	29.00	NaN	35.6	2.13	1.19	0.05
2	Reliance Industries Ltd	RELIANCE	500325	Refineries	1,740,392	2,572	9.93	18.00	316,030	0.31	1,168	50.6	0.00	46.40	18.10	24.2	14.40	9.42	10.00	8.16	9.59	27.10	17.90	24.10	20.70	98.1	0.50	0.63	0.40
3	Asian Paints Ltd	ASIANPAINT	500820	Paints/Varnish	292,090	3,045	15.20	19.00	1,866	0.63	147	52.6	10.60	28.20	14.10	15.6	9.71	29.70	33.60	23.20	25.40	77.60	59.10	20.90	22.20	38.2	1.34	1.04	0.13
4	Berger Paints India Ltd	BERGEPAINT	509480	Paints/Varnish	56,644	583	28.30	7.28	1,634	0.53	42.2	75.0	0.00	24.50	14.00	12.2	14.10	25.90	27.20	22.50	22.90	59.90	63.60	18.30	27.70	9.74	1.33	0.58	0.40
5	Tata Motors Ltd	TATAMOTORS	500570	Automobile	148,207	412	23.20	12.60	144,354	0.00	78.8	46.4	1.82	3.47	0.64	-30,209	NaN	1.40	3.44	-22.30	-5.20	NaN	-2.12	-0.49	4.15	-25.6	0.83	0.68	5.52
6	Tata Consultancy Services Ltd	TCS	532540	IT - Software	1,172,964	3,206	20.80	9.56	7,460	1.34	266	72.3	0.48	16.60	10.20	8.27	7.83	54.90	47.80	43.60	37.20	29.60	21.20	18.80	17.10	108	1.42	2.84	0.08
7	Pidilite Industries Ltd	PIDILITIND	500331	Chemicals	135,022	2,656	9.00	33.60	680	0.38	130	69.9	0.00	26.10	12.00	-3.28	7.10	26.10	31.70	20.20	23.80	104.00	61.70	26.50	29.00	25.6	1.08	0.92	0.10
8	TVS Motor Company Ltd	TVSMOTOR	532343	Automobile	52,544	1,106	6.04	116.00	18,675	0.34	107	50.8	0.00	24.20	14.30	41.5	8.13	11.30	13.20	18.40	20.80	44.40	39.30	9.31	39.50	25.6	1.00	0.89	3.68
9	Rallis India Ltd	RALLIS	500355	Agro Chemicals	4,385	226	24.60	23.60	175	1.33	91.2	50.1	0.00	17.50	13.40	-12.6	2.40	13.60	16.60	10.20	12.60	26.80	20.70	0.03	3.58	8.43	0.96	0.86	0.10
10	Nestle India Ltd	NESTLEIND	500790	FMCG	194,522	20,175	4.17	26.10	280	0.99	241	62.8	0.00	13.50	9.98	4.63	18.10	147.00	95.10	113.00	67.10	82.90	66.50	21.00	15.50	225	1.83	0.51	0.12
11	SBI Cards & Payment Services Ltd	SBICARD	543066	Finance	76,228	807	26.30	23.00	28,186	0.31	94.6	69.4	0.00	25.60	26.10	105	34.10	11.60	12.80	23.00	24.00	36.00	49.70	NaN	NaN	22.5	0.35	0.41	3.16
12	HDFC Bank Ltd	HDFCBANK	500180	Banks	849,930	1,525	11.50	19.90	1,784,970	1.02	446	25.6	0.00	13.90	13.20	22.0	20.00	5.83	6.77	16.60	16.80	20.40	24.20	10.80	16.80	75.2	0.07	2.66	7.22
13	Balkrishna Industries Ltd	BALKRISIND	502355	Tyres	36,364	1,881	25.90	11.80	2,529	0.85	359	58.3	0.00	37.20	17.40	2.56	17.00	23.80	22.00	21.90	19.50	25.80	24.90	14.30	29.90	73.0	0.87	0.48	0.36
14	Bharti Airtel Ltd	BHARTIARTL	532454	Telecomm-Service	476,676	826	1.87	31.30	217,989	0.36	131	55.1	0.00	19.80	4.07	125	-3.36	12.00	8.30	5.86	2.13	76.70	36.80	12.70	12.50	11.4	0.33	0.45	2.99
15	V-Mart Retail Ltd	VMART	534976	Retail	5,594	2,830	41.60	17.60	902	0.03	430	46.1	0.00	76.80	10.70	4,888	-22.40	5.21	11.40	1.46	7.58	92.00	44.20	15.00	NaN	30.8	0.89	0.28	1.06
16	Cipla Ltd	CIPLA	500087	Pharmaceuticals	90,575	1,122	5.31	32.00	1,068	0.45	271	33.6	0.00	5.50	8.62	-2.62	23.10	17.50	13.60	13.20	11.30	34.90	27.90	13.00	11.00	31.8	0.84	1.97	0.05
17	Shalimar Paints Ltd	SHALPAINTS	509874	Paints/Varnish	1,121	155	15.80	64.00	145	0.00	51.5	39.9	0.00	16.30	0.47	17.9	NaN	-7.19	-9.53	-18.00	-21.40	NaN	-10.70	0.54	5.17	-9.00	0.61	1.36	0.39
18	Apollo Hospitals Enterprise Ltd	APOLLOHOSP	508869	Healthcare	61,618	4,285	27.80	27.50	3,998	0.27	417	29.3	16.40	16.20	15.10	7.82	44.80	17.80	11.80	16.60	7.94	73.40	72.40	33.80	18.00	58.4	1.19	1.32	0.67
19	AU Small Finance Bank Ltd	AUBANK	540611	Banks	41,871	628	14.30	35.90	58,575	0.08	119	25.6	0.00	31.30	35.80	11.4	6.58	7.19	8.47	16.60	17.90	33.30	34.70	15.80	NaN	19.7	0.10	2.31	7.84
20	Larsen & Toubro Infotech Ltd	LTI	540005	IT - Software	85,278	4,862	36.00	30.20	824	0.93	524	74.0	0.00	30.40	19.20	21.3	19.20	35.70	38.60	28.50	30.50	33.30	19.50	38.80	NaN	146	1.35	3.09	0.09
21	Deepak Nitrite Ltd	DEEPAKNTR	506401	Chemicals	28,361	2,079	22.70	23.70	270	0.34	268	45.7	0.00	28.90	37.80	-13.6	87.00	44.50	32.80	37.40	34.20	30.90	21.50	57.00	57.00	67.4	1.70	2.26	0.07
22	Aarti Industries Ltd	AARTIIND	524208	Chemicals	25,673	708	30.30	16.60	2,594	0.49	163	44.2	0.00	56.70	17.20	120	32.90	22.10	17.60	27.80	22.20	19.30	26.50	29.10	42.40	36.7	0.81	0.80	0.44
23	Maruti Suzuki India Ltd	MARUTI	532500	Automobile	274,810	9,097	6.88	39.20	625	0.66	1,893	56.4	0.00	24.00	5.34	44.4	-7.94	8.95	13.60	7.25	10.20	45.30	26.30	2.14	20.10	201	1.21	0.80	0.01
24	P I Industries Ltd	PIIND	523642	Agro Chemicals	53,018	3,494	5.52	49.80	280	0.17	436	46.1	0.00	23.60	18.40	29.3	12.90	17.30	21.20	14.70	17.50	51.80	34.70	33.20	41.90	67.5	0.72	2.53	0.04
25	Hindustan Unilever Ltd	HINDUNILVR	500696	FMCG	590,593	2,514	8.06	32.20	1,139	1.35	211	61.9	0.00	14.30	9.60	14.7	15.60	24.30	48.30	18.40	35.80	61.00	55.20	14.30	16.90	41.1	0.75	0.99	0.02
26	Muthoot Finance Ltd	MUTHOOTFIN	533398	Finance	44,377	1,105	35.80	16.40	46,770	1.81	479	73.4	0.00	-5.33	14.00	-7.43	27.40	14.20	15.60	23.60	25.40	12.10	12.50	18.00	17.20	91.2	0.17	3.29	2.43
27	MRF Ltd	MRF	500290	Tyres	37,087	87,469	8.89	39.00	3,118	0.17	33,566	27.9	0.00	17.20	7.57	-53.0	-14.90	7.05	12.60	4.85	9.76	65.30	21.80	5.36	24.00	1,339	0.85	0.78	0.22
28	CEAT Ltd	CEATLTD	500878	Tyres	6,966	1,722	3.68	93.50	2,393	0.17	809	47.2	0.00	20.80	10.20	-89.3	-25.50	6.35	11.60	2.59	9.28	182.00	14.80	-0.14	32.10	5.52	1.08	0.36	0.73
29	Tata Communications Ltd	TATACOMM	500483	Telecomm-Service	37,453	1,314	17.40	53.50	8,840	1.58	31.8	58.9	4.96	3.72	-1.04	34.2	-6.74	21.80	13.20	282.00	NaN	21.10	16.70	24.60	24.40	64.4	0.82	0.61	9.74
30	Vodafone Idea Ltd	IDEA	532822	Telecomm-Service	27,783	8.65	48.50	11.60	248,176	0.00	-23.8	75.0	0.00	4.92	1.60	-6.46	NaN	NaN	NaN	NaN	NaN	NaN	-0.60	-31.80	-17.00	-9.20	0.19	0.24	NaN
31	Trent Ltd	TRENT	500251	Retail	52,289	1,471	6.37	51.60	4,526	0.12	67.0	37.0	0.00	92.00	19.90	550	-14.40	9.29	9.60	1.60	1.76	176.00	124.00	36.40	29.00	10.2	0.67	0.79	1.90
32	Torrent Pharmaceuticals Ltd	TORNTPHARM	500420	Pharmaceuticals	56,339	1,665	4.88	34.00	4,083	0.24	180	71.2	0.00	8.19	7.91	-5.29	4.12	18.90	15.50	18.30	18.10	47.00	32.60	22.10	25.80	23.6	0.63	0.73	0.67
33	Sun Pharmaceuticals Industries Ltd	SUNPHARMA	524715	Pharmaceuticals	242,489	1,011	5.63	37.80	4,366	0.99	221	54.5	4.04	11.80	4.13	9.17	-0.99	18.40	12.90	14.00	10.20	30.60	43.10	13.90	11.40	17.1	0.56	1.74	0.08
34	Kansai Nerolac Paints Ltd	KANSAINER	500165	Paints/Varnish	23,896	443	29.60	23.80	314	0.51	80.9	75.0	0.00	20.70	9.46	-22.5	-6.75	11.40	18.10	8.12	12.80	58.60	53.00	-2.48	15.70	7.84	1.13	1.24	0.07
35	Fortis Healthcare Ltd	FORTIS	532843	Healthcare	21,094	279	14.00	27.10	1,255	0.00	81.8	31.2	0.00	19.90	4.57	120	-2.33	10.80	5.50	6.12	0.31	51.60	22.20	15.80	10.50	5.48	0.51	0.80	0.20
36	State Bank of India	SBIN	500112	Banks	540,608	606	2.75	42.50	4,536,570	1.17	342	57.5	0.00	10.40	4.70	21.7	160.00	4.44	4.56	12.20	5.81	13.10	12.40	12.70	10.80	46.1	0.06	3.06	14.80
37	KPIT Technologies Ltd	KPITTECH	542651	IT - Software	18,200	664	17.10	69.70	186	0.47	50.8	40.1	0.82	22.10	NaN	46.6	NaN	24.10	NaN	21.50	NaN	57.30	32.70	NaN	NaN	11.6	1.13	2.02	0.13
38	Fine Organic Industries Ltd	FINEORG	541557	Chemicals	18,207	5,938	19.00	81.60	45.0	0.15	420	75.0	0.00	93.70	19.20	267	27.20	38.80	34.10	30.70	26.80	34.00	35.50	NaN	NaN	175	1.72	2.61	0.03
39	Bajaj Auto Ltd	BAJAJ-AUTO	532977	Automobile	107,778	3,725	9.85	23.00	124	3.76	900	54.8	0.01	4.61	8.79	-7.75	5.87	23.40	27.50	19.00	20.90	19.30	17.10	2.74	7.29	202	0.96	1.58	0.00
40	Meghmani Organics Ltd	MOL	543331	Agro Chemicals	2,941	116	24.20	43.20	665	1.21	63.1	49.4	0.00	47.20	NaN	59.7	NaN	24.10	NaN	22.60	NaN	8.51	8.71	NaN	NaN	13.6	1.05	0.71	0.41
41	Varun Beverages Ltd	VBL	540180	FMCG	73,987	1,139	7.62	109.00	2,371	0.22	74.1	63.9	0.04	50.80	18.00	115	72.10	17.40	14.60	18.60	15.30	51.40	55.40	50.30	NaN	22.2	0.98	0.34	0.49
42	Power Finance Corporation Ltd	PFC	532810	Finance	30,836	117	17.90	20.30	674,847	10.30	290	56.0	0.00	1.44	22.60	8.39	44.40	9.38	9.66	21.20	19.70	2.18	3.28	-1.61	2.15	53.6	0.10	1.51	8.82
43	Goodyear India Ltd	GOODYEAR	500168	Tyres	2,481	1,075	10.80	37.30	16.7	1.86	310	74.0	0.00	29.60	10.00	-32.2	-4.15	18.00	19.50	13.20	13.50	21.80	15.70	6.03	12.90	49.3	1.77	1.08	0.02
44	Mahanagar Telephone Nigam Ltd	MTNL	500108	Telecomm-Service	1,373	21.8	46.60	30.90	26,820	0.00	-296	56.9	0.00	-16.80	-17.30	-2.26	2.17	-5.08	-11.30	NaN	NaN	NaN	-0.48	-0.89	-2.19	-40.8	0.09	0.43	NaN
45	Medplus Health Services Ltd	MEDPLUS	543427	Retail	6,904	579	56.90	1.52	815	0.00	119	40.4	50.50	23.10	13.40	49.4	25.70	11.40	12.00	10.40	9.25	134.00	126.00	NaN	NaN	9.00	1.82	1.28	0.57
46	Abbott India Ltd	ABBOTINDIA	500488	Pharmaceuticals	40,883	19,239	7.95	24.00	152	0.75	1,327	75.0	0.00	12.20	11.10	14.5	23.60	38.40	37.10	29.50	27.00	50.60	35.70	33.60	29.40	381	1.22	2.63	0.05
47	Dr Lal Pathlabs Ltd	LALPATHLAB	539524	Healthcare	19,670	2,360	40.20	30.70	285	0.51	186	55.0	0.00	1.53	18.00	-38.6	17.40	29.40	32.40	25.10	24.00	79.30	51.70	22.60	NaN	29.8	1.04	1.73	0.18
48	Mindtree Ltd	MINDTREE	532819	IT - Software	57,401	3,480	31.20	31.40	613	1.06	344	61.0	0.00	35.00	15.00	36.4	31.60	41.50	33.40	33.80	26.90	30.40	21.60	47.80	35.00	115	1.45	2.96	0.11
49	Subex Ltd	SUBEXLTD	532348	IT - Software	1,725	30.6	50.10	64.50	14.7	0.00	10.1	0.0	0.00	-7.65	-1.37	-73.2	-19.90	6.18	8.34	3.68	5.34	132.00	20.80	28.30	7.80	0.23	0.46	3.75	0.03

