Module III - Day 3

Python Foundation

Nov-Jan 2025 batch, Vikrant Patil

Date: 04 Mar 2026

Click here for All Notes

login to https://traininghub.vikrant.dev/ with your username and create a notebook with name module3-day3.ipynb

© Vikrant Patil

Stocks API

!pip install yfinance

Requirement already satisfied: yfinance in /home/vikrant/usr/local/default/lib/python3.13/site-packages (1.2.0)
Requirement already satisfied: pandas>=1.3.0 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from yfinance) (3.0.0)
Requirement already satisfied: numpy>=1.16.5 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from yfinance) (2.4.2)
Requirement already satisfied: requests>=2.31 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from yfinance) (2.32.5)
Requirement already satisfied: multitasking>=0.0.7 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from yfinance) (0.0.12)
Requirement already satisfied: platformdirs>=2.0.0 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from yfinance) (4.5.1)
Requirement already satisfied: pytz>=2022.5 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from yfinance) (2025.2)
Requirement already satisfied: frozendict>=2.3.4 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from yfinance) (2.4.7)
Requirement already satisfied: peewee>=3.16.2 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from yfinance) (3.19.0)
Requirement already satisfied: beautifulsoup4>=4.11.1 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from yfinance) (4.14.3)
Requirement already satisfied: curl_cffi<0.14,>=0.7 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from yfinance) (0.13.0)
Requirement already satisfied: protobuf>=3.19.0 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from yfinance) (6.33.5)
Requirement already satisfied: websockets>=13.0 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from yfinance) (16.0)
Requirement already satisfied: cffi>=1.12.0 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from curl_cffi<0.14,>=0.7->yfinance) (2.0.0)
Requirement already satisfied: certifi>=2024.2.2 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from curl_cffi<0.14,>=0.7->yfinance) (2025.11.12)
Requirement already satisfied: soupsieve>=1.6.1 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from beautifulsoup4>=4.11.1->yfinance) (2.8)
Requirement already satisfied: typing-extensions>=4.0.0 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from beautifulsoup4>=4.11.1->yfinance) (4.15.0)
Requirement already satisfied: pycparser in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from cffi>=1.12.0->curl_cffi<0.14,>=0.7->yfinance) (2.23)
Requirement already satisfied: python-dateutil>=2.8.2 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from pandas>=1.3.0->yfinance) (2.9.0.post0)
Requirement already satisfied: six>=1.5 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from python-dateutil>=2.8.2->pandas>=1.3.0->yfinance) (1.17.0)
Requirement already satisfied: charset_normalizer<4,>=2 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from requests>=2.31->yfinance) (3.4.4)
Requirement already satisfied: idna<4,>=2.5 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from requests>=2.31->yfinance) (3.11)
Requirement already satisfied: urllib3<3,>=1.21.1 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from requests>=2.31->yfinance) (2.6.2)

import yfinance as yf

reliance = yf.Ticker("RELIANCE.NS")

help(reliance.history)

Help on method history in module yfinance.base:

history(*args, **kwargs) -> pandas.DataFrame method of yfinance.ticker.Ticker instance

reliance.history()

	Open	High	Low	Close	Volume	Dividends	Stock Splits
Date
2026-02-02 00:00:00+05:30	1340.000000	1392.900024	1335.500000	1390.400024	15711165	0.0	0.0
2026-02-03 00:00:00+05:30	1473.900024	1489.500000	1430.199951	1437.099976	25456171	0.0	0.0
2026-02-04 00:00:00+05:30	1444.000000	1464.000000	1440.300049	1456.800049	8333387	0.0	0.0
2026-02-05 00:00:00+05:30	1457.000000	1461.500000	1439.500000	1443.400024	11763658	0.0	0.0
2026-02-06 00:00:00+05:30	1441.500000	1452.800049	1433.500000	1450.800049	8277304	0.0	0.0
2026-02-09 00:00:00+05:30	1458.000000	1465.900024	1453.000000	1461.599976	5775864	0.0	0.0
2026-02-10 00:00:00+05:30	1471.000000	1471.000000	1452.199951	1458.500000	10259630	0.0	0.0
2026-02-11 00:00:00+05:30	1459.599976	1470.000000	1453.599976	1468.699951	7106877	0.0	0.0
2026-02-12 00:00:00+05:30	1470.000000	1473.000000	1445.500000	1448.900024	11135399	0.0	0.0
2026-02-13 00:00:00+05:30	1445.500000	1450.699951	1416.300049	1419.599976	10755693	0.0	0.0
2026-02-16 00:00:00+05:30	1420.099976	1439.599976	1409.300049	1437.099976	5896514	0.0	0.0
2026-02-17 00:00:00+05:30	1431.099976	1431.800049	1418.599976	1423.000000	10731630	0.0	0.0
2026-02-18 00:00:00+05:30	1427.000000	1442.099976	1419.199951	1441.300049	5521096	0.0	0.0
2026-02-19 00:00:00+05:30	1443.000000	1443.000000	1400.000000	1409.500000	11884664	0.0	0.0
2026-02-20 00:00:00+05:30	1409.500000	1427.400024	1406.099976	1419.400024	8177670	0.0	0.0
2026-02-23 00:00:00+05:30	1425.000000	1434.900024	1418.300049	1428.000000	7758856	0.0	0.0
2026-02-24 00:00:00+05:30	1425.300049	1433.300049	1415.000000	1428.800049	12529409	0.0	0.0
2026-02-25 00:00:00+05:30	1435.000000	1440.500000	1393.500000	1398.500000	10728792	0.0	0.0
2026-02-26 00:00:00+05:30	1398.500000	1412.900024	1391.900024	1406.800049	16683858	0.0	0.0
2026-02-27 00:00:00+05:30	1398.000000	1410.400024	1388.099976	1393.900024	12031440	0.0	0.0
2026-03-02 00:00:00+05:30	1375.500000	1378.599976	1341.500000	1358.000000	23728722	0.0	0.0

