There are several ways of selecting data from a Pandas DataFrame and iloc is one of them. In Pandas, iloc for DataFrame is integer-location based indexing for selection by position.

According to the documentation:

.iloc[] is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array.

Allowed inputs are:

• An integer, e.g. 5.
• A list or array of integers, e.g. [4, 3, 0].
• A slice object with ints, e.g. 1:7.
• A boolean array.
• A callable function with one argument (the calling Series, DataFrame or Panel) and that returns valid output for indexing (one of the above). This is useful in method chains, when you don’t have a reference to the calling object, but would like to base your selection on some value.

Following is the code with comments, description and results of the commands to be run in Python 3 :

Generating a Pandas Dataframe in Python

#import Pandas
import pandas as pd

# Generate a Pandas DataFrame
df_students = pd.DataFrame(
           {'Name': ['John', 'Sally', 'Joe', 'Anthony', 'Jim', 'Alexander', 'Anna'],
            'Salary':[100000, 108000, 100000, 378000, 110000,  80000, 118000]})

# Get first 5 rows of your Pandas DataFrame
df_students.head()

        Name	Salary
0	John	100000
1	Sally	108000
2	Joe	100000
3	Anthony	378000
4	Jim	110000

Using .iloc in different ways to extract rows from Pandas DataFrame

# This will return a Series
print(df_students.iloc[0])

Name        John
Salary    100000
Name: 0, dtype: object

type(df_students.iloc[0])

pandas.core.series.Series

# This will return a DataFrame
print(df_students.iloc[[0]])

  Name  Salary
0  John  100000

type(df_students.iloc[[0]])

pandas.core.frame.DataFrame

print(df_students.iloc[:2])

    Name  Salary
0   John  100000
1  Sally  108000

type(df_students.iloc[:2])

pandas.core.frame.DataFrame

print(df_students.iloc[1:2])

    Name  Salary
1  Sally  108000

print(df_students.iloc[0:1])

   Name  Salary
0  John  100000