---

4.1 Data Frame Information

REPORT CONTENT

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 50 entries, 0 to 49
Data columns (total 29 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   COMPANY                   50 non-null     object 
 1   NSE_CODE                  50 non-null     object 
 2   BSE_CODE                  50 non-null     int64  
 3   SECTOR                    50 non-null     object 
 4   MARKET_CAP(in Cr.)        50 non-null     object 
 5   CURRENT_PRICE             50 non-null     object 
 6   DOWN_FROM_52w_HIGH(in %)  50 non-null     float64
 7   UP_FROM_52w_LOW(in %)     50 non-null     float64
 8   DEBT(in Cr.)              50 non-null     object 
 9   DIV_YIELD(in %)           50 non-null     float64
 10  BOOK_VALUE                50 non-null     object 
 11  PROMOTER_HOLDING(in %)    50 non-null     float64
 12  HOLDING_PLEDGED(in %)     50 non-null     float64
 13  SALES(in %)               50 non-null     float64
 14  SALES_GROWTH_5yrs(in %)   48 non-null     float64
 15  PROFIT(in %)              50 non-null     object 
 16  PROFIT_GROWTH_5yrs(in %)  45 non-null     float64
 17  ROCE(in %)                49 non-null     float64
 18  ROCE_5yr_AVG(in %)        47 non-null     float64
 19  ROE(in %)                 48 non-null     float64
 20  ROE_5yr_AVG(in %)         45 non-null     float64
 21  PE_RATIO                  46 non-null     float64
 22  PE_RATIO_5yr_AVG          50 non-null     float64
 23  CAGR_5yr(in %)            45 non-null     float64
 24  CAGR_10yr(in %)           39 non-null     float64
 25  EPS                       50 non-null     object 
 26  ASSET_TURNOVER            50 non-null     float64
 27  QUICK_RATIO               50 non-null     float64
 28  DEBT_EQUITY_RATIO         48 non-null     float64
dtypes: float64(19), int64(1), object(9)
memory usage: 11.5+ KB

---

---

5. Data Set Cleaning

REPORT CONTENT

need to change the numeric value as a integer or float

---

5.1 Removing Comma's From The Strings

# Replacing the comma with nothing in the data set to make the values clean 
for col in df.columns:
    if df[col].dtype==object:
        df[col]=df[col].str.replace(',','')

df.head(2)
#checking that the above cleaing is done or not 

	COMPANY	NSE_CODE	BSE_CODE	SECTOR	MARKET_CAP(in Cr.)	CURRENT_PRICE	DOWN_FROM_52w_HIGH(in %)	UP_FROM_52w_LOW(in %)	DEBT(in Cr.)	DIV_YIELD(in %)	BOOK_VALUE	PROMOTER_HOLDING(in %)	HOLDING_PLEDGED(in %)	SALES(in %)	SALES_GROWTH_5yrs(in %)	PROFIT(in %)	PROFIT_GROWTH_5yrs(in %)	ROCE(in %)	ROCE_5yr_AVG(in %)	ROE(in %)	ROE_5yr_AVG(in %)	PE_RATIO	PE_RATIO_5yr_AVG	CAGR_5yr(in %)	CAGR_10yr(in %)	EPS	ASSET_TURNOVER	QUICK_RATIO	DEBT_EQUITY_RATIO
0	Bajaj Finance Ltd	BAJFINANCE	500034	Finance	422293	6975	13.3	33.6	183273	0.29	794	55.9	0.0	30.1	26.0	99.4	30.9	10.3	11.0	17.5	17.7	42.6	40.7	31.9	48.5	164	0.16	1.26	3.81
1	Avenue Supermarts Ltd	DMART	540376	Retail	268104	4139	20.1	29.9	711	0.00	232	75.0	0.0	38.5	21.1	68.0	25.8	15.8	18.0	11.5	13.2	116.0	103.0	29.0	NaN	35.6	2.13	1.19	0.05

---

5.2 Changing The Data Type Of Numeric Strings In Integer Or Float

REPORT CONTENT

for col in df.columns:
    df[col]=pd.to_numeric(df[col],errors='ignore')

