Anne - 1 year ago 145

Python Question

I have a dataframe like this:

`A B C`

0 1 0.749065 This

1 2 0.301084 is

2 3 0.463468 a

3 4 0.643961 random

4 1 0.866521 string

5 2 0.120737 !

Calling

`In [10]: print df.groupby("A")["B"].sum()`

will return

`A`

1 1.615586

2 0.421821

3 0.463468

4 0.643961

Now I would like to do "the same" for column "C". Because that column contains strings, sum() doesn't work (although you might think that it would concatenate the strings). What I would really like to see is a list or set of the strings for each group, i.e.

`A`

1 {This, string}

2 {is, !}

3 {a}

4 {random}

I have been trying to find ways to do this.

Series.unique() (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.unique.html) doesn't work, although

`df.groupby("A")["B"]`

is a

`pandas.core.groupby.SeriesGroupBy object`

so I was hoping any Series method would work. Any ideas?

Thanks,

Anne

Answer Source

```
In [4]: df = read_csv(StringIO(data),sep='\s+')
In [5]: df
Out[5]:
A B C
0 1 0.749065 This
1 2 0.301084 is
2 3 0.463468 a
3 4 0.643961 random
4 1 0.866521 string
5 2 0.120737 !
In [6]: df.dtypes
Out[6]:
A int64
B float64
C object
dtype: object
```

When you apply your own function, there is not automatic exclusions of non-numeric columns. This is slower, though (that the applicatino of `.sum()`

to the groupby

```
In [8]: df.groupby('A').apply(lambda x: x.sum())
Out[8]:
A B C
A
1 2 1.615586 Thisstring
2 4 0.421821 is!
3 3 0.463468 a
4 4 0.643961 random
```

Sum by default concatenates

```
In [9]: df.groupby('A')['C'].apply(lambda x: x.sum())
Out[9]:
A
1 Thisstring
2 is!
3 a
4 random
dtype: object
```

You can do pretty much what you want

```
In [11]: df.groupby('A')['C'].apply(lambda x: "{%s}" % ', '.join(x))
Out[11]:
A
1 {This, string}
2 {is, !}
3 {a}
4 {random}
dtype: object
```

Doing this a whole frame group at a time. Key is to return a Series

```
def f(x):
return Series(dict(A = x['A'].sum(),
B = x['B'].sum(),
C = "{%s}" % ', '.join(x['C'])))
In [14]: df.groupby('A').apply(f)
Out[14]:
A B C
A
1 2 1.615586 {This, string}
2 4 0.421821 {is, !}
3 3 0.463468 {a}
4 4 0.643961 {random}
```