rel1yr = reliance.history(period="1y")

rel1yr

	Open	High	Low	Close	Volume	Dividends	Stock Splits
Date
2025-03-03 00:00:00+05:30	1199.210417	1201.650622	1151.401364	1166.590698	17944938	0.0	0.0
2025-03-04 00:00:00+05:30	1157.576688	1169.329796	1154.937327	1157.277954	11377373	0.0	0.0
2025-03-05 00:00:00+05:30	1156.381565	1178.294049	1152.397477	1170.923462	8664095	0.0	0.0
2025-03-06 00:00:00+05:30	1192.238355	1209.120879	1180.435518	1204.788208	14468014	0.0	0.0
2025-03-07 00:00:00+05:30	1211.162776	1249.808479	1207.178688	1244.828369	16474965	0.0	0.0
...	...	...	...	...	...	...	...
2026-02-24 00:00:00+05:30	1425.300049	1433.300049	1415.000000	1428.800049	12529409	0.0	0.0
2026-02-25 00:00:00+05:30	1435.000000	1440.500000	1393.500000	1398.500000	10728792	0.0	0.0
2026-02-26 00:00:00+05:30	1398.500000	1412.900024	1391.900024	1406.800049	16683858	0.0	0.0
2026-02-27 00:00:00+05:30	1398.000000	1410.400024	1388.099976	1393.900024	12031440	0.0	0.0
2026-03-02 00:00:00+05:30	1375.500000	1378.599976	1341.500000	1358.000000	23728722	0.0	0.0

248 rows × 7 columns

type(rel1yr)

pandas.DataFrame

rel1yr.Close.std

<bound method Series.std of Date
2025-03-03 00:00:00+05:30    1166.590698
2025-03-04 00:00:00+05:30    1157.277954
2025-03-05 00:00:00+05:30    1170.923462
2025-03-06 00:00:00+05:30    1204.788208
2025-03-07 00:00:00+05:30    1244.828369
                                ...     
2026-02-24 00:00:00+05:30    1428.800049
2026-02-25 00:00:00+05:30    1398.500000
2026-02-26 00:00:00+05:30    1406.800049
2026-02-27 00:00:00+05:30    1393.900024
2026-03-02 00:00:00+05:30    1358.000000
Name: Close, Length: 248, dtype: float64>

infy1yr = yf.Ticker("INFY.NS").history(period="1y")

rel1yr

	Open	High	Low	Close	Volume	Dividends	Stock Splits
Date
2025-03-03 00:00:00+05:30	1199.210417	1201.650622	1151.401364	1166.590698	17944938	0.0	0.0
2025-03-04 00:00:00+05:30	1157.576688	1169.329796	1154.937327	1157.277954	11377373	0.0	0.0
2025-03-05 00:00:00+05:30	1156.381565	1178.294049	1152.397477	1170.923462	8664095	0.0	0.0
2025-03-06 00:00:00+05:30	1192.238355	1209.120879	1180.435518	1204.788208	14468014	0.0	0.0
2025-03-07 00:00:00+05:30	1211.162776	1249.808479	1207.178688	1244.828369	16474965	0.0	0.0
...	...	...	...	...	...	...	...
2026-02-24 00:00:00+05:30	1425.300049	1433.300049	1415.000000	1428.800049	12529409	0.0	0.0
2026-02-25 00:00:00+05:30	1435.000000	1440.500000	1393.500000	1398.500000	10728792	0.0	0.0
2026-02-26 00:00:00+05:30	1398.500000	1412.900024	1391.900024	1406.800049	16683858	0.0	0.0
2026-02-27 00:00:00+05:30	1398.000000	1410.400024	1388.099976	1393.900024	12031440	0.0	0.0
2026-03-02 00:00:00+05:30	1375.500000	1378.599976	1341.500000	1358.000000	23728722	0.0	0.0

248 rows × 7 columns

infy1yr

	Open	High	Low	Close	Volume	Dividends	Stock Splits
Date
2025-03-03 00:00:00+05:30	1643.657119	1678.913652	1643.657119	1659.488525	7504969	0.0	0.0
2025-03-04 00:00:00+05:30	1646.279465	1650.164490	1621.998057	1639.772095	6759673	0.0	0.0
2025-03-05 00:00:00+05:30	1643.802696	1683.138576	1643.802696	1662.305176	8180782	0.0	0.0
2025-03-06 00:00:00+05:30	1665.704607	1672.794826	1642.491557	1666.530151	10610547	0.0	0.0
2025-03-07 00:00:00+05:30	1654.146545	1655.991955	1627.339895	1637.538086	8019331	0.0	0.0
...	...	...	...	...	...	...	...
2026-02-24 00:00:00+05:30	1301.000000	1303.599976	1264.099976	1275.500000	27839061	0.0	0.0
2026-02-25 00:00:00+05:30	1286.199951	1323.599976	1286.199951	1290.099976	16669618	0.0	0.0
2026-02-26 00:00:00+05:30	1305.500000	1314.000000	1286.300049	1289.099976	11493914	0.0	0.0
2026-02-27 00:00:00+05:30	1326.300049	1335.000000	1294.199951	1300.099976	23141724	0.0	0.0
2026-03-02 00:00:00+05:30	1291.000000	1298.800049	1273.000000	1288.900024	9700361	0.0	0.0

248 rows × 7 columns

together = pd.concat([rel1yr, infy1yr])

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[18], line 1
----> 1 together = pd.concat([rel1yr, infy1yr])

NameError: name 'pd' is not defined

import pandas as pd

together = pd.concat([rel1yr, infy1yr])

together

	Open	High	Low	Close	Volume	Dividends	Stock Splits
Date
2025-03-03 00:00:00+05:30	1199.210417	1201.650622	1151.401364	1166.590698	17944938	0.0	0.0
2025-03-04 00:00:00+05:30	1157.576688	1169.329796	1154.937327	1157.277954	11377373	0.0	0.0
2025-03-05 00:00:00+05:30	1156.381565	1178.294049	1152.397477	1170.923462	8664095	0.0	0.0
2025-03-06 00:00:00+05:30	1192.238355	1209.120879	1180.435518	1204.788208	14468014	0.0	0.0
2025-03-07 00:00:00+05:30	1211.162776	1249.808479	1207.178688	1244.828369	16474965	0.0	0.0
...	...	...	...	...	...	...	...
2026-02-24 00:00:00+05:30	1301.000000	1303.599976	1264.099976	1275.500000	27839061	0.0	0.0
2026-02-25 00:00:00+05:30	1286.199951	1323.599976	1286.199951	1290.099976	16669618	0.0	0.0
2026-02-26 00:00:00+05:30	1305.500000	1314.000000	1286.300049	1289.099976	11493914	0.0	0.0
2026-02-27 00:00:00+05:30	1326.300049	1335.000000	1294.199951	1300.099976	23141724	0.0	0.0
2026-03-02 00:00:00+05:30	1291.000000	1298.800049	1273.000000	1288.900024	9700361	0.0	0.0

496 rows × 7 columns

We lost track of ticker!