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 50 entries, 0 to 49
Data columns (total 29 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   COMPANY                   50 non-null     object 
 1   NSE_CODE                  50 non-null     object 
 2   BSE_CODE                  50 non-null     int64  
 3   SECTOR                    50 non-null     object 
 4   MARKET_CAP(in Cr.)        50 non-null     int64  
 5   CURRENT_PRICE             50 non-null     float64
 6   DOWN_FROM_52w_HIGH(in %)  50 non-null     float64
 7   UP_FROM_52w_LOW(in %)     50 non-null     float64
 8   DEBT(in Cr.)              50 non-null     float64
 9   DIV_YIELD(in %)           50 non-null     float64
 10  BOOK_VALUE                50 non-null     float64
 11  PROMOTER_HOLDING(in %)    50 non-null     float64
 12  HOLDING_PLEDGED(in %)     50 non-null     float64
 13  SALES(in %)               50 non-null     float64
 14  SALES_GROWTH_5yrs(in %)   48 non-null     float64
 15  PROFIT(in %)              50 non-null     float64
 16  PROFIT_GROWTH_5yrs(in %)  45 non-null     float64
 17  ROCE(in %)                49 non-null     float64
 18  ROCE_5yr_AVG(in %)        47 non-null     float64
 19  ROE(in %)                 48 non-null     float64
 20  ROE_5yr_AVG(in %)         45 non-null     float64
 21  PE_RATIO                  46 non-null     float64
 22  PE_RATIO_5yr_AVG          50 non-null     float64
 23  CAGR_5yr(in %)            45 non-null     float64
 24  CAGR_10yr(in %)           39 non-null     float64
 25  EPS                       50 non-null     float64
 26  ASSET_TURNOVER            50 non-null     float64
 27  QUICK_RATIO               50 non-null     float64
 28  DEBT_EQUITY_RATIO         48 non-null     float64
dtypes: float64(24), int64(2), object(3)
memory usage: 11.5+ KB

---

5.3 Checking For Null Values

REPORT CONTENT

df.isnull().sum()

COMPANY                      0
NSE_CODE                     0
BSE_CODE                     0
SECTOR                       0
MARKET_CAP(in Cr.)           0
CURRENT_PRICE                0
DOWN_FROM_52w_HIGH(in %)     0
UP_FROM_52w_LOW(in %)        0
DEBT(in Cr.)                 0
DIV_YIELD(in %)              0
BOOK_VALUE                   0
PROMOTER_HOLDING(in %)       0
HOLDING_PLEDGED(in %)        0
SALES(in %)                  0
SALES_GROWTH_5yrs(in %)      2
PROFIT(in %)                 0
PROFIT_GROWTH_5yrs(in %)     5
ROCE(in %)                   1
ROCE_5yr_AVG(in %)           3
ROE(in %)                    2
ROE_5yr_AVG(in %)            5
PE_RATIO                     4
PE_RATIO_5yr_AVG             0
CAGR_5yr(in %)               5
CAGR_10yr(in %)             11
EPS                          0
ASSET_TURNOVER               0
QUICK_RATIO                  0
DEBT_EQUITY_RATIO            2
dtype: int64

As we can observe in few columns have null value and as there Data type and requirement for analysis we can replace them with zero

---

5.4 Replacing The Null Values

REPORT CONTENT

df.fillna(0,inplace=True,downcast='infer')

df.isnull().sum()

COMPANY                     0
NSE_CODE                    0
BSE_CODE                    0
SECTOR                      0
MARKET_CAP(in Cr.)          0
CURRENT_PRICE               0
DOWN_FROM_52w_HIGH(in %)    0
UP_FROM_52w_LOW(in %)       0
DEBT(in Cr.)                0
DIV_YIELD(in %)             0
BOOK_VALUE                  0
PROMOTER_HOLDING(in %)      0
HOLDING_PLEDGED(in %)       0
SALES(in %)                 0
SALES_GROWTH_5yrs(in %)     0
PROFIT(in %)                0
PROFIT_GROWTH_5yrs(in %)    0
ROCE(in %)                  0
ROCE_5yr_AVG(in %)          0
ROE(in %)                   0
ROE_5yr_AVG(in %)           0
PE_RATIO                    0
PE_RATIO_5yr_AVG            0
CAGR_5yr(in %)              0
CAGR_10yr(in %)             0
EPS                         0
ASSET_TURNOVER              0
QUICK_RATIO                 0
DEBT_EQUITY_RATIO           0
dtype: int64

---

---

6. Exploratory Data Analysis (E.D.A.)

REPORT CONTENT

df.head()

	COMPANY	NSE_CODE	BSE_CODE	SECTOR	MARKET_CAP(in Cr.)	CURRENT_PRICE	DOWN_FROM_52w_HIGH(in %)	UP_FROM_52w_LOW(in %)	DEBT(in Cr.)	DIV_YIELD(in %)	BOOK_VALUE	PROMOTER_HOLDING(in %)	HOLDING_PLEDGED(in %)	SALES(in %)	SALES_GROWTH_5yrs(in %)	PROFIT(in %)	PROFIT_GROWTH_5yrs(in %)	ROCE(in %)	ROCE_5yr_AVG(in %)	ROE(in %)	ROE_5yr_AVG(in %)	PE_RATIO	PE_RATIO_5yr_AVG	CAGR_5yr(in %)	CAGR_10yr(in %)	EPS	ASSET_TURNOVER	QUICK_RATIO	DEBT_EQUITY_RATIO
0	Bajaj Finance Ltd	BAJFINANCE	500034	Finance	422293	6975.0	13.30	33.60	183273.0	0.29	794.0	55.9	0.0	30.1	26.0	99.4	30.90	10.30	11.0	17.50	17.70	42.6	40.7	31.9	48.5	164.00	0.16	1.26	3.81
1	Avenue Supermarts Ltd	DMART	540376	Retail	268104	4139.0	20.10	29.90	711.0	0.00	232.0	75.0	0.0	38.5	21.1	68.0	25.80	15.80	18.0	11.50	13.20	116.0	103.0	29.0	0.0	35.60	2.13	1.19	0.05
2	Reliance Industries Ltd	RELIANCE	500325	Refineries	1740392	2572.0	9.93	18.00	316030.0	0.31	1168.0	50.6	0.0	46.4	18.1	24.2	14.40	9.42	10.0	8.16	9.59	27.1	17.9	24.1	20.7	98.10	0.50	0.63	0.40
3	Asian Paints Ltd	ASIANPAINT	500820	Paints/Varnish	292090	3045.0	15.20	19.00	1866.0	0.63	147.0	52.6	10.6	28.2	14.1	15.6	9.71	29.70	33.6	23.20	25.40	77.6	59.1	20.9	22.2	38.20	1.34	1.04	0.13
4	Berger Paints India Ltd	BERGEPAINT	509480	Paints/Varnish	56644	583.0	28.30	7.28	1634.0	0.53	42.2	75.0	0.0	24.5	14.0	12.2	14.10	25.90	27.2	22.50	22.90	59.9	63.6	18.3	27.7	9.74	1.33	0.58	0.40

---

Getting Basic Necessary Details from Data Set

---

6.1 Name & Number Of Sectors

REPORT CONTENT

pd.DataFrame({'SECTOR':df['SECTOR'].unique()})

	SECTOR
0	Finance
1	Retail
2	Refineries
3	Paints/Varnish
4	Automobile
5	IT - Software
6	Chemicals
7	Agro Chemicals
8	FMCG
9	Banks
10	Tyres
11	Telecomm-Service
12	Pharmaceuticals
13	Healthcare

• Or with the help of group by we can also done the same task

df[['SECTOR']].groupby('SECTOR').count().reset_index()

	SECTOR
0	Agro Chemicals
1	Automobile
2	Banks
3	Chemicals
4	FMCG
5	Finance
6	Healthcare
7	IT - Software
8	Paints/Varnish
9	Pharmaceuticals
10	Refineries
11	Retail
12	Telecomm-Service
13	Tyres

• Count of different sectors in Data set

print('Number of Total Sectors= '+str(df['SECTOR'].nunique()))

Number of Total Sectors= 14

• So, in this data set we have 14 sectors

---

6.2 Number Of Shares Of Each Company

REPORT CONTENT

• The number of share can be calculate with the help of dividing market capitalization by current price of each share

share_num=df[['COMPANY','MARKET_CAP(in Cr.)','CURRENT_PRICE']].copy()

share_num['NUM_OF_SHARES(in Cr.)']=(share_num['MARKET_CAP(in Cr.)']/share_num['CURRENT_PRICE'])
share_num

	COMPANY	MARKET_CAP(in Cr.)	CURRENT_PRICE	NUM_OF_SHARES(in Cr.)
0	Bajaj Finance Ltd	422293	6975.00	60.543799
1	Avenue Supermarts Ltd	268104	4139.00	64.775066
2	Reliance Industries Ltd	1740392	2572.00	676.668740
3	Asian Paints Ltd	292090	3045.00	95.924466
4	Berger Paints India Ltd	56644	583.00	97.159520
5	Tata Motors Ltd	148207	412.00	359.725728
6	Tata Consultancy Services Ltd	1172964	3206.00	365.865253
7	Pidilite Industries Ltd	135022	2656.00	50.836596
8	TVS Motor Company Ltd	52544	1106.00	47.508137
9	Rallis India Ltd	4385	226.00	19.402655
10	Nestle India Ltd	194522	20175.00	9.641735
11	SBI Cards & Payment Services Ltd	76228	807.00	94.458488
12	HDFC Bank Ltd	849930	1525.00	557.331148
13	Balkrishna Industries Ltd	36364	1881.00	19.332270
14	Bharti Airtel Ltd	476676	826.00	577.089588
15	V-Mart Retail Ltd	5594	2830.00	1.976678
16	Cipla Ltd	90575	1122.00	80.726381
17	Shalimar Paints Ltd	1121	155.00	7.232258
18	Apollo Hospitals Enterprise Ltd	61618	4285.00	14.379930
19	AU Small Finance Bank Ltd	41871	628.00	66.673567
20	Larsen & Toubro Infotech Ltd	85278	4862.00	17.539696
21	Deepak Nitrite Ltd	28361	2079.00	13.641655
22	Aarti Industries Ltd	25673	708.00	36.261299
23	Maruti Suzuki India Ltd	274810	9097.00	30.208860
24	P I Industries Ltd	53018	3494.00	15.174013
25	Hindustan Unilever Ltd	590593	2514.00	234.921639
26	Muthoot Finance Ltd	44377	1105.00	40.160181
27	MRF Ltd	37087	87469.00	0.424002
28	CEAT Ltd	6966	1722.00	4.045296
29	Tata Communications Ltd	37453	1314.00	28.503044
30	Vodafone Idea Ltd	27783	8.65	3211.907514
31	Trent Ltd	52289	1471.00	35.546567
32	Torrent Pharmaceuticals Ltd	56339	1665.00	33.837237
33	Sun Pharmaceuticals Industries Ltd	242489	1011.00	239.850643
34	Kansai Nerolac Paints Ltd	23896	443.00	53.941309
35	Fortis Healthcare Ltd	21094	279.00	75.605735
36	State Bank of India	540608	606.00	892.092409
37	KPIT Technologies Ltd	18200	664.00	27.409639
38	Fine Organic Industries Ltd	18207	5938.00	3.066184
39	Bajaj Auto Ltd	107778	3725.00	28.933691
40	Meghmani Organics Ltd	2941	116.00	25.353448
41	Varun Beverages Ltd	73987	1139.00	64.957858
42	Power Finance Corporation Ltd	30836	117.00	263.555556
43	Goodyear India Ltd	2481	1075.00	2.307907
44	Mahanagar Telephone Nigam Ltd	1373	21.80	62.981651
45	Medplus Health Services Ltd	6904	579.00	11.924007
46	Abbott India Ltd	40883	19239.00	2.125006
47	Dr Lal Pathlabs Ltd	19670	2360.00	8.334746
48	Mindtree Ltd	57401	3480.00	16.494540
49	Subex Ltd	1725	30.60	56.372549

### 6.2.1 Visualization

plt.figure(figsize=(13,6), dpi=200)
plt.xticks(rotation=90)
plt.title('Comparison Between Share numbers & MarketCapitalization',size=25)
chart_1=sns.barplot(data=share_num, x='COMPANY', y='NUM_OF_SHARES(in Cr.)',)
plt.ylabel('Numbre Of Shares',size=20)
plt.xlabel('Companies',size=25)
chart_2=chart_1.twinx()
sns.lineplot(data=share_num, x='COMPANY', y='MARKET_CAP(in Cr.)', marker='o', color='grey')
plt.ylabel('Market Capitalization',size=20)

Text(0, 0.5, 'Market Capitalization')

• So, as we can see The MRF share is 90K approx value and Vodafone_idea is 8rs stock but they both have same market cap but the Number of stocks has a great different which makes to feel that the MRF is a costly company and Vodafone_idea is a penny company but it's not true in terms of market cap they have same value

---

6.3 Number Of Companies On Each Sector

REPORT CONTENT

df_sector=df[['SECTOR','COMPANY']].groupby('SECTOR').count().reset_index()
df_sector

	SECTOR	COMPANY
0	Agro Chemicals	3
1	Automobile	4
2	Banks	3
3	Chemicals	4
4	FMCG	3
5	Finance	4
6	Healthcare	3
7	IT - Software	5
8	Paints/Varnish	4
9	Pharmaceuticals	4
10	Refineries	1
11	Retail	4
12	Telecomm-Service	4
13	Tyres	4

6.3.1 Visualization

plt.figure(figsize=(13,6), dpi=150)
plt.xticks(rotation=45)
plt.title('Number Of Companies',size=20)
sns.barplot(data=df_sector, x='SECTOR', y='COMPANY',palette='mako')
plt.xlabel('Sectors',size=20)
plt.ylabel('Company Numbers',size=20)

Text(0, 0.5, 'Company Numbers')

• Total we have 14 sectors and 50 different companies in each sector

---

6.4 Number Of Companies According To Market Capitalization

REPORT CONTENT

• So, in stock market every one has different theory to keep the companies in different size capitalization. I am doing the differentiation on the basis of their Market capitalization

SMALL_CAP_COUNT=0
MID_CAP_COUNT=0
LARGE_CAP_COUNT=0
BLUE_CHIP_COUNT=0

MARKET_CAP_DICT={}

MARKET_CAPITAL=list(df['MARKET_CAP(in Cr.)'])

for CAPITAL in MARKET_CAPITAL:
    if CAPITAL<50000:
        SMALL_CAP_COUNT+=1
        MARKET_CAP_DICT['SMALL_CAP']=SMALL_CAP_COUNT
    elif CAPITAL>50000 and CAPITAL<100000:
        MID_CAP_COUNT+=1
        MARKET_CAP_DICT['MID_CAP']=MID_CAP_COUNT
    elif CAPITAL>100000 and CAPITAL<1000000:
        LARGE_CAP_COUNT+=1
        MARKET_CAP_DICT['LARGE_CAP']=LARGE_CAP_COUNT
    elif CAPITAL>1000000:
        BLUE_CHIP_COUNT+=1
        MARKET_CAP_DICT['BLUE_CHIP']=BLUE_CHIP_COUNT

MARKET_CAP_DF=pd.DataFrame({'POSITION':MARKET_CAP_DICT.keys(),'NUMBER_OF_COMPANY':MARKET_CAP_DICT.values()})
MARKET_CAP_DF

	POSITION	NUMBER_OF_COMPANY
0	LARGE_CAP	13
1	BLUE_CHIP	2
2	MID_CAP	11
3	SMALL_CAP	24

6.4.1 Visualization

plt.figure(figsize=(10,5),dpi=150)
plt.xticks(rotation=45)
plt.title('Number of Companies According To Market Capitalization',size=15)
sns.barplot(data=MARKET_CAP_DF, x='POSITION', y='NUMBER_OF_COMPANY', palette='twilight_r')
plt.xlabel('Capitalization',size=15)
plt.ylabel('Number Of Companies',size=15)

Text(0, 0.5, 'Number Of Companies')

• So, as in above method use proper coding to get which company belongs to which Cap but we also have Masking method to do the same task

MARKET_CAP_DICT={'SMALL_CAP':len(df[(df['MARKET_CAP(in Cr.)']<50000)]),\
                 'MID_CAP':len(df[(df['MARKET_CAP(in Cr.)']>50000)&(df['MARKET_CAP(in Cr.)']<100000)]),\
                'LARGE_CAP':len(df[(df['MARKET_CAP(in Cr.)']>100000) & (df['MARKET_CAP(in Cr.)']<1000000)]),\
                "BLUE_CHIP":len(df[(df['MARKET_CAP(in Cr.)']>1000000)])}
MARKET_CAP_DF=pd.DataFrame({'POSITION':MARKET_CAP_DICT.keys(),'NUMBER_OF_COMPANY':MARKET_CAP_DICT.values()})
MARKET_CAP_DF

	POSITION	NUMBER_OF_COMPANY
0	SMALL_CAP	24
1	MID_CAP	11
2	LARGE_CAP	13
3	BLUE_CHIP	2

---

6.5 Company according to their sectors

REPORT CONTENT

df_sectors=df[['COMPANY','SECTOR','MARKET_CAP(in Cr.)']].copy()

market_cap=df['MARKET_CAP(in Cr.)']

names=[]
for i in market_cap:
    if i<50000:
        a='SMALL_CAP'
        names.append(a)
    elif i>50000 and i<100000:
        b='MID_CAP'
        names.append(b)   
    elif i>100000 and i<1000000:
        c='LARGE_CAP'
        names.append(c) 
    elif i>1000000:
        d='BLUE_CHIP'
        names.append(d)
df_sectors["POSITION_"]=names

df_sectors

	COMPANY	SECTOR	MARKET_CAP(in Cr.)	POSITION_
0	Bajaj Finance Ltd	Finance	422293	LARGE_CAP
1	Avenue Supermarts Ltd	Retail	268104	LARGE_CAP
2	Reliance Industries Ltd	Refineries	1740392	BLUE_CHIP
3	Asian Paints Ltd	Paints/Varnish	292090	LARGE_CAP
4	Berger Paints India Ltd	Paints/Varnish	56644	MID_CAP
5	Tata Motors Ltd	Automobile	148207	LARGE_CAP
6	Tata Consultancy Services Ltd	IT - Software	1172964	BLUE_CHIP
7	Pidilite Industries Ltd	Chemicals	135022	LARGE_CAP
8	TVS Motor Company Ltd	Automobile	52544	MID_CAP
9	Rallis India Ltd	Agro Chemicals	4385	SMALL_CAP
10	Nestle India Ltd	FMCG	194522	LARGE_CAP
11	SBI Cards & Payment Services Ltd	Finance	76228	MID_CAP
12	HDFC Bank Ltd	Banks	849930	LARGE_CAP
13	Balkrishna Industries Ltd	Tyres	36364	SMALL_CAP
14	Bharti Airtel Ltd	Telecomm-Service	476676	LARGE_CAP
15	V-Mart Retail Ltd	Retail	5594	SMALL_CAP
16	Cipla Ltd	Pharmaceuticals	90575	MID_CAP
17	Shalimar Paints Ltd	Paints/Varnish	1121	SMALL_CAP
18	Apollo Hospitals Enterprise Ltd	Healthcare	61618	MID_CAP
19	AU Small Finance Bank Ltd	Banks	41871	SMALL_CAP
20	Larsen & Toubro Infotech Ltd	IT - Software	85278	MID_CAP
21	Deepak Nitrite Ltd	Chemicals	28361	SMALL_CAP
22	Aarti Industries Ltd	Chemicals	25673	SMALL_CAP
23	Maruti Suzuki India Ltd	Automobile	274810	LARGE_CAP
24	P I Industries Ltd	Agro Chemicals	53018	MID_CAP
25	Hindustan Unilever Ltd	FMCG	590593	LARGE_CAP
26	Muthoot Finance Ltd	Finance	44377	SMALL_CAP
27	MRF Ltd	Tyres	37087	SMALL_CAP
28	CEAT Ltd	Tyres	6966	SMALL_CAP
29	Tata Communications Ltd	Telecomm-Service	37453	SMALL_CAP
30	Vodafone Idea Ltd	Telecomm-Service	27783	SMALL_CAP
31	Trent Ltd	Retail	52289	MID_CAP
32	Torrent Pharmaceuticals Ltd	Pharmaceuticals	56339	MID_CAP
33	Sun Pharmaceuticals Industries Ltd	Pharmaceuticals	242489	LARGE_CAP
34	Kansai Nerolac Paints Ltd	Paints/Varnish	23896	SMALL_CAP
35	Fortis Healthcare Ltd	Healthcare	21094	SMALL_CAP
36	State Bank of India	Banks	540608	LARGE_CAP
37	KPIT Technologies Ltd	IT - Software	18200	SMALL_CAP
38	Fine Organic Industries Ltd	Chemicals	18207	SMALL_CAP
39	Bajaj Auto Ltd	Automobile	107778	LARGE_CAP
40	Meghmani Organics Ltd	Agro Chemicals	2941	SMALL_CAP
41	Varun Beverages Ltd	FMCG	73987	MID_CAP
42	Power Finance Corporation Ltd	Finance	30836	SMALL_CAP
43	Goodyear India Ltd	Tyres	2481	SMALL_CAP
44	Mahanagar Telephone Nigam Ltd	Telecomm-Service	1373	SMALL_CAP
45	Medplus Health Services Ltd	Retail	6904	SMALL_CAP
46	Abbott India Ltd	Pharmaceuticals	40883	SMALL_CAP
47	Dr Lal Pathlabs Ltd	Healthcare	19670	SMALL_CAP
48	Mindtree Ltd	IT - Software	57401	MID_CAP
49	Subex Ltd	IT - Software	1725	SMALL_CAP

