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Pandas is a popular Python library used for data analysis and manipulation. One of the common tasks when working with dataframes is retrieving column names. This can be done using various methods in Pandas, such as the .columns attribute, the .keys() method, or the .get_column_names() function. These methods allow for easy and efficient retrieval of column names, providing users with flexibility in choosing the most suitable method for their specific needs. By utilizing these methods, users can quickly access and work with column names, resulting in a smoother and more efficient data analysis process.
Get Column Names in Pandas (3 Methods)
You can use the following methods to get the column names in a pandas DataFrame:
Method 1: Get All Column Names
list(df)Method 2: Get Column Names in Alphabetical Order
sorted(df)
Method 3: Get Column Names with Specific Data Type
list(df.select_dtypes(include=['int64', 'bool']))
The following examples show how to use each method with the following pandas DataFrame:
import pandas as pd #create DataFrame df = pd.DataFrame({'team': ['A', 'B', 'C', 'D', 'E', 'F'], 'points': [18, 22, 19, 14, 14, 11], 'assists': [5, 7, 7, 9, 12, 9], 'playoffs': [True, False, False, True, True, True]}) #view DataFrame print(df) team points assists playoffs 0 A 18 5 True 1 B 22 7 False 2 C 19 7 False 3 D 14 9 True 4 E 14 12 True 5 F 11 9 True
Example 1: Get All Column Names
The easiest way to get all of the column names in a pandas DataFrame is to use list() as follows:
#get all column nameslist(df)
['team', 'points', 'assists', 'playoffs']
The result is a list that contains all four column names from the pandas DataFrame.
Example 2: Get Column Names in Alphabetical Order
To get the column names in a pandas DataFrame in alphabetical order, you can use the sorted() function as follows:
#get column names in alphabetical ordersorted(df)
['assists', 'playoffs', 'points', 'team']
The result is a list that contains all four column names from the pandas DataFrame listed in alphabetical order.
#get column names in reverse alphabetical ordersorted(df, reverse=True)
['team', 'points', 'playoffs', 'assists']Example 3: Get Column Names with Specific Data Type
You can use the following syntax to view the data type of each column in the DataFrame:
#view data type of each columndf.dtypes
team object
points int64
assists int64
playoffs bool
dtype: object
You can then use the select_dtypes() function to only get the column names with a specific data type.
For example, we can use the following syntax to only get the column names that have a data type of int64 or bool:
#get all columns that have data type of int64 or boollist(df.select_dtypes(include=['int64', 'bool']))
['points', 'assists', 'playoffs']The result is a list of column names that have a data type of int64 or bool.
The following tutorials explain how to perform other common operations in pandas:
Cite this article
stats writer (2024). How can we retrieve column names in Pandas using different methods?. PSYCHOLOGICAL SCALES. Retrieved from https://scales.arabpsychology.com/stats/how-can-we-retrieve-column-names-in-pandas-using-different-methods/
stats writer. "How can we retrieve column names in Pandas using different methods?." PSYCHOLOGICAL SCALES, 26 Jun. 2024, https://scales.arabpsychology.com/stats/how-can-we-retrieve-column-names-in-pandas-using-different-methods/.
stats writer. "How can we retrieve column names in Pandas using different methods?." PSYCHOLOGICAL SCALES, 2024. https://scales.arabpsychology.com/stats/how-can-we-retrieve-column-names-in-pandas-using-different-methods/.
stats writer (2024) 'How can we retrieve column names in Pandas using different methods?', PSYCHOLOGICAL SCALES. Available at: https://scales.arabpsychology.com/stats/how-can-we-retrieve-column-names-in-pandas-using-different-methods/.
[1] stats writer, "How can we retrieve column names in Pandas using different methods?," PSYCHOLOGICAL SCALES, vol. X, no. Y, ص Z-Z, June, 2024.
stats writer. How can we retrieve column names in Pandas using different methods?. PSYCHOLOGICAL SCALES. 2024;vol(issue):pages.
