Jitter in scatterplot for non-numeric x-axis

Question

I am looking for a way to descriptively scatter a pandas.DataFrame similar to this:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 3 columns):
 #   Column  Non-Null Count  Dtype
---  ------  --------------  -----
 0   type    1000 non-null   object
 1   value   1000 non-null   int64
 2   count   1000 non-null   int64
dtypes: int64(2), object(1)
memory usage: 23.6+ KB

Using pandas.DataFrame.plot or seaborn.scatterplot, the points for each type are all placed on one vertical line overlapping each other. To mitigate this issue I want to introduce at least some jitter in the x-direction but I don't know how.

My plots so far:

import pandas as pd
import matplotlib.pyplot as plt
import random

df = pd.DataFrame({
    'type': [random.choice(['t1', 't2', 't3']) for _ in range(1000)],
    'value': [random.randint(0, 500) for _ in range(1000)],
    'count': [random.randint(0,250) for _ in range(1000)],
    })

df.plot(kind='scatter', x='type', y='value', c='count', cmap='Blues')
plt.show()

import seaborn as sns

sns.scatterplot(x='type', y='value', data=df, hue='count')
plt.show()

Answer 1

I managed to jitter the types by encoding the types with numeric values and then jitter them instead. However, this requires at least 1 more column in the DataFrame.

import pandas as pd
import matplotlib.pyplot as plt
import random

df = pd.DataFrame({
    'type': [random.choice(['t1', 't2', 't3']) for _ in range(1000)],
    'value': [random.randint(0, 500) for _ in range(1000)],
    'count': [random.randint(0,250) for _ in range(1000)],
    })

def jitter(x):
    return x + random.uniform(0, .5) -.25

type_ids = {'t1': 1, 't2': 2, 't3': 3}

df['type_id'] = df['type'].apply(lambda x: type_ids[x])
df['jitter_type'] = df['type_id'].apply(lambda x: jitter(x))

df.plot(kind='scatter', x='jitter_type', y='value', c='count', cmap='Blues')
plt.xticks([1,2,3])
plt.gca().set_xticklabels(['t1', 't2', 't3'])
plt.show()

Answer 2

The problem with your approach is that seaborn's scatterplot lacks specific functionality that makes sense in the context of categorical data, e.g., jitter. Hence, seaborn provides "scatterplots for caterogical data": stripplot or swarmplot. But seaborn creates an ... interesting figure legend. We have to get rid of this and replace it with a colorbar:

#fake data generation
import pandas as pd
import numpy as np

np.random.seed(123)
ndf = 1000
df = pd.DataFrame({
    'Type': [np.random.choice(['t1', 't2', 't3']) for _ in range(ndf)],
    'Val': [np.random.randint(0, 700) for _ in range(ndf)],
    'Cou': [np.random.randint(0, 500) for _ in range(ndf)],
    })
    
#now the actual plotting  
import seaborn as sns
from matplotlib import colors, cm
import matplotlib.pyplot as plt
    
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))

#preparation for the colorbars
pal = "coolwarm"
normpal = colors.Normalize(df.Cou.min(), df.Cou.max())

#stripplot display
sns.stripplot(x="Type", y="Val", data=df, hue="Cou", palette=pal, ax=ax1, jitter=0.2)
ax1.get_legend().remove()
ax1.set_title("stripplot")
fig.colorbar(cm.ScalarMappable(cmap=pal, norm=normpal), ax=ax1)

#swarmplot display
sns.swarmplot(x="Type", y="Val", data=df, hue="Cou", palette=pal, ax=ax2)
ax2.get_legend().remove()
ax2.set_title("swarmplot")
fig.colorbar(cm.ScalarMappable(cmap=pal, norm=normpal), ax=ax2)

plt.tight_layout()
plt.show()

Sample output: