Creating multiple pandas dataframes from a single dataframe based on iteration

From a single dataframe(tr), I'm trying to create multiple dataframes based on a set of columns(cat_col). New dataframe names must be tr_'colname'.
Could someone help me with the below code?

for col in cat_col:
tr_ = tr[[col,'TARGET']].groupby([col,'TARGET']).size().reset_index(name='Counts')
tr_ = pivot_table(tr_,values='Counts',index=[col],columns=['TARGET'])
print tr_.shape


Output:
(3, 2)
(7, 2)
(8, 2)
(5, 2)
(6, 2)
(6, 2)
(18, 2)
(7, 2)
(58, 2)
(4, 2)
(3, 2)
(7, 2)

tr[['col1','TARGET']].head(10)


col1 TARGET
0 Unaccompanied 1
1 Family 0
2 Unaccompanied 0
3 Unaccompanied 0
4 Unaccompanied 0
5 Spouse, partner 0
6 Unaccompanied 0
7 Unaccompanied 0
8 Children 0
9 Unaccompanied 0

tr_col1.head(3)


TARGET 0 1
col1
Family 37140 3009
Spouse, partner 10475 895
Unaccompanied 228189 20337

tr_

DataFrame

DataFrame

print (type(tr_))

1 Answer
1

I think need:

tr = pd.DataFrame({'A':list('abcdefabcd'),
'B':list('abcdeabffe'),
'TARGET':[1,1,0,0,1,0,1,1,0,1]})

print (tr)
A B TARGET
0 a a 1
1 b b 1
2 c c 0
3 d d 0
4 e e 1
5 f a 0
6 a b 1
7 b f 1
8 c f 0
9 d e 1

cat_col = ['A','B']

d = {}
for col in cat_col:
tr_ = (tr[[col,'TARGET']].groupby([col,'TARGET'])
.size()
.unstack()
.reset_index()
.rename_axis(None, axis=1))
#some another processes if necessary

#check if outout is DataFrame
print (type(tr_))

print (tr_)
#if necessary store to dict
d[col] = tr_


#select df from dict
print (d['A'])
A 0 1
0 a NaN 2.0
1 b NaN 2.0
2 c 2.0 NaN
3 d 1.0 1.0
4 e NaN 1.0
5 f 1.0 NaN


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