Open In Colab

Pandas functions for Time Series data

import pandas as pd
from datetime import datetime

Date Time Index

my_dates = ["04/07/2025","05/07/2025","06/07/2025"]
my_dates
['04/07/2025', '05/07/2025', '06/07/2025']
a= pd.to_datetime(my_dates, format="%d/%m/%Y")
a
DatetimeIndex(['2025-07-04', '2025-07-05', '2025-07-06'], dtype='datetime64[ns]', freq=None)
a.month
Index([7, 7, 7], dtype='int32')
a.day
Index([4, 5, 6], dtype='int32')
a.year
Index([2025, 2025, 2025], dtype='int32')
a.hour
Index([0, 0, 0], dtype='int32')
datetime.strptime('31/01/22 23:59:59.999999','%d/%m/%y %H:%M:%S.%f')
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
/tmp/ipython-input-3468975073.py in <cell line: 0>()
----> 1 datetime.strptime('31/01/22 23:59:59.999999','%d/%m/%y %H:%M:%S.%f')

NameError: name 'datetime' is not defined
import gdown
# https://drive.google.com/file/d/1ugXf9514sOZx5izMY7Mt6_HX8doCQLcO/view?usp=sharing
gdown.download("https://drive.google.com/uc?id=1ugXf9514sOZx5izMY7Mt6_HX8doCQLcO")
Downloading...
From: https://drive.google.com/uc?id=1ugXf9514sOZx5izMY7Mt6_HX8doCQLcO
To: /content/Preprocessing3.csv
100%|██████████| 919k/919k [00:00<00:00, 78.0MB/s]
'Preprocessing3.csv'
df = pd.read_csv("/content/Preprocessing3.csv")
df.head()
Date Year Locality Estimated Value Sale Price Property Residential num_rooms num_bathrooms carpet_area property_tax_rate Face
0 2009-01-02 2009 Greenwich NaN 5187000.0 ? Detached House 3 2 1026.0 1.025953 South
1 2009-01-02 2009 Norwalk NaN 480000.0 Single Family Detached House 3 2 1051.0 1.025953 West
2 2009-01-02 2009 Waterbury 57890.0 152000.0 Single Family Detached House 3 2 943.0 1.025953 East
3 2009-01-02 2009 NaN 44520.0 60000.0 Single Family Detached House 3 2 1099.0 1.025953 North
4 2009-01-03 2009 Bridgeport 91071.0 250000.0 Two Family Duplex 4 2 1213.0 1.025953 South 
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10000 entries, 0 to 9999
Data columns (total 12 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   Date               10000 non-null  object 
 1   Year               10000 non-null  int64  
 2   Locality           8715 non-null   object 
 3   Estimated Value    8719 non-null   float64
 4   Sale Price         10000 non-null  float64
 5   Property           10000 non-null  object 
 6   Residential        10000 non-null  object 
 7   num_rooms          10000 non-null  int64  
 8   num_bathrooms      10000 non-null  int64  
 9   carpet_area        8718 non-null   float64
 10  property_tax_rate  10000 non-null  float64
 11  Face               10000 non-null  object 
dtypes: float64(4), int64(3), object(5)
memory usage: 937.6+ KB
df["Date"] = pd.to_datetime(df["Date"])
df['Date'].max()
Timestamp('2022-09-30 00:00:00')
df["Date"].min()
Timestamp('2009-01-02 00:00:00')
df["Date"].dt.day
Date
0 2
1 2
2 2
3 2
4 3
... ...
9995 30
9996 30
9997 30
9998 30
9999 30

10000 rows × 1 columns


take date feature and extract day month year from that

new_df = pd.DataFrame(df["Date"])
new_df.head()
Date
0 2009-01-02
1 2009-01-02
2 2009-01-02
3 2009-01-02
4 2009-01-03 
new_df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10000 entries, 0 to 9999
Data columns (total 1 columns):
 #   Column  Non-Null Count  Dtype         
---  ------  --------------  -----         
 0   Date    10000 non-null  datetime64[ns]
dtypes: datetime64[ns](1)
memory usage: 78.3 KB
new_df["day"] = new_df["Date"].dt.day
new_df["month"] = new_df["Date"].dt.month
new_df["year"] = new_df["Date"].dt.year
new_df
Date day month year
0 2009-01-02 2 1 2009
1 2009-01-02 2 1 2009
2 2009-01-02 2 1 2009
3 2009-01-02 2 1 2009
4 2009-01-03 3 1 2009
... ... ... ... ...
9995 2022-09-30 30 9 2022
9996 2022-09-30 30 9 2022
9997 2022-09-30 30 9 2022
9998 2022-09-30 30 9 2022
9999 2022-09-30 30 9 2022

