Pandas DataFrame: Convert all string values to lower/upper case

Updated: February 23, 2024 By: Guest Contributor Post a comment


When working with data in Python, the Pandas library is an indispensable tool for data manipulation and analysis. One common task when preprocessing data is converting string values to a uniform case (either all lowercase or uppercase). This can be crucial for tasks such as string matching, where case inconsistencies can prevent matches. This tutorial will guide you through various methods to convert all string values in a Pandas DataFrame to either lower or upper case.

Before diving into string conversions, let’s briefly discuss what Pandas DataFrames are. A DataFrame is a two-dimensional, size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Essentially, it’s a powerful tool for data analysis that allows you to store and manipulate data in a table where each column can be of a different datatype.

Setup and Basic Example

First, make sure you have pandas installed. If not, you can install it using pip:

pip install pandas

Let’s start with a simple DataFrame:

import pandas as pd
data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Occupation': ['Engineer', 'Doctor', 'Artist']}
df = pd.DataFrame(data)

Which outputs:

      Name Occupation
0    Alice   Engineer
1      Bob     Doctor
2  Charlie     Artist

Our goal is to convert all string values in ‘df’ to lowercase. Let’s see how to achieve this.

Lowercasing All String Values

Using applymap()

applymap() is a DataFrame function that applies a function to each element. For our case, we use the built-in str.lower() method:

df = df.applymap(lambda x: x.lower() if type(x) == str else x)


      Name Occupation
0    alice   engineer
1      bob     doctor
2  charlie     artist

Using apply() with axis parameter

Another approach is using apply() with the axis=1 parameter to manipulate entire rows:

df.apply(lambda row: row.astype(str).str.lower(), axis=1)

This method is useful when your DataFrame contains non-string values, and you want to avoid type checking for each element.

Uppercasing All String Values

Similarly, to convert all string values to uppercase, you can use the str.upper() function in combination with applymap() or apply().


import pandas as pd

# Sample DataFrame with string values
df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'City': ['New York', 'Los Angeles', 'Chicago']

# Convert all string values to uppercase
df = df.applymap(lambda x: x.upper() if isinstance(x, str) else x)


This code snippet creates a DataFrame with two columns (Name and City) containing string values. It then uses applymap() to apply a function to each element of the DataFrame. The lambda function checks if the element is a string (isinstance(x, str)) and, if so, converts it to uppercase using x.upper(). This method ensures that all string values in the DataFrame are converted to uppercase, while non-string values are left unchanged.

Advanced Techniques

Selective Conversion

In some cases, you might want to convert strings in certain columns only. You can do this by applying str.lower() or str.upper() directly to the selected columns:

df['Name'] = df['Name'].str.upper()
df['Occupation'] = df['Occupation'].str.lower()


      Name Occupation
0    ALICE   engineer
1      BOB     doctor
2  CHARLIE     artist

Dealing with Missing Values

It’s important to handle missing values aptly to avoid errors during conversion. One approach is to fill missing values with a placeholder string before conversion, then replace it back:

df.fillna('missing').applymap(lambda x: x.lower()).replace('missing', np.nan)

This ensures that the structure of your DataFrame stays intact while also allowing conversions to proceed smoothly.

Performance Tips

When working with large DataFrames, performance can become an issue. Here are some tips to improve speed:

  • Apply conversions to selected columns rather than the entire DataFrame whenever possible. This reduces the amount of data being processed.
  • Consider using vectorized operations with pandas.Series.str methods instead of applying functions with applymap() or apply(), which are inherently slower due to their iterative nature.


Converting all string values in a Pandas DataFrame to lower or upper case is a common data preprocessing step. This tutorial covered several methods to achieve this, from simple one-liners to more advanced techniques accommodating various data types and missing values. By applying these techniques, you’ll enhance the consistency and possibly the quality of your data, paving the way for more accurate analyses.