Small overview of HoloViz capability of data exploration

This notebook is intended to present a small overview of HoloViz and the capability for data exploration, with interactive plots (show difference between matplotlib and bokeh). Many parts are based on or copied from the official HoloViz Tutorial (highly recommended for a more extensive overview of the possibilities of HoloViz).

Note: In June 2019 the project name changed from PyViz to HoloViz. The reason for this is explained in this blog post.

HoloViz Packages used for this notebook

Exploring Pandas Dataframes

If your data is in a Pandas dataframe, it’s natural to explore it using the .plot() method (based on Matplotlib). Let’s have a look at some automatic weather station data from Langenferner:

import pandas as pd
url = 'https://cluster.klima.uni-bremen.de/~oggm/tutorials/aws_data_Langenferner_UTC+2.csv'
df = pd.read_csv(url, index_col=0, parse_dates=True)
df.head()

	TEMP	RH	SWIN	SWOUT	LWIN	LWOUT	WINDSPEED	WINDDIR	PRESSURE
2013-07-13 00:00:00	1.634333	67.595753	0.0	0.0	212.744817	303.656833	4.436833	211.533333	692.622250
2013-07-13 01:00:00	1.388667	68.150512	0.0	0.0	209.781683	302.588717	5.544000	206.166667	692.395683
2013-07-13 02:00:00	1.064500	66.853977	0.0	0.0	207.234933	300.872133	5.573167	210.750000	692.200800
2013-07-13 03:00:00	0.985167	55.827547	0.0	0.0	207.913533	295.684267	3.970167	203.250000	692.163967
2013-07-13 04:00:00	1.155333	43.371014	0.0	0.0	211.513517	292.688400	3.267000	203.366667	692.001667

Just calling .plot() won’t give anything meaningful, because of the different magnitudes of the parameters:

df.plot();

Of course we can have a look at one variable only:

df.TEMP.plot();

This creates a static plot using matplotlib. With this approach we also can make some further explorations, like calculating the monthly mean temperature:

dfm = df.resample('m').mean()
dfm.TEMP.plot();

/tmp/ipykernel_15035/2820268743.py:1: FutureWarning: 'm' is deprecated and will be removed in a future version, please use 'ME' instead.
  dfm = df.resample('m').mean()

We can see the course of the parameter but we can not tell what was the exact temperature at January and we also cannot zoom in.

Exploring Data with hvPlot and Bokeh

If we are using hvplot instead we can create interactive plots with the same plotting API:

you might need to install first hvplot via e.g. conda install -c pyviz hvplot

import hvplot.pandas

df.TEMP.hvplot()

Now you have an interactive plot using bokeh with zooming option and hover with additional information (get the exact values and timestamps), also possible for all variables but again not very meaningful:

plot = df.hvplot()
plot