# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session

Importing libraries

import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import os
sns.set_style("darkgrid")
%matplotlib inline

Read Dataset

df = pd.read_csv('D:/Datasets/nifty.csv')

df

	Company Name	Symbol	Industry	Series	Open	High	Low	Previous Close	Last Traded Price	Change	Percentage Change	Share Volume	Value (Indian Rupee)	52 Week High	52 Week Low	365 Day Percentage Change	30 Day Percentage Change
0	3M India Ltd.	3MINDIA	Diversified	EQ	21950.00	21999.00	21126.05	21854.05	21575.00	-279.05	-1.28	4159	8.965357e+07	27800.00	17273.00	-10.18	8.22
1	Aarti Drugs Ltd.	AARTIDRUGS	Healthcare	EQ	400.50	401.80	394.10	403.85	400.00	-3.85	-0.95	31782	1.262731e+07	749.75	378.00	-42.92	-5.10
2	Aavas Financiers Ltd.	AAVAS	Financial Services	EQ	1997.10	2004.05	1894.50	2015.45	1943.15	-72.30	-3.59	150704	2.907728e+08	3340.00	1815.00	-25.69	-9.39
3	ABB India Ltd.	ABB	Capital Goods	EQ	2260.35	2311.50	2260.35	2300.90	2280.00	-20.90	-0.91	97053	2.210935e+08	2487.85	1618.05	27.25	-5.21
4	Abbott India Ltd.	ABBOTINDIA	Healthcare	EQ	18700.40	19200.00	18605.00	18760.40	19199.80	439.40	2.34	12396	2.346770e+08	23934.45	15514.00	11.61	5.84
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
496	Zensar Technolgies Ltd.	ZENSARTECH	Information Technology	EQ	273.15	273.55	268.40	272.10	270.00	-2.10	-0.77	186578	5.046748e+07	587.00	246.65	-12.61	-11.02
497	ZF Commercial Vehicle Control Systems India Ltd.	ZFCVINDIA	Automobile and Auto Components	EQ	7748.00	7900.00	7525.30	7716.60	7680.00	-36.60	-0.47	12790	9.884624e+07	8780.05	6793.30	-	1.43
498	Zomato Ltd.	ZOMATO	Consumer Services	EQ	54.15	56.70	52.55	53.85	56.00	2.15	3.99	112078641	6.193466e+09	169.00	50.05	-	-27.86
499	Zydus Lifesciences Ltd.	ZYDUSLIFE	Healthcare	EQ	356.90	364.05	354.30	357.00	364.00	7.00	1.96	862373	3.100145e+08	651.90	319.00	-	-5.64
500	Zydus Wellness Ltd.	ZYDUSWELL	Fast Moving Consumer Goods	EQ	1635.00	1635.00	1605.00	1636.85	1627.00	-9.85	-0.60	10803	1.749567e+07	2476.85	1430.00	-22.08	6.25

501 rows × 17 columns

df.head()

	Company Name	Symbol	Industry	Series	Open	High	Low	Previous Close	Last Traded Price	Change	Percentage Change	Share Volume	Value (Indian Rupee)	52 Week High	52 Week Low	365 Day Percentage Change	30 Day Percentage Change
0	3M India Ltd.	3MINDIA	Diversified	EQ	21950.00	21999.00	21126.05	21854.05	21575.00	-279.05	-1.28	4159	8.965357e+07	27800.00	17273.00	-10.18	8.22
1	Aarti Drugs Ltd.	AARTIDRUGS	Healthcare	EQ	400.50	401.80	394.10	403.85	400.00	-3.85	-0.95	31782	1.262731e+07	749.75	378.00	-42.92	-5.10
2	Aavas Financiers Ltd.	AAVAS	Financial Services	EQ	1997.10	2004.05	1894.50	2015.45	1943.15	-72.30	-3.59	150704	2.907728e+08	3340.00	1815.00	-25.69	-9.39
3	ABB India Ltd.	ABB	Capital Goods	EQ	2260.35	2311.50	2260.35	2300.90	2280.00	-20.90	-0.91	97053	2.210935e+08	2487.85	1618.05	27.25	-5.21
4	Abbott India Ltd.	ABBOTINDIA	Healthcare	EQ	18700.40	19200.00	18605.00	18760.40	19199.80	439.40	2.34	12396	2.346770e+08	23934.45	15514.00	11.61	5.84

