Filter row based on counting the same thing value happening more

Question

I have DataFrame where the "name" variable is sometimes repetitive. I want to filter only those row where we have the repetitive name. For example,

name=['A','B','C','A','C','A']
value=[4,7,8,9,7,6]
a=pd.DataFrame(name, value)
a.reset_index(level=0, inplace=True)
a.columns=['Value','Name']
a

Now I want to filter and create another DataFrame where we have only those row where same names occurs at least twice. Like for this one I don't want the row that include B as it occurs only once, like in the image:

I think, I might need to use something like count and unique or something like that in the condition

Answer 1

You could use duplicated with keep=False. It marks all duplicates as True, so what the below code does is it filters out all rows without any duplicates.

out = a[a['Name'].duplicated(keep=False)]

Output:

   Value Name
0      4    A
2      8    C
3      9    A
4      7    C
5      6    A

If we want to filter rows with a certain number of "Name" occurrences, we could use value_counts + map:

out = a[a['Name'].map(a['Name'].value_counts()>=3)]

or groupby + size:

out = a[a.groupby('Name')['Value'].transform('count')>=3]

Output:

   Value Name
0      4    A
3      9    A
5      6    A

Answer 2

You can use:

a[a["Name"].apply(lambda x: a["Name"].tolist().count(x)) >= 2]

Output

	Value	Name
0	4	A
2	8	C
3	9	A
4	7	C
5	6	A

If you are intersted in having the values based on the repetition number, you can use:

repNum = 3
a[a["Name"].apply(lambda x: a["Name"].tolist().count(x)) == 3]

Output

	Value	Name
0	4	A
3	9	A
5	6	A

I suggest to initialize a["Name"].tolist() before using it in the apply function since it might be more efficient:

nameList = a["Name"].tolist()
# Rest of the code

Answer 3

You can check with groupby transform count

cnt = a.groupby('Name')['Name'].transform('count')
n = 3
a[cnt==n]
Out[166]: 
   Value Name
0      4    A
3      9    A
5      6    A