How to use dates & times with pandas

Manipulating Time Series Data in Python

Stefan Jansen

Founder & Lead Data Scientist at Applied Artificial Intelligence

Date & time series functionality

At the root: data types for date & time information
- Objects for points in time and periods
- Attributes & methods reflect time-related details
Sequences of dates & periods:
- Series or DataFrame columns
- Index: convert object into Time Series
Many Series/DataFrame methods rely on time information in the index to provide time-series functionality

Basic building block: pd.Timestamp

import pandas as pd  # assumed imported going forward
from datetime import datetime  # To manually create dates

time_stamp = pd.Timestamp(datetime(2017, 1, 1))

pd.Timestamp('2017-01-01') == time_stamp

True # Understands dates as strings

time_stamp # type: pandas.tslib.Timestamp

Timestamp('2017-01-01 00:00:00')

Basic building block: pd.Timestamp

Timestamp object has many attributes to store time-specific information

time_stamp.year

time_stamp.day_name()

'Sunday'

More building blocks: pd.Period & freq

period = pd.Period('2017-01')

period # default: month-end

Period('2017-01', 'M')

period.asfreq('D') # convert to daily

Period('2017-01-31', 'D')

period.to_timestamp().to_period('M')

Period('2017-01', 'M')

Period object has freq attribute to store frequency info

Convert pd.Period() to pd.Timestamp() and back

More building blocks: pd.Period & freq

period + 2

Period('2017-03', 'M')

pd.Timestamp('2017-01-31', 'M') + 1

Timestamp('2017-02-28 00:00:00', freq='M')

Frequency info enables basic date arithmetic

Sequences of dates & times

pd.date_range: start, end, periods, freq

index = pd.date_range(start='2017-1-1', periods=12, freq='M')

index

DatetimeIndex(['2017-01-31', '2017-02-28', '2017-03-31', ...,
               '2017-09-30', '2017-10-31', '2017-11-30', '2017-12-31'],
              dtype='datetime64[ns]', freq='M')

pd.DateTimeIndex: sequence of Timestamp objects with frequency info

Sequences of dates & times

index[0]

Timestamp('2017-01-31 00:00:00', freq='M')

index.to_period()

PeriodIndex(['2017-01', '2017-02', '2017-03', '2017-04', ..., 
             '2017-11', '2017-12'], dtype='period[M]', freq='M')

Create a time series: pd.DateTimeIndex

pd.DataFrame({'data': index}).info()

RangeIndex: 12 entries, 0 to 11
Data columns (total 1 columns):
data    12 non-null datetime64[ns]
dtypes: datetime64[ns](1)

Create a time series: pd.DateTimeIndex

np.random.random:
- Random numbers: [0,1]
- 12 rows, 2 columns

data = np.random.random((size=12,2))

pd.DataFrame(data=data, index=index).info()

DatetimeIndex: 12 entries, 2017-01-31 to 2017-12-31
Freq: M
Data columns (total 2 columns):
0    12 non-null float64
1    12 non-null float64
dtypes: float64(2)

Frequency aliases & time info

ch1_1_v2 -How to use Dates & Times with pandas.036.png

Let's practice!

Manipulating Time Series Data in Python