How to Convert Pandas Columns to String

Pandas, a powerful data manipulation library for Python, provides extensive functionality for handling and transforming data. One common task is converting columns to strings, which is useful in scenarios where you need to perform string operations on numerical or categorical data. In this blog post, we’ll explore the concepts related to converting columns to strings in Pandas and delve into three different methods to achieve this.

Data Types in Pandas

Before we jump into converting columns to strings, it’s crucial to understand the different data types in Pandas. The primary data types include integers, floats, strings, and categorical data. Converting between these types is a common requirement when dealing with diverse datasets.

Object Type in Pandas

In Pandas, when a column contains a mixture of data types, it is often assigned the data type ‘object.’ While this type is versatile, it may not be suitable for certain operations, necessitating the need to convert specific columns to string format.

Convert Columns to String in Pandas

Create a sample dataset:

Python

import pandas as pd
import numpy as np

# Creating a DataFrame with random numerical and string columns
np.random.seed(42)  # Setting seed for reproducibility

data = {
    'Numeric_Column': np.random.randint(1, 100, 4),
    'String_Column': np.random.choice(['A', 'B', 'C', 'D'], 4)
}

df = pd.DataFrame(data)
df.info()

Output:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4 entries, 0 to 3
Data columns (total 2 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   Numeric_Column  4 non-null      int64 
 1   String_Column   4 non-null      object
dtypes: int64(1), object(1)

Method 1: Using astype() Method:

The astype() method in Pandas is used to change the data type of a column. It’s a simple and efficient way to convert the data type of a specific column to another. In this case, we use it to convert a numeric column to a string.

Python

# Using astype() Method
df_astype = df.copy()
df_astype['Numeric_Column'] = df_astype['Numeric_Column'].astype(str)
print("\nDataFrame after converting 'Numeric_Column' to string using astype():")
print(df_astype)
print(df_astype.info())

Output:

DataFrame after converting 'Numeric_Column' to string using astype():
  Numeric_Column String_Column
0             52             A
1             93             A
2             15             C
3             72             B
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4 entries, 0 to 3
Data columns (total 2 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   Numeric_Column  4 non-null      object
 1   String_Column   4 non-null      object
dtypes: object(2)
memory usage: 192.0+ bytes

Method 2:Using the map() Function:

The map() function in Pandas is a versatile tool for element-wise transformations. It is commonly used to apply a function to each element of a Series. Here, we use map() to convert the ‘Numeric_Column’ to strings.

Python

df_map = df.copy()
df_map['Numeric_Column'] = df_map['Numeric_Column'].map(str)
print("\nDataFrame after converting 'Numeric_Column' to string using map():")
print(df_map)
print(df_map.info())

Output:

DataFrame after converting 'Numeric_Column' to string using map():
  Numeric_Column String_Column
0             52             A
1             93             A
2             15             C
3             72             B
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4 entries, 0 to 3
Data columns (total 2 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   Numeric_Column  4 non-null      object
 1   String_Column   4 non-null      object
dtypes: object(2)
memory usage: 192.0+ bytes

Method 3: Using the apply() Function:

The apply() function in Pandas is a powerful tool that allows you to apply a custom function along the axis of a DataFrame. Here, we use apply() to apply the str function to each element of the ‘Numeric_Column’, converting numeric values to strings.

Python

# Utilizing apply() Function
df_apply = df.copy()
df_apply['Numeric_Column'] = df_apply['Numeric_Column'].apply(str)
print("\nDataFrame after converting 'Numeric_Column' to string using apply():")
print(df_apply)
print(df_apply.info())

Output:

 DataFrame after converting 'Numeric_Column' to string using apply():
  Numeric_Column String_Column
0             52             A
1             93             A
2             15             C
3             72             B
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4 entries, 0 to 3
Data columns (total 2 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   Numeric_Column  4 non-null      object
 1   String_Column   4 non-null      object
dtypes: object(2)
memory usage: 192.0+ bytes