Python is a versatile and powerful programming language that allows you to perform various data manipulation tasks. One common task is changing the data type of a variable to numeric.

This can be useful when you need to perform mathematical operations or numerical analysis on your data. In this tutorial, we will explore different methods to change the data type to numeric in Python.

First, let’s understand what a data type is. In programming, every value has a specific type associated with it, such as integers, floating-point numbers, strings, etc. Python provides built-in functions that allow you to convert variables from one data type to another.

**Method 1: Using the int() function**

If your variable contains an integer value represented as a string, you can use the __int()__ function to convert it into an integer.

Example:

“`python

number = “5”

converted_number = int(number)

print(converted_number)

“`

Output:

“`

5

“`

In this example, we have a string variable `number` containing the value “5”. By applying the `int()` function on `number`, we converted it into an integer and stored it in a new variable called `converted_number`. When we print `converted_number`, it displays the output as 5.

**Method 2: Using the float() function**

If your variable contains a decimal number represented as a string or an integer, you can use the __float()__ function to convert it into a floating-point number.

“`python

number = “3.14”

converted_number = float(number)

print(converted_number)

“`

Output:

“`

3.14

“`

In this example, we have a string variable `number` containing the value “3.14”. By applying the `float()` function on `number`, we converted it into a floating-point number and stored it in a new variable called `converted_number`. When we print `converted_number`, it displays the output as 3.14.

**Method 3: Using the astype() method**

If you are working with pandas, a popular data manipulation library in Python, you can use the __astype()__ method to change the data type of a column in a DataFrame.

“`python

import pandas as pd

data = {‘numbers’: [‘1’, ‘2’, ‘3’, ‘4’, ‘5’]}

df = pd.DataFrame(data)

df[‘numbers’] = df[‘numbers’].astype(int)

print(df.dtypes)

“`

Output:

“`

numbers int64

dtype: object

“`

In this example, we have created a DataFrame with a column named “numbers” containing string values. By using the `astype()` method on the “numbers” column and specifying `int` as the desired data type, we converted the column into integers. When we print the data types of the columns using `dtypes`, it shows that the “numbers” column is now of integer type.

**Method 4: Using list comprehension**

If you have a list of numeric strings and want to convert them into numeric values, you can use list comprehension and one of the conversion functions mentioned earlier.

“`python

numbers = [“1”, “2”, “3”, “4”, “5”]

converted_numbers = [float(x) for x in numbers]

print(converted_numbers)

“`

Output:

“`

[1.0, 2.0, 3.0, 4.0, 5.0]

“`

In this example, we have a list variable `numbers` containing numeric strings. By using list comprehension and applying the `float()` function on each element of `numbers`, we converted the strings into floating-point numbers. The resulting list `converted_numbers` contains the converted values.

Changing the data type to numeric is a common requirement in data analysis and scientific computing. Python provides various methods to handle such conversions, depending on your specific needs. By using the __int()__ and __float()__ functions, the __astype()__ method in pandas, or list comprehension, you can easily convert strings or other data types into numeric values.

Now that you have learned different methods to change the data type to numeric in Python, you can apply them in your own projects to manipulate numerical data efficiently. Remember to choose the appropriate method based on the type of variable or data structure you are working with.