rel1yr

	Open	High	Low	Close	Volume	Dividends	Stock Splits
Date
2025-03-03 00:00:00+05:30	1199.210417	1201.650622	1151.401364	1166.590698	17944938	0.0	0.0
2025-03-04 00:00:00+05:30	1157.576688	1169.329796	1154.937327	1157.277954	11377373	0.0	0.0
2025-03-05 00:00:00+05:30	1156.381565	1178.294049	1152.397477	1170.923462	8664095	0.0	0.0
2025-03-06 00:00:00+05:30	1192.238355	1209.120879	1180.435518	1204.788208	14468014	0.0	0.0
2025-03-07 00:00:00+05:30	1211.162776	1249.808479	1207.178688	1244.828369	16474965	0.0	0.0
...	...	...	...	...	...	...	...
2026-02-24 00:00:00+05:30	1425.300049	1433.300049	1415.000000	1428.800049	12529409	0.0	0.0
2026-02-25 00:00:00+05:30	1435.000000	1440.500000	1393.500000	1398.500000	10728792	0.0	0.0
2026-02-26 00:00:00+05:30	1398.500000	1412.900024	1391.900024	1406.800049	16683858	0.0	0.0
2026-02-27 00:00:00+05:30	1398.000000	1410.400024	1388.099976	1393.900024	12031440	0.0	0.0
2026-03-02 00:00:00+05:30	1375.500000	1378.599976	1341.500000	1358.000000	23728722	0.0	0.0

248 rows × 7 columns

rel1yr['Symbol'] = 'RELIANCE.NS'

rel1yr

	Open	High	Low	Close	Volume	Dividends	Stock Splits	Symbol
Date
2025-03-03 00:00:00+05:30	1199.210417	1201.650622	1151.401364	1166.590698	17944938	0.0	0.0	RELIANCE.NS
2025-03-04 00:00:00+05:30	1157.576688	1169.329796	1154.937327	1157.277954	11377373	0.0	0.0	RELIANCE.NS
2025-03-05 00:00:00+05:30	1156.381565	1178.294049	1152.397477	1170.923462	8664095	0.0	0.0	RELIANCE.NS
2025-03-06 00:00:00+05:30	1192.238355	1209.120879	1180.435518	1204.788208	14468014	0.0	0.0	RELIANCE.NS
2025-03-07 00:00:00+05:30	1211.162776	1249.808479	1207.178688	1244.828369	16474965	0.0	0.0	RELIANCE.NS
...	...	...	...	...	...	...	...	...
2026-02-24 00:00:00+05:30	1425.300049	1433.300049	1415.000000	1428.800049	12529409	0.0	0.0	RELIANCE.NS
2026-02-25 00:00:00+05:30	1435.000000	1440.500000	1393.500000	1398.500000	10728792	0.0	0.0	RELIANCE.NS
2026-02-26 00:00:00+05:30	1398.500000	1412.900024	1391.900024	1406.800049	16683858	0.0	0.0	RELIANCE.NS
2026-02-27 00:00:00+05:30	1398.000000	1410.400024	1388.099976	1393.900024	12031440	0.0	0.0	RELIANCE.NS
2026-03-02 00:00:00+05:30	1375.500000	1378.599976	1341.500000	1358.000000	23728722	0.0	0.0	RELIANCE.NS

248 rows × 8 columns

infy1yr['Symbol'] = "INFY.NS"

both  = pd.concat([rel1yr, infy1yr])

both

	Open	High	Low	Close	Volume	Dividends	Stock Splits	Symbol
Date
2025-03-03 00:00:00+05:30	1199.210417	1201.650622	1151.401364	1166.590698	17944938	0.0	0.0	RELIANCE.NS
2025-03-04 00:00:00+05:30	1157.576688	1169.329796	1154.937327	1157.277954	11377373	0.0	0.0	RELIANCE.NS
2025-03-05 00:00:00+05:30	1156.381565	1178.294049	1152.397477	1170.923462	8664095	0.0	0.0	RELIANCE.NS
2025-03-06 00:00:00+05:30	1192.238355	1209.120879	1180.435518	1204.788208	14468014	0.0	0.0	RELIANCE.NS
2025-03-07 00:00:00+05:30	1211.162776	1249.808479	1207.178688	1244.828369	16474965	0.0	0.0	RELIANCE.NS
...	...	...	...	...	...	...	...	...
2026-02-24 00:00:00+05:30	1301.000000	1303.599976	1264.099976	1275.500000	27839061	0.0	0.0	INFY.NS
2026-02-25 00:00:00+05:30	1286.199951	1323.599976	1286.199951	1290.099976	16669618	0.0	0.0	INFY.NS
2026-02-26 00:00:00+05:30	1305.500000	1314.000000	1286.300049	1289.099976	11493914	0.0	0.0	INFY.NS
2026-02-27 00:00:00+05:30	1326.300049	1335.000000	1294.199951	1300.099976	23141724	0.0	0.0	INFY.NS
2026-03-02 00:00:00+05:30	1291.000000	1298.800049	1273.000000	1288.900024	9700361	0.0	0.0	INFY.NS

