How to select a subset of a DataFrame?

In this article, we are going to discuss how to select a subset of columns and rows from a DataFrame. We are going to use the nba.csv dataset to perform all operations.

Python3

# import required module
import pandas as pd

# assign dataframe
data = pd.read_csv("nba.csv")

# display dataframe
data.head()

Output:

Below are various operations by using which we can select a subset for a given dataframe: 

• Select a specific column from a dataframe

To select a single column, we can use a square bracket [ ]:

Python3

# import required module
import pandas as pd

# assign dataframe
data = pd.read_csv("nba.csv")

# get a single columns
ages = data["Age"]

# display the column
ages.head()

Output:

• Select multiple columns from a dataframe

We can pass a list of column names inside the square bracket [] to get multiple columns: 

Python3

# import required module
import pandas as pd

# assign dataframe
data = pd.read_csv("nba.csv")

# get a single columns
name_sex = data[["Name","Age"]]

# display the column
name_sex.head()

Output:

• Select a subset of rows from a dataframe

To select rows of people older than 25 years in the given dataset, we can put conditions within the brackets to select specific rows depending on the condition. 

Python3

# importing pandas library
import pandas as pd

# reading csv file
data = pd.read_csv("nba.csv")

# subset of dataframe
above_25 = data[data["Age"] > 35]

# display subset
print(above_25.head())

Output:

• Select a subset of rows and columns combined

In this case, a subset of all rows and columns is made in one go, and select [] is not sufficient now. The loc or iloc operators are needed. The section before the comma is the rows you choose, and the part after the comma is the columns you want to pick by using loc or iloc. Here we select only names of people older than 25.

Python3

# importing pandas library
import pandas as pd

# reading csv file
data = pd.read_csv("nba.csv")

# subset of dataframe
adults = data.loc[data["Age"] > 25, "Name"]

# display subset
print(adults.head())

Output: