Pandas loc Function

Pandas loc Function

The loc function in pandas is a powerful tool for selecting data from a DataFrame based on label information. It is one of the most commonly used functions for data manipulation and querying in the pandas library. This article will explore the loc function in detail, providing a comprehensive guide on its usage with various examples.

Introduction to Pandas loc

Pandas is a Python library used for data manipulation and analysis. It provides several functions to perform complex data operations with ease. One of these functions is loc, which is used for label-based indexing. This function allows users to access a group of rows and columns by labels or a boolean array.

Basic Syntax of loc

The basic syntax of the loc function is:

dataframe.loc[row_labels, column_labels]

Here, row_labels and column_labels can be labels, lists of labels, a slice object with labels, or a boolean array.

Examples of Using loc

Example 1: Selecting a Single Row by Label

import pandas as pd

data = {
    'Website': ['pandasdataframe.com', 'example.com', 'testsite.com'],
    'Visits': [1000, 1500, 800]
}
df = pd.DataFrame(data)
result = df.loc[0]
print(result)

Output:

Example 2: Selecting Multiple Rows by Labels

import pandas as pd

data = {
    'Website': ['pandasdataframe.com', 'example.com', 'testsite.com'],
    'Visits': [1000, 1500, 800]
}
df = pd.DataFrame(data)
result = df.loc[[0, 2]]
print(result)

Output:

Example 3: Selecting Slices of Rows

import pandas as pd

data = {
    'Website': ['pandasdataframe.com', 'example.com', 'testsite.com'],
    'Visits': [1000, 1500, 800]
}
df = pd.DataFrame(data)
result = df.loc[0:1]
print(result)

Output:

Example 4: Selecting Columns by Label

import pandas as pd

data = {
    'Website': ['pandasdataframe.com', 'example.com', 'testsite.com'],
    'Visits': [1000, 1500, 800]
}
df = pd.DataFrame(data)
result = df.loc[:, 'Website']
print(result)

Output:

Example 5: Selecting Multiple Columns by Labels

import pandas as pd

data = {
    'Website': ['pandasdataframe.com', 'example.com', 'testsite.com'],
    'Visits': [1000, 1500, 800]
}
df = pd.DataFrame(data)
result = df.loc[:, ['Website', 'Visits']]
print(result)

Output:

Example 6: Selecting Rows and Columns

import pandas as pd

data = {
    'Website': ['pandasdataframe.com', 'example.com', 'testsite.com'],
    'Visits': [1000, 1500, 800]
}
df = pd.DataFrame(data)
result = df.loc[0, 'Website']
print(result)

Output:

Example 7: Selecting Rows by Condition

import pandas as pd

data = {
    'Website': ['pandasdataframe.com', 'example.com', 'testsite.com'],
    'Visits': [1000, 1500, 800]
}
df = pd.DataFrame(data)
result = df.loc[df['Visits'] > 1000]
print(result)

Output:

Example 8: Using Boolean Arrays

import pandas as pd

data = {
    'Website': ['pandasdataframe.com', 'example.com', 'testsite.com'],
    'Visits': [1000, 1500, 800]
}
df = pd.DataFrame(data)
result = df.loc[df['Website'].str.contains('pandasdataframe.com')]
print(result)

Output:

Example 9: Updating Data Using loc

import pandas as pd

data = {
    'Website': ['pandasdataframe.com', 'example.com', 'testsite.com'],
    'Visits': [1000, 1500, 800]
}
df = pd.DataFrame(data)
df.loc[0, 'Visits'] = 1200
print(df)

Output:

Example 10: Adding a New Column Using loc

import pandas as pd

data = {
    'Website': ['pandasdataframe.com', 'example.com', 'testsite.com'],
    'Visits': [1000, 1500, 800]
}
df = pd.DataFrame(data)
df.loc[:, 'NewColumn'] = 'Sample'
print(df)

Output:

Advanced Usage of loc

Example 11: Using loc with MultiIndex DataFrames

import pandas as pd

tuples = [('pandasdataframe.com', '2021'), ('pandasdataframe.com', '2022'), ('example.com', '2021')]
index = pd.MultiIndex.from_tuples(tuples, names=['Website', 'Year'])
data = {
    'Visits': [1000, 1200, 1500]
}
df = pd.DataFrame(data, index=index)
result = df.loc[('pandasdataframe.com', '2021')]
print(result)

Output:

Example 12: Conditional Selection with loc and Functions

import pandas as pd

data = {
    'Website': ['pandasdataframe.com', 'example.com', 'testsite.com'],
    'Visits': [1000, 1500, 800]
}
df = pd.DataFrame(data)
result = df.loc[df['Visits'].apply(lambda x: x > 1000)]
print(result)

Output:

Example 13: Selecting Rows Using Index Data

import pandas as pd

data = {
    'Website': ['pandasdataframe.com', 'example.com', 'testsite.com'],
    'Visits': [1000, 1500, 800]
}
df = pd.DataFrame(data, index=['A', 'B', 'C'])
result = df.loc['A']
print(result)

Output:

Example 14: Complex Conditions

import pandas as pd

data = {
    'Website': ['pandasdataframe.com', 'example.com', 'testsite.com'],
    'Visits': [1000, 1500, 800]
}
df = pd.DataFrame(data)
result = df.loc[(df['Visits'] > 800) & (df['Website'].str.contains('com'))]
print(result)

Output:

Example 15: Using loc to Select Rows and Columns Simultaneously

import pandas as pd

data = {
    'Website': ['pandasdataframe.com', 'example.com', 'testsite.com'],
    'Visits': [1000, 1500, 800]
}
df = pd.DataFrame(data)
result = df.loc[df['Visits'] > 1000, ['Website']]
print(result)

Output:

Example 16: Modifying a Slice of the DataFrame

import pandas as pd

data = {
    'Website': ['pandasdataframe.com', 'example.com', 'testsite.com'],
    'Visits': [1000, 1500, 800]
}
df = pd.DataFrame(data)
df.loc[df['Visits'] > 1000, 'Visits'] = 2000
print(df)

Output:

Example 17: Using loc with a Callable

import pandas as pd

data = {
    'Website': ['pandasdataframe.com', 'example.com', 'testsite.com'],
    'Visits': [1000, 1500, 800]
}
df = pd.DataFrame(data)
result = df.loc[lambda df: df['Visits'] > 1000, ['Website']]
print(result)

Output:

Example 18: Selecting Data with a List of Labels

import pandas as pd

data = {
    'Website': ['pandasdataframe.com', 'example.com', 'testsite.com'],
    'Visits': [1000, 1500, 800]
}
df = pd.DataFrame(data)
result = df.loc[[0, 2], 'Website']
print(result)

Output:

Example 19: Usingloc to Update Multiple Columns

import pandas as pd

data = {
    'Website': ['pandasdataframe.com', 'example.com', 'testsite.com'],
    'Visits': [1000, 1500, 800],
    'Revenue': [200, 300, 150]
}
df = pd.DataFrame(data)
df.loc[df['Visits'] > 800, ['Visits', 'Revenue']] = [1600, 400]
print(df)

Output:

Example 20: Inserting a New Row Using loc

import pandas as pd

data = {
    'Website': ['pandasdataframe.com', 'example.com', 'testsite.com'],
    'Visits': [1000, 1500, 800]
}
df = pd.DataFrame(data)
df.loc[3] = ['newsite.com', 500]
print(df)

Output:

Pandas loc Function Conclusion

The loc function in pandas is a versatile tool that can be used for a wide range of data selection and manipulation tasks. It allows for label-based indexing, which is intuitive and useful especially when dealing with complex data sets. The examples provided in this article demonstrate the flexibility of loc in accessing and modifying data within a DataFrame. Whether you are filtering data, updating values, or adding new rows and columns, loc provides a robust way to perform these operations efficiently.

Understanding and mastering the use of loc can significantly enhance your data manipulation capabilities in Python using pandas. It is recommended to practice these examples and experiment with different variations to fully grasp the potential of the loc function.