496 rows × 8 columns

altair ploting

!pip install altair

Requirement already satisfied: altair in /home/vikrant/usr/local/default/lib/python3.13/site-packages (6.0.0)
Requirement already satisfied: jinja2 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from altair) (3.1.6)
Requirement already satisfied: jsonschema>=3.0 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from altair) (4.25.1)
Requirement already satisfied: narwhals>=1.27.1 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from altair) (2.17.0)
Requirement already satisfied: packaging in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from altair) (25.0)
Requirement already satisfied: typing-extensions>=4.12.0 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from altair) (4.15.0)
Requirement already satisfied: attrs>=22.2.0 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from jsonschema>=3.0->altair) (25.4.0)
Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from jsonschema>=3.0->altair) (2025.9.1)
Requirement already satisfied: referencing>=0.28.4 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from jsonschema>=3.0->altair) (0.37.0)
Requirement already satisfied: rpds-py>=0.7.1 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from jsonschema>=3.0->altair) (0.30.0)
Requirement already satisfied: MarkupSafe>=2.0 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from jinja2->altair) (3.0.3)

import altair as alt

alt.Chart(rel1yr.reset_index()).mark_line().encode(
    x = 'Date',
    y = "Close"
)

alt.Chart(rel1yr.reset_index()).mark_circle().encode(
    x = 'Date',
    y = "Close"
)

alt.Chart(rel1yr.reset_index()).mark_bar().encode(
    x = 'Date',
    y = "Close"
)

alt.Chart(rel1yr.reset_index()).mark_area().encode(
    x = 'Date',
    y = "Close"
)

alt.Chart(both.reset_index()).mark_line().encode(
    x = 'Date',
    y = 'Close',
    color = 'Symbol'
)

def get_data(tickers, period="1y"):
    dfs = []
    for t in tickers:
        d = yf.Ticker(t).history(period=period)
        d['Symbol'] = t
    return pd.concat(dfs)

def get_data(tickers, period="1y"):
    dfs = [yf.Ticker(t).history(period=period) for t in tickers]
    for d, t in zip(dfs, tickers):
        d['Symbol'] = t
    return pd.concat(dfs)

data = get_data(['RELIANCE.NS','INFY.NS','TMPV.NS'])

data

	Open	High	Low	Close	Volume	Dividends	Stock Splits	Symbol
Date
2025-03-03 00:00:00+05:30	1199.210417	1201.650622	1151.401364	1166.590698	17944938	0.0	0.0	RELIANCE.NS
2025-03-04 00:00:00+05:30	1157.576688	1169.329796	1154.937327	1157.277954	11377373	0.0	0.0	RELIANCE.NS
2025-03-05 00:00:00+05:30	1156.381565	1178.294049	1152.397477	1170.923462	8664095	0.0	0.0	RELIANCE.NS
2025-03-06 00:00:00+05:30	1192.238355	1209.120879	1180.435518	1204.788208	14468014	0.0	0.0	RELIANCE.NS
2025-03-07 00:00:00+05:30	1211.162776	1249.808479	1207.178688	1244.828369	16474965	0.0	0.0	RELIANCE.NS
...	...	...	...	...	...	...	...	...
2026-02-24 00:00:00+05:30	376.850006	380.299988	371.649994	377.549988	7213746	0.0	0.0	TMPV.NS
2026-02-25 00:00:00+05:30	378.000000	385.600006	376.100006	381.850006	7748663	0.0	0.0	TMPV.NS
2026-02-26 00:00:00+05:30	381.700012	393.299988	380.100006	391.549988	14697480	0.0	0.0	TMPV.NS
2026-02-27 00:00:00+05:30	393.649994	394.049988	380.950012	382.649994	15951040	0.0	0.0	TMPV.NS
2026-03-02 00:00:00+05:30	358.000000	378.399994	355.500000	370.600006	11804900	0.0	0.0	TMPV.NS

590 rows × 8 columns

def lineplot_stocks(data, column):
    c = alt.Chart(data.reset_index()).mark_line().encode(
            x = 'Date',
            y = 'Close',
            color = 'Symbol'
    )
    return c

lineplot_stocks(data, 'Close')

yf.Ticker("TMPV.NS").history(period="1y")

TMPV.NS: Period '1yr' is invalid, must be one of: 1d, 5d, 1mo, 3mo, 6mo, ytd, max

	Open	High	Low	Close	Adj Close	Volume
Date

yf.Ticker("TMPV.NS").history(period="1y")

