I am using .size() on a groupby result in order to count how many items are in each group.

I would like the result to be saved to a new column name without manually editing the column names array, how can it be done?

Thanks

This is what I have tried:

```
grpd = df.groupby(['A','B'])
grpd['size'] = grpd.size()
grpd
```

and the error I got:

TypeError: ‘DataFrameGroupBy’ object does not support item assignment

(on the second line)

The result of `df.groupby(...)`

is not a DataFrame. To get a DataFrame back, you have to apply a function to each group, transform each element of a group, or filter the groups.

It seems like you want a DataFrame that contains (1) all your original data in `df`

and (2) the count of how much data is in each group. These things have different lengths, so if they need to go into the same DataFrame, you’ll need to list the size redundantly, i.e., for each row in each group.

```
df['size'] = df.groupby(['A','B']).transform(np.size)
```

(Aside: It’s helpful if you can show succinct sample input and expected results.)

### Answer：

The `.size()`

built-in method of DataFrameGroupBy objects actually returns a Series object with the group sizes and not a DataFrame. If you want a DataFrame whose column is the group sizes, indexed by the groups, with a custom name, you can use the `.to_frame()`

method and use the desired column name as its argument.

```
grpd = df.groupby(['A','B']).size().to_frame('size')
```

If you wanted the groups to be columns again you could add a `.reset_index()`

at the end.

### Answer：

You need `transform`

`size`

– `len`

of `df`

is same as before:

Notice:

*Here it is necessary to add one column after groupby, else you get an error. Because GroupBy.size count NaNs too, what column is used is not important. All columns working same.*

```
import pandas as pd
df = pd.DataFrame({'A': ['x', 'x', 'x','y','y']
, 'B': ['a', 'c', 'c','b','b']})
print (df)
A B
0 x a
1 x c
2 x c
3 y b
4 y b
df['size'] = df.groupby(['A', 'B'])['A'].transform('size')
print (df)
A B size
0 x a 1
1 x c 2
2 x c 2
3 y b 2
4 y b 2
```

If need set column name in aggregating `df`

– `len`

of `df`

is obviously **NOT** same as before:

```
import pandas as pd
df = pd.DataFrame({'A': ['x', 'x', 'x','y','y']
, 'B': ['a', 'c', 'c','b','b']})
print (df)
A B
0 x a
1 x c
2 x c
3 y b
4 y b
df = df.groupby(['A', 'B']).size().reset_index(name='Size')
print (df)
A B Size
0 x a 1
1 x c 2
2 y b 2
```

### Answer：

lets say n is the name of dataframe and cst is the no of items being repeted.

Below code gives the count in next column

```
cstn=Counter(n.cst)
cstlist = pd.DataFrame.from_dict(cstn, orient='index').reset_index()
cstlist.columns=['name','cnt']
n['cnt']=n['cst'].map(cstlist.loc[:, ['name','cnt']].set_index('name').iloc[:,0].to_dict())
```

Hope this will work