python list或pandas dataframe任意索引和切片

Question

I have used both R and Python extensively in my work, and at times I get the syntax between them confused.

In R, if I wanted to create a model from only some features of my data set, I can do something like this:

subset = df[1:1000, c(1,5,14:18,24)]

This would take the first 1000 rows (yes, R starts on index 1), and it would take the 1st, 5th, 14th through 18th, and 24th columns.

I have tried to do any combination of slice , range , and similar sorts of functions, and have not been able to duplicate this sort of flexibility. In the end, I just enumerated all of the values.

How can this be done in Python?

Pick an arbitrary subset of elements from a list, some of which are selected individually (as in the commas shown above) and some selected sequentially (as in the colons shown above)?

Answer 1

In a file of index_tricks , numpy defines a class instance that converts a scalars and slices into an enumerated list, using the r_ method:

In [560]: np.r_[1,5,14:18,24]
Out[560]: array([ 1,  5, 14, 15, 16, 17, 24])

It's an instance with a __getitem__ method, so it uses the indexing syntax. It expands 14:18 into np.arange(14,18) . It can also expand values with linspace .

So I think you'd rewrite

subset = df[1:1000, c(1,5,14:18,24)]

as

df.iloc[:1000, np.r_[0,4,13:17,23]]

Answer 2

You can use iloc for integer indexing in pandas:

df.iloc[0:10000, [0, 4] + range(13,18) + [23]]

As commented by @root, in Python 3, you need to explicitly convert range() to list by df.iloc[0:10000, [0, 4] + list(range(13,18)) + [23]]

Answer 3

Try this, The first square brackets filter. The second set of square brackets slice.

df[[0,4]+ range(13,18)+[23]][:1000]