Python Pandas Series with .iloc

Pandas is one of the most popular and powerful data analysis libraries in Python. One of the key components of Pandas is Series, which is a one-dimensional array of labeled data. Series is very similar to a Python list, but with extra functionality.

The .iloc property in Pandas Series is used to access data using integer-based indexing. It allows you to select a single value or a subset of values from a Series based on their position rather than their label.

Accessing a Single Value

To access a single value from a Pandas Series using .iloc, you can pass the index position of the value you want to the .iloc property. For example:

import pandas as pd

s = pd.Series([10, 20, 30, 40, 50])
value = s.iloc[2]

print(value)  # Output: 30

In this example, we create a Pandas Series with five values and then use .iloc[2] to access the value at index position 2.

Accessing a Subset of Values

To access a subset of values from a Pandas Series using .iloc, you can pass a list of index positions to the .iloc property. For example:

import pandas as pd

s = pd.Series([10, 20, 30, 40, 50])
subset = s.iloc[[1, 3, 4]]

print(subset)  # Output: 1    20
               #         3    40
               #         4    50
               #         dtype: int64

In this example, we create a Pandas Series with five values and then use .iloc[[1, 3, 4]] to access the values at index positions 1, 3, and 4.

Conclusion

The .iloc property in Pandas Series is a powerful tool for accessing data using integer-based indexing. Whether you need to retrieve a single value or a subset of values, .iloc makes it easy to retrieve the data you need. Try it out in your next data analysis project!