python - Access index of last element in data frame

Question

I've looking around for this but I can't seem to find it (though it must be extremely trivial).

The problem that I have is that I would like to retrieve the value of a column for the first and last entries of a data frame. But if I do:

df.ix[0]['date']

I get:

datetime.datetime(2011, 1, 10, 16, 0)

but if I do:

df[-1:]['date']

I get:

myIndex
13         2011-12-20 16:00:00
Name: mydate

with a different format. Ideally, I would like to be able to access the value of the last index of the data frame, but I can't find how.

I even tried to create a column (IndexCopy) with the values of the index and try:

df.ix[df.tail(1)['IndexCopy']]['mydate']

but this also yields a different format (since df.tail(1)['IndexCopy'] does not output a simple integer).

Any ideas?

Answer 1

The former answer is now superseded by .iloc:

>>> df = pd.DataFrame({"date": range(10, 64, 8)})
>>> df.index += 17
>>> df
    date
17    10
18    18
19    26
20    34
21    42
22    50
23    58
>>> df["date"].iloc[0]
10
>>> df["date"].iloc[-1]
58

---

The shortest way I can think of uses .iget():

>>> df = pd.DataFrame({"date": range(10, 64, 8)})
>>> df.index += 17
>>> df
    date
17    10
18    18
19    26
20    34
21    42
22    50
23    58
>>> df['date'].iget(0)
10
>>> df['date'].iget(-1)
58

Alternatively:

>>> df['date'][df.index[0]]
10
>>> df['date'][df.index[-1]]
58

There's also .first_valid_index() and .last_valid_index(), but depending on whether or not you want to rule out NaNs they might not be what you want.

Remember that df.ix[0] doesn't give you the first, but the one indexed by 0. For example, in the above case, df.ix[0] would produce

>>> df.ix[0]
Traceback (most recent call last):
  File "<ipython-input-489-494245247e87>", line 1, in <module>
    df.ix[0]
[...]
KeyError: 0