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Python Pandas Dataframe: Divide values in two rows based on column values

I have a pandas dataframe:
A B C D
1 1 0 32
1 4
2 0 43
1 12
3 0 58
1 34
2 1 0 37
1 5
[..]
where A, B and C are index columns. What I want to compute is for every group of rows with unique values for A and B: D WHERE C=1 / D WHERE C=0.
The result should look like this:
A B NEW
1 1 4/32
2 12/43
3 58/34
2 1 37/5
[..]
Can you help me?

Use Series.unstack first, so possible divide columns 0,1:
new = df['D'].unstack()
new = new[1].div(new[0]).to_frame('NEW')
print (new)
NEW
A B
1 1 0.125000
2 0.279070
3 0.586207
2 2 0.135135

Filter rows with more than 1 value in a set and count their occurrence pandas python

Let's assume, I have the following data frame.
Id Combinations
1 (A,B)
2 (C,)
3 (A,D)
4 (D,E,F)
5 (F)
I would like to filter out Combination column values with more than value in a set. Something like below. AND I would like count the number of occurrence as whole in Combination column. For example, ID number 2 and 5 should be removed since their value in a set is only 1.
The result I am looking for is:
ID Combination Frequency
1 A 2
1 B 1
3 A 2
3 D 2
4 D 2
4 E 1
4 F 2
Can anyone help to get the above result in Python pandas?

First if necessary convert values to lists:
df['Combinations'] = df['Combinations'].str.strip('(,)').str.split(',')
If need count after filtering only one values by Series.str.len in boolean indexing, then use DataFrame.explode and count values by Series.map with Series.value_counts:
df1 = df[df['Combinations'].str.len().gt(1)].explode('Combinations')
df1['Frequency'] = df1['Combinations'].map(df1['Combinations'].value_counts())
print (df1)
Id Combinations Frequency
0 1 A 2
0 1 B 1
2 3 A 2
2 3 D 2
3 4 D 2
3 4 E 1
3 4 F 1
Or if need count before removing them filter them by Series.duplicated in last step:
df2 = df.explode('Combinations')
df2['Frequency'] = df2['Combinations'].map(df2['Combinations'].value_counts())
df2 = df2[df2['Id'].duplicated(keep=False)]
Alternative:
df2 = df2[df2.groupby('Id').Id.transform('size') > 1]
Or:
df2 = df2[df2['Id'].map(df2['Id'].value_counts() > 1]
print (df2)
Id Combinations Frequency
0 1 A 2
0 1 B 1
2 3 A 2
2 3 D 2
3 4 D 2
3 4 E 1
3 4 F 2

How to sum up values of 'D' column for every row with the same combination of values from columns 'A','B' and 'C?

I need to sum up values of 'D' column for every row with the same combination of values from columns 'A','B' and 'C. Eventually I need to create DataFrame with unique combinations of values from
columns 'A','B' and 'C' with corresponding sum in column D.
import numpy as np
df = pd.DataFrame(np.random.randint(0,3,size=(10,4)),columns=list('ABCD'))
df
OT:
A B C D
0 0 2 0 2
1 0 1 2 1
2 0 0 2 0
3 1 2 2 2
4 0 2 2 2
5 0 2 2 2
6 2 2 2 1
7 2 1 1 1
8 1 0 2 0
9 1 2 0 0
I've tried to create temporary data frame with empty cells
D = pd.DataFrame([i for i in range(len(df))]).rename(columns = {0:'D'})
D['D'] = ''
D
OT:
D
0
1
2
3
4
5
6
7
8
9
And use apply() to sum up all 'D' column values for unique row consisted of columns 'A','B' and 'C'. For example below line returns sum of values from 'D' column for 'A'=0,'B'=2,'C'=2:
df[(df['A']==0) & (df['B']==2) & (df['C']==2)]['D'].sum()
OT:
4
function:
def Sumup(cols):
A = cols[0]
B = cols[1]
C = cols[2]
D = cols[3]
sum = df[(df['A']==A) & (df['B']==B) & (df['C']==C)]['D'].sum()
return sum
apply on df and saved in temp df D['D']:
D['D'] = df[['A','B','C','D']].apply(Sumup)
Later I wanted to use drop_duplicates but I receive dataframe consisted of NaN's.
D
OT:
D
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
5 NaN
6 NaN
7 NaN
8 NaN
9 NaN
Anyone could give me a hint how to manage the NaN problem or what other approach can I apply to solve the original
problem?

