
import pandas as pd


df = pd.read_csv('aapl.csv', parse_dates=['Date'], index_col = "Date")


df.head()


df.index # DatetimeIndex

DatetimeIndex(['2008-10-14', '2008-10-13', '2008-10-10', '2008-10-09',
               '2008-10-08', '2008-10-07', '2008-10-06', '2008-10-03',
               '2008-10-02', '2008-10-01',
               ...
               '1984-09-20', '1984-09-19', '1984-09-18', '1984-09-17',
               '1984-09-14', '1984-09-13', '1984-09-12', '1984-09-11',
               '1984-09-10', '1984-09-07'],
              dtype='datetime64[ns]', name='Date', length=6081, freq=None)


ts = pd.Timestamp("2018-01-05")
ts

Timestamp('2018-01-05 00:00:00')


ts.day_name()

'Friday'


ts+pd.Timedelta("1 day") # add one day

Timestamp('2018-01-06 00:00:00')


ts+pd.offsets.BDay() # add one business day

Timestamp('2018-01-08 00:00:00')


# Null data time
pd.Timestamp(pd.NaT)

NaT


df['2008-01'] # select one month data
# or df.loc['2008-01']

/var/folders/99/z39gcgsd2_z4nj1wpdp68jm00000gp/T/ipykernel_57501/3730440384.py:1: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.
  df['2008-01'] # select one month data


df.index

DatetimeIndex(['2008-10-14', '2008-10-13', '2008-10-10', '2008-10-09',
               '2008-10-08', '2008-10-07', '2008-10-06', '2008-10-03',
               '2008-10-02', '2008-10-01',
               ...
               '1984-09-20', '1984-09-19', '1984-09-18', '1984-09-17',
               '1984-09-14', '1984-09-13', '1984-09-12', '1984-09-11',
               '1984-09-10', '1984-09-07'],
              dtype='datetime64[ns]', name='Date', length=6081, freq=None)


# String slicing
df['2008-01-02':'2008-01-03'] # inclusive


df.asfreq('Y', method='pad') # select data every year


df.index[0]

Timestamp('2008-10-14 00:00:00')


df.head()


# exact match, Series
series_minute = pd.Series(
    [1, 2, 3],
    pd.DatetimeIndex(
        ["2011-12-31 23:59:00", "2012-01-01 00:00:00", "2012-01-01 00:02:00"]
    ),
)

series_minute["2011-12-31 23:59:00"]
# or series_minute.loc["2011-12-31 23:59:00"]

1


# exact match, DateFrame
df.loc['2008-10-14 00:00:00']

Open              116.26
High              116.40
Low               103.14
Close             104.08
Volume       70749800.00
Adj Close         104.08
Name: 2008-10-14 00:00:00, dtype: float64


# fancy indexing
df.index[[1, 3, 5]]

DatetimeIndex(['2008-10-13', '2008-10-09', '2008-10-07'], dtype='datetime64[ns]', name='Date', freq=None)


df.resample('M').mean() # monthly mean


df['Close'].resample('M').mean().plot()

<Axes: xlabel='Date'>


# create DatetimeIndex from a list
pd.DatetimeIndex(["2018-01-01", "2018-01-03", "2018-01-05"])

DatetimeIndex(['2018-01-01', '2018-01-03', '2018-01-05'], dtype='datetime64[ns]', freq=None)


import numpy as np

dates = [
    pd.Timestamp("2012-05-01"),
    pd.Timestamp("2012-05-02"),
    pd.Timestamp("2012-05-03"),
]

s = pd.Series(np.random.randn(3), index = dates) # Timestamp to DatetimeIndex
s.index

DatetimeIndex(['2012-05-01', '2012-05-02', '2012-05-03'], dtype='datetime64[ns]', freq=None)


dates = [
    pd.Timestamp("2012-05-01"),
    pd.Timestamp("2012-05-02"),
    pd.Timestamp("2012-05-03"),
]

pd.Index(dates) # Timestamp to DatetimeIndex

DatetimeIndex(['2012-05-01', '2012-05-02', '2012-05-03'], dtype='datetime64[ns]', freq=None)


rng = pd.date_range('2001-01-01', '2002-01-01', freq = 'M') # Datetime Index
rng, rng.resolution

