Pandas – Find Column Names that Start with Specific String

In this tutorial, we will look at how to get the column names in a pandas dataframe that start with a specific string (in the column name) with the help of some examples.

How to find columns whose name starts with a specific string?

You can apply the string startswith() function with the help of the .str accessor on df.columns to check if column names (of a pandas dataframe) start with a specific string.

You can use the .str accessor to apply string functions to all the column names in a pandas dataframe.

Pass the start string as an argument to the startswith() function. The following is the syntax.

# get column names that start with a specific string, s
df.columns[df.columns.str.startswith(s)]

The idea is to get a boolean array using df.columns.str.startswith() and then use it to filter the column names in df.columns.

Alternatively, you can use a list comprehension to iterate through the column names and check if it starts with the specified string or not.

Examples

Let’s now look at some examples of using the above syntax.

First, we will create a pandas dataframe that we will be using throughout this tutorial.

import pandas as pd

# employee data
data = {
    "Emp_Name": ["Jim", "Dwight", "Angela", "Tobi"],
    "Emp_Age": [26, 28, 27, 32],
    "Department": ["Sales", "Sales", "Accounting", "HR"]
}

# create pandas dataframe
df = pd.DataFrame(data)

# display the dataframe
df

Output:

Here, we created a dataframe with information about some employees in an office. The dataframe has the columns – “Emp_Name”, “Emp_Age”, and “Department”.

Example 1 – Get column names that start with a specific string

Let’s get the column names in the above dataframe that start with the string “Emp_” in their column labels.

We’ll apply the string startswith() function with the help of the .str accessor to df.columns.

# check if column name starts with the string, "Emp_"
df.columns.str.startswith("Emp_")

Output:

array([ True,  True, False])

You can see that we get a boolean array indicating which columns in the dataframe start with the string “Emp_”.

We can use the above boolean array to filter df.columns to get only the columns that start with the specified string (in this example, “Emp_”)

# get column names that start with the string, "Emp_"
df.columns[df.columns.str.startswith("Emp_")]

Output:

Index(['Emp_Name', 'Emp_Age'], dtype='object')

We get the column names starting with “Emp_” in the above dataframe.

Example 2 – Get column names that start with a specific string using list comprehension

Alternatively, we can use a list comprehension to iterate through the column names in df.columns and select the ones that start with the given string.

# get column names that start with the string, "Emp_"
[col for col in df.columns if col.startswith("Emp_")]

Output:

['Emp_Name', 'Emp_Age']

We get the column names that start with “Emp_”. The “Emp_Name” and the “Emp_Age” columns are the only ones that start with the string “Emp_” in the above dataframe.

Summary

In this tutorial, we looked at how to get the column names that start with a specified string in a pandas dataframe. The following are the key takeaways –

• Use the string startswith() function (applied using the .str accessor on df.columns) to check if a column name starts with a given string or not (and use this result to filter df.columns).
• You can also get column names that start with a specified string with the help of a list comprehension.

You might also be interested in –

• Pandas – Find Column Names that Contain Specific String
• Pandas – Get Columns with Missing Values
• Pandas – Add Prefix to Column Names
• Pandas – Add Suffix to Column Names
• Pandas – Change Column Names to Uppercase
• Pandas – Change Column Names to Lowercase
• Remove Prefix or Suffix from Pandas Column Names
• Get Column Names as List in Pandas DataFrame
• Pandas – Rename Column Names