· Pandas · 4 min read

Pandas iloc[] Function: A Comprehensive Guide.

Selecting Rows with Pandas iloc[] Function

Pandas iloc[] function is a very powerful tool for selecting rows from a pandas DataFrame.

iloc[] function selects rows based on integer positions. This allows you to easily slice and manipulate data without having to manually loop through the rows.

To use iloc[], simply pass it one or more integer positions. You can specify a single integer to get a specific row at that position or you can specify a range of integers to get a range of rows.

Here is an example:

import pandas as pd

# define some data
data = {
    'name': ['Alice', 'Bob', 'Charlie', 'Dave', 'Edith'],
    'age': [25, 32, 18, 47, 33],
    'gender': ['F', 'M', 'M', 'M', 'F'],
}
df = pd.DataFrame.from_dict(data)

# select the first row
df.iloc[0]

# select the first two rows
df.iloc[0:2]

# select the last two rows
df.iloc[-2:]

In this example, we are using iloc[] to select different ranges of rows from a DataFrame.

You can use iloc[] in many different ways to manipulate and slice data in a pandas DataFrame. It is a powerful tool that can save you a lot of time and effort in your data analysis workflows.

So the takeaway here is, if you need to select rows based on their position in a DataFrame, look no further than the iloc[] function!

Indexing and Slicing with iloc[] Function

One of the most powerful features of iloc[] function is its ability to select and slice data within a DataFrame based on index.

The DataFrame may have an index, which is a label that uniquely identifies each row. It can be a simple numeric index or a more complex multi-level index.

To use iloc[] with an index, you can specify the label(s) of the row(s) you want to select or slice.

Here is an example:

import pandas as pd

# define some data
data = {
    'name': ['Alice', 'Bob', 'Charlie', 'Dave', 'Edith'],
    'age': [25, 32, 18, 47, 33],
    'gender': ['F', 'M', 'M', 'M', 'F'],
}
df = pd.DataFrame.from_dict(data)

# set the index to the 'name' column
df = df.set_index('name')

# select a single row by label
df.loc['Bob']

# select a range of rows by label
df.loc['Charlie':'Edith']

# select specific rows by label
df.loc[['Bob','Dave']]

# select a specific column by label and a specific row by label
df.loc['Bob','age']

# select a specific column by label and a range of rows by label
df.loc['Charlie':'Edith','age']

In this example, we are using iloc[] to select and slice data within a DataFrame based on index. By setting the index and using loc[] function in combination with iloc[], we can easily and efficiently slice and manipulate large amounts of data sets.

In conclusion, iloc[] function is a very powerful tool for selecting, indexing and slicing rows in a pandas DataFrame. By using the right combination of techniques, you can quickly and easily navigate through your data and extract the specific information you need.

Filtering Data with Pandas iloc[] Function

Sometimes you need to select rows from a DataFrame that meet certain conditions. This is where filtering comes in handy. Pandas iloc[] Function can be used to filter data in a DataFrame based on various criteria.

Here is an example:

import pandas as pd

# define some data
data = {
    'name': ['Alice', 'Bob', 'Charlie', 'Dave', 'Edith'],
    'age': [25, 32, 18, 47, 33],
    'gender': ['F', 'M', 'M', 'M', 'F'],
}
df = pd.DataFrame.from_dict(data)

# filter by age greater than 30
df[df['age'] > 30]

# filter by gender equals to 'F'
df[df['gender'] == 'F']

# filter by age greater than 20 and gender equals to 'M'
df[(df['age'] > 20) & (df['gender'] == 'M')]

In the above example, we are filtering data from a DataFrame based on different conditions such as age and gender.

We use the logical & to combine multiple conditions. Similarly, we can use the | operator to combine conditions with an or statement.

By using iloc[] to filter the data, we can easily extract information that meets our criteria. This is useful when working with large data sets where finding specific information may be difficult.

In conclusion, Pandas iloc[] Function is a powerful tool for filtering data within a pandas DataFrame. By using conditional operators and logical operators, you can easily extract specific information that meets your criteria.

Summary

In this blog post, we have explored the different ways of selecting, slicing, indexing, and filtering data with Pandas iloc[] Function. We have seen how it can be used to manipulate and slice data without having to loop through rows manually. By setting the index and using logical operators, we can quickly filter and select specific rows and columns of data that meet our criteria. If you’re working with large data sets in Python, mastering Pandas iloc[] Function is a must. Follow the examples and tips shared here to improve your data analysis skills and simplify your workflows.

Share: