Pandas Convert Column To String Type
Last Updated :
12 Jul, 2024
Pandas is a Python library widely used for data analysis and manipulation of huge datasets. One of the major applications of the Pandas library is the ability to handle and transform data. Mostly during data preprocessing, we are required to convert a column into a specific data type. In this article, we'll look into the process of converting a Pandas column to a string type.
Let us understand the different ways of converting Pandas columns to string types:
astype() Method:
The astype() method in Pandas is a straightforward way to change the data type of a column to any desired type. The astype method has the following syntax:
.astype(data_Type)
Let us understand this using an example:
Here we define that the numeric type for the dataset should be converted to a string (Str). This will convert the data type “int” to "string.”.
Python
import pandas as pd
# sample data
data = {'NumericColumn': [1, 2, 3, 4]}
df = pd.DataFrame(data)
df['NumericColumn'] = df['NumericColumn'].astype(str)
df.info()
Output:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4 entries, 0 to 3
Data columns (total 1 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 NumericColumn 4 non-null object
dtypes: object(1)
memory usage: 160.0+ bytes
apply() Function:
The apply() function is another way of converting the data type. This function allows us for more flexibility in data transformations. Lambda function is used in this method.
Lambda Function
However, before we understand how we can convert to string type using apply() function let's study lambda function. In python, lambda is a anonymous function that can be be defined in short without using the def keyword. It has a very concise syntax where lambda keyword is followed by arguments and expression.
It can take multiple arguments but has only one expression which is evaluated and returned. The syntax of lambda function is as follows:
lambda arguments: expression
You can even study the example of using lambda function below:
Python
add = lambda x, y: x + y # Adds two numbers
result = add(5, 3) # Calls the lambda function
print(result)
Output:
8
Now, lets see how lambda can help us along with apply function to convert column to string type. Lambda function will be a quick way of telling the computer to apply the changes for each value
Python
import pandas as pd
# sample data
data = {'NumericColumn': [1, 2, 3, 4]}
df = pd.DataFrame(data)
df['NumericColumn'] = df['NumericColumn'].apply(lambda x: str(x))
df.info()
Output:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4 entries, 0 to 3
Data columns (total 1 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 NumericColumn 4 non-null object
dtypes: object(1)
memory usage: 160.0+ bytes
map() Function:
The map() function is our next method for conversion. This method is useful when we need to apply conversion based on a mapping dictionary:
Python
import pandas as pd
# Create a sample DataFrame
data = {'NumericColumn': [1, 2, 3, 4, 5]}
df = pd.DataFrame(data)
# Define a mapping dictionary for conversion
mapping_dict = {1: 'One', 2: 'Two', 3: 'Three', 4: 'Four', 5: 'Five'}
# Use map() to convert 'NumericColumn' based on the mapping dictionary
df['NumericColumn'] = df['NumericColumn'].map(mapping_dict)
# Check the DataFrame
df.info()
Output:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 1 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 NumericColumn 5 non-null object
dtypes: object(1)
Here, the list [1, 2, 3, 4, 5] will change to [one, two, three, four, five].
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