NIFTY 500 is India’s first broad-based stock market index of the Indian stock market. It contains top 500 listed companies on the NSE. The NIFTY 500 index represents about 96.1% of free float market capitalization and about 96.5% of the total turnover on the National Stock Exchange (NSE).

NIFTY 500 companies are disaggregated into 72 industry indices.Industry weights in the index reflect industry weights in the market. For example, if the banking sector has a 5% weight in the universe of stocks traded on the NSE, banking stocks in the index would also have an approximate representation of 5% in the index.NIFTY 500 can be used for a variety of purposes such as benchmarking fund portfolios, launching of index funds, ETFs and other structured products

NIFTY 500/S&P CNX 500 Type:Stock market index Location:Mumbai, India Owner:National Stock Exchange of India Currency:Indian Rupee Website:Nifty500

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 501 entries, 0 to 500
Data columns (total 17 columns):
 #   Column                     Non-Null Count  Dtype  
---  ------                     --------------  -----  
 0   Company Name               501 non-null    object 
 1   Symbol                     501 non-null    object 
 2   Industry                   501 non-null    object 
 3   Series                     501 non-null    object 
 4   Open                       501 non-null    float64
 5   High                       501 non-null    float64
 6   Low                        501 non-null    float64
 7   Previous Close             501 non-null    float64
 8   Last Traded Price          501 non-null    float64
 9   Change                     501 non-null    object 
 10  Percentage Change          501 non-null    object 
 11  Share Volume               501 non-null    int64  
 12  Value (Indian Rupee)       501 non-null    float64
 13  52 Week High               501 non-null    float64
 14  52 Week Low                501 non-null    float64
 15  365 Day Percentage Change  501 non-null    object 
 16  30 Day Percentage Change   501 non-null    object 
dtypes: float64(8), int64(1), object(8)
memory usage: 66.7+ KB

The Open and Previous Close segments show the opening and shutting cost of the stocks on a specific day Industry Sectors to which the stocks has a place The High and Low segments give the most elevated and the least cost for the stock on a specific day, individually. The Share Volume section lets us know the all out volume of stocks exchanged on a specific day. 52 Week High and Low colums Provides the yearly high and low assumed worth of the stock

Finding Null Values and Missing Values

df.isnull().sum()

Company Name                 0
Symbol                       0
Industry                     0
Series                       0
Open                         0
High                         0
Low                          0
Previous Close               0
Last Traded Price            0
Change                       0
Percentage Change            0
Share Volume                 0
Value (Indian Rupee)         0
52 Week High                 0
52 Week Low                  0
365 Day Percentage Change    0
30 Day Percentage Change     0
dtype: int64

Finding Duplicate Values

df.duplicated().sum()

0

df.nunique()

Company Name                 501
Symbol                       501
Industry                      21
Series                         2
Open                         492
High                         495
Low                          493
Previous Close               495
Last Traded Price            493
Change                       372
Percentage Change            354
Share Volume                 501
Value (Indian Rupee)         501
52 Week High                 497
52 Week Low                  494
365 Day Percentage Change    441
30 Day Percentage Change     458
dtype: int64

Object Values

object_dtype = df.select_dtypes(include=object)
object_dtype.columns

Index(['Company Name', 'Symbol', 'Industry', 'Series', 'Change',
       'Percentage Change', '365 Day Percentage Change',
       '30 Day Percentage Change'],
      dtype='object')

Numerical Values

object_dtype = df.select_dtypes(exclude=object)
object_dtype.columns

Index(['Open', 'High', 'Low', 'Previous Close', 'Last Traded Price',
       'Share Volume', 'Value (Indian Rupee)', '52 Week High', '52 Week Low'],
      dtype='object')

