Pandas Column Type to String

By the end of this tutorial, you will understand how to use the astype() function in Pandas. This function allows you to cast an object to a specific data type.

Let us go exploring.

Function Syntax

The function syntax is as illustrated below:

DataFrame.astype(dtype, copy=True, errors=‘raise’)

The function parameters are as shown:

  1. dtype – specifies the target data type to which the Pandas object is cast. You can also provide a dictionary with the data type of each target column.
  2. copy ­– specifies if the operation is performed in-place, i.e., affects the innovador DataFrame or creating a copy.
  3. errors – sets the errors to either ‘raise’ or ‘ignore.’

Return Value

The function returns a DataFrame with the specified object converted to the target data type.


Take a look at the example code shown below:

# import pandas
import pandas as pd
df = pd.DataFrame({
    ‘col1’: [10,20,30,40,50],
    ‘col2’: [60,70,80,90,100],
    ‘col3’: [110,120,130,140,150]},

Convert Int to Float

To convert the ‘col1’ to floating-point values, we can do:

df.col1.astype(‘float64’, copy=True)

The code above should convert ‘col1’ to floats as shown in the output below:

Convert to Multiple Types

We can also convert multiple columns to different data types. For example, we convert ‘col1’ to float64 and ‘col2’ to string in the code below.

print(f«before: {df.dtypes}n«)
df = df.astype({
    ‘col1’: ‘float64’,
    ‘col2’: ‘string’
print(f«after: {df.dtypes}»)

In the code above, we pass the column and the target data type as a dictionary.

The resulting types are as shown:

Convert DataFrame to String

To convert the entire DataFrame to string type, we can do the following:

The above should cast the entire DataFrame into string types.


In this article, we covered how to convert a Pandas column from one data type to another. We also covered how to convert an entire DataFrame into string type.

Happy coding!!

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