Pandas:¶

Datetime
aggregate
Groupby
loc & iloc

In [15]:

import pandas as pd
import numpy as np
import datetime as dt

In [16]:

df = pd.read_csv('https://raw.githubusercontent.com/abulbasar/data/master/stocks.small.csv')
df.head()

Out[16]:

	date	open	high	low	close	volume	adjclose	symbol
0	2000-07-18	144.81250	144.828125	141.437500	143.0000	50683600.0	50.155473	INTC
1	2000-07-20	32.93751	34.250010	32.812500	33.7500	3288300.0	8.789734	BEN
2	2000-07-24	64.25000	67.312477	64.187523	64.7500	948800.0	7.689567	APH
3	2000-07-26	21.87500	22.125000	20.937500	20.9375	1464300.0	15.618320	SHW
4	2000-07-26	42.00000	42.312481	41.625000	41.8750	1397600.0	9.402721	STJ

Datetime info¶

https://www.programiz.com/python-programming/datetime/strptime

In [17]:

df['date'] = pd.to_datetime(df['date'], format = "%Y-%m-%d")
df

Out[17]:

	date	open	high	low	close	volume	adjclose	symbol
0	2000-07-18	144.812500	144.828125	141.437500	143.000000	50683600.0	50.155473	INTC
1	2000-07-20	32.937510	34.250010	32.812500	33.750000	3288300.0	8.789734	BEN
2	2000-07-24	64.250000	67.312477	64.187523	64.750000	948800.0	7.689567	APH
3	2000-07-26	21.875000	22.125000	20.937500	20.937500	1464300.0	15.618320	SHW
4	2000-07-26	42.000000	42.312481	41.625000	41.875000	1397600.0	9.402721	STJ
...	...	...	...	...	...	...	...	...
1841	2016-07-29	114.089996	116.550003	114.089996	115.209999	2520700.0	115.209999	AET
1842	2016-08-01	128.809998	130.020004	127.830002	128.220001	1576600.0	128.220001	UHS
1843	2016-08-02	24.340000	24.430000	23.990000	24.170000	7115500.0	24.170000	BSX
1844	2016-08-02	63.779999	63.840000	62.490002	62.840000	2740500.0	62.840000	ADI
1845	2016-08-04	28.010000	28.190001	27.900000	28.049999	3899900.0	28.049999	CSX

1846 rows × 8 columns

In [18]:

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1846 entries, 0 to 1845
Data columns (total 8 columns):
 #   Column    Non-Null Count  Dtype         
---  ------    --------------  -----         
 0   date      1846 non-null   datetime64[ns]
 1   open      1846 non-null   float64       
 2   high      1846 non-null   float64       
 3   low       1846 non-null   float64       
 4   close     1846 non-null   float64       
 5   volume    1846 non-null   float64       
 6   adjclose  1846 non-null   float64       
 7   symbol    1846 non-null   object        
dtypes: datetime64[ns](1), float64(6), object(1)
memory usage: 115.5+ KB

In [19]:

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1846 entries, 0 to 1845
Data columns (total 8 columns):
 #   Column    Non-Null Count  Dtype         
---  ------    --------------  -----         
 0   date      1846 non-null   datetime64[ns]
 1   open      1846 non-null   float64       
 2   high      1846 non-null   float64       
 3   low       1846 non-null   float64       
 4   close     1846 non-null   float64       
 5   volume    1846 non-null   float64       
 6   adjclose  1846 non-null   float64       
 7   symbol    1846 non-null   object        
dtypes: datetime64[ns](1), float64(6), object(1)
memory usage: 115.5+ KB

In [ ]:

dt.timedelta(days = 35)