Question: I have a DF in Pandas, which looks like:

```
Letters Numbers
A       1
A       3
A       2
A       1
B       1
B       2
B       3
C       2
C       2

```

I'm looking to count the number of similar rows and save the result in a third column. For example, the output I'm looking for:

```
Letters Numbers Events
A       1       2
A       2       1
A       3       1
B       1       1
B       2       1
B       3       1
C       2       2

```

An example of what I'm looking to do is [here](https://stackoverflow.com/questions/15453237/count-occurences-of-a-row-with-pandas-in-python). The best idea I've come up with is to use `count_values()`, but I think this is just for one column. Another idea is to use [`duplicated()`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.duplicated.html), anyway I don't want construct any `for`-loop. I'm pretty sure, that a Pythonic alternative to a for loop exists.

Answer: 
You can groupby these two columns and then calculate the sizes of the groups:

```
In [16]: df.groupby(['Letters', 'Numbers']).size()
Out[16]: 
Letters  Numbers
A        1          2
         2          1
         3          1
B        1          1
         2          1
         3          1
C        2          2
dtype: int64

```

To get a DataFrame like in your example output, you can reset the index with `reset_index`.
You can use a combination of `groupby`, `transform` and then `drop_duplicates`

```
In [84]:

df['Events'] = df.groupby('Letters')['Numbers'].transform(pd.Series.value_counts)
df.drop_duplicates()
Out[84]:
  Letters  Numbers  Events
0       A        1       2
1       A        3       1
2       A        2       1
4       B        1       1
5       B        2       1
6       B        3       1
7       C        2       2

```