Indexing & resampling time series

Manipulating Time Series Data in Python

Stefan Jansen

Founder & Lead Data Scientist at Applied Artificial Intelligence

Time series transformation

Basic time series transformations include:

Parsing string dates and convert to datetime64
Selecting & slicing for specific subperiods
Setting & changing DateTimeIndex frequency
- Upsampling vs Downsampling

Getting GOOG stock prices

google = pd.read_csv('google.csv')  # import pandas as pd

google.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 504 entries, 0 to 503
Data columns (total 2 columns):
date     504 non-null object
price    504 non-null float64
dtypes: float64(1), object(1)

google.head()

         date   price
0  2015-01-02  524.81
1  2015-01-05  513.87
2  2015-01-06  501.96
3  2015-01-07  501.10
4  2015-01-08  502.68

Converting string dates to datetime64

pd.to_datetime():
- Parse date string
- Convert to datetime64

google.date = pd.to_datetime(google.date)

google.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 504 entries, 0 to 503
Data columns (total 2 columns):
date     504 non-null datetime64[ns]
price    504 non-null float64
dtypes: datetime64[ns](1), float64(1)

Converting string dates to datetime64

.set_index():
- Date into index
- inplace:
  - don't create copy

google.set_index('date', inplace=True)

google.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 504 entries, 2015-01-02 to 2016-12-30
Data columns (total 1 columns):
price    504 non-null float64
dtypes: float64(1)

Plotting the Google stock time series

google.price.plot(title='Google Stock Price')

plt.tight_layout(); plt.show()

ch1_2_v2 - Indexing & Resampling Time Series.013.png

Partial string indexing

Selecting/indexing using strings that parse to dates

google['2015'].info() # Pass string for part of date

DatetimeIndex: 252 entries, 2015-01-02 to 2015-12-31
Data columns (total 1 columns):
price    252 non-null float64
dtypes: float64(1)

google['2015-3': '2016-2'].info() # Slice includes last month

DatetimeIndex: 252 entries, 2015-03-02 to 2016-02-29
Data columns (total 1 columns):
price    252 non-null float64
dtypes: float64(1)
memory usage: 3.9 KB

Partial string indexing

google.loc['2016-6-1', 'price'] # Use full date with .loc[]

734.15

.asfreq(): set frequency

.asfreq('D'):
- Convert DateTimeIndex to calendar day frequency

google.asfreq('D').info() # set calendar day frequency

DatetimeIndex: 729 entries, 2015-01-02 to 2016-12-30
Freq: D
Data columns (total 1 columns):
price    504 non-null float64
dtypes: float64(1)

.asfreq(): set frequency

Upsampling:
- Higher frequency implies new dates => missing data

google.asfreq('D').head()

             price
date              
2015-01-02  524.81
2015-01-03     NaN
2015-01-04     NaN
2015-01-05  513.87
2015-01-06  501.96

.asfreq(): reset frequency

.asfreq('B'):
- Convert DateTimeIndex to business day frequency

google = google.asfreq('B') # Change to calendar day frequency

google.info()

DatetimeIndex: 521 entries, 2015-01-02 to 2016-12-30
Freq: B
Data columns (total 1 columns):
price    504 non-null float64
dtypes: float64(1)

.asfreq(): reset frequency

google[google.price.isnull()] # Select missing 'price' values

            price
date             
2015-01-19    NaN
2015-02-16    NaN
...
2016-11-24    NaN
2016-12-26    NaN

Business days that were not trading days

Let's practice!

Manipulating Time Series Data in Python