Python dataframe drop rows which occur less frequently [duplicate]

Question

I have a data frame with repeatedly occurring rows with different names. I want to delete less occurring rows. My data frame is very big. I am giving only a small size here.

dataframe:

df = 
         name     value
    0      A      10
    1      B      20
    2      A      30
    3      A      40
    4      C      50
    5      C      60
    6      D      70

In the above data frame B and D rows occurred fewer times. That is less than 1. I want to delete/drop all such rows that occur less than 2.

My code:

##### Net strings
net_strs = df['name'].unique().tolist()
strng_list = df.group.unique().tolist()
tempdf = df.groupby('name').count()
##### strings that have less than 2 measurements in whole data set
lesstr = tempdf[tempdf['value']<2].index
##### Strings that have more than 2 measurements in whole data set
strng_list = np.setdiff1d(net_strs,lesstr).tolist()
##### Removing the strings with less measurements
df = df[df['name']==strng_list]

My present output:

ValueError: Lengths must match to compare

My expected output:

         name     value
    0      A      10
    1      A      30
    2      A      40
    3      C      50
    4      C      60

Answer 1

You could find the count of each element in name and then select rows only those rows having names that occur more than once.

v = df.name.value_counts()
df[df.name.isin(v.index[v.gt(1)])]

Output :

    name    value
0   A   10
2   A   30
3   A   40
4   C   50
5   C   60

Answer 2

I believe this code should give you what you want.

df['count'] = df.groupby('name').transform('count')
df2 = df.loc[df['count'] >= 2].drop(columns='count')