	Open	High	Low	Close	Volume	Dividends	Stock Splits
Date
2025-10-15 00:00:00+05:30	403.000000	403.000000	388.000000	390.850006	26953550	0.0	0.0
2025-10-16 00:00:00+05:30	395.450012	400.450012	389.549988	396.799988	21983750	0.0	0.0
2025-10-17 00:00:00+05:30	396.799988	402.500000	392.250000	396.600006	16564540	0.0	0.0
2025-10-20 00:00:00+05:30	400.049988	403.399994	396.600006	399.750000	10471670	0.0	0.0
2025-10-21 00:00:00+05:30	402.799988	403.399994	400.049988	401.850006	3449156	0.0	0.0
...	...	...	...	...	...	...	...
2026-02-24 00:00:00+05:30	376.850006	380.299988	371.649994	377.549988	7213746	0.0	0.0
2026-02-25 00:00:00+05:30	378.000000	385.600006	376.100006	381.850006	7748663	0.0	0.0
2026-02-26 00:00:00+05:30	381.700012	393.299988	380.100006	391.549988	14697480	0.0	0.0
2026-02-27 00:00:00+05:30	393.649994	394.049988	380.950012	382.649994	15951040	0.0	0.0
2026-03-02 00:00:00+05:30	358.000000	378.399994	355.500000	370.600006	11804900	0.0	0.0

94 rows × 7 columns

!pip install nsetools

Collecting nsetools

  Downloading nsetools-2.0.1-py3-none-any.whl.metadata (15 kB)

Requirement already satisfied: six in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from nsetools) (1.17.0)

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Downloading nsetools-2.0.1-py3-none-any.whl (23 kB)

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Installing collected packages: dateutils, nsetools

   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2/2 [nsetools]

Successfully installed dateutils-0.6.12 nsetools-2.0.1

import nsetools as nst

nse = nst.Nse() 

tickers= nse.get_stock_codes()

jitickers = [f"{j}.NS" for j in tickers if j.startswith("JI")]

jidata = get_data(jitickers)
lineplot_stocks(jidata, column="Close")

Example

calculate percentage change in prices for following tickers in year 2021

tickers_dict = {'INTC': 'Intel',
               'MSFT': 'Microsoft',
               'IBM': 'IBM',
               'BHP': 'BHP',
               'TM': 'Toyota',
               'AAPL': 'Apple',
               'AMZN': 'Amazon',
               'C': 'Citigroup',
               'QCOM': 'Qualcomm',
               'KO': 'Coca-Cola',
               'GOOG': 'Google'}

def fetch_data(tickers_dict, start, end):
    dfs = []
    for t, company in tickers_dict.items(): # we will iteartor over keys and values
        d = yf.Ticker(t).history(start=start, end=end)
        d['Symbol'] = company
        dfs.append(d)
    return pd.concat(dfs)

import datetime

start = datetime.datetime(2021, 1, 1)
end = datetime.datetime(2021, 12, 31)

data = fetch_data(tickers_dict, start, end)

d = yf.Ticker("INTC").history(start=start, end=end)

d

	Open	High	Low	Close	Volume	Dividends	Stock Splits
Date
2021-01-04 00:00:00-05:00	45.101818	46.457856	44.658847	44.902931	46102500	0.0	0.0
2021-01-05 00:00:00-05:00	44.704054	45.951610	44.595572	45.752724	24866600	0.0	0.0
2021-01-06 00:00:00-05:00	45.599037	47.144923	45.382073	46.195694	36809600	0.0	0.0
2021-01-07 00:00:00-05:00	46.548269	47.633096	46.376502	47.181084	32092300	0.0	0.0
2021-01-08 00:00:00-05:00	47.416137	47.488457	46.349386	46.692917	34625400	0.0	0.0
...	...	...	...	...	...	...	...
2021-12-23 00:00:00-05:00	47.374982	47.977833	47.347156	47.588299	25198200	0.0	0.0
2021-12-27 00:00:00-05:00	47.625398	48.283902	47.579026	48.172604	20985400	0.0	0.0
2021-12-28 00:00:00-05:00	48.163328	48.339546	47.885089	48.005657	19290400	0.0	0.0
2021-12-29 00:00:00-05:00	48.052037	48.358099	47.912916	48.070587	14174800	0.0	0.0
2021-12-30 00:00:00-05:00	48.154055	48.358098	47.903642	47.987114	18359400	0.0	0.0