df.groupby(['A','B','C']).sum()

import numpy as np
df = pd.DataFrame(np.random.randint(0,3,size=(10,4)),columns=list('ABCD'))
df.groupby(["A", "B", "C"])["D"].sum()

Duplicate row of low occurrence in pandas dataframe

In the following dataset what's the best way to duplicate row with groupby(['Type']) count < 3 to 3. df is the input, and df1 is my desired outcome. You see row 3 from df was duplicated by 2 times at the end. This is only an example deck. the real data has approximately 20mil lines and 400K unique Types, thus a method that does this efficiently is desired.
>>> df
Type Val
0 a 1
1 a 2
2 a 3
3 b 1
4 c 3
5 c 2
6 c 1
>>> df1
Type Val
0 a 1
1 a 2
2 a 3
3 b 1
4 c 3
5 c 2
6 c 1
7 b 1
8 b 1
Thought about using something like the following but do not know the best way to write the func.
df.groupby('Type').apply(func)
Thank you in advance.

Use value_counts with map and repeat:
counts = df.Type.value_counts()
repeat_map = 3 - counts[counts < 3]
df['repeat_num'] = df.Type.map(repeat_map).fillna(0,downcast='infer')
df = df.append(df.set_index('Type')['Val'].repeat(df['repeat_num']).reset_index(),
sort=False, ignore_index=True)[['Type','Val']]
print(df)
Type Val
0 a 1
1 a 2
2 a 3
3 b 1
4 c 3
5 c 2
6 c 1
7 b 1
8 b 1
Note : sort=False for append is present in pandas>=0.23.0, remove if using lower version.
EDIT : If data contains multiple val columns then make all columns columns as index expcept one column and repeat and then reset_index as:
df = df.append(df.set_index(['Type','Val_1','Val_2'])['Val'].repeat(df['repeat_num']).reset_index(),
sort=False, ignore_index=True)

Is it possible to split a Pandas dataframe using groupby and merge each group with separate dataframes

I have a Pandas dataframe that contains a grouping variable. I would like to merge each group with other dataframes based on the contents of one of the columns. So, for example, I have a dataframe, dfA, which can be defined as:
dfA = pd.DataFrame({'a':[1,2,3,4,5,6],
'b':[0,1,0,0,1,1],
'c':['a','b','c','d','e','f']})
a b c
0 1 0 a
1 2 1 b
2 3 0 c
3 4 0 d
4 5 1 e
5 6 1 f
Two other dataframes, dfB and dfC, contain a common column ('a') and an extra column ('d') and can be defined as:
dfB = pd.DataFrame({'a':[1,2,3],
'd':[11,12,13]})
a d
0 1 11
1 2 12
2 3 13
dfC = pd.DataFrame({'a':[4,5,6],
'd':[21,22,23]})
a d
0 4 21
1 5 22
2 6 23
I would like to be able to split dfA based on column 'b' and merge one of the groups with dfB and the other group with dfC to produce an output that looks like:
a b c d
0 1 0 a 11
1 2 1 b 12
2 3 0 c 13
3 4 0 d 21
4 5 1 e 22
5 6 1 f 23
In this simplified version, I could concatenate dfB and dfC and merge with dfA without splitting into groups as shown below:
dfX = pd.concat([dfB,dfC])
dfA = dfA.merge(dfX,on='a',how='left')
print(dfA)
a b c d
0 1 0 a 11
1 2 1 b 12
2 3 0 c 13
3 4 0 d 21
4 5 1 e 22
5 6 1 f 23
However, in the real-world situation, the smaller dataframes will be generated from multiple different complex sources; generating the dataframes and combining into a single dataframe beforehand may not be feasible because there may be overlapping data on the column that will be used for merging the dataframes (but this will be avoided if the dataframe can be split based on the grouping variable). Is it possible to use Pandas groupby() method to do this instead? I was thinking of something like the following (which doesn't work, perhaps because I'm not combining the groups into a new dataframe correctly):
grouped = dfA.groupby('b')
for name, group in grouped:
if name == 0:
group = group.merge(dfB,on='a',how='left')
elif name == 1:
group = group.merge(dfC,on='a',how='left')
Any thoughts would be appreciated.

This will fix your code
l=[]
grouped = dfA.groupby('b')
for name, group in grouped:
if name == 0:
group = group.merge(dfB,on='a',how='left')
elif name == 1:
group = group.merge(dfC,on='a',how='left')
l.append(group)
pd.concat(l)
Out[215]:
a b c d
0 1 0 a 11.0
1 3 0 c 13.0
2 4 0 d NaN
0 2 1 b NaN
1 5 1 e 22.0
2 6 1 f 23.0