plt.figure(figsize=(10,5),dpi=150)
plt.xticks(rotation=45)
plt.title(' Companies According To market Capitalization',size=20)
sns.barplot(data=df_sectors, x='POSITION_', y='MARKET_CAP(in Cr.)',hue='COMPANY', palette='Dark2' )
plt.xlabel('Sectors',size=20)
plt.ylabel('Market Capitalization',size=20)
plt.legend(loc=(1.05,-1.7))

<matplotlib.legend.Legend at 0x221cee458e0>

---

6.6 Loss making Sector & Companies Detail

REPORT CONTENT

Details of those companies which have negative earnings which show that the companies are in loss

loss_companies=df[df['EPS']<0]
loss_companies

	COMPANY	NSE_CODE	BSE_CODE	SECTOR	MARKET_CAP(in Cr.)	CURRENT_PRICE	DOWN_FROM_52w_HIGH(in %)	UP_FROM_52w_LOW(in %)	DEBT(in Cr.)	DIV_YIELD(in %)	BOOK_VALUE	PROMOTER_HOLDING(in %)	HOLDING_PLEDGED(in %)	SALES(in %)	SALES_GROWTH_5yrs(in %)	PROFIT(in %)	PROFIT_GROWTH_5yrs(in %)	ROCE(in %)	ROCE_5yr_AVG(in %)	ROE(in %)	ROE_5yr_AVG(in %)	PE_RATIO	PE_RATIO_5yr_AVG	CAGR_5yr(in %)	CAGR_10yr(in %)	EPS	ASSET_TURNOVER	QUICK_RATIO	DEBT_EQUITY_RATIO
5	Tata Motors Ltd	TATAMOTORS	500570	Automobile	148207	412.00	23.2	12.6	144354.0	0.0	78.8	46.4	1.82	3.47	0.64	-30209.00	0.00	1.40	3.44	-22.3	-5.2	0.0	-2.12	-0.49	4.15	-25.6	0.83	0.68	5.52
17	Shalimar Paints Ltd	SHALPAINTS	509874	Paints/Varnish	1121	155.00	15.8	64.0	145.0	0.0	51.5	39.9	0.00	16.30	0.47	17.90	0.00	-7.19	-9.53	-18.0	-21.4	0.0	-10.70	0.54	5.17	-9.0	0.61	1.36	0.39
30	Vodafone Idea Ltd	IDEA	532822	Telecomm-Service	27783	8.65	48.5	11.6	248176.0	0.0	-23.8	75.0	0.00	4.92	1.60	-6.46	0.00	0.00	0.00	0.0	0.0	0.0	-0.60	-31.80	-17.00	-9.2	0.19	0.24	0.00
44	Mahanagar Telephone Nigam Ltd	MTNL	500108	Telecomm-Service	1373	21.80	46.6	30.9	26820.0	0.0	-296.0	56.9	0.00	-16.80	-17.30	-2.26	2.17	-5.08	-11.30	0.0	0.0	0.0	-0.48	-0.89	-2.19	-40.8	0.09	0.43	0.00

Getting the number of loss making companies according to sector.

high_loss_sector=loss_companies[['SECTOR','COMPANY']].groupby('SECTOR').count().reset_index()
high_loss_sector

	SECTOR	COMPANY
0	Automobile	1
1	Paints/Varnish	1
2	Telecomm-Service	2

6.6.1 Visualization of loss making Sector according to their companies

plt.figure(figsize=(10,6), dpi=150)
plt.xticks(rotation=45)
plt.title('Number Of Loss Making Companies',size=20)
sns.barplot(data=high_loss_sector, x='SECTOR', y='COMPANY',palette='twilight')
plt.xlabel('Sectors',size=20)
plt.ylabel('Number of Companies',size=20)

Text(0, 0.5, 'Number of Companies')

• According to Data Set "Telecomm-Service" based Sector has high number of loss making companies