Univariate Analysis

sns.distplot(x = df['Open'])

C:\ProgramData\Anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

<AxesSubplot:ylabel='Density'>

sns.distplot(x = df['High'])

C:\ProgramData\Anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

<AxesSubplot:ylabel='Density'>

sns.distplot(x = df['Open'])

C:\ProgramData\Anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

<AxesSubplot:ylabel='Density'>

sns.distplot(x = df['Previous Close'])

C:\ProgramData\Anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

<AxesSubplot:ylabel='Density'>

sns.countplot(df['Industry'])
plt.xticks(rotation=90)

C:\ProgramData\Anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
        17, 18, 19, 20]),
 [Text(0, 0, 'Diversified'),
  Text(1, 0, 'Healthcare'),
  Text(2, 0, 'Financial Services'),
  Text(3, 0, 'Capital Goods'),
  Text(4, 0, 'Consumer Services'),
  Text(5, 0, 'Construction Materials'),
  Text(6, 0, 'Metals & Mining'),
  Text(7, 0, 'Power'),
  Text(8, 0, 'Services'),
  Text(9, 0, 'Fast Moving Consumer Goods'),
  Text(10, 0, 'Oil Gas & Consumable Fuels'),
  Text(11, 0, 'Information Technology'),
  Text(12, 0, 'Chemicals'),
  Text(13, 0, 'Textiles'),
  Text(14, 0, 'Automobile and Auto Components'),
  Text(15, 0, 'Consumer Durables'),
  Text(16, 0, 'Telecommunication'),
  Text(17, 0, 'Realty'),
  Text(18, 0, 'Forest Materials'),
  Text(19, 0, 'Construction'),
  Text(20, 0, 'Media Entertainment & Publication')])

sns.countplot(df['Series'])

C:\ProgramData\Anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='Series', ylabel='count'>

Bi-Variant Analysis

fx = df[['High','Low']].plot(figsize=(20,7))
fx.set_title("High vs Low",fontsize=25)

Text(0.5, 1.0, 'High vs Low')

fx = df[['Open','Previous Close']].plot(figsize=(20,5))
fx.set_title("Open Vs Previous Close",fontsize=20)

Text(0.5, 1.0, 'Open Vs Previous Close')

fx = df[['Open','Last Traded Price']].plot(figsize=(20,5))
fx.set_title("Open Vs Last Traded Price",fontsize=20)

Text(0.5, 1.0, 'Open Vs Last Traded Price')

sns.jointplot(x='Open', y='Previous Close', data=df, kind="reg", truncate=False, color="m", height=7);

sns.jointplot(x='High', y='Low', data=df, kind="reg", truncate=False, color="m", height=7);

sns.jointplot(x= 'Share Volume', y= 'Value (Indian Rupee)', data=df, kind="reg", truncate=False, color="m", height=7);

sns.jointplot(x= 'High', y= 'Value (Indian Rupee)', data=df, kind="reg", truncate=False, color="m", height=7);

sns.jointplot(x= 'Low', y= 'Value (Indian Rupee)', data=df, kind="reg", truncate=False, color="m", height=7);

Multi- Variate Analysis

sns.pairplot(df)
plt.show()

plt.figure(figsize = (10, 10))
cor=df.corr()
sns.heatmap(cor, annot = True, cmap="YlGnBu")
plt.show()

Outliers from the Dataset

fig, axs = plt.subplots(5, 2, figsize=(8,10))
fig.tight_layout(pad=4.0)
features = ['Open', 'High', 'Low', 'Previous Close', 'Last Traded Price','Share Volume', 'Value (Indian Rupee)', '52 Week High', '52 Week Low']
for f,ax in zip(features,axs.ravel()):
    ax=sns.boxplot(ax=ax,data=df,y=df[f])
    ax.set_title('Feature:'+ f)

df.plot(subplots=True,figsize=(29,15))

array([<AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>,
       <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>,
       <AxesSubplot:>], dtype=object)