Changing the data type of a variable is a common task when working with data analysis in Jupyter Notebook. In this tutorial, we will explore different methods to change the data type of a variable in Jupyter Notebook using Python.
Method 1: Using the ‘astype()’ Function
To change the data type of a variable, we can use the astype() function provided by the pandas library. This function allows us to convert a series or column of data to a different data type.
First, we need to import the pandas library into our Jupyter Notebook. We can do this by adding the following line of code at the beginning of our code:
import pandas as pd
Next, let’s assume we have a DataFrame named ‘df’ with a column named ‘age’ containing numerical values as strings. To convert these values to integers, we can use the astype() function as follows:
df['age'] = df['age'].astype(int)
If any value in the ‘age’ column cannot be converted to an integer, an error will be raised. It is important to ensure that all values in the column can be converted before using this method.
Method 2: Using Built-in Conversion Functions
In addition to using the astype() function, Python provides built-in conversion functions that allow us to change the data type of variables.
a) Converting Strings to Integers or Floats:
To convert a string variable containing numerical values to an integer or float, we can use the int() or float() functions, respectively.
age = '25'
age = int(age) # Converts 'age' to an integer
height = '1.75'
height = float(height) # Converts 'height' to a float
b) Converting Integers or Floats to Strings:
If we need to convert an integer or float variable to a string, we can use the str() function.
age = 25
age = str(age) # Converts 'age' to a string
In both methods mentioned above, it is important to ensure that the data can be converted to the desired data type. For example, converting a string that contains non-numeric characters to an integer will raise an error.
In this tutorial, we have learned two methods for changing the data type of variables in Jupyter Notebook. We explored using the ‘astype()’ function from the pandas library and also demonstrated how built-in conversion functions like ‘int()’, ‘float()’, and ‘str()’ can be used for data type conversions. Remember to handle potential errors when converting data types and always validate your inputs before applying these methods.