---

6.7 Profit making Sector & Companies Detail

REPORT CONTENT

profit_companies=df[df['EPS']>0]
profit_companies

	COMPANY	NSE_CODE	BSE_CODE	SECTOR	MARKET_CAP(in Cr.)	CURRENT_PRICE	DOWN_FROM_52w_HIGH(in %)	UP_FROM_52w_LOW(in %)	DEBT(in Cr.)	DIV_YIELD(in %)	BOOK_VALUE	PROMOTER_HOLDING(in %)	HOLDING_PLEDGED(in %)	SALES(in %)	SALES_GROWTH_5yrs(in %)	PROFIT(in %)	PROFIT_GROWTH_5yrs(in %)	ROCE(in %)	ROCE_5yr_AVG(in %)	ROE(in %)	ROE_5yr_AVG(in %)	PE_RATIO	PE_RATIO_5yr_AVG	CAGR_5yr(in %)	CAGR_10yr(in %)	EPS	ASSET_TURNOVER	QUICK_RATIO	DEBT_EQUITY_RATIO
0	Bajaj Finance Ltd	BAJFINANCE	500034	Finance	422293	6975.0	13.30	33.60	183273.0	0.29	794.0	55.9	0.00	30.10	26.00	99.40	30.90	10.30	11.00	17.50	17.70	42.60	40.70	31.90	48.50	164.00	0.16	1.26	3.81
1	Avenue Supermarts Ltd	DMART	540376	Retail	268104	4139.0	20.10	29.90	711.0	0.00	232.0	75.0	0.00	38.50	21.10	68.00	25.80	15.80	18.00	11.50	13.20	116.00	103.00	29.00	0.00	35.60	2.13	1.19	0.05
2	Reliance Industries Ltd	RELIANCE	500325	Refineries	1740392	2572.0	9.93	18.00	316030.0	0.31	1168.0	50.6	0.00	46.40	18.10	24.20	14.40	9.42	10.00	8.16	9.59	27.10	17.90	24.10	20.70	98.10	0.50	0.63	0.40
3	Asian Paints Ltd	ASIANPAINT	500820	Paints/Varnish	292090	3045.0	15.20	19.00	1866.0	0.63	147.0	52.6	10.60	28.20	14.10	15.60	9.71	29.70	33.60	23.20	25.40	77.60	59.10	20.90	22.20	38.20	1.34	1.04	0.13
4	Berger Paints India Ltd	BERGEPAINT	509480	Paints/Varnish	56644	583.0	28.30	7.28	1634.0	0.53	42.2	75.0	0.00	24.50	14.00	12.20	14.10	25.90	27.20	22.50	22.90	59.90	63.60	18.30	27.70	9.74	1.33	0.58	0.40
6	Tata Consultancy Services Ltd	TCS	532540	IT - Software	1172964	3206.0	20.80	9.56	7460.0	1.34	266.0	72.3	0.48	16.60	10.20	8.27	7.83	54.90	47.80	43.60	37.20	29.60	21.20	18.80	17.10	108.00	1.42	2.84	0.08
7	Pidilite Industries Ltd	PIDILITIND	500331	Chemicals	135022	2656.0	9.00	33.60	680.0	0.38	130.0	69.9	0.00	26.10	12.00	-3.28	7.10	26.10	31.70	20.20	23.80	104.00	61.70	26.50	29.00	25.60	1.08	0.92	0.10
8	TVS Motor Company Ltd	TVSMOTOR	532343	Automobile	52544	1106.0	6.04	116.00	18675.0	0.34	107.0	50.8	0.00	24.20	14.30	41.50	8.13	11.30	13.20	18.40	20.80	44.40	39.30	9.31	39.50	25.60	1.00	0.89	3.68
9	Rallis India Ltd	RALLIS	500355	Agro Chemicals	4385	226.0	24.60	23.60	175.0	1.33	91.2	50.1	0.00	17.50	13.40	-12.60	2.40	13.60	16.60	10.20	12.60	26.80	20.70	0.03	3.58	8.43	0.96	0.86	0.10
10	Nestle India Ltd	NESTLEIND	500790	FMCG	194522	20175.0	4.17	26.10	280.0	0.99	241.0	62.8	0.00	13.50	9.98	4.63	18.10	147.00	95.10	113.00	67.10	82.90	66.50	21.00	15.50	225.00	1.83	0.51	0.12
11	SBI Cards & Payment Services Ltd	SBICARD	543066	Finance	76228	807.0	26.30	23.00	28186.0	0.31	94.6	69.4	0.00	25.60	26.10	105.00	34.10	11.60	12.80	23.00	24.00	36.00	49.70	0.00	0.00	22.50	0.35	0.41	3.16
12	HDFC Bank Ltd	HDFCBANK	500180	Banks	849930	1525.0	11.50	19.90	1784970.0	1.02	446.0	25.6	0.00	13.90	13.20	22.00	20.00	5.83	6.77	16.60	16.80	20.40	24.20	10.80	16.80	75.20	0.07	2.66	7.22
13	Balkrishna Industries Ltd	BALKRISIND	502355	Tyres	36364	1881.0	25.90	11.80	2529.0	0.85	359.0	58.3	0.00	37.20	17.40	2.56	17.00	23.80	22.00	21.90	19.50	25.80	24.90	14.30	29.90	73.00	0.87	0.48	0.36
14	Bharti Airtel Ltd	BHARTIARTL	532454	Telecomm-Service	476676	826.0	1.87	31.30	217989.0	0.36	131.0	55.1	0.00	19.80	4.07	125.00	-3.36	12.00	8.30	5.86	2.13	76.70	36.80	12.70	12.50	11.40	0.33	0.45	2.99
15	V-Mart Retail Ltd	VMART	534976	Retail	5594	2830.0	41.60	17.60	902.0	0.03	430.0	46.1	0.00	76.80	10.70	4888.00	-22.40	5.21	11.40	1.46	7.58	92.00	44.20	15.00	0.00	30.80	0.89	0.28	1.06
16	Cipla Ltd	CIPLA	500087	Pharmaceuticals	90575	1122.0	5.31	32.00	1068.0	0.45	271.0	33.6	0.00	5.50	8.62	-2.62	23.10	17.50	13.60	13.20	11.30	34.90	27.90	13.00	11.00	31.80	0.84	1.97	0.05
18	Apollo Hospitals Enterprise Ltd	APOLLOHOSP	508869	Healthcare	61618	4285.0	27.80	27.50	3998.0	0.27	417.0	29.3	16.40	16.20	15.10	7.82	44.80	17.80	11.80	16.60	7.94	73.40	72.40	33.80	18.00	58.40	1.19	1.32	0.67
19	AU Small Finance Bank Ltd	AUBANK	540611	Banks	41871	628.0	14.30	35.90	58575.0	0.08	119.0	25.6	0.00	31.30	35.80	11.40	6.58	7.19	8.47	16.60	17.90	33.30	34.70	15.80	0.00	19.70	0.10	2.31	7.84
20	Larsen & Toubro Infotech Ltd	LTI	540005	IT - Software	85278	4862.0	36.00	30.20	824.0	0.93	524.0	74.0	0.00	30.40	19.20	21.30	19.20	35.70	38.60	28.50	30.50	33.30	19.50	38.80	0.00	146.00	1.35	3.09	0.09
21	Deepak Nitrite Ltd	DEEPAKNTR	506401	Chemicals	28361	2079.0	22.70	23.70	270.0	0.34	268.0	45.7	0.00	28.90	37.80	-13.60	87.00	44.50	32.80	37.40	34.20	30.90	21.50	57.00	57.00	67.40	1.70	2.26	0.07
22	Aarti Industries Ltd	AARTIIND	524208	Chemicals	25673	708.0	30.30	16.60	2594.0	0.49	163.0	44.2	0.00	56.70	17.20	120.00	32.90	22.10	17.60	27.80	22.20	19.30	26.50	29.10	42.40	36.70	0.81	0.80	0.44
23	Maruti Suzuki India Ltd	MARUTI	532500	Automobile	274810	9097.0	6.88	39.20	625.0	0.66	1893.0	56.4	0.00	24.00	5.34	44.40	-7.94	8.95	13.60	7.25	10.20	45.30	26.30	2.14	20.10	201.00	1.21	0.80	0.01
24	P I Industries Ltd	PIIND	523642	Agro Chemicals	53018	3494.0	5.52	49.80	280.0	0.17	436.0	46.1	0.00	23.60	18.40	29.30	12.90	17.30	21.20	14.70	17.50	51.80	34.70	33.20	41.90	67.50	0.72	2.53	0.04
25	Hindustan Unilever Ltd	HINDUNILVR	500696	FMCG	590593	2514.0	8.06	32.20	1139.0	1.35	211.0	61.9	0.00	14.30	9.60	14.70	15.60	24.30	48.30	18.40	35.80	61.00	55.20	14.30	16.90	41.10	0.75	0.99	0.02
26	Muthoot Finance Ltd	MUTHOOTFIN	533398	Finance	44377	1105.0	35.80	16.40	46770.0	1.81	479.0	73.4	0.00	-5.33	14.00	-7.43	27.40	14.20	15.60	23.60	25.40	12.10	12.50	18.00	17.20	91.20	0.17	3.29	2.43
27	MRF Ltd	MRF	500290	Tyres	37087	87469.0	8.89	39.00	3118.0	0.17	33566.0	27.9	0.00	17.20	7.57	-53.00	-14.90	7.05	12.60	4.85	9.76	65.30	21.80	5.36	24.00	1339.00	0.85	0.78	0.22
28	CEAT Ltd	CEATLTD	500878	Tyres	6966	1722.0	3.68	93.50	2393.0	0.17	809.0	47.2	0.00	20.80	10.20	-89.30	-25.50	6.35	11.60	2.59	9.28	182.00	14.80	-0.14	32.10	5.52	1.08	0.36	0.73
29	Tata Communications Ltd	TATACOMM	500483	Telecomm-Service	37453	1314.0	17.40	53.50	8840.0	1.58	31.8	58.9	4.96	3.72	-1.04	34.20	-6.74	21.80	13.20	282.00	0.00	21.10	16.70	24.60	24.40	64.40	0.82	0.61	9.74
31	Trent Ltd	TRENT	500251	Retail	52289	1471.0	6.37	51.60	4526.0	0.12	67.0	37.0	0.00	92.00	19.90	550.00	-14.40	9.29	9.60	1.60	1.76	176.00	124.00	36.40	29.00	10.20	0.67	0.79	1.90
32	Torrent Pharmaceuticals Ltd	TORNTPHARM	500420	Pharmaceuticals	56339	1665.0	4.88	34.00	4083.0	0.24	180.0	71.2	0.00	8.19	7.91	-5.29	4.12	18.90	15.50	18.30	18.10	47.00	32.60	22.10	25.80	23.60	0.63	0.73	0.67
33	Sun Pharmaceuticals Industries Ltd	SUNPHARMA	524715	Pharmaceuticals	242489	1011.0	5.63	37.80	4366.0	0.99	221.0	54.5	4.04	11.80	4.13	9.17	-0.99	18.40	12.90	14.00	10.20	30.60	43.10	13.90	11.40	17.10	0.56	1.74	0.08
34	Kansai Nerolac Paints Ltd	KANSAINER	500165	Paints/Varnish	23896	443.0	29.60	23.80	314.0	0.51	80.9	75.0	0.00	20.70	9.46	-22.50	-6.75	11.40	18.10	8.12	12.80	58.60	53.00	-2.48	15.70	7.84	1.13	1.24	0.07
35	Fortis Healthcare Ltd	FORTIS	532843	Healthcare	21094	279.0	14.00	27.10	1255.0	0.00	81.8	31.2	0.00	19.90	4.57	120.00	-2.33	10.80	5.50	6.12	0.31	51.60	22.20	15.80	10.50	5.48	0.51	0.80	0.20
36	State Bank of India	SBIN	500112	Banks	540608	606.0	2.75	42.50	4536570.0	1.17	342.0	57.5	0.00	10.40	4.70	21.70	160.00	4.44	4.56	12.20	5.81	13.10	12.40	12.70	10.80	46.10	0.06	3.06	14.80
37	KPIT Technologies Ltd	KPITTECH	542651	IT - Software	18200	664.0	17.10	69.70	186.0	0.47	50.8	40.1	0.82	22.10	0.00	46.60	0.00	24.10	0.00	21.50	0.00	57.30	32.70	0.00	0.00	11.60	1.13	2.02	0.13
38	Fine Organic Industries Ltd	FINEORG	541557	Chemicals	18207	5938.0	19.00	81.60	45.0	0.15	420.0	75.0	0.00	93.70	19.20	267.00	27.20	38.80	34.10	30.70	26.80	34.00	35.50	0.00	0.00	175.00	1.72	2.61	0.03
39	Bajaj Auto Ltd	BAJAJ-AUTO	532977	Automobile	107778	3725.0	9.85	23.00	124.0	3.76	900.0	54.8	0.01	4.61	8.79	-7.75	5.87	23.40	27.50	19.00	20.90	19.30	17.10	2.74	7.29	202.00	0.96	1.58	0.00
40	Meghmani Organics Ltd	MOL	543331	Agro Chemicals	2941	116.0	24.20	43.20	665.0	1.21	63.1	49.4	0.00	47.20	0.00	59.70	0.00	24.10	0.00	22.60	0.00	8.51	8.71	0.00	0.00	13.60	1.05	0.71	0.41
41	Varun Beverages Ltd	VBL	540180	FMCG	73987	1139.0	7.62	109.00	2371.0	0.22	74.1	63.9	0.04	50.80	18.00	115.00	72.10	17.40	14.60	18.60	15.30	51.40	55.40	50.30	0.00	22.20	0.98	0.34	0.49
42	Power Finance Corporation Ltd	PFC	532810	Finance	30836	117.0	17.90	20.30	674847.0	10.30	290.0	56.0	0.00	1.44	22.60	8.39	44.40	9.38	9.66	21.20	19.70	2.18	3.28	-1.61	2.15	53.60	0.10	1.51	8.82
43	Goodyear India Ltd	GOODYEAR	500168	Tyres	2481	1075.0	10.80	37.30	16.7	1.86	310.0	74.0	0.00	29.60	10.00	-32.20	-4.15	18.00	19.50	13.20	13.50	21.80	15.70	6.03	12.90	49.30	1.77	1.08	0.02
45	Medplus Health Services Ltd	MEDPLUS	543427	Retail	6904	579.0	56.90	1.52	815.0	0.00	119.0	40.4	50.50	23.10	13.40	49.40	25.70	11.40	12.00	10.40	9.25	134.00	126.00	0.00	0.00	9.00	1.82	1.28	0.57
46	Abbott India Ltd	ABBOTINDIA	500488	Pharmaceuticals	40883	19239.0	7.95	24.00	152.0	0.75	1327.0	75.0	0.00	12.20	11.10	14.50	23.60	38.40	37.10	29.50	27.00	50.60	35.70	33.60	29.40	381.00	1.22	2.63	0.05
47	Dr Lal Pathlabs Ltd	LALPATHLAB	539524	Healthcare	19670	2360.0	40.20	30.70	285.0	0.51	186.0	55.0	0.00	1.53	18.00	-38.60	17.40	29.40	32.40	25.10	24.00	79.30	51.70	22.60	0.00	29.80	1.04	1.73	0.18
48	Mindtree Ltd	MINDTREE	532819	IT - Software	57401	3480.0	31.20	31.40	613.0	1.06	344.0	61.0	0.00	35.00	15.00	36.40	31.60	41.50	33.40	33.80	26.90	30.40	21.60	47.80	35.00	115.00	1.45	2.96	0.11
49	Subex Ltd	SUBEXLTD	532348	IT - Software	1725	30.6	50.10	64.50	14.7	0.00	10.1	0.0	0.00	-7.65	-1.37	-73.20	-19.90	6.18	8.34	3.68	5.34	132.00	20.80	28.30	7.80	0.23	0.46	3.75	0.03

