TSAP(3) : Load Data and

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TSAP : TimeSeries Analysis with Python

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

data = pd.read_csv('data/AirPassengers.csv') 
data.head(3)

	Month	#Passengers
0	1949-01	112
1	1949-02	118
2	1949-03	132

data.rename(index=str, columns={'Month':'Date'}, inplace=True)

data['Year'] = data.Date.apply(lambda x: x.split('-')[0])

data['Month'] = data.Date.apply(lambda x: x.split('-')[1])

data.set_index('Date',inplace=True)

data.head(3)

	#Passengers	Year	Month
Date
1949-01	112	1949	01
1949-02	118	1949	02
1949-03	132	1949	03

data['1949-01':'1949-05']

	#Passengers	Year	Month
Date
1949-01	112	1949	01
1949-02	118	1949	02
1949-03	132	1949	03
1949-04	129	1949	04
1949-05	121	1949	05

# How to visualize?
data[['#Passengers']].plot(grid=True, figsize=(12, 6))

dateparse = lambda x, y: pd.datetime.strptime('%s-%s'%(x,y), '%Y-%m')

# create dataframe
df = pd.DataFrame({'year': [2015, 2016, 2017, 2018],
                   'month': [2, 3, 4, 5],
                   'day': [4, 5, 6, 7],
                   'hour': [2, 3, 4, 5]}) 
df

	day	hour	month	year
0	4	2	2	2015
1	5	3	3	2016
2	6	4	4	2017
3	7	5	5	2018

pd.to_datetime(df)

0   2015-02-04 02:00:00
1   2016-03-05 03:00:00
2   2017-04-06 04:00:00
3   2018-05-07 05:00:00
dtype: datetime64[ns]

pd.to_datetime(df[['year', 'month', 'day']])

0   2015-02-04
1   2016-03-05
2   2017-04-06
3   2018-05-07
dtype: datetime64[ns]

# truncate convenience function
ts = pd.Series(range(10), index = pd.date_range('8/31/2017', freq = 'M', periods = 10))
ts.truncate(before='10/31/2017', after='5/31/2018')

2017-10-31    2
2017-11-30    3
2017-12-31    4
2018-01-31    5
2018-02-28    6
2018-03-31    7
2018-04-30    8
2018-05-31    9
Freq: M, dtype: int64

# Series
ts[[0, 2, 6]].index

DatetimeIndex(['2017-08-31', '2017-10-31', '2018-02-28'], dtype='datetime64[ns]', freq=None)