Reputation: 305
I am having difficulty with this. I have the results from my initial model (`Unfiltered´), that I plot like so:
df = pd.DataFrame(
{'class': ['foot', 'bike', 'bus', 'car', 'metro'],
'Precision': [0.7, 0.66, 0.41, 0.61, 0.11],
'Recall': [0.58, 0.35, 0.13, 0.89, 0.02],
'F1-score': [0.64, 0.45, 0.2, 0.72, 0.04]}
)
groups = df.melt(id_vars=['class'], var_name=['Metric'])
sns.barplot(data=groups, x='class', y='value', hue='Metric')
Now, I obtained a second results from my improved model (filtered
), so I add a column (status
) to my df
to indicate the results from each model like this:
df2 = pd.DataFrame(
{'class': ['foot','foot','bike','bike','bus','bus',
'car','car','metro','metro'],
'Precison': [0.7, 0.62, 0.66, 0.96, 0.41, 0.42, 0.61, 0.75, 0.11, 0.3],
'Recall': [0.58, 0.93, 0.35, 0.4, 0.13, 0.1, 0.89, 0.86, 0.02, 0.01],
'F1-score': [0.64, 0.74, 0.45, 0.56, 0.2, 0.17, 0.72, 0.8, 0.04, 0.01],
'status': ['Unfiltered', 'Filtered', 'Unfiltered','Filtered','Unfiltered',
'Filtered','Unfiltered','Filtered','Unfiltered','Filtered']}
)
df2.head()
class Precison Recall F1-score status
0 foot 0.70 0.58 0.64 Unfiltered
1 foot 0.62 0.93 0.74 Filtered
2 bike 0.66 0.35 0.45 Unfiltered
3 bike 0.96 0.40 0.56 Filtered
4 bus 0.41 0.13 0.20 Unfiltered
And I want to plot this, in similar grouping as above (i.e. foot
, bike
, bus
, car
, metro
). However, for each of the metrics, I want to place the two values side-by-side. Take for example, the foot
group, I would have two bars Precision[Unfiltered, filtered]
, then 2 bars for Recall[Unfiltered, filtered]
and also 2 bars for F1-score[Unfiltered, filtered]
. Likewise all other groups.
My attempt:
group2 = df2.melt(id_vars=['class', 'status'], var_name=['Metric'])
sns.barplot(data=group2, x='class', y='value', hue='Metric')
Upvotes: 1
Views: 349
Reputation: 402
Seaborn grouped barplots don't allow for multiple grouping variables. One workaround is to recode the two grouping variables (Metric and status) as one variable with 6 levels. Another possibility is to use facets. If you are open to another plotting package, I might recommend plotnine, which allows multiple grouping variables as follows:
import plotnine as p9
fig = (
p9.ggplot(group2)
+ p9.geom_col(
p9.aes(x="class", y="value", fill="Metric", color="Metric", alpha="status"),
position=p9.position_dodge(1),
size=1,
width=0.5,
)
+ p9.scale_color_manual(("red", "blue", "green"))
+ p9.scale_fill_manual(("red", "blue", "green"))
)
fig.draw()
This generates the following image:
Upvotes: 1
Reputation: 895
You can pass in hue
any sequence as long as it has the same length as your data, and assign colours through it.
So you could try with
group2 = df2.melt(id_vars=['class', 'status'], var_name=['Metric'])
sns.barplot(data=group2, x='class', y='value', hue=group2[['Metric','status']].agg(tuple, axis=1))
plt.legend(fontsize=7)
But the result is a bit hard to read:
Upvotes: 2