high_profit_sector=profit_companies[['SECTOR','COMPANY']].groupby('SECTOR').count().reset_index()
high_profit_sector

	SECTOR	COMPANY
0	Agro Chemicals	3
1	Automobile	3
2	Banks	3
3	Chemicals	4
4	FMCG	3
5	Finance	4
6	Healthcare	3
7	IT - Software	5
8	Paints/Varnish	3
9	Pharmaceuticals	4
10	Refineries	1
11	Retail	4
12	Telecomm-Service	2
13	Tyres	4

Visualization of profit making Sector according to their companies

plt.figure(figsize=(10,5), dpi=150)
plt.xticks(rotation=60)
plt.title('Number Of profit Making Companies',size=20)
sns.barplot(data=high_profit_sector, x='SECTOR', y='COMPANY',palette='vlag_r')
plt.xlabel('Sectors',size=20)
plt.ylabel('Number Of Companies',size=20)

Text(0, 0.5, 'Number Of Companies')

• As per above analysis "Telecomm-Service" Sector has equal profit making and loss making companies so according to data set investing in this sector will not give handsome returns
• To make sure this thing we do more analysis on "Telecomm-Service" Sector

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7 "Telecomm-Service" SECTOR ANALYSIS

REPORT CONTENT

Tele_sector=df[df['SECTOR']=='Telecomm-Service']
Tele_sector

	COMPANY	NSE_CODE	BSE_CODE	SECTOR	MARKET_CAP(in Cr.)	CURRENT_PRICE	DOWN_FROM_52w_HIGH(in %)	UP_FROM_52w_LOW(in %)	DEBT(in Cr.)	DIV_YIELD(in %)	BOOK_VALUE	PROMOTER_HOLDING(in %)	HOLDING_PLEDGED(in %)	SALES(in %)	SALES_GROWTH_5yrs(in %)	PROFIT(in %)	PROFIT_GROWTH_5yrs(in %)	ROCE(in %)	ROCE_5yr_AVG(in %)	ROE(in %)	ROE_5yr_AVG(in %)	PE_RATIO	PE_RATIO_5yr_AVG	CAGR_5yr(in %)	CAGR_10yr(in %)	EPS	ASSET_TURNOVER	QUICK_RATIO	DEBT_EQUITY_RATIO
14	Bharti Airtel Ltd	BHARTIARTL	532454	Telecomm-Service	476676	826.00	1.87	31.3	217989.0	0.36	131.0	55.1	0.00	19.80	4.07	125.00	-3.36	12.00	8.3	5.86	2.13	76.7	36.80	12.70	12.50	11.4	0.33	0.45	2.99
29	Tata Communications Ltd	TATACOMM	500483	Telecomm-Service	37453	1314.00	17.40	53.5	8840.0	1.58	31.8	58.9	4.96	3.72	-1.04	34.20	-6.74	21.80	13.2	282.00	0.00	21.1	16.70	24.60	24.40	64.4	0.82	0.61	9.74
30	Vodafone Idea Ltd	IDEA	532822	Telecomm-Service	27783	8.65	48.50	11.6	248176.0	0.00	-23.8	75.0	0.00	4.92	1.60	-6.46	0.00	0.00	0.0	0.00	0.00	0.0	-0.60	-31.80	-17.00	-9.2	0.19	0.24	0.00
44	Mahanagar Telephone Nigam Ltd	MTNL	500108	Telecomm-Service	1373	21.80	46.60	30.9	26820.0	0.00	-296.0	56.9	0.00	-16.80	-17.30	-2.26	2.17	-5.08	-11.3	0.00	0.00	0.0	-0.48	-0.89	-2.19	-40.8	0.09	0.43	0.00

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7.1 Analyzing Debt and Market_Cap of Companies

REPORT CONTENT

• So, as a investor we should make sure that the debt which a company owns can repay smoothly and also invest the profit for it's growth

Tele_sector[Tele_sector["MARKET_CAP(in Cr.)"]>Tele_sector["DEBT(in Cr.)"]]

	COMPANY	NSE_CODE	BSE_CODE	SECTOR	MARKET_CAP(in Cr.)	CURRENT_PRICE	DOWN_FROM_52w_HIGH(in %)	UP_FROM_52w_LOW(in %)	DEBT(in Cr.)	DIV_YIELD(in %)	BOOK_VALUE	PROMOTER_HOLDING(in %)	HOLDING_PLEDGED(in %)	SALES(in %)	SALES_GROWTH_5yrs(in %)	PROFIT(in %)	PROFIT_GROWTH_5yrs(in %)	ROCE(in %)	ROCE_5yr_AVG(in %)	ROE(in %)	ROE_5yr_AVG(in %)	PE_RATIO	PE_RATIO_5yr_AVG	CAGR_5yr(in %)	CAGR_10yr(in %)	EPS	ASSET_TURNOVER	QUICK_RATIO	DEBT_EQUITY_RATIO
14	Bharti Airtel Ltd	BHARTIARTL	532454	Telecomm-Service	476676	826.0	1.87	31.3	217989.0	0.36	131.0	55.1	0.00	19.80	4.07	125.0	-3.36	12.0	8.3	5.86	2.13	76.7	36.8	12.7	12.5	11.4	0.33	0.45	2.99
29	Tata Communications Ltd	TATACOMM	500483	Telecomm-Service	37453	1314.0	17.40	53.5	8840.0	1.58	31.8	58.9	4.96	3.72	-1.04	34.2	-6.74	21.8	13.2	282.00	0.00	21.1	16.7	24.6	24.4	64.4	0.82	0.61	9.74

7.1.1 Visualization

Tele_sector.plot(x="COMPANY", y=["DEBT(in Cr.)","MARKET_CAP(in Cr.)"], kind="bar",figsize=(39,18),\
                 rot=0, title='Debt & Market_cap comparison',fontsize=25,colormap='RdYlGn' )
plt.xlabel('Company',size=40)
plt.ylabel('Value in Cr.',size=40)
plt.title('Number of profit Making Companies',size=40)

Text(0.5, 1.0, 'Number of profit Making Companies')

-So, in above analysis we can see 2 companies has debt more than there Market_Cap than we can avoid to invest in these companies

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7.2 CAGR check

REPORT CONTENT

• As investor we basically need handsome returns on our investment with the help of CAGR(compound annual growth rate) we can check that at what rate of compounding the price of stock has changed.
• As per basic ROI(Return on investment) we will keep that the CAGR should me more than 15%.

Tele_sector[(Tele_sector['CAGR_5yr(in %)']>20)& (Tele_sector['CAGR_10yr(in %)']>20)]

	COMPANY	NSE_CODE	BSE_CODE	SECTOR	MARKET_CAP(in Cr.)	CURRENT_PRICE	DOWN_FROM_52w_HIGH(in %)	UP_FROM_52w_LOW(in %)	DEBT(in Cr.)	DIV_YIELD(in %)	BOOK_VALUE	PROMOTER_HOLDING(in %)	HOLDING_PLEDGED(in %)	SALES(in %)	SALES_GROWTH_5yrs(in %)	PROFIT(in %)	PROFIT_GROWTH_5yrs(in %)	ROCE(in %)	ROCE_5yr_AVG(in %)	ROE(in %)	ROE_5yr_AVG(in %)	PE_RATIO	PE_RATIO_5yr_AVG	CAGR_5yr(in %)	CAGR_10yr(in %)	EPS	ASSET_TURNOVER	QUICK_RATIO	DEBT_EQUITY_RATIO
29	Tata Communications Ltd	TATACOMM	500483	Telecomm-Service	37453	1314.0	17.4	53.5	8840.0	1.58	31.8	58.9	4.96	3.72	-1.04	34.2	-6.74	21.8	13.2	282.0	0.0	21.1	16.7	24.6	24.4	64.4	0.82	0.61	9.74

The "IT-software" sector has highest profit making companies as per the Data set so will analyze this sector

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8. "IT - Software" SECTOR ANALYSIS

