[datetime]S7_03_Practical_example1

Avocado.csv

0.83MB

 

 

In [59]:

import pandas as pd
import datetime as dt

 

In [60]:

avo_df = pd.read_csv('Avocado.csv')
avo_df

 

Out[60]:

	Date	AveragePrice	Total Volume	type	region
0	2015-12-27	1.33	64236.62	conventional	Albany
1	2015-12-20	1.35	54876.98	conventional	Albany
2	2015-12-13	0.93	118220.22	conventional	Albany
3	2015-12-06	1.08	78992.15	conventional	Albany
4	2015-11-29	1.28	51039.60	conventional	Albany
...	...	...	...	...	...
18244	2018-02-04	1.63	17074.83	organic	WestTexNewMexico
18245	2018-01-28	1.71	13888.04	organic	WestTexNewMexico
18246	2018-01-21	1.87	13766.76	organic	WestTexNewMexico
18247	2018-01-14	1.93	16205.22	organic	WestTexNewMexico
18248	2018-01-07	1.62	17489.58	organic	WestTexNewMexico

18249 rows × 5 columns

 

In [61]:

avo_df['Date'] = pd.to_datetime(avo_df['Date'])
avo_df

 

Out[61]:

	Date	AveragePrice	Total Volume	type	region
0	2015-12-27	1.33	64236.62	conventional	Albany
1	2015-12-20	1.35	54876.98	conventional	Albany
2	2015-12-13	0.93	118220.22	conventional	Albany
3	2015-12-06	1.08	78992.15	conventional	Albany
4	2015-11-29	1.28	51039.60	conventional	Albany
...	...	...	...	...	...
18244	2018-02-04	1.63	17074.83	organic	WestTexNewMexico
18245	2018-01-28	1.71	13888.04	organic	WestTexNewMexico
18246	2018-01-21	1.87	13766.76	organic	WestTexNewMexico
18247	2018-01-14	1.93	16205.22	organic	WestTexNewMexico
18248	2018-01-07	1.62	17489.58	organic	WestTexNewMexico

18249 rows × 5 columns

 

In [62]:

avo_df.info()

 

 

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 18249 entries, 0 to 18248
Data columns (total 5 columns):
 #   Column        Non-Null Count  Dtype         
---  ------        --------------  -----         
 0   Date          18249 non-null  datetime64[ns]
 1   AveragePrice  18249 non-null  float64       
 2   Total Volume  18249 non-null  float64       
 3   type          18249 non-null  object        
 4   region        18249 non-null  object        
dtypes: datetime64[ns](1), float64(2), object(2)
memory usage: 713.0+ KB

 

In [63]:

avo_df.set_index(keys=['Date'], inplace = True)
avo_df

 

Out[63]:

	AveragePrice	Total Volume	type	region
Date
2015-12-27	1.33	64236.62	conventional	Albany
2015-12-20	1.35	54876.98	conventional	Albany
2015-12-13	0.93	118220.22	conventional	Albany
2015-12-06	1.08	78992.15	conventional	Albany
2015-11-29	1.28	51039.60	conventional	Albany
...	...	...	...	...
2018-02-04	1.63	17074.83	organic	WestTexNewMexico
2018-01-28	1.71	13888.04	organic	WestTexNewMexico
2018-01-21	1.87	13766.76	organic	WestTexNewMexico
2018-01-14	1.93	16205.22	organic	WestTexNewMexico
2018-01-07	1.62	17489.58	organic	WestTexNewMexico

18249 rows × 4 columns

 

In [64]:

avo_df.values

 

Out[64]:

array([[1.33, 64236.62, 'conventional', 'Albany'],
       [1.35, 54876.98, 'conventional', 'Albany'],
       [0.93, 118220.22, 'conventional', 'Albany'],
       ...,
       [1.87, 13766.76, 'organic', 'WestTexNewMexico'],
       [1.93, 16205.22, 'organic', 'WestTexNewMexico'],
       [1.62, 17489.58, 'organic', 'WestTexNewMexico']], dtype=object)

 

In [65]:

avo_df.columns

 

Out[65]:

Index(['AveragePrice', 'Total Volume', 'type', 'region'], dtype='object')

 

In [66]:

avo_df.index

 

Out[66]:

DatetimeIndex(['2015-12-27', '2015-12-20', '2015-12-13', '2015-12-06',
               '2015-11-29', '2015-11-22', '2015-11-15', '2015-11-08',
               '2015-11-01', '2015-10-25',
               ...
               '2018-03-11', '2018-03-04', '2018-02-25', '2018-02-18',
               '2018-02-11', '2018-02-04', '2018-01-28', '2018-01-21',
               '2018-01-14', '2018-01-07'],
              dtype='datetime64[ns]', name='Date', length=18249, freq=None)