CODEWITHSUNDEEP
pythonpandastimetime-series
crbl
crbl

Reputation: 397

Python: Time Series with Pandas

I want to use time series with Pandas. I read multiple time series one by one, from a csv file which has the date in the column named "Date" as (YYYY-MM-DD):

Date,Business,Education,Holiday
2005-01-01,6665,8511,86397
2005-02-01,8910,12043,92453
2005-03-01,8834,12720,78846
2005-04-01,8127,11667,52644
2005-05-01,7762,11092,33789
2005-06-01,7652,10898,34245
2005-07-01,7403,12787,42020
2005-08-01,7968,13235,36190
2005-09-01,8345,12141,36038
2005-10-01,8553,12067,41089
2005-11-01,8880,11603,59415
2005-12-01,8331,9175,70736


df = pd.read_csv(csv_file, index_col = 'Date',header=0)
Series_list = df.keys()

The time series can have different frequencies: day, week, month, quarter, year and I want to index the time series according to a frequency I decide before I generate the Arima model. Could someone please explain how can I define the frequency of the series?

stepwise_fit = auto_arima(df[Series_name]....

Upvotes: 2

Views: 809

Answers (1)

ALollz
ALollz

Reputation: 59519

pandas has a built in function pandas.infer_freq()

import pandas as pd
df = pd.DataFrame({'Date': ['2005-01-01', '2005-02-01', '2005-03-01', '2005-04-01'],
                  'Date1': ['2005-01-01', '2005-01-02', '2005-01-03', '2005-01-04'],
                  'Date2': ['2006-01-01', '2007-01-01', '2008-01-01', '2009-01-01'],
                  'Date3': ['2006-01-01', '2006-02-06', '2006-03-11', '2006-04-01']})
df['Date'] = pd.to_datetime(df['Date'])
df['Date1'] = pd.to_datetime(df['Date1'])
df['Date2'] = pd.to_datetime(df['Date2'])
df['Date3'] = pd.to_datetime(df['Date3'])

pd.infer_freq(df.Date)
#'MS'
pd.infer_freq(df.Date1)
#'D'
pd.infer_freq(df.Date2)
#'AS-JAN'

Alternatively you could also make use of the datetime functionality of the columns.

df.Date.dt.freq
#'MS'

Of course if your data doesn't actually have a real frequency, then you won't get anything.

pd.infer_freq(df.Date3)
#

The frequency descriptions are docmented under offset-aliases.

Upvotes: 3

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