(DatetimeIndex(['2001-01-31', '2001-02-28', '2001-03-31', '2001-04-30',
                '2001-05-31', '2001-06-30', '2001-07-31', '2001-08-31',
                '2001-09-30', '2001-10-31', '2001-11-30', '2001-12-31'],
               dtype='datetime64[ns]', freq='M'),
 'day')


rng = pd.date_range('2001-01-01', periods = 10, freq = 'M')
rng

DatetimeIndex(['2001-01-31', '2001-02-28', '2001-03-31', '2001-04-30',
               '2001-05-31', '2001-06-30', '2001-07-31', '2001-08-31',
               '2001-09-30', '2001-10-31'],
              dtype='datetime64[ns]', freq='M')


# slicing
rng[:5]

DatetimeIndex(['2001-01-31', '2001-02-28', '2001-03-31', '2001-04-30',
               '2001-05-31'],
              dtype='datetime64[ns]', freq='M')


# select by datetime index
df[df.index.isin(rng)]


s = pd.Series(range(len(rng)), index = rng)
s, type(s), s.index

(2001-01-31     0
 2001-02-28     1
 2001-03-31     2
 2001-04-30     3
 2001-05-31     4
 2001-06-30     5
 2001-07-31     6
 2001-08-31     7
 2001-09-30     8
 2001-10-31     9
 2001-11-30    10
 2001-12-31    11
 Freq: M, dtype: int64,
 pandas.core.series.Series,
 DatetimeIndex(['2001-01-31', '2001-02-28', '2001-03-31', '2001-04-30',
                '2001-05-31', '2001-06-30', '2001-07-31', '2001-08-31',
                '2001-09-30', '2001-10-31', '2001-11-30', '2001-12-31'],
               dtype='datetime64[ns]', freq='M'))


# convert DatetimeIndex to PeriodIndex
rng = pd.date_range('2001-01-01', periods = 10, freq = 'M')
rng.to_period()

PeriodIndex(['2001-01', '2001-02', '2001-03', '2001-04', '2001-05', '2001-06',
             '2001-07', '2001-08', '2001-09', '2001-10'],
            dtype='period[M]')


# convert a Series with DatetimeIndex to a Series with PeriodIndex
s = pd.Series(range(len(rng)), index = rng)
s.to_period()

PeriodIndex(['2001-01', '2001-02', '2001-03', '2001-04', '2001-05', '2001-06',
             '2001-07', '2001-08', '2001-09', '2001-10'],
            dtype='period[M]')

	Open	High	Low	Close	Volume	Adj Close
Date
2008-10-14	116.26	116.40	103.14	104.08	70749800	104.08
2008-10-13	104.55	110.53	101.02	110.26	54967000	110.26
2008-10-10	85.70	100.00	85.00	96.80	79260700	96.80
2008-10-09	93.35	95.80	86.60	88.74	57763700	88.74
2008-10-08	85.91	96.33	85.68	89.79	78847900	89.79

	Open	High	Low	Close	Volume	Adj Close
Date
2008-01-31	129.45	136.65	129.40	135.36	48059800	135.36
2008-01-30	131.37	135.45	130.00	132.18	44394700	132.18
2008-01-29	131.15	132.79	129.05	131.54	39285100	131.54
2008-01-28	128.16	133.20	126.45	130.01	52673000	130.01
2008-01-25	138.99	139.09	129.61	130.01	55526400	130.01
2008-01-24	139.99	140.70	132.01	135.60	71638100	135.60
2008-01-23	136.19	140.00	126.14	139.07	120463200	139.07
2008-01-22	148.06	159.98	146.00	155.64	86955500	155.64
2008-01-18	161.71	165.75	159.61	161.36	61583700	161.36
2008-01-17	161.51	165.36	158.42	160.89	62780700	160.89
2008-01-16	165.23	169.01	156.70	159.64	79065900	159.64
2008-01-15	177.72	179.22	164.66	169.04	83688500	169.04
2008-01-14	177.52	179.42	175.17	178.78	39301800	178.78
2008-01-11	176.00	177.85	170.00	172.69	44010200	172.69
2008-01-10	177.58	181.00	175.41	178.02	52963400	178.02
2008-01-09	171.30	179.50	168.30	179.40	64781500	179.40
2008-01-08	180.14	182.46	170.80	171.25	54422000	171.25
2008-01-07	181.25	183.60	170.23	177.64	74006900	177.64
2008-01-04	191.45	193.00	178.89	180.05	51994000	180.05
2008-01-03	195.41	197.39	192.69	194.93	30073800	194.93
2008-01-02	199.27	200.26	192.55	194.84	38542100	194.84

	Open	High	Low	Close	Volume	Adj Close
Date
2008-01-03	195.41	197.39	192.69	194.93	30073800	194.93
2008-01-02	199.27	200.26	192.55	194.84	38542100	194.84

	Open	High	Low	Close	Volume	Adj Close
Date
1984-12-31	29.12	29.25	29.12	29.12	7453600	3.32
1985-12-31	22.25	22.37	22.00	22.00	3158400	2.51
1986-12-31	41.00	41.38	40.38	40.50	4742400	4.62
1987-12-31	42.50	43.00	41.88	42.00	4200000	9.63
1988-12-31	40.25	40.50	40.00	40.38	3578800	9.34
1989-12-31	35.25	37.50	35.00	37.25	6555600	8.70
1990-12-31	43.00	43.25	42.75	43.00	1593200	10.17
1991-12-31	57.38	58.00	56.00	56.38	4802000	13.46
1992-12-31	58.75	60.00	58.75	59.75	3302000	14.39
1993-12-31	29.75	30.25	29.25	29.25	5765200	7.13
1994-12-31	38.88	38.88	37.88	38.38	3726400	9.49
1995-12-31	32.25	32.25	31.75	32.13	4983200	8.03
1996-12-31	21.37	21.50	20.75	20.87	13719200	5.22
1997-12-31	13.13	13.63	12.94	13.13	14531200	3.28
1998-12-31	40.50	41.38	39.50	40.94	9716400	10.23
1999-12-31	100.94	102.87	99.50	102.81	5856400	25.70
2000-12-31	14.88	15.25	14.56	14.88	16161800	7.44
2001-12-31	22.51	22.66	21.83	21.90	4920800	10.95
2002-12-31	14.00	14.36	13.95	14.33	7168800	7.16
2003-12-31	21.35	21.53	21.18	21.37	6230400	10.69
2004-12-31	64.89	65.00	64.03	64.40	9949600	32.20
2005-12-31	72.38	74.75	72.25	74.75	28829800	74.75
2006-12-31	86.29	86.58	81.90	83.80	44225700	83.80
2007-12-31	199.50	200.50	197.75	198.08	19261900	198.08

	Open	High	Low	Close	Volume	Adj Close
Date
2008-10-14	116.26	116.40	103.14	104.08	70749800	104.08
2008-10-13	104.55	110.53	101.02	110.26	54967000	110.26
2008-10-10	85.70	100.00	85.00	96.80	79260700	96.80
2008-10-09	93.35	95.80	86.60	88.74	57763700	88.74
2008-10-08	85.91	96.33	85.68	89.79	78847900	89.79

DateTime¶

Load Data¶

Timestamp¶

Selection¶

Resample¶

DatetimeIndex¶

Series¶

Convert to Period¶

Reference¶

	Open	High	Low	Close	Volume	Adj Close
Date
1984-09-30	26.981250	27.333125	26.606250	26.738750	4.807300e+06	3.051875
1984-10-31	25.035652	25.313478	24.780435	24.806957	5.559409e+06	2.830435
1984-11-30	24.545238	24.782857	24.188095	24.236190	5.749562e+06	2.764762
1984-12-31	27.060000	27.378500	26.841000	26.947500	6.195360e+06	3.075000
1985-01-31	29.520000	29.855909	29.140000	29.253182	1.035382e+07	3.337727
...	...	...	...	...	...	...
2008-06-30	178.928571	181.549524	175.414286	178.485714	3.311754e+07	178.485714
2008-07-31	167.790455	170.846818	164.117273	167.748636	3.206130e+07	167.748636
2008-08-31	170.620476	173.298571	168.745238	171.069524	2.225062e+07	171.069524
2008-09-30	143.129524	146.230000	137.534286	141.243333	4.091339e+07	141.243333
2008-10-31	100.214000	104.699000	93.997000	98.326000	6.696761e+07	98.326000

	Open	High	Low	Close	Volume	Adj Close
Date
2001-10-31	17.73	18.40	17.44	17.56	9776800	8.78
2001-08-31	17.73	18.60	17.65	18.55	7746600	9.27
2001-07-31	19.27	19.42	18.51	18.79	8393800	9.40
2001-05-31	19.80	20.24	19.49	19.95	15817600	9.98
2001-04-30	26.70	27.12	24.87	25.49	17670600	12.74
2001-02-28	19.37	19.44	18.12	18.25	18157600	9.12
2001-01-31	21.50	22.50	21.44	21.62	26106000	10.81