Pandas Series boolean maps and plotting

Question

I am just trying to up my understanding of plotting Pandas Series data using Booleans to mask out values I don't want. I am not sure that what I have is the correct or efficient way to do it.

Don't get me wrong, I do get the chart I am after but are my assumptions on the syntax correct?

All I want to do is plot the non zero values on my chart. I have not formatted the charts as I would normally as this was just a test of Booleans and masking data and not for creating report grade charts.

If I masked this as a Pandas DataFrame I would do the following if df1 were my DataFrame. I understand this and it makes sense that the df1[mask] returns my values as required

# Plot our graph with only items that are non-zero
fig = px.bar(df1[mask], x = 'Animals', y = 'Count')
fig.show()

Doing it as a Pandas Series This is the snippet that creates the graph I require

# Plot our graph with only items that are non-zero
fig = px.bar(sf, x = sf.index[sf_mask], y = sf[sf_mask])
fig.show()

After my initial test with adding my mask to sf and getting an error. I deduced that I needed to add the mask against the x and y parameters. I take it this is because a Series is just a single column and the index is set as my "animals". Therefore by mapping the sf.index[sf_mask] I get the returned animals in the index and sf[sf_mask] returns me the values. failure to add either one would give a "ValueError" stating that the arguments should have the same length.

Here is what I did to test my workings

My initial imports and setting up Plotly as my plotting backend

import pandas as pd
import plotly.express as px

# Set our plotting backend to Plotly
pd.options.plotting.backend = "plotly"

I just created a test dataset from a dictionary

animals = {'rabbits' : 1,
       'dogs' : 3,
       'cats' : 0,
       'ferrets' : 3,
       'horses' : 8,
       'goldfish' : 0,
       'guinea_pigs' : 2,
       'hamsters' : 6,
       'mice' : 3,
       'rats' : 0
      }

Then converted it to a pandas Series

sf = pd.Series(animals)

I then create my boolean mask to mask out all our non-Zero entries on our Pandas Series

sf_mask = sf != 0

And if I then view the mask I can see I only get non zero values which is exactly what I am looking for.

sf[sf_mask]

Which outputs my non-zero items in my series.

rabbits        1
dogs           3
ferrets        3
horses         8
guinea_pigs    2
hamsters       6
mice           3
dtype: int64

If I plot without my Boolean mask 'sf_mask' using the following syntax I get my complete Pandas Series charted

# Plot our Series showing all items
fig = px.bar(sf, x = sf.index, y = sf)
fig.show()

Which outputs the following chart

If I plot with my Boolean mask 'sf_mask' using the following syntax I get the chart I want which excludes the gaps with zero value items.

# Plot our graph with only items that are non-zero
fig = px.bar(sf, x = sf.index[sf_mask], y = sf[sf_mask])
fig.show()

Which outputs the correct chart.

Answer 1

Your understanding of booleans and masking is correct.

You can simplify your syntax a little though: if you take a look at the plotly.express.bar documentation, you'll see that the arguments 'x' and 'y' are optional. You don't need to pass 'x' or 'y' because by default plotly.express will create the bars using the index of the Series as x and the values of the Series as y. You can also pass the masked series in place of the entire series.

For example, this will produce the same bar chart:

fig = px.bar(sf[sf>0])
fig.update_layout(showlegend=False)