Data Manipulation with loc and iloc

This notebook demonstrates how to select rows and columns in a Pandas DataFrame using .loc and .iloc. Each section explains a different selection technique.

Import Libraries

Import Pandas and NumPy for data manipulation.

import pandas as pd
import numpy as np

Create DataFrame with Custom Index

Create a DataFrame with columns A, B, C and custom row labels (‘a’, ‘b’, ‘c’, ‘d’, ‘e’).

data = {'A': [1,2,3,4,5],
        'B': [6,7,8,9,10],
        'C': [11,12,13,14,15]
        }
df = pd.DataFrame(data, index=['a','b','c','d','e'])
df

	A	B	C
a	1	6	11
b	2	7	12
c	3	8	13
d	4	9	14
e	5	10	15

Select Rows by Label Range with loc

Use df.loc["a":'c'] to select rows from label ‘a’ to ‘c’ (inclusive).

df.loc["a":'c']

	A	B	C
a	1	6	11
b	2	7	12
c	3	8	13

Select Specific Rows by Label with loc

Use df.loc[['a','c']] to select rows with labels ‘a’ and ‘c’.

df.loc[['a','c']]

	A	B	C
a	1	6	11
c	3	8	13

Select Specific Rows and Columns by Label with loc

Use df.loc[['a','c'],['A','C']] to select rows ‘a’ and ‘c’ and columns ‘A’ and ‘C’.

df.loc[['a','c'],['A','C']]

	A	C
a	1	11
c	3	13

Display the DataFrame

Show the entire DataFrame for reference.

df

	A	B	C
a	1	6	11
b	2	7	12
c	3	8	13
d	4	9	14
e	5	10	15

Select Row by Integer Position with iloc

Use df.iloc[0] to select the first row by its integer position.

df.iloc[0]

A     1
B     6
C    11
Name: a, dtype: int64

Select Multiple Rows by Integer Range with iloc

Use df.iloc[0:3] to select rows from position 0 to 2 (Python slicing is exclusive of the end).

df.iloc[0:3]

	A	B	C
a	1	6	11
b	2	7	12
c	3	8	13

Select Rows from a Position to End with iloc

Use df.iloc[3:] to select all rows from position 3 to the end.

df.iloc[3:]

	A	B	C
d	4	9	14
e	5	10	15

Select Specific Rows by Integer Position with iloc

Use df.iloc[[0,3]] to select rows at positions 0 and 3.

df.iloc[[0,3]]

	A	B	C
a	1	6	11
d	4	9	14

Select Specific Rows and Columns by Integer Position with iloc

Use df.iloc[[0,3],[0,2]] to select rows at positions 0 and 3, and columns at positions 0 and 2.

df.iloc[[0,3],[0,2]]

	A	C
a	1	11
d	4	14