I have a dataframe which results from:

df_grouped = df.groupby(['A', 'B'])['A'].count().sort_values(ascending=False)
df_grouped = pd.DataFrame(df_grouped)
new_index = pd.MultiIndex.from_tuples(df_grouped.index)
df_grouped.index = new_index
df_grouped.reset_index(inplace=True)
df_grouped.columns = ['A', 'B', 'count']

Then, df_grouped is something like:

I have plotted a seaborn.histplot using the following code:

fig, ax = plt.subplots(1, 1, figsize=(10,5))
sns.histplot(x='A', hue='B', data=df_grouped, ax=ax, multiple='stack', weights='count')

and results in the following image:

What I would like is to order the plot based on the total counts of each value of A. I have tried different methods, but I am not able to get a successful result.

Edit

I found a way to do what I wanted.

What I did, is to calculate the total counts by df['A'] values:

df['total_count'] = df.groupby(by='A')['count'].transform('sum')
df = df.sort_values(by=['total_count'], ascending=False)

Then, by using the same plot code from above, I got the desired result.

The answer is similar to what Redox proposed.

In any case, I will try the other options proposed.

CodePudding user response：

• To be clear, the visualization is a stacked bar chart, it's not a histogram, as a histrogram represents the distribution of continuous values, while this is the counts of discrete categorical values.
• This answer starts with the raw dataframe, not the dataframe created with .groupby.

1. The easiest way to do this is create a frequency table of the raw dataframe using

   Data Views

   df

        A    B
   0  A_5  B_5
   1  A_3  B_1
   2  A_4  B_5
   3  A_3  B_4
   4  A_3  B_5
   

   dfc

   B    B_1  B_2  B_3  B_4  B_5  tot_A
   A                                  
   A_1   86  131   15   55   90    377
   A_3   47   90    9   33   61    240
   A_5   37   83   13   33   56    222
   A_4   43   65    9   27   50    194
   A_2   16   21    1    5   24     67
   

   CodePudding user response：

   You can use below code... you need to get the order that you want the graph sorted (A_1, A_3, ...), then sort your dataframe df_grouped before plotting.

   #Groupby A and sort by count - send the output as list to ord
   my_ord = list(df_grouped.groupby(by=['A']).sum().sort_values('count', ascending = False).index)
   
   # Order and sort the dataframe so that the final graph is also sorted
   df_grouped['A'] = pd.Categorical(df_grouped['A'], categories=my_ord, ordered=True)
   df_grouped = df_grouped.sort_values('A')
   
   #Plot graph - no change here
   fig, ax = plt.subplots(1, 1, figsize=(10,5))
   sns.histplot(x='A', hue='B', data=df_grouped, ax=ax, multiple='stack', weights='count')
   

   Output