5.5. Numbers as Indices

Enough about movie budgets, it’s time to budget my time instead. Because I schedule my day to the minute, I like to be able to look up movies by their runtime, so that when I have a spare two hours and 34 minutes, I can find all the movies that would fit precisely in that time slot. (Popcorn-making time is budgeted separately).

Before you start, here is a refresher on the index operator in Pandas.

Selecting Columns of a DataFrame

Selecting Rows of a DataFrame

If you use an integer in any of the last four examples, it works just like the string, but the index values are numeric instead. What is important (and confusing) about this is that they use the index, not the position. So, if you create a data frame with 4 rows of some data, it will have an index that is created by default where the first row starts with 0, the next row is 1 and so on. However, if you sort the data frame such that the last row becomes the first and the first row becomes the last, using df.loc[0] on the sorted data frame will return the last row.

If you want to be strictly positional, you should use df.iloc[0], which will return the first row regardless of the index value. df.iloc[0:5] is the same as doing df.head(), and df.iloc[[1, 3, 5, 7]] will return four rows: the 2nd, 4th, 6th and 8th.

import pandas as pd
df = pd.DataFrame({'a':list("pythonrocks"), 'b':[1,2,3,4,5,6,7,8,9,10,11]})
df = df.set_index('a')
df.loc['p':'n']
b
a
p 1
y 2
t 3
h 4
o 5
n 6

OK, but what if we do this:

df.loc['p':'o']
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
Cell In[2], line 1
----> 1 df.loc['p':'o']

File ~/rs/.venv/lib/python3.11/site-packages/pandas/core/indexing.py:1073, in _LocationIndexer.__getitem__(self, key)
   1070 axis = self.axis or 0
   1072 maybe_callable = com.apply_if_callable(key, self.obj)
-> 1073 return self._getitem_axis(maybe_callable, axis=axis)

File ~/rs/.venv/lib/python3.11/site-packages/pandas/core/indexing.py:1290, in _LocIndexer._getitem_axis(self, key, axis)
   1288 if isinstance(key, slice):
   1289     self._validate_key(key, axis)
-> 1290     return self._get_slice_axis(key, axis=axis)
   1291 elif com.is_bool_indexer(key):
   1292     return self._getbool_axis(key, axis=axis)

File ~/rs/.venv/lib/python3.11/site-packages/pandas/core/indexing.py:1324, in _LocIndexer._get_slice_axis(self, slice_obj, axis)
   1321     return obj.copy(deep=False)
   1323 labels = obj._get_axis(axis)
-> 1324 indexer = labels.slice_indexer(slice_obj.start, slice_obj.stop, slice_obj.step)
   1326 if isinstance(indexer, slice):
   1327     return self.obj._slice(indexer, axis=axis)

File ~/rs/.venv/lib/python3.11/site-packages/pandas/core/indexes/base.py:6559, in Index.slice_indexer(self, start, end, step, kind)
   6516 """
   6517 Compute the slice indexer for input labels and step.
   6518 
   (...)
   6555 slice(1, 3, None)
   6556 """
   6557 self._deprecated_arg(kind, "kind", "slice_indexer")
-> 6559 start_slice, end_slice = self.slice_locs(start, end, step=step)
   6561 # return a slice
   6562 if not is_scalar(start_slice):

File ~/rs/.venv/lib/python3.11/site-packages/pandas/core/indexes/base.py:6773, in Index.slice_locs(self, start, end, step, kind)
   6771 end_slice = None
   6772 if end is not None:
-> 6773     end_slice = self.get_slice_bound(end, "right")
   6774 if end_slice is None:
   6775     end_slice = len(self)

File ~/rs/.venv/lib/python3.11/site-packages/pandas/core/indexes/base.py:6694, in Index.get_slice_bound(self, label, side, kind)
   6692     slc = lib.maybe_booleans_to_slice(slc.view("u1"))
   6693     if isinstance(slc, np.ndarray):
-> 6694         raise KeyError(
   6695             f"Cannot get {side} slice bound for non-unique "
   6696             f"label: {repr(original_label)}"
   6697         )
   6699 if isinstance(slc, slice):
   6700     if side == "left":

KeyError: "Cannot get right slice bound for non-unique label: 'o'"

Pandas raises an error because there are two ‘o’s in the index. It doesn’t know which one you mean, first? last? If you argue it should use the last then consider the performance implications if this was a really large index? In that case it would be very time consuming to search the index for the last occurance.

On the other hand, if we sort the index then the last instance can be found quite quickly, and with a sorted index loc will work for this example.

df = df.sort_index()
df.loc['c':'o']
b
a
c 9
h 4
k 10
n 6
o 5
o 8

5.5.1. Practice Questions

Create a Series called time_scheduler that is indexed by runtime and has the movie’s title as its values. Note that you will need to use sort_index() in order to be able to look up movies by their duration. Base yourself on df rather than budget_df.

While you’re at it, remove any movie that is less than 10 minutes (you can’t get into it if it’s too short) or longer than 3 hours (who’s got time for that?).

Hint: You may have to use pd.to_numeric to force the runtimes to be numbers (instead of numbers in a string).

Here is a simpler example that shows the movies that are 7 minutes long

 import pandas as pd
 df = pd.read_csv("https://runestone.academy/ns/books/published/httlads/_static/movies_metadata.csv").dropna(axis=1, how='all')
time_scheduler = df.set_index('runtime')
time_scheduler = time_scheduler[['title', 'release_date']]
time_scheduler.loc[7].head()
title release_date
runtime
7.0 Balance 1989-01-01
7.0 Killer Bean 2: The Party 2000-08-08
7.0 The Employment 2008-01-01
7.0 Moscow Clad in Snow 1909-04-09
7.0 Paperman 2012-11-02

Now let’s find all those two-hour-and-34-minute movies.

But what is the 155th shortest movie in this collection?

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