Pandas DataFrame loc Multiple Columns

Pandas DataFrame loc Multiple Columns

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 structures in pandas is DataFrame, which is a two-dimensional labeled data structure with columns of potentially different types.

In this article, we will focus on the loc function in pandas DataFrame. The loc function 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. Unlike, the iloc function which is an index-based selection method and it does not include the last element of the range.

More specifically, we will discuss how to use loc to select multiple columns in a DataFrame. We will provide 10-20 complete, context-independent, and directly runnable pandas codes.

Creating a DataFrame

Before we dive into the usage of loc, let’s first create a DataFrame. Here is an example:

import pandas as pd

data = {
    'Name': ['John', 'Anna', 'Peter', 'Linda'],
    'Age': [28, 24, 35, 32],
    'City': ['New York', 'Paris', 'Berlin', 'London']
}

df = pd.DataFrame(data)

print(df)

Output:

Selecting a Single Column with loc

The loc function can be used to select a single column. Here is an example:

import pandas as pd

data = {
    'Name': ['John', 'Anna', 'Peter', 'Linda'],
    'Age': [28, 24, 35, 32],
    'City': ['New York', 'Paris', 'Berlin', 'London']
}

df = pd.DataFrame(data)

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

Output:

Selecting Multiple Columns with loc

The loc function can also be used to select multiple columns. Here is an example:

import pandas as pd

data = {
    'Name': ['John', 'Anna', 'Peter', 'Linda'],
    'Age': [28, 24, 35, 32],
    'City': ['New York', 'Paris', 'Berlin', 'London']
}

df = pd.DataFrame(data)

print(df.loc[:, ['Name', 'Age']])

Output:

Selecting Multiple Columns with a Slice

The loc function can also select multiple columns with a slice. Here is an example:

import pandas as pd

data = {
    'Name': ['John', 'Anna', 'Peter', 'Linda'],
    'Age': [28, 24, 35, 32],
    'City': ['New York', 'Paris', 'Berlin', 'London']
}

df = pd.DataFrame(data)

print(df.loc[:, 'Name':'Age'])

Output:

Selecting Multiple Columns with a Boolean Array

The loc function can also select multiple columns with a Boolean array. Here is an example:

import pandas as pd

data = {
    'Name': ['John', 'Anna', 'Peter', 'Linda'],
    'Age': [28, 24, 35, 32],
    'City': ['New York', 'Paris', 'Berlin', 'London']
}

df = pd.DataFrame(data)

print(df.loc[:, [True, False, True]])

Output:

Selecting Multiple Columns with a Callable

The loc function can also select multiple columns with a callable. Here is an example:

import pandas as pd

data = {
    'Name': ['John', 'Anna', 'Peter', 'Linda'],
    'Age': [28, 24, 35, 32],
    'City': ['New York', 'Paris', 'Berlin', 'London']
}

df = pd.DataFrame(data)

print(df.loc[:, lambda df: ['Name', 'Age']])

Output:

Selecting Rows and Columns with loc

The loc function can also select rows and columns at the same time. Here is an example:

import pandas as pd

data = {
    'Name': ['John', 'Anna', 'Peter', 'Linda'],
    'Age': [28, 24, 35, 32],
    'City': ['New York', 'Paris', 'Berlin', 'London']
}

df = pd.DataFrame(data)

print(df.loc[0:2, ['Name', 'Age']])

Output:

Selecting Rows and Columns with a Callable

The loc function can also select rows and columns with a callable. Here is an example:

import pandas as pd

data = {
    'Name': ['John', 'Anna', 'Peter', 'Linda'],
    'Age': [28, 24, 35, 32],
    'City': ['New York', 'Paris', 'Berlin', 'London']
}

df = pd.DataFrame(data)

print(df.loc[lambda df: df.Age > 30, ['Name', 'Age']])

Output:

Selecting Rows and Columns with a Boolean Array

The loc function can also select rows and columns with a Boolean array. Here is an example:

import pandas as pd

data = {
    'Name': ['John', 'Anna', 'Peter', 'Linda'],
    'Age': [28, 24, 35, 32],
    'City': ['New York', 'Paris', 'Berlin', 'London']
}

df = pd.DataFrame(data)

print(df.loc[df.Age > 30, [True, False, True]])

Output:

Selecting Rows and Columns with a Slice

The loc function can also select rows and columns with a slice. Here is an example:

import pandas as pd

data = {
    'Name': ['John', 'Anna', 'Peter', 'Linda'],
    'Age': [28, 24, 35, 32],
    'City': ['New York', 'Paris', 'Berlin', 'London']
}

df = pd.DataFrame(data)

print(df.loc[0:2, 'Name':'Age'])

Output:

Pandas DataFrame loc Multiple Columns Conclusion

In this article, we have discussed how to use the loc function in pandas DataFrame to select multiple columns. We have provided 10 complete, context-independent, and directly runnable pandas codes. We hope this article can help you understand and use the loc function better. For more information, please visit pandasdataframe.com.