Plotting AxesSubPlot from python list

Question

I have a function that is returning a plot from a DF to show the cluster sizes in an ML model depending on different parameters.

I want to plot each of the different parameter combinations in subplots.

my thinkng was to append each of the plots to a list and plot from there. However, when I append the plot to the list it is giving me the same plot.

function to return the plots

def ngram_range(ranges, DF, DF_col, number_clusters, lst):
  CV = CountVectorizer(ngram_range=(ranges), analyzer='char')
  CV_values = CV.fit_transform(DF_col)
  clusters = MiniBatchKMeans(n_clusters=number_clusters, init_size=1024, batch_size=2048, random_state=20).fit_predict(CV_values)
  DF['Cluster'] = clusters
  new_DF = DF.groupby('Cluster')['Cluster'].count()
  plot = new_DF.plot(kind = 'bar', title = 'Cluster Frequency Ngram range from {} to {}'.format(ranges[0],ranges[1]), xlabel = 'Cluster', ylabel = 'Freq')
  lst.append(plot)
  return(lst)

loop to items to the list

ranges = [[2,2], [2,3], [2,4], [3,3],[3,4],[4,4]]
plots = []
for R in ranges:
  ngram_range(R,df_ru,df_ru['RU_desc'],8,plots)

I tried to solve this as above by appending the list inside the function but is still produces the same result.

Lucas M. Uriarte · Accepted Answer · 2022-09-11 17:08:37Z

the problem here is that pandas.plot() uses the last axes available to plot, therefore before you use the plot you need to create a new axes.

def ngram_range(ranges, DF, DF_col, number_clusters, lst):
  CV = CountVectorizer(ngram_range=(ranges), analyzer='char')
  CV_values = CV.fit_transform(DF_col)
  clusters = MiniBatchKMeans(n_clusters=number_clusters, init_size=1024, batch_size=2048, random_state=20).fit_predict(CV_values)
  DF['Cluster'] = clusters
  new_DF = DF.groupby('Cluster')['Cluster'].count()
  fig, ax = plt.subplots() # create the new figure and axes
  plot = new_DF.plot(kind = 'bar', title = 'Cluster Frequency Ngram range from {} to {}'.format(ranges[0],ranges[1]), xlabel = 'Cluster', ylabel = 'Freq', ax=ax)
  lst.append([fig, plot])
  plt.show()  # use this line in case you want to plot every time the function is call
  return(lst)

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