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 moduleimport pandas as pd# assign dataframedata = pd.read_csv("nba.csv")# display dataframedata.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 moduleimport pandas as pd# assign dataframedata = pd.read_csv("nba.csv")# get a single columnsages = data["Age"]# display the columnages.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 moduleimport pandas as pd# assign dataframedata = pd.read_csv("nba.csv")# get a single columnsname_sex = data[["Name","Age"]]# display the columnname_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 libraryimport pandas as pd# reading csv filedata = pd.read_csv("nba.csv")# subset of dataframeabove_25 = data[data["Age"] > 35]# display subsetprint(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 libraryimport pandas as pd# reading csv filedata = pd.read_csv("nba.csv")# subset of dataframeadults = data.loc[data["Age"] > 25, "Name"]# display subsetprint(adults.head()) |
Output:
