Excel to Python: LEN Function - A Complete Guide | Mito
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How to Use Excel's LEN Function in Pandas

Excel's LEN function returns the number of characters in a text string. This is commonly used in string processing tasks, like data validation or cleaning.

This page explains how to use Excel's LEN function in Python using pandas.

Mito is an open source library that lets you write Excel formulas in Python. Either write the formula directly in Python or use the LEN formula in the Mito Spreadsheet and generate the equivalent Python code automatically.

Mito's LEN function works exactly like it does in Excel. That means you don't need worry about managing data types, handling errors, or the edge case differences between Excel and Python formulas.

Install Mito to start using Excel formulas in Python.

# Import the mitosheet Excel functions
from mitosheet.public.v3 import *;

# Use Mito's LEN function
df['length'] = LEN(df['A'])

To replicate the LEN function in Excel using Python and pandas, you have the following common implementations:

In Excel, you would use =LEN("Your String"). In pandas, to find the length of a single string, you can use Python's built-in len function

A common use for this is to validate the length of a string. For example, if you want to ensure that a string is exactly 10 characters long, you could use the following code:

# Create a string
my_string = "Hello World"

# Find the length of the string
if len(my_string) == 10:
    print("String is 10 characters long")
    print("String is not 10 characters long")

Assuming you want to calculate the length of each string in column 'A' in Excel, you would use the function LEN(A1) and drag it down the entire dataset.

In pandas, you can do the same using the `str` accessor followed by the `len` function on the desired column:

# Calculate the length of each value in column A
df['B'] = df['A'].str.len()

In Excel, you might use a formula like =IF(LEN(A1)>10, "Long", "Short") to classify strings based on length.

In pandas, you can use the `numpy.where` method combined with the `str` accessor and `len` function to achieve the same result:

import numpy as np
df['classification'] = np.where(df['column_name'].str.len() > 10, 'Long', 'Short')

While using the LEN function in pandas, it's common to run into a few pitfalls. Here are some mistakes to be aware of and how to avoid them.

Pandas requires you to use the `str` accessor to apply string-specific methods to a Series.

If you attempt to use `len()` directly on a pandas Series without the `str` accessor, you'll calculate the get unexpected results. See the example below.

# Calculate the length of each string in column A
df['length'] = df['A'].str.len()

# This will error because you forgot the str accessor
df['length'] = df['A'].len()

# Find the number of rows in the DataFrame
df['length'] = len(df['A'])

The LEN function in Excel is quite straightforward. It takes a single argument which is the text string you want to measure, and returns its length.


LEN Excel Syntax

ParameterDescriptionData Type
textThe text string you want to measure.string


=LEN("Hello World")Calculate the length of the string 'Hello World'.11
=LEN(A1)Calculate the length of the value in cell A1.Length of the value in A1

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