Python – Statsmodels Example – Video

Statsmodels is a nice statistics library in Python, which eases the data processing and analysis with Python. The library is available here – statsmodels.org and on its official web site the installation guide is always up to date.

In the live coding video, the following points are mentioned:

• imporing libraries and loading macrodata dataset to pandas
• displaying data from dataset in all columns and all rows
• inserting index to the data
• plotting the data with matplotlib
• getting the trend and the cycle of the data with hp_filter.hpfilter (https://www.statsmodels.org/dev/generated/statsmodels.tsa.filters.hp_filter.hpfilter.html)
• presenting the data, zooming to specific dates

Imporing libraries and loading macrodata dataset to pandas is the standard start.

Displaying data from dataset in all columns and all rows is actually a 1-liner. 1 per column, 1 per row:

Inserting the index to the data is a standard trick, which could be carried out through different ways – e.g. via length or via from-to period:

code.py

Python

index = pd.Index(sm.tsa.datetools.dates_from_range('1959Q1', '2009Q3')) df.index = index

1 2	index = pd.Index(sm.tsa.datetools.dates_from_range('1959Q1', '2009Q3')) df.index = index

Plotting the data with matplotlib is the interesting part. In general, this is why pandas and jupyter are powerful – the display of the data is quite fast – just a single df.plot() starts the magic.

code.py

Python

df[['trend', 'cycle']].plot()

1	df[['trend', 'cycle']].plot()

Getting the trend and the cycile of the dataset with  hp_filter.hpfilter is actually parsing data from a tuple to the dataset:

code.py

Python

df[['trend', 'cycle']]["1985":"1999"].plot()

1	df[['trend', 'cycle']]["1985":"1999"].plot()