251 rows × 7 columns

data

	Open	High	Low	Close	Volume	Dividends	Stock Splits	Symbol
Date
2021-01-04 00:00:00-05:00	45.101818	46.457856	44.658847	44.902931	46102500	0.0	0.0	Intel
2021-01-05 00:00:00-05:00	44.704054	45.951610	44.595572	45.752724	24866600	0.0	0.0	Intel
2021-01-06 00:00:00-05:00	45.599037	47.144923	45.382073	46.195694	36809600	0.0	0.0	Intel
2021-01-07 00:00:00-05:00	46.548269	47.633096	46.376502	47.181084	32092300	0.0	0.0	Intel
2021-01-08 00:00:00-05:00	47.416137	47.488457	46.349386	46.692917	34625400	0.0	0.0	Intel
...	...	...	...	...	...	...	...	...
2021-12-23 00:00:00-05:00	145.994520	147.466588	145.856911	146.047134	13818000	0.0	0.0	Google
2021-12-27 00:00:00-05:00	146.365753	147.321581	146.153841	146.961777	13256000	0.0	0.0	Google
2021-12-28 00:00:00-05:00	147.269930	147.269930	144.849091	145.357773	18624000	0.0	0.0	Google
2021-12-29 00:00:00-05:00	145.339441	146.088068	144.421324	145.413879	17022000	0.0	0.0	Google
2021-12-30 00:00:00-05:00	145.359767	145.967710	144.673417	144.915604	12978000	0.0	0.0	Google

2761 rows × 8 columns

lineplot_stocks(data, column='Volume')

data.reset_index().groupby("Symbol").first()

	Date	Open	High	Low	Close	Volume	Dividends	Stock Splits
Symbol
Amazon	2021-01-04 00:00:00-05:00	163.500000	163.600006	157.201004	159.331497	88228000	0.0	0.0
Apple	2021-01-04 00:00:00-05:00	129.853862	129.941387	123.279474	125.856712	143301900	0.0	0.0
BHP	2021-01-04 00:00:00-05:00	43.681438	43.848454	43.148268	43.263893	3227471	0.0	0.0
Citigroup	2021-01-04 00:00:00-05:00	51.251057	51.350912	49.703290	50.044464	21286200	0.0	0.0
Coca-Cola	2021-01-04 00:00:00-05:00	46.691080	47.000805	44.763899	45.391953	25611100	0.0	0.0
Google	2021-01-04 00:00:00-05:00	87.222813	87.377158	84.756811	85.768723	38038000	0.0	0.0
IBM	2021-01-04 00:00:00-05:00	97.265757	97.319854	95.093991	95.789574	5417443	0.0	0.0
Intel	2021-01-04 00:00:00-05:00	45.101818	46.457856	44.658847	44.902931	46102500	0.0	0.0
Microsoft	2021-01-04 00:00:00-05:00	213.040133	213.490091	205.649354	208.406540	37130100	0.0	0.0
Qualcomm	2021-01-04 00:00:00-05:00	137.387108	139.308606	132.116461	133.337601	9827000	0.0	0.0
Toyota	2021-01-04 00:00:00-05:00	137.289771	137.289771	133.950273	135.069305	315600	0.0	0.0

data.reset_index().groupby("Symbol").last()

	Date	Open	High	Low	Close	Volume	Dividends	Stock Splits
Symbol
Amazon	2021-12-30 00:00:00-05:00	169.699997	170.888000	168.524002	168.644501	37584000	0.0	0.0
Apple	2021-12-30 00:00:00-05:00	175.618190	176.694587	174.267803	174.375443	59773000	0.0	0.0
BHP	2021-12-30 00:00:00-05:00	42.669834	42.782120	42.262786	42.290855	2306570	0.0	0.0
Citigroup	2021-12-30 00:00:00-05:00	51.984263	52.370158	51.787029	51.821331	11380500	0.0	0.0
Coca-Cola	2021-12-30 00:00:00-05:00	52.414075	52.564944	52.147831	52.165581	7703900	0.0	0.0
Google	2021-12-30 00:00:00-05:00	145.359767	145.967710	144.673417	144.915604	12978000	0.0	0.0
IBM	2021-12-30 00:00:00-05:00	113.566732	114.093168	113.210113	113.702591	3158100	0.0	0.0
Intel	2021-12-30 00:00:00-05:00	48.154055	48.358098	47.903642	47.987114	18359400	0.0	0.0
Microsoft	2021-12-30 00:00:00-05:00	330.072962	331.250726	327.089942	327.572632	15994500	0.0	0.0
Qualcomm	2021-12-30 00:00:00-05:00	169.977254	170.562499	166.712675	167.096741	4108400	0.0	0.0
Toyota	2021-12-30 00:00:00-05:00	166.870227	167.286784	166.363108	166.698166	123200	0.0	0.0

initial = data.reset_index().groupby("Symbol").first()
final = data.reset_index().groupby("Symbol").last()

percentage_change = (final.Close - initial.Close)/initial.Close*100

percentage_change

Symbol
Amazon        5.845049
Apple        38.550769
BHP          -2.249076
Citigroup     3.550576
Coca-Cola    14.922531
Google       68.960898
IBM          18.700383
Intel         6.868555
Microsoft    57.179632
Qualcomm     25.318545
Toyota       23.416764
Name: Close, dtype: float64