REPORT CONTENT

IT_sector=df[df['SECTOR']=='IT - Software']
IT_sector

	COMPANY	NSE_CODE	BSE_CODE	SECTOR	MARKET_CAP(in Cr.)	CURRENT_PRICE	DOWN_FROM_52w_HIGH(in %)	UP_FROM_52w_LOW(in %)	DEBT(in Cr.)	DIV_YIELD(in %)	BOOK_VALUE	PROMOTER_HOLDING(in %)	HOLDING_PLEDGED(in %)	SALES(in %)	SALES_GROWTH_5yrs(in %)	PROFIT(in %)	PROFIT_GROWTH_5yrs(in %)	ROCE(in %)	ROCE_5yr_AVG(in %)	ROE(in %)	ROE_5yr_AVG(in %)	PE_RATIO	PE_RATIO_5yr_AVG	CAGR_5yr(in %)	CAGR_10yr(in %)	EPS	ASSET_TURNOVER	QUICK_RATIO	DEBT_EQUITY_RATIO
6	Tata Consultancy Services Ltd	TCS	532540	IT - Software	1172964	3206.0	20.8	9.56	7460.0	1.34	266.0	72.3	0.48	16.60	10.20	8.27	7.83	54.90	47.80	43.60	37.20	29.6	21.2	18.8	17.1	108.00	1.42	2.84	0.08
20	Larsen & Toubro Infotech Ltd	LTI	540005	IT - Software	85278	4862.0	36.0	30.20	824.0	0.93	524.0	74.0	0.00	30.40	19.20	21.30	19.20	35.70	38.60	28.50	30.50	33.3	19.5	38.8	0.0	146.00	1.35	3.09	0.09
37	KPIT Technologies Ltd	KPITTECH	542651	IT - Software	18200	664.0	17.1	69.70	186.0	0.47	50.8	40.1	0.82	22.10	0.00	46.60	0.00	24.10	0.00	21.50	0.00	57.3	32.7	0.0	0.0	11.60	1.13	2.02	0.13
48	Mindtree Ltd	MINDTREE	532819	IT - Software	57401	3480.0	31.2	31.40	613.0	1.06	344.0	61.0	0.00	35.00	15.00	36.40	31.60	41.50	33.40	33.80	26.90	30.4	21.6	47.8	35.0	115.00	1.45	2.96	0.11
49	Subex Ltd	SUBEXLTD	532348	IT - Software	1725	30.6	50.1	64.50	14.7	0.00	10.1	0.0	0.00	-7.65	-1.37	-73.20	-19.90	6.18	8.34	3.68	5.34	132.0	20.8	28.3	7.8	0.23	0.46	3.75	0.03

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8.1 Analyzing Debt and Market_Cap of companies

REPORT CONTENT

IT_sector[IT_sector["MARKET_CAP(in Cr.)"]>IT_sector["DEBT(in Cr.)"]]

	COMPANY	NSE_CODE	BSE_CODE	SECTOR	MARKET_CAP(in Cr.)	CURRENT_PRICE	DOWN_FROM_52w_HIGH(in %)	UP_FROM_52w_LOW(in %)	DEBT(in Cr.)	DIV_YIELD(in %)	BOOK_VALUE	PROMOTER_HOLDING(in %)	HOLDING_PLEDGED(in %)	SALES(in %)	SALES_GROWTH_5yrs(in %)	PROFIT(in %)	PROFIT_GROWTH_5yrs(in %)	ROCE(in %)	ROCE_5yr_AVG(in %)	ROE(in %)	ROE_5yr_AVG(in %)	PE_RATIO	PE_RATIO_5yr_AVG	CAGR_5yr(in %)	CAGR_10yr(in %)	EPS	ASSET_TURNOVER	QUICK_RATIO	DEBT_EQUITY_RATIO
6	Tata Consultancy Services Ltd	TCS	532540	IT - Software	1172964	3206.0	20.8	9.56	7460.0	1.34	266.0	72.3	0.48	16.60	10.20	8.27	7.83	54.90	47.80	43.60	37.20	29.6	21.2	18.8	17.1	108.00	1.42	2.84	0.08
20	Larsen & Toubro Infotech Ltd	LTI	540005	IT - Software	85278	4862.0	36.0	30.20	824.0	0.93	524.0	74.0	0.00	30.40	19.20	21.30	19.20	35.70	38.60	28.50	30.50	33.3	19.5	38.8	0.0	146.00	1.35	3.09	0.09
37	KPIT Technologies Ltd	KPITTECH	542651	IT - Software	18200	664.0	17.1	69.70	186.0	0.47	50.8	40.1	0.82	22.10	0.00	46.60	0.00	24.10	0.00	21.50	0.00	57.3	32.7	0.0	0.0	11.60	1.13	2.02	0.13
48	Mindtree Ltd	MINDTREE	532819	IT - Software	57401	3480.0	31.2	31.40	613.0	1.06	344.0	61.0	0.00	35.00	15.00	36.40	31.60	41.50	33.40	33.80	26.90	30.4	21.6	47.8	35.0	115.00	1.45	2.96	0.11
49	Subex Ltd	SUBEXLTD	532348	IT - Software	1725	30.6	50.1	64.50	14.7	0.00	10.1	0.0	0.00	-7.65	-1.37	-73.20	-19.90	6.18	8.34	3.68	5.34	132.0	20.8	28.3	7.8	0.23	0.46	3.75	0.03

8.1.1 Visualization

IT_sector.plot(x="COMPANY", y=["DEBT(in Cr.)","MARKET_CAP(in Cr.)"], kind="bar", figsize=(39, 18),\
                 rot=0 , colormap='RdYlGn',fontsize=25 )
plt.xlabel('Company',size=40)
plt.ylabel('Value in Cr.',size=40)
plt.title('Debt & Market_cap comparison',size=40)

Text(0.5, 1.0, 'Debt & Market_cap comparison')

---

8.2 CAGR check

REPORT CONTENT

IT_sector[(IT_sector['CAGR_5yr(in %)']>20)& (IT_sector['CAGR_10yr(in %)']>20)]

	COMPANY	NSE_CODE	BSE_CODE	SECTOR	MARKET_CAP(in Cr.)	CURRENT_PRICE	DOWN_FROM_52w_HIGH(in %)	UP_FROM_52w_LOW(in %)	DEBT(in Cr.)	DIV_YIELD(in %)	BOOK_VALUE	PROMOTER_HOLDING(in %)	HOLDING_PLEDGED(in %)	SALES(in %)	SALES_GROWTH_5yrs(in %)	PROFIT(in %)	PROFIT_GROWTH_5yrs(in %)	ROCE(in %)	ROCE_5yr_AVG(in %)	ROE(in %)	ROE_5yr_AVG(in %)	PE_RATIO	PE_RATIO_5yr_AVG	CAGR_5yr(in %)	CAGR_10yr(in %)	EPS	ASSET_TURNOVER	QUICK_RATIO	DEBT_EQUITY_RATIO
48	Mindtree Ltd	MINDTREE	532819	IT - Software	57401	3480.0	31.2	31.4	613.0	1.06	344.0	61.0	0.0	35.0	15.0	36.4	31.6	41.5	33.4	33.8	26.9	30.4	21.6	47.8	35.0	115.0	1.45	2.96	0.11

As, we are getting Mindtree Ltd from "IT- Sector" as per our all criteria and from "Telecomm-Service" sector Tata Communications Ltd. So, will do a comparison in these both stocks.

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9. Stock comparison

REPORT CONTENT

selected_stocks = df[(df['NSE_CODE']=='MINDTREE') | (df['NSE_CODE']=='TATACOMM')]
selected_stocks

	COMPANY	NSE_CODE	BSE_CODE	SECTOR	MARKET_CAP(in Cr.)	CURRENT_PRICE	DOWN_FROM_52w_HIGH(in %)	UP_FROM_52w_LOW(in %)	DEBT(in Cr.)	DIV_YIELD(in %)	BOOK_VALUE	PROMOTER_HOLDING(in %)	HOLDING_PLEDGED(in %)	SALES(in %)	SALES_GROWTH_5yrs(in %)	PROFIT(in %)	PROFIT_GROWTH_5yrs(in %)	ROCE(in %)	ROCE_5yr_AVG(in %)	ROE(in %)	ROE_5yr_AVG(in %)	PE_RATIO	PE_RATIO_5yr_AVG	CAGR_5yr(in %)	CAGR_10yr(in %)	EPS	ASSET_TURNOVER	QUICK_RATIO	DEBT_EQUITY_RATIO
29	Tata Communications Ltd	TATACOMM	500483	Telecomm-Service	37453	1314.0	17.4	53.5	8840.0	1.58	31.8	58.9	4.96	3.72	-1.04	34.2	-6.74	21.8	13.2	282.0	0.0	21.1	16.7	24.6	24.4	64.4	0.82	0.61	9.74
48	Mindtree Ltd	MINDTREE	532819	IT - Software	57401	3480.0	31.2	31.4	613.0	1.06	344.0	61.0	0.00	35.00	15.00	36.4	31.60	41.5	33.4	33.8	26.9	30.4	21.6	47.8	35.0	115.0	1.45	2.96	0.11

9.1 Profit growth check

REPORT CONTENT

• So for selecting a stock for a investor profit growths matter alot because if the company earns then the stock will run

plt.figure(figsize=(10,5), dpi=150)
plt.xticks(rotation=0)
plt.title('Avg 5 Year Profit Growth',size=20)
chart_A=sns.barplot(data=selected_stocks, x='COMPANY', y='PROFIT_GROWTH_5yrs(in %)',palette='gist_earth')
plt.xlabel('Sectors',size=20)
plt.ylabel('Number of Companies',size=20)

Text(0, 0.5, 'Number of Companies')

• In above analysis we found that the Avg profit growth of TCL is not up to mark. So, for final conclusion will check the Quick ratio of both the companies.

9.2 Quick ratio check

REPORT CONTENT

plt.figure(figsize=(10,5), dpi=150)
plt.xticks(rotation=0)
plt.title('Quick Ratio',size=20)
chart_A=sns.barplot(data=selected_stocks, x='COMPANY', y='QUICK_RATIO',palette='cividis_r')
plt.xlabel('Company',size=20)

Text(0.5, 0, 'Company')

So, we can see that the Mind Tree Ltd. holds a great quick ratio(repayment of loans capability) which shows that we can keep a track on this company

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Thank You