The take() method in pandas helps us select rows and columns based on their position in the dataframe. We provide an array of ints indicating which positions to take.
syntax –
DataFrame.take(indices, axis=0)indices – An array of ints indicating which positions to take.
axis – The axis on which to select elements. 0 means that we are selecting rows, 1 means that we are selecting columns.
Examples –
Let’s create a dataframe to work with.
import pandas as pd
data = {'Apple':[89, 89, 90, 110, 125, 84, 131, 123, 123, 140, 145, 145],
'Orange': [46, 46, 50, 65, 63, 48, 110, 120, 60, 42, 47, 62],
'Banana': [26, 30, 30, 25, 38, 22, 22, 36, 20, 27, 23, 34 ],
'Mango': [80, 80, 90, 125, 130, 150, 140, 140, 135, 135, 80, 90]}
index = ['Jan','Feb','Mar','Apr','May','June','Jul','Aug','Sep','Oct','Nov','Dec']
df = pd.DataFrame(data, index=index)
df
1 . Select Rows Based on Row Position –
Let’s say that we want to select the 1st row, 5th row and the 10th row. Remember that indexing in python starts with 0.
# select 1st, 5th and 10th row
df.take([0, 4, 9])
By default the axis is set to axis=0 or index i.e. it will select rows. If you want you can explicitly set this like shown below.
# select 1st, 5th and 10th row
df.take([0, 4, 9], axis='index')2 . Select Columns based on Column Position –
We can also select columns based on column position. Let’s say that we want to select the 1st and the 3rd columns i.e. Apple and Banana.
# select 1st and 3rd column
df.take([0, 2], axis='columns')