final['percentage_change'] = percentage_change

final

	Date	Open	High	Low	Close	Volume	Dividends	Stock Splits	percentage_change
Symbol
Amazon	2021-12-30 00:00:00-05:00	169.699997	170.888000	168.524002	168.644501	37584000	0.0	0.0	5.845049
Apple	2021-12-30 00:00:00-05:00	175.618190	176.694587	174.267803	174.375443	59773000	0.0	0.0	38.550769
BHP	2021-12-30 00:00:00-05:00	42.669834	42.782120	42.262786	42.290855	2306570	0.0	0.0	-2.249076
Citigroup	2021-12-30 00:00:00-05:00	51.984263	52.370158	51.787029	51.821331	11380500	0.0	0.0	3.550576
Coca-Cola	2021-12-30 00:00:00-05:00	52.414075	52.564944	52.147831	52.165581	7703900	0.0	0.0	14.922531
Google	2021-12-30 00:00:00-05:00	145.359767	145.967710	144.673417	144.915604	12978000	0.0	0.0	68.960898
IBM	2021-12-30 00:00:00-05:00	113.566732	114.093168	113.210113	113.702591	3158100	0.0	0.0	18.700383
Intel	2021-12-30 00:00:00-05:00	48.154055	48.358098	47.903642	47.987114	18359400	0.0	0.0	6.868555
Microsoft	2021-12-30 00:00:00-05:00	330.072962	331.250726	327.089942	327.572632	15994500	0.0	0.0	57.179632
Qualcomm	2021-12-30 00:00:00-05:00	169.977254	170.562499	166.712675	167.096741	4108400	0.0	0.0	25.318545
Toyota	2021-12-30 00:00:00-05:00	166.870227	167.286784	166.363108	166.698166	123200	0.0	0.0	23.416764

alt.Chart(final.reset_index()).mark_bar().encode(
    x = 'Symbol',
    y = 'percentage_change')

def plot_percentage_change(data):
    initial = data.reset_index().groupby("Symbol").first()
    final = data.reset_index().groupby("Symbol").last()
    percentage_change = (final.Close - initial.Close)/initial.Close*100
    final['percentage_change'] = percentage_change
    return alt.Chart(final.reset_index()).mark_bar().encode(
        x = 'Symbol',
        y = 'percentage_change')
    

plot_percentage_change(data)

Yearwise percentage change

inputs
 - tickers
 - years

- get_data for given years
- compute percenatge change for each year

def get_data_yearwise(tickers, year):
    start = datetime.datetime(year, 1, 1)
    end = datetime.datetime(year, 12, 31)
    dfs = []
    for t, company in tickers.items():
        d = yf.Ticker(t).history(start=start, end=end)
        d['Symbol'] = company
        d['Year'] = year
        dfs.append(d)
    return pd.concat(dfs)


def get_data_all_years(tickers, years):
    dfs = [get_data_yearwise(tickers, y) for y in years]
    return pd.concat(dfs)

def compute_percentage(allyears_data):
    grpby = allyears_data.groupby(['Year', 'Symbol'])
    percentage = (grpby.Close.last() - grpby.Close.first()) / grpby.Close.first() * 100
    percentage.name = 'percentage'
    return percentage.reset_index()

tickers_dict

{'INTC': 'Intel',
 'MSFT': 'Microsoft',
 'IBM': 'IBM',
 'BHP': 'BHP',
 'TM': 'Toyota',
 'AAPL': 'Apple',
 'AMZN': 'Amazon',
 'C': 'Citigroup',
 'QCOM': 'Qualcomm',
 'KO': 'Coca-Cola',
 'GOOG': 'Google'}

years = list(range(2015, 2025))

years

[2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024]

alldata = get_data_all_years(tickers_dict, years)

compute_percentage(alldata)

	Year	Symbol	percentage
0	2015	Amazon	123.346944
1	2015	Apple	-0.165979
2	2015	BHP	-38.584500
3	2015	Citigroup	-3.330121
4	2015	Coca-Cola	6.835213
...	...	...	...
105	2024	IBM	41.307768
106	2024	Intel	-57.980238
107	2024	Microsoft	15.406165
108	2024	Qualcomm	12.407000
109	2024	Toyota	10.683975

110 rows × 3 columns

p = compute_percentage(alldata)

alt.Chart(p).mark_line().encode(
    x = 'Year:N',
    y = 'percentage',
    color = 'Symbol').properties(
    height = 400,
    width = 600)