CODEWITHSUNDEEP
pandasholoviews
Jeff
Jeff

Reputation: 417

How can I make different columns render as different colors in holoviews / hvplot?

I have a pandas dataframe with two columns of time series data. In my actual data, these columns are large enough that the render is unwieldy without datashader. I am attempting to compare events from these two timeseries. However, I need to be able to tell which data point is from which column. A simple functional example is below. How would I get columns A and B to use different color maps?

import numpy as np
import hvplot.pandas
import pandas as pd

A = np.random.randint(10, size=10000)
B = np.random.randint(30, size=10000)
d = {'A':A,'B':B}
df = pd.DataFrame(d)

df.hvplot(kind='scatter',datashade=True, height=500, width=1000, dynspread=False)

Upvotes: 0

Views: 1185

Answers (1)

philippjfr
philippjfr

Reputation: 4080

You will have to use the count_cat aggregator that counts each category separately, e.g. in the example above that would look like this:

import datashader as ds
df.hvplot(kind='scatter', aggregator=ds.count_cat('Variable'), datashade=True,
          height=500, width=1000)

The 'Variable' here corresponds to the default group_label that hvplot assigns to the columns. If you provided a different group_label you would have to update the aggregator to match. However instead of supplying an aggregator explicitly you can also use the by keyword:

df.hvplot(kind='scatter', by='Variable', datashade=True,
          height=500, width=1000)

Once hvplot 0.3.1 is released you'll also be able to supply an explicit cmap, e.g.:

df.hvplot(kind='scatter', by='Variable', datashade=True,
          height=500, width=1000, cmap={'A': 'red', 'B': 'blue'})

enter image description here

Upvotes: 3

Related Questions