10000 rows × 4 columns

df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 144 entries, 0 to 143
Data columns (total 2 columns):
 #   Column                   Non-Null Count  Dtype         
---  ------                   --------------  -----         
 0   Month                    144 non-null    datetime64[ns]
 1   Thousands of Passengers  144 non-null    int64         
dtypes: datetime64[ns](1), int64(1)
memory usage: 2.4 KB
import yfinance as yf
ticker_symbol = 'RELIANCE.NS'
stock_data = yf.download(ticker_symbol, start='2024-01-01', end='2025-08-01', interval="1d")
stock_data
/tmp/ipython-input-65770057.py:3: FutureWarning: YF.download() has changed argument auto_adjust default to True
  stock_data = yf.download(ticker_symbol, start='2024-01-01', end='2025-08-01', interval="1d")
[*********************100%***********************]  1 of 1 completed
Price Close High Low Open Volume
Ticker RELIANCE.NS RELIANCE.NS RELIANCE.NS RELIANCE.NS RELIANCE.NS
Date
2024-01-01 1290.744263 1299.016237 1282.223134 1285.910692 4030540
2024-01-02 1301.432983 1303.077427 1282.148458 1288.128164 7448800
2024-01-03 1287.281006 1312.545235 1284.241274 1300.585825 9037536
2024-01-04 1293.933350 1300.511122 1285.188128 1289.623028 9612778
2024-01-05 1299.439697 1305.494222 1294.606127 1297.047791 8086406
... ... ... ... ... ...
2025-07-25 1391.699951 1401.000000 1384.099976 1398.900024 11854722
2025-07-28 1387.599976 1407.800049 1385.000000 1392.300049 7748361
2025-07-29 1417.099976 1420.199951 1383.000000 1383.000000 10750072
2025-07-30 1410.099976 1423.300049 1401.300049 1418.099976 7209849
2025-07-31 1390.199951 1402.599976 1382.199951 1388.099976 17065827

392 rows × 5 columns

import yfinance as yf
ticker_symbol = 'RELIANCE.NS'
stock_data = yf.download(ticker_symbol, period="3mo", interval="1d")
stock_data
/tmp/ipython-input-1785857247.py:3: FutureWarning:

YF.download() has changed argument auto_adjust default to True

[*********************100%***********************]  1 of 1 completed
Price Close High Low Open Volume
Ticker RELIANCE.NS RELIANCE.NS RELIANCE.NS RELIANCE.NS RELIANCE.NS
Date
2025-05-02 1419.800049 1426.699951 1409.099976 1414.000000 13007841
2025-05-05 1431.300049 1439.500000 1426.900024 1431.000000 12685649
2025-05-06 1420.900024 1432.000000 1410.599976 1431.000000 14084117
2025-05-07 1406.000000 1424.400024 1402.699951 1420.900024 13440169
2025-05-08 1407.000000 1420.800049 1398.000000 1404.099976 16106175
... ... ... ... ... ...
2025-07-28 1387.599976 1407.800049 1385.000000 1392.300049 7748361
2025-07-29 1417.099976 1420.199951 1383.000000 1383.000000 10750072
2025-07-30 1410.099976 1423.300049 1401.300049 1418.099976 7209849
2025-07-31 1390.199951 1402.599976 1382.199951 1388.099976 17065827
2025-08-01 1393.699951 1405.900024 1384.300049 1386.900024 10321171

66 rows × 5 columns

stock_data["Close"].plot()

!pip install mplfinance
Collecting mplfinance

  Downloading mplfinance-0.12.10b0-py3-none-any.whl.metadata (19 kB)

Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (from mplfinance) (3.10.0)

Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (from mplfinance) (2.2.2)

Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->mplfinance) (1.3.2)

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Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->mplfinance) (1.4.8)

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Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.7->matplotlib->mplfinance) (1.17.0)

Downloading mplfinance-0.12.10b0-py3-none-any.whl (75 kB)

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Installing collected packages: mplfinance

