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How to access pandas DataFrame elements using the .iloc attribute?
The pandas.DataFrame.iloc attribute is used to access elements from a pandas DataFrame using the integer position. And It is very similar to the pandas.DataFrame “iat” attribute but the difference is, the “iloc” attribute can access a group of elements whereas the “iat” attribute accesses only a single element.
The “.iloc” attribute allows inputs like an integer value, a list of integer values, and a slicing object with integers, and boolean array, etc.
The attribute will raise an “IndexError” if the requested index is out of bounds, except for the slicing indexer object.
Example 1
In this following example, we created a pandas DataFrame using a nested list with integer values. After that, we have applied the iloc attribute with a single integer value.
# importing pandas package
import pandas as pd
# create a Pandas DataFrame
df = pd.DataFrame([[1,2,3,4],[10,20,30,40],[100,200,300,400]], columns=list('abcd'))
print("DataFrame:")
print(df)
# Access the elements using iloc attribute
result = df.iloc[0]
print("Output:")
print(result)Output
The output is given below −
DataFrame: a b c d 0 1 2 3 4 1 10 20 30 40 2 100 200 300 400 Output: a 1 b 2 c 3 d 4 Name: 0, dtype: int64
The iloc attribute accessed the entire 0th-row from the given DataFrame.
Example 2
Let’s access the elements from pandas.DataFrame by providing the list of integer values to the iloc attribute. In this example, we have specified [0,1] to the “iloc” attribute.
# importing pandas package
import pandas as pd
# create a Pandas DataFrame
df = pd.DataFrame([[1,2,3,4],[10,20,30,40],[100,200,300,400]], columns=list('abcd'))
print("DataFrame:")
print(df)
# Access the elements using a list of integers
result = df.iloc[[0,1]]
print("Output:")
print(result)Output
The output is given below −
DataFrame: a b c d 0 1 2 3 4 1 10 20 30 40 2 100 200 300 400 Output: a b c d 0 1 2 3 4 1 10 20 30 40
We have successfully accessed the 0th and 1st-row elements from the pandas DataFrame using the “iloc” attribute. As a result, it returns a new DataFrame object which is displayed in the above output block.
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