Pandas DataFrame loc Example

Pandas DataFrame loc Example

Pandas is a powerful data manipulation library in Python. It provides data structures and functions needed to manipulate structured data. One of the most commonly used data structure in pandas is DataFrame. DataFrame is a two-dimensional labeled data structure with columns of potentially different types.

One of the most important features of pandas DataFrame is the loc attribute. It is a label-based data selection method which means that we have to pass the name of the row or column which we want to select. This method includes the last element of the range as well, unlike iloc.

In this article, we will explore various examples of using the loc attribute with pandas DataFrame.

Example 1: Selecting a Single Row by Label

import pandas as pd

data = {
    'website': ['pandasdataframe.com', 'pandasdataframe.com', 'pandasdataframe.com'],
    'visitors': [1000, 700, 1250],
    'signups': [50, 36, 48]
}

df = pd.DataFrame(data, index=['Day 1', 'Day 2', 'Day 3'])

print(df.loc['Day 1'])

Output:

Example 2: Selecting Multiple Rows by Label

import pandas as pd

data = {
    'website': ['pandasdataframe.com', 'pandasdataframe.com', 'pandasdataframe.com'],
    'visitors': [1000, 700, 1250],
    'signups': [50, 36, 48]
}

df = pd.DataFrame(data, index=['Day 1', 'Day 2', 'Day 3'])

print(df.loc[['Day 1', 'Day 3']])

Output:

Example 3: Selecting Rows with a Boolean / Conditional Lookup

import pandas as pd

data = {
    'website': ['pandasdataframe.com', 'pandasdataframe.com', 'pandasdataframe.com'],
    'visitors': [1000, 700, 1250],
    'signups': [50, 36, 48]
}

df = pd.DataFrame(data, index=['Day 1', 'Day 2', 'Day 3'])

print(df.loc[df['visitors'] > 800])

Output:

Example 4: Selecting a Single Column by Label

import pandas as pd

data = {
    'website': ['pandasdataframe.com', 'pandasdataframe.com', 'pandasdataframe.com'],
    'visitors': [1000, 700, 1250],
    'signups': [50, 36, 48]
}

df = pd.DataFrame(data, index=['Day 1', 'Day 2', 'Day 3'])

print(df.loc[:, 'visitors'])

Output:

Example 5: Selecting Multiple Columns by Label

import pandas as pd

data = {
    'website': ['pandasdataframe.com', 'pandasdataframe.com', 'pandasdataframe.com'],
    'visitors': [1000, 700, 1250],
    'signups': [50, 36, 48]
}

df = pd.DataFrame(data, index=['Day 1', 'Day 2', 'Day 3'])

print(df.loc[:, ['visitors', 'signups']])

Output:

Example 6: Selecting Rows and Columns Simultaneously

import pandas as pd

data = {
    'website': ['pandasdataframe.com', 'pandasdataframe.com', 'pandasdataframe.com'],
    'visitors': [1000, 700, 1250],
    'signups': [50, 36, 48]
}

df = pd.DataFrame(data, index=['Day 1', 'Day 2', 'Day 3'])

print(df.loc['Day 1', 'visitors'])

Output:

Example 7: Selecting a Range of Rows and a Range of Columns

import pandas as pd

data = {
    'website': ['pandasdataframe.com', 'pandasdataframe.com', 'pandasdataframe.com'],
    'visitors': [1000, 700, 1250],
    'signups': [50, 36, 48]
}

df = pd.DataFrame(data, index=['Day 1', 'Day 2', 'Day 3'])

print(df.loc['Day 1':'Day 3', 'visitors':'signups'])

Output:

Example 8: Selecting with a MultiIndex

import pandas as pd

index = pd.MultiIndex.from_tuples([(i, j) for i in ['Day 1', 'Day 2', 'Day 3'] for j in ['Morning', 'Afternoon']])
data = {
    'website': ['pandasdataframe.com']*6,
    'visitors': [500, 500, 350, 350, 625, 625],
    'signups': [25, 25, 18, 18, 24, 24]
}

df = pd.DataFrame(data, index=index)

print(df.loc[('Day 1', 'Morning')])

Output:

Example 9: Using Slice in loc

import pandas as pd

data = {
    'website': ['pandasdataframe.com', 'pandasdataframe.com', 'pandasdataframe.com'],
    'visitors': [1000, 700, 1250],
    'signups': [50, 36, 48]
}

df = pd.DataFrame(data, index=['Day 1', 'Day 2', 'Day 3'])

print(df.loc['Day 1':'Day 2'])

Output:

Example 10: Using loc with a Boolean Series

import pandas as pd

data = {
    'website': ['pandasdataframe.com', 'pandasdataframe.com', 'pandasdataframe.com'],
    'visitors': [1000, 700, 1250],
    'signups': [50, 36, 48]
}

df = pd.DataFrame(data, index=['Day 1', 'Day 2', 'Day 3'])

print(df.loc[df['signups'] > 40])

Output:

In conclusion, the loc attribute provides a powerful and flexible way to access data in a pandas DataFrame. It allows us to select data by label, with a boolean array, or with a callable function. It also supports slicing and multi-indexing, making it a versatile tool for data selection and manipulation.