Importing Necessary Libraries¶

In [1]:

import pandas as pd
import datetime as dt

`date`¶

In [2]:

today = dt.date(2021, 8, 22)

In [3]:

today.day

Out[3]:

In [4]:

today.month

Out[4]:

In [5]:

today.year

Out[5]:

`datetime`¶

In [6]:

today = dt.datetime(2021, 8, 22, 17, 45, 51)

In [8]:

print(today)

2021-08-22 17:45:51

In [9]:

today.hour

Out[9]:

In [10]:

today.minute

Out[10]:

In [11]:

today.second

Out[11]:

`Timestamp`¶

In [12]:

pd.Timestamp('2021-12-22')

Out[12]:

Timestamp('2021-12-22 00:00:00')

In [13]:

pd.Timestamp('2019/02/13')

Out[13]:

Timestamp('2019-02-13 00:00:00')

In [14]:

pd.Timestamp('2020, 4, 21')

Out[14]:

Timestamp('2020-04-21 00:00:00')

In [19]:

pd.Timestamp('2020, 4, 21 18:33:12')

Out[19]:

Timestamp('2020-04-21 18:33:12')

`DatetimeIndex`¶

In [23]:

dates = ['2019/12/22', '2012/8/22', '2015/2/22']

In [24]:

pd.DatetimeIndex(dates)

Out[24]:

DatetimeIndex(['2019-12-22', '2012-08-22', '2015-02-22'], dtype='datetime64[ns]', freq=None)

In [26]:

type(dates)

Out[26]:

list

In [27]:

dates[1]

Out[27]:

'2012/8/22'

In [38]:

dates2 = [dt.date(2021, 9, 8), dt.date(2021, 9, 9), dt.date(2021, 9, 10)]

In [39]:

dates2

Out[39]:

[datetime.date(2021, 9, 8),
 datetime.date(2021, 9, 9),
 datetime.date(2021, 9, 10)]

In [40]:

dateIndex = pd.DatetimeIndex(dates2)
dateIndex

Out[40]:

DatetimeIndex(['2021-09-08', '2021-09-09', '2021-09-10'], dtype='datetime64[ns]', freq=None)

In [42]:

values = [12, 7, 30]
pd.Series(data=values, index=dateIndex)

Out[42]:

2021-09-08    12
2021-09-09     7
2021-09-10    30
dtype: int64

`pd.to_datetime()`¶

In [44]:

pd.to_datetime('2021, 1, 5')

Out[44]:

Timestamp('2021-01-05 00:00:00')

In [45]:

pd.to_datetime(dt.date(2001, 12, 12))

Out[45]:

Timestamp('2001-12-12 00:00:00')

In [47]:

pd.to_datetime(dt.datetime(2012, 2, 12, 18, 33, 12))

Out[47]:

Timestamp('2012-02-12 18:33:12')

In [48]:

dates = ['2019/12/22', '2012/8/22', '2015/2/22']
pd.to_datetime(dates)

Out[48]:

DatetimeIndex(['2019-12-22', '2012-08-22', '2015-02-22'], dtype='datetime64[ns]', freq=None)

In [50]:

dates3 = pd.Series(
    ['December 22nd, 1999', '2019, 12, 12', "This is Date", 'Jan 5th, 2021'])
dates3

Out[50]:

0    December 22nd, 1999
1           2019, 12, 12
2           This is Date
3          Jan 5th, 2021
dtype: object

In [51]:

pd.to_datetime(dates3, errors='coerce')  #invalid parsing will be set as NaT

Out[51]:

0   1999-12-22
1   2019-12-12
2          NaT
3   2021-01-05
dtype: datetime64[ns]

In [54]:

unixtime = [3244563, 3545463, 3242037, 7363236]
pd.to_datetime(unixtime, unit='s')

Out[54]:

DatetimeIndex(['1970-02-07 13:16:03', '1970-02-11 00:51:03',
               '1970-02-07 12:33:57', '1970-03-27 05:20:36'],
              dtype='datetime64[ns]', freq=None)

Create Range in the form of Date `pd.date_range()`¶

In [58]:

times = pd.date_range(start='2021-01-01', end='2021-12-31', freq='D')
times

Out[58]:

DatetimeIndex(['2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04',
               '2021-01-05', '2021-01-06', '2021-01-07', '2021-01-08',
               '2021-01-09', '2021-01-10',
               ...
               '2021-12-22', '2021-12-23', '2021-12-24', '2021-12-25',
               '2021-12-26', '2021-12-27', '2021-12-28', '2021-12-29',
               '2021-12-30', '2021-12-31'],
              dtype='datetime64[ns]', length=365, freq='D')

In [61]:

times = pd.date_range(start='2021-01-01', end='2029-12-31', freq='A')
times

Out[61]:

DatetimeIndex(['2021-12-31', '2022-12-31', '2023-12-31', '2024-12-31',
               '2025-12-31', '2026-12-31', '2027-12-31', '2028-12-31',
               '2029-12-31'],
              dtype='datetime64[ns]', freq='A-DEC')

In [62]:

times = pd.date_range(start='2021-01-01', end='2021-12-31', freq='5H')
times

Out[62]:

DatetimeIndex(['2021-01-01 00:00:00', '2021-01-01 05:00:00',
               '2021-01-01 10:00:00', '2021-01-01 15:00:00',
               '2021-01-01 20:00:00', '2021-01-02 01:00:00',
               '2021-01-02 06:00:00', '2021-01-02 11:00:00',
               '2021-01-02 16:00:00', '2021-01-02 21:00:00',
               ...
               '2021-12-29 02:00:00', '2021-12-29 07:00:00',
               '2021-12-29 12:00:00', '2021-12-29 17:00:00',
               '2021-12-29 22:00:00', '2021-12-30 03:00:00',
               '2021-12-30 08:00:00', '2021-12-30 13:00:00',
               '2021-12-30 18:00:00', '2021-12-30 23:00:00'],
              dtype='datetime64[ns]', length=1748, freq='5H')

In [63]:

times = pd.date_range(start='2021-01-01', end='2021-12-31', freq='4H')
times

Out[63]:

DatetimeIndex(['2021-01-01 00:00:00', '2021-01-01 04:00:00',
               '2021-01-01 08:00:00', '2021-01-01 12:00:00',
               '2021-01-01 16:00:00', '2021-01-01 20:00:00',
               '2021-01-02 00:00:00', '2021-01-02 04:00:00',
               '2021-01-02 08:00:00', '2021-01-02 12:00:00',
               ...
               '2021-12-29 12:00:00', '2021-12-29 16:00:00',
               '2021-12-29 20:00:00', '2021-12-30 00:00:00',
               '2021-12-30 04:00:00', '2021-12-30 08:00:00',
               '2021-12-30 12:00:00', '2021-12-30 16:00:00',
               '2021-12-30 20:00:00', '2021-12-31 00:00:00'],
              dtype='datetime64[ns]', length=2185, freq='4H')

In [ ]:

times = pd.date_range(start='2021-01-01', end='2021-12-31', freq='5H')
times

Importing Stock Data using `datareader`¶

In [66]:

from pandas_datareader import data

In [67]:

# !pip install pandas_datareader

In [69]:

company = 'MSFT'
start = '2020-12-22'
end = '2021-06-22'

In [71]:

stocks = data.DataReader(name=company,
                         data_source='yahoo',
                         start=start,
                         end=end)

In [72]:

stocks

Out[72]:

	High	Low	Open	Close	Volume	Adj Close
Date
2020-12-21	224.000000	217.279999	217.550003	222.589996	37181900.0	221.143524
2020-12-22	225.630005	221.850006	222.690002	223.940002	22612200.0	222.484772
2020-12-23	223.559998	220.800003	223.110001	221.020004	18699600.0	219.583740
2020-12-24	223.610001	221.199997	221.419998	222.750000	10550600.0	221.302490
2020-12-28	226.029999	223.020004	224.449997	224.960007	17933500.0	223.498138
...	...	...	...	...	...	...
2021-06-16	260.579987	254.419998	259.399994	257.380005	27220000.0	256.888214
2021-06-17	261.750000	256.010010	256.070007	260.899994	27565500.0	260.401489
2021-06-18	262.299988	258.750000	259.630005	259.429993	37202200.0	258.934296
2021-06-21	263.519989	257.920013	259.820007	262.630005	26696100.0	262.128174
2021-06-22	265.790009	262.399994	262.720001	265.510010	24694100.0	265.002686

126 rows × 6 columns

In [73]:

stocks.loc['2021-06-17']

Out[73]:

High         2.617500e+02
Low          2.560100e+02
Open         2.560700e+02
Close        2.609000e+02
Volume       2.756550e+07
Adj Close    2.604015e+02
Name: 2021-06-17 00:00:00, dtype: float64

In [84]:

stocks.iloc[111]

Out[84]:

High         2.492700e+02
Low          2.458400e+02
Open         2.481300e+02
Close        2.473000e+02
Volume       1.940670e+07
Adj Close    2.468275e+02
Name: 2021-06-02 00:00:00, dtype: float64

In [88]:

stocks.loc['2020-12-24':'2021-01-18']

Out[88]:

	High	Low	Open	Close	Volume	Adj Close
Date
2020-12-24	223.610001	221.199997	221.419998	222.750000	10550600.0	221.302490
2020-12-28	226.029999	223.020004	224.449997	224.960007	17933500.0	223.498138
2020-12-29	227.179993	223.580002	226.309998	224.149994	17403200.0	222.693390
2020-12-30	225.630005	221.470001	225.229996	221.679993	20272300.0	220.239456
2020-12-31	223.000000	219.679993	221.699997	222.419998	20942100.0	220.974640
2021-01-04	223.000000	214.809998	222.529999	217.690002	37130100.0	216.275375
2021-01-05	218.520004	215.699997	217.259995	217.899994	23823000.0	216.484009
2021-01-06	216.490005	211.940002	212.169998	212.250000	35930700.0	210.870728
2021-01-07	219.339996	213.710007	214.039993	218.289993	27694500.0	216.871460
2021-01-08	220.580002	217.029999	218.679993	219.619995	22956200.0	218.192841
2021-01-11	218.910004	216.729996	218.470001	217.490005	23031300.0	216.076675
2021-01-12	217.100006	213.320007	216.500000	214.929993	23249300.0	213.533310
2021-01-13	216.759995	213.929993	214.020004	216.339996	20087100.0	214.934143
2021-01-14	217.460007	212.740005	215.910004	213.020004	29480800.0	211.635742
2021-01-15	214.509995	212.029999	213.520004	212.649994	31746500.0	211.268112

In [89]:

stocks.truncate(before='2020-12-30', after='2021-01-11')

Out[89]:

	High	Low	Open	Close	Volume	Adj Close
Date
2020-12-30	225.630005	221.470001	225.229996	221.679993	20272300.0	220.239456
2020-12-31	223.000000	219.679993	221.699997	222.419998	20942100.0	220.974640
2021-01-04	223.000000	214.809998	222.529999	217.690002	37130100.0	216.275375
2021-01-05	218.520004	215.699997	217.259995	217.899994	23823000.0	216.484009
2021-01-06	216.490005	211.940002	212.169998	212.250000	35930700.0	210.870728
2021-01-07	219.339996	213.710007	214.039993	218.289993	27694500.0	216.871460
2021-01-08	220.580002	217.029999	218.679993	219.619995	22956200.0	218.192841
2021-01-11	218.910004	216.729996	218.470001	217.490005	23031300.0	216.076675

`Timedelta`¶

In [97]:

timeA = pd.Timestamp('2021-01-01 10:26:00')
timeB = pd.Timestamp('2020-01-15 10:33:00')

In [98]:

timeA - timeB

Out[98]:

Timedelta('351 days 23:53:00')

In [99]:

pd.Timedelta(weeks=10, days=9, minutes=12)

Out[99]:

Timedelta('79 days 00:12:00')

In [101]:

#parsing/converting dates into datetime from csv files
df = pd.read_csv('catalog.csv', index_col='id')
df.head()

