Incorrect arrangement of bars in facet plot-CodePudding

Situation

I'm having a table with the most important features from a random forrest model. This table looks like:

I used a screenshot as it the easiest way to get an overview, below you'll find a MWE.

My goal is to plot for each Label (the column which is named "Label") a barplot.

Problem

If we melt the table and have a look at the label p24D...

...we see that p24D in combination with m24U shows a value of 0. But if I create my plot it looks like:

We see in the p24D-facet a bar that is greater than 0 which is labeled with m24U.

Conclusion

For me it seems like it takes the correct bar heights and distribution for each group, but the x-labels are wrong, as they are taken from the last plotted facet.

Question

I want to keep the same labeling for all facets, but the bars should be assigned to the correct labels. How can I do this?

MWE

The dataframe and the code for the plot

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

df_imp = pd.DataFrame({'m12D': {0: 0.25987975843758654, 1: 0.18727707779383243,  2: 0.39100295701375354,  3: 0.06384800127268568,
  4: 0.07999849502412754,  5: 0.13640019148970256,  6: 0.1412876367877005,  7: 0.09121120297702168,
  8: 0.0,  9: 0.0}, 
 'm01D': {0: 0.0724816118081828, 1: 0.0,  2: 0.0,  3: 0.0,  4: 0.0,  5: 0.06251755368842625,  6: 0.0,
  7: 0.0,  8: 0.0,  9: 0.0},
 'm06D': {0: 0.06145081213633308,  1: 0.15137716985355018,  2: 0.10217899239161463,  3: 0.0,
  4: 0.0,  5: 0.0,  6: 0.06612051972885429,  7: 0.09406588670435026,  8: 0.0,  9: 0.0},
 'm12U': {0: 0.047766259908712215,  1: 0.11029620061232079,  2: 0.058189273034798476,  3: 0.0,
  4: 0.0,  5: 0.13754236428929292,  6: 0.24967144685607753,  7: 0.3109784881004455,
  8: 0.07243867278541272,  9: 0.06597783007344389},
 'm05D': {0: 0.04166653999225189,  1: 0.07952487761091377,  2: 0.0,  3: 0.0,  4: 0.0,  5: 0.0,
  6: 0.0,  7: 0.0,  8: 0.0,  9: 0.0},
 'Label': {0: 'p03D',  1: 'p06D',  2: 'p12D',  3: 'p24D',  4: 'p48D',  5: 'p03U',  6: 'p06U',
  7: 'p12U',  8: 'p24U',  9: 'p48U'}, 
 'down': {0: 0.0,  1: 0.05780607422803258,  2: 0.0,  3: 0.06127000511754594,  4: 0.05020367686447687,
  5: 0.0,  6: 0.0,  7: 0.0,  8: 0.0,  9: 0.0},
 'm06U': {0: 0.0,  1: 0.0,  2: 0.08466651640727402,  3: 0.0,  4: 0.0,  5: 0.05309415831396426,  
  6: 0.10234265392288792,  7: 0.10916311424468256,  8: 0.039101554776822,  9: 0.04173916824046613},
 'p06T': {0: 0.0,  1: 0.0,  2: 0.056553945901284826,  3: 0.0,  4: 0.0,  5: 0.0,  6: 0.0,  7: 0.0,
  8: 0.0,  9: 0.0},
 'm24D': {0: 0.0,  1: 0.0,  2: 0.0,  3: 0.05209052270599571,  4: 0.06155322163002999,  5: 0.0,
  6: 0.0,  7: 0.0,  8: 0.0,  9: 0.0},
 'p24T': {0: 0.0,  1: 0.0,  2: 0.0,  3: 0.05411155410020583,  4: 0.0,  5: 0.0,  6: 0.0,  7: 0.0,
  8: 0.0,  9: 0.0},
 'wday_6': {0: 0.0,  1: 0.0,  2: 0.0,  3: 0.059271604760677284,  4: 0.0,  5: 0.0,
  6: 0.0,  7: 0.0,  8: 0.035176474643147146,  9: 0.0},
 'p48T': {0: 0.0,  1: 0.0,  2: 0.0,  3: 0.0,  4: 0.04623563899446924,  5: 0.0,  6: 0.0,  7: 0.0,
  8: 0.0,  9: 0.0},
 'wday_4': {0: 0.0,  1: 0.0,  2: 0.0,  3: 0.0,  4: 0.04342742013282083,  5: 0.0,  6: 0.0,
  7: 0.0,  8: 0.0,  9: 0.0},
 'm02D': {0: 0.0,  1: 0.0,  2: 0.0,  3: 0.0,  4: 0.0,  5: 0.049231996352442,
  6: 0.0,  7: 0.0,  8: 0.0,  9: 0.0},
 'up_sum': {0: 0.0,  1: 0.0,  2: 0.0,  3: 0.0,  4: 0.0,  5: 0.0,  6: 0.05893778686091927,
  7: 0.0,  8: 0.0667566604504024,  9: 0.09670821121474267},
 'm05U': {0: 0.0,  1: 0.0,  2: 0.0,  3: 0.0,  4: 0.0,  5: 0.0,  6: 0.0,  7: 0.0590711833092717,
  8: 0.0,  9: 0.0},
 'm24U': {0: 0.0,  1: 0.0,  2: 0.0,  3: 0.0,  4: 0.0,  5: 0.0,  6: 0.0,
  7: 0.0,  8: 0.0844568188980733,  9: 0.11262510249213625},
 'wday_5': {0: 0.0,  1: 0.0,  2: 0.0,  3: 0.0,  4: 0.0,  5: 0.0,  6: 0.0,  7: 0.0,  8: 0.0,
  9: 0.03522099307752065}})

df_imp2 = df_imp.fillna(0)
df_imp2 = df_imp2.melt(id_vars="Label")
df_imp2 = df_imp2.rename(columns={"value":"Importance", "variable":"Feature"})
sns.set_theme(style="white")
g = sns.FacetGrid(df_imp2, col="Label", height=1.5, aspect=5, col_wrap=2, margin_titles=True, despine=False)
g.map(sns.barplot, "Feature", "Importance", order=['m12D', 'm01D', 'm06D', 'm12U', 'm05D',  'down', 'm06U', 'p06T',
       'm24D', 'p24T', 'wday_6', 'p48T', 'wday_4', 'm02D', 'up_sum', 'm05U',
       'm24U', 'wday_5']))
g.figure.subplots_adjust(wspace=0.02, hspace=0.4)
g.set(yticks=np.arange(0,0.4,0.1))

g.set_xticklabels(rotation=30)

plt.show()

CodePudding user response：

so how about you sort your data first before plotting them :

df_imp2 = df_imp.fillna(0)
df_imp2 = df_imp2.melt(id_vars="Label")
df_imp2 = df_imp2.rename(columns={"value":"Importance", "variable":"Feature"})
df_imp2 = df_imp2.sort_values(['Label','Feature']) # sort them before plot
sns.set_theme(style="white")
g = sns.FacetGrid(df_imp2, col="Label", height=1.5, aspect=5, col_wrap=2, margin_titles=True, despine=False)
g.map(sns.barplot, "Feature", "Importance")
g.figure.subplots_adjust(wspace=0.02, hspace=0.4)
g.set(yticks=np.arange(0,0.4,0.1))

g.set_xticklabels(rotation=30)