Pandas Convert All Columns to String

Pandas, a Python software package, offers data processing and evaluation. It tackles the missing data with ease and is quick, adaptable, and clear. The robust data structure not just offers but also improves the functionality of tools for data modification and analysis.

A datatype is a fundamental building block used by computer languages to understand how to preserve and alter the data. In a Pandas DataFrame, you may frequently want to turn the single or multiple columns into strings. Conveniently, using the various native functions of Pandas, this is simple to accomplish.

This article will teach you how to transform the values in a column into a string data type using the Pandas library in Python. You will then discover how to turn the floats and integers into Pandas strings. You’ll discover how to convert the columns in a Pandas DataFrame to a string in addition to the four distinct ways to do it. The DataFrame.astype(str), DataFrame.values.astype(str), DataFrame.apply(str),, and DataFrame.applymap(str) are some of the methods used in the demonstration to convert any type to a string type.

Constructing DataFrame Using Pandas.DataFrame() Method

The first and foremost requirement for the program is to import the Pandas library as pd to avail the Pandas features. The next step is to create a Pandas DataFrame. We’ll build a DataFrame with three distinct columns, one of them runs as a string and the other two will load as integers. Then, we employ the print() function to display its five records.

In the previous illustration, we created a DataFrame that utilizes the Pandas.DataFrame() method. This DataFrame has three columns: “Name”, “Age”, and “Salary”. Each column stores five records or values. We created a DataFrame object “data” and assigned it the output of calling the pd.DataFrame() method. So, the Pandas DataFrame is accessible by using this object. We then utilized the print() function to display the DataFrame.

The DataFrame we just created appears on the terminal which can be seen in the snapshot attached in the following:

Now, we find out the datatypes of all the columns of the DataFrame. For this, we use the Pandas .info() function. The .info() method displays us the details about the DataFrame, providing the datatypes for every column, allowing us to examine how the Pandas processes the string data.

We employed the Pandas method in the previous code. The print() function is then invoked having the as its parameters to display the information about the DataFrame’s columns datatype.

This is the output displayed on the terminal:

In the previous example, we can observe that Pandas always handles the strings as objects by default. Strings and mixed data types are both handled by the object data type. However, it is not notably obvious.

Pandas have a specific string datatype from version 1.0. Although this datatype does not yet provide any clear storage or efficiency enhancements, the Pandas developer team said that this will happen in the future. As a consequence, the lesson will exclusively employ the string datatype.

Let’s begin by converting a column to a string using the preferred Pandas approach.

Example 1:

The first method that we utilize is the Pandas astype() function. The column (series) technique is a feature of Pandas.

If you’re operating the Pandas 1.0 or later, pass in the “string”. Otherwise, use the “str” for the editions of Pandas earlier than 1.0. By adopting this, you can be certain that the string datatype is used instead of the object datatype.

Let’s begin with its practical demonstration in a Python environment.

In the previous code using the previously created DataFrame “data”, we now utilized the Pandas “.astype()” function. We wrote the DataFrame’s name with the “.astype()” function and provided the “string” datatype as its parameter. Afterward, we invoked the “.info()” method within the print() function’s parentheses to display the updated datatype of the DataFrame columns.

Executing the previous Python script yields us the following outcome:

The “Age” column of our Pandas DataFrame, which was initially kept as an int64, is presently handled as a string datatype as can be seen.

Example 2:

You can employ the .map() technique to change a Pandas column to strings in a manner that is identical to the .astype() Pandas series function.

Let’s explore what this appears to be:

The illustration begins by utilizing the code of the DataFrame that we explained in the previous example. After printing the DataFrame, we then displayed the datatypes with the aid of the “.info()” function. Now, we choose a column whose datatype are neded to convert into a string. We selected the “Age” column again for the said purpose. Then, we invoked the “.map()” function with the DataFrame object and the specified column name. We also defined the datatype within the “.map(str)” function’s braces. We need to check the updated datatype with the “.info()” method.

The output can be seen in the following image:

It is clear from this that the string datatype cannot be used when utilizing the .map() method. The data is stored in the object datatype as a consequence. Due to this, if you’re running an edition higher than 1.0, we advise not to employ this method.

Example 3:

Like the technique described previously, we can likewise change the datatype of a Pandas column to strings using the .apply() method. The same restrictions apply, in that we are only able to transform them into object datatypes. We are still unable to transform them to string datatypes.

Let’s have a look at it:

As you can see in the previous script provided, the apply() method is employed. Between the braces of this function, we specify the datatype. This method does not take the “string”. Instead, it works with “str”. Then, we displayed the datatypes with the info() method utilization.

The resultant terminal shows that the updated datatype changed from int64 to object which is a string.

Example 4:

Furthermore, we may utilize the Pandas’ value.astype() function to directly transform the values of a column into strings.

Here, we explore its works with the Python code implementation:

With the same DataFrame and column, we utilized the Pandas “values.astype()” method and passed the datatype “str” as its argument. Finally, we displayed the datatypes with the info() method.

This gives us the following output:

Example 5:

Our last segment teaches you how to utilize the .applymap() function to transform all the Pandas DataFrame columns into strings.

In this method, we used the .applymap() method. As we want to convert all the columns, we need not mention any specific column name with the DataFrame object as we did earlier. We simply used the DataFrame object with the .applymap() function and passed the datatype “str” as a parameter.

This is the following output:


This lesson is based on the Pandas’ different methods to convert the columns to string datatypes. We utilized all the possible ways to give you a bunch of easy choices when we encounter such problems. We demonstrated some practical example codes to help you learn the various Pandas techniques. We hope that this writing helps you understand the Pandas modules.

About the author

Aqsa Yasin

I am a self-motivated information technology professional with a passion for writing. I am a technical writer and love to write for all Linux flavors and Windows.