Highlight stripplot points based on a condition

Question

I am trying to do something like this:

import seaborn as sns
import matplotlib.pyplot as plt

# edit me
fig, ax = plt.subplots(figsize=(9, 6))

tips = sns.load_dataset("tips")
#sns.stripplot(data=tips, x = ['f1','f2'], y=[combined_df.r1_feature1,combined_df.r2_feature1], hue="size", palette="deep")
# wide form with x,y missing

params_anno = dict(jitter=0.25, size=5, palette="flare",
                    dodge=True)
if(value of df1 = value of df2):
    params_anno["linewidth"].append(2)
    params_anno["edgecolor"].append("green")
    
ax = sns.stripplot(data=combined_df.drop(
    "cycle_number", axis=1), **params_anno)
ax.set_ylim([0, 25])
ax.set_xlabel(xlabel="Different reads")
ax.set_ylabel(ylabel="values")

The if condition should determine if certain dots aka data points on the sns.stripplot should stand out with an edge color which makes them stand out. However, this approach above would fail because after meeting the first True instance, everything else afterwards would get annotated.

How should I go about it? I am unaware of how agile sns.stripplot(data=combined_df) is when it comes to handling each and every data point. Is ax.annotate the way to do this?

I have tried using sns.stripplot, but the struggle lies in accessing each data point in isolation, so it can be marked differently if it meets the if condition.

Answer 1

Option 1

• Conditionally separate the data with Boolean indexing in pandas, and then plot multiple stripplots to the same axes.
  • Indexing and selecting data
  • Boolean indexing

import seaborn as sns

# load data
tips = sns.load_dataset("tips")

# separate the data by a condition
mask = tips.total_bill.gt(20)
gt20 = tips[mask]
lt20 = tips[~mask]

# plot the dataframes in separate stripplots
ax = sns.stripplot(x="day", y="total_bill", hue="smoker", data=lt20, jitter=True, dodge=True)
sns.stripplot(x="day", y="total_bill", hue="smoker", data=gt20, jitter=True, dodge=True, edgecolor='r', linewidth=1, legend=None, ax=ax)

# move the legend if needed
sns.move_legend(ax, bbox_to_anchor=(1, 1.02), loc='upper left')

Option 2

• Use pandas.DataFrame.assign to create a new column, 'condition', based on the condition, and use that for hue=.

import seaborn as sns

# load data
tips = sns.load_dataset("tips")

# new column based on a condition
tips = tips.assign(condition=tips.total_bill.gt(20))

# plot the stripplot with hue set to the new column
ax = sns.stripplot(x="day", y="total_bill", hue="condition", data=tips, jitter=True, dodge=True)

# move the legend
sns.move_legend(ax, bbox_to_anchor=(1, 1.02), loc='upper left')

• With dodge=False