Convert Column to Int using Pandas in Python

Introduction:

In this tutorial we are learning about how to convert the Column to Int using Pandas in Python. Python is one of the useful languages for data analysis and data manipulation, and good reason. With its simple syntax and libraries, Python provides powerful documentation. Pandas is one such library that contains many tools for data manipulation and analysis. Pandas allow us to easily manage and manipulate data in many ways, making it an essential part of a data analyst or researcher's toolset.

As data analysts, we often encounter data stored as strings, even though it is more useful than string data. In the next section of this article, we will explore different ways to convert string data to equivalent data in Pandas, including using the astype() and to_numeric() functions. We will also discuss some best practices and considerations to keep in mind when dealing with data conversions.

How to convert the Column to Int using Pandas in Python?

There we three ways to convert the Column to Int using Pandas in Python, which are given below -

1. Using the astype() method
2. Using the to_numeric() method
3. Using the apply() method

Now we learn about these methods, which are given as follows -

1. Using the astype() method:

Using the astype() method to convert the Column to Int using Pandas in Python. This method is very simple and useful when converting data in Pandas to integer data. We can use the astype() function for the desired field and specify the desired data type (in this case, "int"). To use the astype() function in data conversion, we simply call this method on the DataFrame and then specify the data type we want to convert the column to.

Program Code 1:

Here we give the program code to convert the Column to Int using Pandas in Python by using the astype() method. In this example, the following code uses the Pandas library to create a DataFrame named "d" containing the column "Col1" containing the string values. Then use the astype() method to change the "Col1" value to the integer. Then copy the DataFrame and use the "dtype" property to display the "Col1" data type. The code is given below -

Output:

Now we run the above code and find the result from it. The result is given below -

   Col1
0    10
1    12
2    20
3    45
4    56
5    70

Program Code 2:

Here we give another program code to convert the Column to Int using Pandas in Python by using the astype() method. Here we print the data on the column using the "dtypes" property and print the DataFrame by using the "print()" function to see the changes made in the "Age" column. The code is given below -

Output:

Now we run the above code and find the result from it. The result is given below -

Name      object
Age        int64
Gender    object
dtype: object
     Name  Age  Gender
0   Priya   22  Female
1    Mita   26  Female
2    Diya   20  Female
3   Rudra   30    Male
4  Badsha   29    Male

From the above output, we can see that the "Age" column has been successfully converted to aggregate data represented by the "int32" value in the data types output. The DataFrame now has three columns: Name, Age, and Gender. The age column contains numeric values, excluding string values. It should be noted that when using the astype() function, the string value must be convertible to an integer; otherwise, a ValueError error will occur. Additionally, if the row contains non-numeric characters or missing values, this method will not work as expected.

2. Using the to_numeric() method:

Using the to_numeric() method to convert the Column to Int using Pandas in Python. This function allows us to convert many data types, including strings. It provides greater flexibility in handling incorrect conversions and provides additional parameters to adjust the conversion process. To use the to_numeric() function, we call this function on the line we want to change and specify the data type we want to change.

Program Code 1:

Here we give the program code to convert the Column to Int using Pandas in Python by using the to_numeric() method. In this example, the following code uses pandas to create a DataFrame with columns ("Col1"). The "to_numeric()" method optimizes memory usage by converting strings to integers. Conversion errors are resolved by replacing it with NaN. Type the created DataFrame and display the data type "Col1". The code is given below -

Output:

Now we run the above code and find the result from it. The result is given below -

   Col1
0    10
1    12
2    20
3    45
4    56
5    70

Program Code 2:

Here we give another program code to convert the Column to Int using Pandas in Python by using the to_numeric() method. Here we print the data on the column using the "dtypes" property and print the DataFrame by using the "print()" function to see the changes made in the "Age" column. The code is given below -

Output:

Now we run the above code and find the result from it. The result is given below -

In the above output, we can see that the "Age" column has been converted to Int8 data type, which represents a signed number from -128 to 127. The "Name" and "Gender" columns remain as object (string) data types. The DataFrame itself shows updated column ages where string values have been changed to their equivalent values. So, we have successfully converted strings in Pandas Dataframe to integers using the to_numeric() function.

3. Using the apply() method:

Using the apply() method to convert the Column to Int using Pandas in Python. To use the apply() method, we simply call this function on the line we want to change and specify the data type we want to change.

Program Code:

Here we give the program code to convert the Column to Int using Pandas in Python by using the apply() method. In this example, the following code creates a pandas DataFrame "d" with the string "Col1". The "apply()" method is used with the lambda function to convert each value in "Col1" to an integer. The DataFrame will be printed using the "dtype" attribute to display the "Col1" data type. The code is given below -

Output:

Now we run the above code and find the result from it. The result is given below -

   Col1
0    10
1    12
2    20
3    45
4    56
5    70

Conclusion:

In this tutorial we are learning about how to convert the Column to Int using Pandas in Python. The astype() function, the to_numeric() function, and the apply() method are three of the most popular methods of converting strings to integers. The astype() method is useful when we know that the array contains only numbers; to_numeric() is more useful for handling conversion errors. Here we also provide some examples for each method in Python.