Getting count of rows from breakpoints of different column - pandas

Question

Consider there are two columns A and B in a dataframe. How can I decile column A and use those breakpoints of column A deciles to calculate the count of rows in column B??
import pandas as pd
import numpy as np
df=pd.read_excel("E:\Sai\Development\UCG\qcut.xlsx")
df['Range']=pd.qcut(df['a'],10)
df_gb=df.groupby('Range',as_index=False).agg({'a':[min,max,np.size]})
df_gb.columns = df_gb.columns.droplevel()
df_gb=df_gb.rename(columns={'':'Range','size':'count_A'})
df['Range_B']=0
df['Range_B'].loc[df['b']<=df_gb['max'][0]]=1
df['Range_B'].loc[(df['b']>df_gb['max'][0]) & (df['b']<=df_gb['max'][1])]=2
df['Range_B'].loc[(df['b']>df_gb['max'][1]) & (df['b']<=df_gb['max'][2])]=3
df['Range_B'].loc[(df['b']>df_gb['max'][2]) & (df['b']<=df_gb['max'][3])]=4
df['Range_B'].loc[(df['b']>df_gb['max'][3]) & (df['b']<=df_gb['max'][4])]=5
df['Range_B'].loc[(df['b']>df_gb['max'][4]) & (df['b']<=df_gb['max'][5])]=6
df['Range_B'].loc[(df['b']>df_gb['max'][5]) & (df['b']<=df_gb['max'][6])]=7
df['Range_B'].loc[(df['b']>df_gb['max'][6]) & (df['b']<=df_gb['max'][7])]=8
df['Range_B'].loc[(df['b']>df_gb['max'][7]) & (df['b']<=df_gb['max'][8])]=9
df['Range_B'].loc[df['b']>df_gb['max'][8]]=10
df_gb_b=df.groupby('Range_B',as_index=False).agg({'b':np.size})
df_gb_b=df_gb_b.rename(columns={'b':'count_B'})
df_final = pd.concat([df_gb, df_gb_b], axis=1)
df_final=df_final[['Range','count_A','count_B']]
Is there any simple solution, as I intend to do for so many columns

Answer 1

I hope this would help:
df['Range'] = pd.qcut(df['a'], 10)
df2 = df.groupby(['Range'])['a'].count().reset_index().rename(columns = {'a':'count_A'})
for item in df2['Range'].values:
df2.loc[df2['Range'] == item, 'count_B'] = df['b'].apply(lambda x: x in item).sum()
df2 = df2.sort_values('Range', ascending = True)
if you want to additionally count values b that are out of range a:
min_border = df2['Range'].values[0].left
max_border = df2['Range'].values[-1].right
df2.loc[0, 'count_B'] += df.loc[df['b'] <= min_border, 'b'].count()
df2.iloc[-1, 2] += df.loc[df['b'] > max_border, 'b'].count()

Answer 2

One way -
df = pd.DataFrame({'A': np.random.randint(0, 100, 20), 'B': np.random.randint(0, 10, 20)})
bins = [0, 1, 4, 8, 16, 32, 60, 100, 200, 500, 5999]
labels = ["{0} - {1}".format(i, j) for i, j in zip(bins, bins[1:])]
df['group_A'] = pd.cut(df['A'], bins, right=False, labels=labels)
df['group_B'] = pd.cut(df.B, bins, right=False, labels=labels)
df1 = df.groupby(['group_A'])['A'].count().reset_index()
df2 = df.groupby(['group_B'])['B'].count().reset_index()
df_final = pd.merge(df1, df2, left_on =['group_A'], right_on =['group_B']).drop(['group_B'], axis=1).rename(columns={'group_A': 'group'})
print(df_final)
Output
group A B
0 0 - 1 0 1
1 1 - 4 1 3
2 4 - 8 1 9
3 8 - 16 2 7
4 16 - 32 3 0
5 32 - 60 7 0
6 60 - 100 6 0
7 100 - 200 0 0
8 200 - 500 0 0
9 500 - 5999 0 0