How to apply the slicing indexer to the pandas DataFrame.iloc attribute?

The pandas DataFrame.iloc is an attribute that is used to access the elements of the DataFrame using integer-location-based index values.

The attribute .iloc only takes the integer values which are specifying the row and column index positions. Generally, the position-based index values are represented from 0 to length-1.

Beyond this range only we can access the DataFrame elements otherwise it will raise an “IndexError”. But the slice indexer won’t raise “IndexError” for out-of-bound index value, because it allows out-of-bounds index values.

Example 1

In this following example, we have applied the slicing indexer to the iloc attribute to access the values from the 1st -3rd row. Here, 3 is excluded.

# importing pandas package
import pandas as pd

# create a Pandas DataFrame
df = pd.DataFrame([['a','b'],['c','d'],['e','f'],['g','h']], columns=['col1','col2'])

print("DataFrame:")
print(df)

# Access the elements using slicing indexer
result = df.iloc[1:3]
print("Output:")
print(result)

Output

The output is given below −

DataFrame:
  col1 col2
0    a   b
1    c   d
2    e   f
3    g   h

Output:
 col1 col2
1   c   d
2   e   f

The iloc attribute successfully accessed the 2 rows elements from the given DataFrame by specifying the slicing indexer object to the “.iloc” attribute.

Example 2

Now, let us apply the slicing indexer with negative bound values to the iloc attribute.

# importing pandas package
import pandas as pd

# create a Pandas DataFrame
df = pd.DataFrame([['a','b'],['c','d'],['e','f'],['g','h']], columns=['col1','col2'])

print("DataFrame:")
print(df)

# Apply slicing indexer with negative bound values
result = df.iloc[-4:-1]
print("Output:")
print(result)

Output

The output is given below −

DataFrame:
 col1 col2
0   a   b
1   c   d
2   e   f
3   g   h

Output:
 col1 col2
0  a   b
1  c   d
2  e   f

The negative bound values [-4:-1] are given to the iloc attribute. Then it returns a new DataFrame with accessed elements