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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
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