pandas dataframe iloc vs loc

Guri Bee

2 min read

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19-10-2024

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Pandas: The Power of iloc and loc for Data Selection

Pandas DataFrames are the backbone of many data science workflows. They offer a powerful and intuitive way to manipulate and analyze data. A core feature of Pandas is its ability to select specific data using indices, and here, iloc and loc play a crucial role.

What are iloc and loc?

Both iloc and loc are methods used to select rows and columns from a Pandas DataFrame, but they work with different indexing systems.

• iloc: Stands for integer location. It uses integer-based indexing to select data. This means you specify the row and column numbers (starting from zero) to select the desired data.
• loc: Stands for label location. It uses label-based indexing to select data. You specify the row and column labels (e.g., row names, column names) to access the desired data.

Understanding the Differences:

Let's consider a simple DataFrame:

import pandas as pd

data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],
        'Age': [25, 30, 22, 28],
        'City': ['New York', 'London', 'Paris', 'Tokyo']}
df = pd.DataFrame(data)

Here's how iloc and loc differ:

Name  Age  <br>
1   Bob     30 <br>
2   Charlie  22 <br>
``` |

| loc | Label-based indexing | df.loc[1:3, 'Name':'Age'] | Selects rows with labels 1, 2, and 3, and columns labeled 'Name' and 'Age'. This gives you:
| | | | | python <br> Name Age <br> 1 Bob 30 <br> 2 Charlie 22 <br> 3 David 28 <br> |

Choosing the Right Method:

• iloc: Use iloc when you need to select data based on its numerical position in the DataFrame. It's efficient and straightforward for numerical operations.
• loc: Use loc when you need to select data based on its row and column labels. It provides a more intuitive way to interact with data when labels are meaningful.

Practical Examples:

• iloc: To get the first five rows of a DataFrame: df.iloc[:5]
• loc: To filter rows where 'City' is 'New York': df.loc[df['City'] == 'New York']

Additional Considerations:

• iloc and loc can also be used to modify data within a DataFrame. For instance, df.iloc[0, 0] = 'Eva' would change the value in the first row and first column to 'Eva'.
• You can use Boolean indexing with loc to select specific rows based on conditions.

Conclusion:

Mastering iloc and loc is crucial for efficient data manipulation in Pandas. Remember, iloc uses numerical indexing, while loc uses label-based indexing. Choosing the right method depends on your specific data and task.

Additional Resources:

• Pandas Documentation: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.iloc.html
• Real Python: https://realpython.com/pandas-dataframe-select-columns/

Remember to always test your code and understand the logic behind each operation. Happy coding!