Using this code I created a seaborn plot to visualize multiple variables in a long format dataset.
import pandas as pd
import seaborn as sns
data = {'Patient ID': [11111, 11111, 11111, 11111, 22222, 22222, 22222, 22222, 33333, 33333, 33333, 33333, 44444, 44444, 44444, 44444, 55555, 55555, 55555, 55555],
'Lab Attribute': ['% Saturation- Iron', 'ALK PHOS', 'ALT(SGPT)', 'AST (SGOT)', '% Saturation- Iron', 'ALK PHOS', 'ALT(SGPT)', 'AST (SGOT)', '% Saturation- Iron', 'ALK PHOS', 'ALT(SGPT)', 'AST (SGOT)', '% Saturation- Iron', 'ALK PHOS', 'ALT(SGPT)', 'AST (SGOT)', '% Saturation- Iron', 'ALK PHOS', 'ALT(SGPT)', 'AST (SGOT)'],
'Baseline': [46.0, 94.0, 21.0, 18.0, 56.0, 104.0, 31.0, 12.0, 50.0, 100.0, 33.0, 18.0, 46.0, 94.0, 21.0, 18.0, 46.0, 94.0, 21.0, 18.0],
'3 Month': [33.0, 92.0, 19.0, 25.0, 33.0, 92.0, 21.0, 11.0, 33.0, 102.0, 18.0, 17.0, 23.0, 82.0, 13.0, 17.0, 23.0, 82.0, 13.0, 17.0],
'6 Month': [34.0, 65.0, 10.0, 14.0, 34.0, 65.0, 10.0, 14.0, 34.0, 65.0, 10.0, 14.0, 34.0, 65.0, 10.0, 14.0, 34.0, 65.0, 10.0, 14.0]}
df = pd.DataFrame(data)
# reshape the dataframe
dfm = df_labs.melt(id_vars=['Patient_ID', 'Lab_Attribute'], var_name='Months')
# change the Months values to numeric
dfm.Months = dfm.Months.map({'Baseline': 0, '3 Month': 3, '6 Month': 6})
# plot a figure level line plot with seaborn
p = sns.relplot(data=dfm, col='Lab_Attribute', x='Months', y='value', hue='Patient_ID', kind='line', col_wrap=5, marker='o', palette='husl',facet_kws={'sharey': False, 'sharex': True},err_style="bars", ci=95,)
plt.savefig('gmb_nw_labs.jpg')
The plots work great, though for some reason the error bars are not displaying, even after adding:
err_style="bars", ci=95,
to sns.replot()
p = sns.relplot(data=dfm, col='Lab_Attribute', x='Months', y='value', hue='Patient_ID', kind='line', col_wrap=5, marker='o', palette='husl',facet_kws={'sharey': False, 'sharex': True},err_style="bars", ci=95,)
Can anyone tell me why this is, are there maybe just too few data points in my data set?
hue, so there are no error bars because no data is being combined. Remove hue='Patient ID' to only show the mean line and error bars.seaborn.lineplot can be mapped onto the seaborn.relplot. By not specifying hue the API will create the error bars
linestyle='' is specified so the mean line is not drawnpython 3.8.12, pandas 1.3.4, matplotlib 3.4.3, seaborn 0.11.2data = {'Patient ID': [11111, 11111, 11111, 11111, 22222, 22222, 22222, 22222, 33333, 33333, 33333, 33333, 44444, 44444, 44444, 44444, 55555, 55555, 55555, 55555],
'Lab Attribute': ['% Saturation- Iron', 'ALK PHOS', 'ALT(SGPT)', 'AST (SGOT)', '% Saturation- Iron', 'ALK PHOS', 'ALT(SGPT)', 'AST (SGOT)', '% Saturation- Iron', 'ALK PHOS', 'ALT(SGPT)', 'AST (SGOT)', '% Saturation- Iron', 'ALK PHOS', 'ALT(SGPT)', 'AST (SGOT)', '% Saturation- Iron', 'ALK PHOS', 'ALT(SGPT)', 'AST (SGOT)'],
'Baseline': [46.0, 94.0, 21.0, 18.0, 56.0, 104.0, 31.0, 12.0, 50.0, 100.0, 33.0, 18.0, 46.0, 94.0, 21.0, 18.0, 46.0, 94.0, 21.0, 18.0],
'3 Month': [33.0, 92.0, 19.0, 25.0, 33.0, 92.0, 21.0, 11.0, 33.0, 102.0, 18.0, 17.0, 23.0, 82.0, 13.0, 17.0, 23.0, 82.0, 13.0, 17.0],
'6 Month': [34.0, 65.0, 10.0, 14.0, 34.0, 65.0, 10.0, 14.0, 34.0, 65.0, 10.0, 14.0, 34.0, 65.0, 10.0, 14.0, 34.0, 65.0, 10.0, 14.0]}
df = pd.DataFrame(data)
# reshape the dataframe
dfm = df.melt(id_vars=['Patient ID', 'Lab Attribute'], var_name='Months')
# change the Months values to numeric
dfm.Months = dfm.Months.map({'Baseline': 0, '3 Month': 3, '6 Month': 6})
# plot a figure level line plot with seaborn
p = sns.relplot(data=dfm, col='Lab Attribute', x='Months', y='value', hue='Patient ID', kind='line', col_wrap=3, marker='o', palette='husl', facet_kws={'sharey': False, 'sharex': True}, err_style="bars", ci=95,)
p.map(sns.lineplot, 'Months', 'value', linestyle='', err_style="bars", color='k')

hue='Patient ID'p = sns.relplot(data=dfm, col='Lab Attribute', x='Months', y='value', kind='line', col_wrap=3, marker='o', palette='husl', facet_kws={'sharey': False, 'sharex': True}, err_style="bars", ci=95)

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