How to get numpy arrays indexing equivalent in pandas data frame?

Question

I have a numpy array as following:

    array([[1, 2],
           [3, 4],
           [5, 6],
           [7, 8]])

The array is called myArray, and I perform two indexing operations on the 2D array and get following results:

    In[1]: a1 = myArray[1:]
           a1

    Out[1]:array([[3, 4],
                 [5, 6],
                 [7, 8]])


    In[2]: a2 = myArray[:-1]
           a2

    Out[2]:array([[1, 2],
                  [3, 4],
                  [5, 6]])

Now, I have the same data in the form of a pandas df in two columns, let the data frame be df

How to do the equivalent indexing/ slicing on two columns to get the same results as above for a1 and a2.

You can access the underlying numpy object using df.values. — Arya McCarthy
– Arya McCarthy, Commented May 8, 2017 at 3:29

akuiper · Accepted Answer · 2017-05-08 03:31:58Z

1

Use iloc:

df.iloc[1:]

#   x   y
#1  3   4
#3  5   6
#4  7   8

df.iloc[:-1]

#   x   y
#0  1   2
#1  3   4
#3  5   6

Use head/tail:

df.head(-1)       # equivalent to df.iloc[:-1]

#   x   y
#0  1   2
#1  3   4
#3  5   6

df.tail(-1)       # equivalent to df.iloc[1:]

#   x   y
#1  3   4
#3  5   6
#4  7   8

answered May 8, 2017 at 3:31

akuiper

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4 Comments

Liza Over a year ago

What if there are more columns in the df, and I just want to get the results on"x" and "y" columns. I know I can delete the columns after indexing, but is there a way to perform indexing on selected columns only?

akuiper Over a year ago

How about df[['x', 'y']].iloc[1:]? Is this what you are looking for?

Liza Over a year ago

Thanks a lot, this is what I wanted.

akhetos Over a year ago

@Psidom how to select multiple line which doesnt follow each other? e.g select lines from list=[1,10,25,100]

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