Successfully installed mplfinance-0.12.10b0

import yfinance as yf
import mplfinance as mpf
import pandas as pd

# Download stock data
ticker_symbol = 'RELIANCE.NS'
stock_data = yf.download(ticker_symbol, period="3mo", interval="1d")
stock_data
/tmp/ipython-input-3328782981.py:7: FutureWarning:

YF.download() has changed argument auto_adjust default to True

[*********************100%***********************]  1 of 1 completed
Price Close High Low Open Volume
Ticker RELIANCE.NS RELIANCE.NS RELIANCE.NS RELIANCE.NS RELIANCE.NS
Date
2025-05-02 1419.800049 1426.699951 1409.099976 1414.000000 13007841
2025-05-05 1431.300049 1439.500000 1426.900024 1431.000000 12685649
2025-05-06 1420.900024 1432.000000 1410.599976 1431.000000 14084117
2025-05-07 1406.000000 1424.400024 1402.699951 1420.900024 13440169
2025-05-08 1407.000000 1420.800049 1398.000000 1404.099976 16106175
... ... ... ... ... ...
2025-07-28 1387.599976 1407.800049 1385.000000 1392.300049 7748361
2025-07-29 1417.099976 1420.199951 1383.000000 1383.000000 10750072
2025-07-30 1410.099976 1423.300049 1401.300049 1418.099976 7209849
2025-07-31 1390.199951 1402.599976 1382.199951 1388.099976 17065827
2025-08-01 1393.699951 1405.900024 1384.300049 1386.900024 10321171

66 rows × 5 columns

stock_data.isna().sum()
0
Price Ticker
Close RELIANCE.NS 0
High RELIANCE.NS 0
Low RELIANCE.NS 0
Open RELIANCE.NS 0
Volume RELIANCE.NS 0 

stock_data.index
DatetimeIndex(['2025-05-02', '2025-05-05', '2025-05-06', '2025-05-07',
               '2025-05-08', '2025-05-09', '2025-05-12', '2025-05-13',
               '2025-05-14', '2025-05-15', '2025-05-16', '2025-05-19',
               '2025-05-20', '2025-05-21', '2025-05-22', '2025-05-23',
               '2025-05-26', '2025-05-27', '2025-05-28', '2025-05-29',
               '2025-05-30', '2025-06-02', '2025-06-03', '2025-06-04',
               '2025-06-05', '2025-06-06', '2025-06-09', '2025-06-10',
               '2025-06-11', '2025-06-12', '2025-06-13', '2025-06-16',
               '2025-06-17', '2025-06-18', '2025-06-19', '2025-06-20',
               '2025-06-23', '2025-06-24', '2025-06-25', '2025-06-26',
               '2025-06-27', '2025-06-30', '2025-07-01', '2025-07-02',
               '2025-07-03', '2025-07-04', '2025-07-07', '2025-07-08',
               '2025-07-09', '2025-07-10', '2025-07-11', '2025-07-14',
               '2025-07-15', '2025-07-16', '2025-07-17', '2025-07-18',
               '2025-07-21', '2025-07-22', '2025-07-23', '2025-07-24',
               '2025-07-25', '2025-07-28', '2025-07-29', '2025-07-30',
               '2025-07-31', '2025-08-01'],
              dtype='datetime64[ns]', name='Date', freq=None)
stock_data.info()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 66 entries, 2025-05-02 to 2025-08-01
Data columns (total 5 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   (Close, RELIANCE.NS)   66 non-null     float64
 1   (High, RELIANCE.NS)    66 non-null     float64
 2   (Low, RELIANCE.NS)     66 non-null     float64
 3   (Open, RELIANCE.NS)    66 non-null     float64
 4   (Volume, RELIANCE.NS)  66 non-null     int64  
dtypes: float64(4), int64(1)
memory usage: 3.1 KB
stock_data.columns =stock_data.columns.droplevel("Ticker")
# Plot candlestick chart with 10-day and 20-day moving averages
mpf.plot(
    stock_data,
    type='candle',
    style='yahoo',
    title=f'{ticker_symbol} Candlestick Chart with Moving Averages',
    mav=(10, 20),
    volume=True,
    ylabel='Price',
    ylabel_lower='Volume'
)

import plotly.graph_objects as go

fig = go.Figure(data=[go.Candlestick(x=stock_data.index,
                open=stock_data['Open'],
                high=stock_data['High'],
                low=stock_data['Low'],
                close=stock_data['Close'])])

fig.update_layout(title='Candlestick Chart',
                  xaxis_title='Date',
                  yaxis_title='Price')

fig.show()