Out[101]:

	date	time	continent_code	country_name	country_code	state/province	population	city/town	distance	location_description	...	geolocation	hazard_type	landslide_type	landslide_size	trigger	storm_name	injuries	fatalities	source_name	source_link
id
34	3/2/07	Night	NaN	United States	US	Virginia	16000	Cherry Hill	3.40765	Unknown	...	(38.600900000000003, -77.268199999999993)	Landslide	Landslide	Small	Rain	NaN	NaN	NaN	NBC 4 news	http://www.nbc4.com/news/11186871/detail.html
42	3/22/07	NaN	NaN	United States	US	Ohio	17288	New Philadelphia	3.33522	NaN	...	(40.517499999999998, -81.430499999999995)	Landslide	Landslide	Small	Rain	NaN	NaN	NaN	Canton Rep.com	http://www.cantonrep.com/index.php?ID=345054&C...
56	4/6/07	NaN	NaN	United States	US	Pennsylvania	15930	Wilkinsburg	2.91977	Urban area	...	(40.4377, -79.915999999999997)	Landslide	Landslide	Small	Rain	NaN	NaN	NaN	The Pittsburgh Channel.com	https://web.archive.org/web/20080423132842/htt...
59	4/14/07	NaN	NaN	Canada	CA	Quebec	42786	Châteauguay	2.98682	Above river	...	(45.322600000000001, -73.777100000000004)	Landslide	Riverbank collapse	Small	Rain	NaN	NaN	NaN	Le Soleil	http://www.hebdos.net/lsc/edition162007/articl...
61	4/15/07	NaN	NaN	United States	US	Kentucky	6903	Pikeville	5.66542	Below road	...	(37.432499999999997, -82.493099999999998)	Landslide	Landslide	Small	Downpour	NaN	NaN	0.0	Matthew Crawford (KGS)	NaN

5 rows × 22 columns

In [102]:

df = pd.read_csv('catalog.csv', index_col='id', parse_dates=['date'])
df.head()

Out[102]:

	date	time	continent_code	country_name	country_code	state/province	population	city/town	distance	location_description	...	geolocation	hazard_type	landslide_type	landslide_size	trigger	storm_name	injuries	fatalities	source_name	source_link
id
34	2007-03-02	Night	NaN	United States	US	Virginia	16000	Cherry Hill	3.40765	Unknown	...	(38.600900000000003, -77.268199999999993)	Landslide	Landslide	Small	Rain	NaN	NaN	NaN	NBC 4 news	http://www.nbc4.com/news/11186871/detail.html
42	2007-03-22	NaN	NaN	United States	US	Ohio	17288	New Philadelphia	3.33522	NaN	...	(40.517499999999998, -81.430499999999995)	Landslide	Landslide	Small	Rain	NaN	NaN	NaN	Canton Rep.com	http://www.cantonrep.com/index.php?ID=345054&C...
56	2007-04-06	NaN	NaN	United States	US	Pennsylvania	15930	Wilkinsburg	2.91977	Urban area	...	(40.4377, -79.915999999999997)	Landslide	Landslide	Small	Rain	NaN	NaN	NaN	The Pittsburgh Channel.com	https://web.archive.org/web/20080423132842/htt...
59	2007-04-14	NaN	NaN	Canada	CA	Quebec	42786	Châteauguay	2.98682	Above river	...	(45.322600000000001, -73.777100000000004)	Landslide	Riverbank collapse	Small	Rain	NaN	NaN	NaN	Le Soleil	http://www.hebdos.net/lsc/edition162007/articl...
61	2007-04-15	NaN	NaN	United States	US	Kentucky	6903	Pikeville	5.66542	Below road	...	(37.432499999999997, -82.493099999999998)	Landslide	Landslide	Small	Downpour	NaN	NaN	0.0	Matthew Crawford (KGS)	NaN

5 rows × 22 columns

Importing Necessary Libraries¶

date¶

datetime¶

Timestamp¶

DatetimeIndex¶

pd.to_datetime()¶

Create Range in the form of Date pd.date_range()¶

Importing Stock Data using datareader¶

Timedelta¶