Python is a really handy language that can do a lot of things, especially when it comes to dealing with data, especially with Pandas library. In this article, we’re going to dive into astype() in Pandas. It lets us change the type of data in Pandas to whatever we want. Plus, it’s got this extra power where it can turn existing columns into special categories.<\/p>\n\n\n\n
The astype() method in Pandas is used to cast a pandas object, such as a DataFrame or Series, to a specified data type.\u00a0 <\/strong>Hence, it provides a flexible way to convert the data types of one or more columns in a DataFrame. It is truly useful when we are required to change the data type of a specific column or multiple columns simultaneously.<\/p>\n\n\n\n Besides changing the data type of columns, the astype() method also allows us to convert columns to categorical types. This is useful when dealing with variables that only have a limited number of unique values, such as categorical variables or factors.<\/p>\n\n\n\n Now let us explore the syntax of the astype() method.<\/p>\n\n\n\n Here is the breakdown of the syntax:<\/p>\n\n\n\n Now let us see various use cases of the astype() function.<\/p>\n\n\n\n The astype() method is commonly used to change the data type of a specific column in a DataFrame. <\/strong> Let’s consider an example: We have a DataFrame with columns representing different attributes of a person, such as Name, Age, and Weight. We want to convert the Weight column to an integer data type.<\/p>\n\n\n\n We can use the astype() method in this way:<\/p>\n\n\n\n Output:<\/strong><\/p>\n\n\n\n In addition to changing the data type of a single column, the astype() method also allows us to change the data types of multiple columns simultaneously. This can be achieved by providing a dictionary containing column names as keys and their corresponding data types as values.<\/p>\n\n\n\n Let us take an example, same as above let us now try to change the data types of the Weight and Age columns. Check the Python code below:<\/p>\n\n\n\n Output:<\/strong><\/p>\n\n\n\n Astype() can also be used to convert the column type into categorical. Categorical types are useful when dealing with variables that have a limited number of unique values or represent categories of factors.<\/p>\n\n\n\n Consider a scenario where we have a DataFrame with a column representing the gender of individuals. We want to convert the Gender column to a categorical type. Converting the column to categorical data type will allow for more efficient data storage. Here is the code:<\/p>\n\n\n\n Output:<\/strong><\/p>\n\n\n\nSyntax and Parameters of astype() Method<\/strong><\/h3>\n\n\n\n
DataFrame.astype(dtype, copy=True, errors='raise', **kwargs)\n<\/code><\/pre>\n\n\n\n\n
Casting the Data Type of a Single Column <\/strong><\/h3>\n\n\n\n
import pandas as pd\n\ndata = {\n "Name": ["John", "Emma", "Michael"],\n "Age": [25, 30, 35],\n "Weight": [65.2, 68.5, 73.1]\n}\ndf = pd.DataFrame(data)\n# Display the original DataFrame\nprint('Original DataFrame:\\n', df)\n\n# Change the data type of 'Weight' column\ndf["Weight"] = df["Weight"].astype('int64')\n\n# Display the new DataFrame. \nprint('Updated DataFrame:\\n',df)<\/pre><\/div>\n\n\n\nOriginal DataFrame:\n Name Age Weight\n0 John 25 65.2\n1 Emma 30 68.5\n2 Michael 35 73.1\n\nUpdated DataFrame:\n Name Age Weight\n0 John 25 65\n1 Emma 30 68\n2 Michael 35 73\n<\/code><\/pre>\n\n\n\nCasting the Data Type of Multiple Columns <\/strong><\/h3>\n\n\n\n
import pandas as pd\n\ndata = {\n "Name": ["John", "Emma", "Michael"],\n "Age": [25, 30, 35],\n "Weight": [65.2, 68.5, 73.1]\n}\ndf = pd.DataFrame(data)\n\n# Display the original DataFrame\nprint('Original DataFrame:\\n', df)\n\n# Change the data type of 'Weight' and 'Age' columns\ndf = df.astype({"Age": 'float', "Weight": 'int64'})\n\n# Display the new DataFrame. \nprint('Updated DataFrame:\\n',df)<\/pre><\/div>\n\n\n\nOriginal DataFrame:\n Name Age Weight\n0 John 25 65.2\n1 Emma 30 68.5\n2 Michael 35 73.1\n\nUpdated DataFrame:\n Name Age Weight\n0 John 25.0 65\n1 Emma 30.0 68\n2 Michael 35.0 73\n<\/code><\/pre>\n\n\n\nConverting Columns to Categorical Type <\/strong><\/h3>\n\n\n\n
import pandas as pd\n\ndata = {\n "Name": ["John", "Emma", "Michael"],\n "Gender": ["Male", "Female", "Male"],\n "Age": [25, 30, 35]\n}\ndf = pd.DataFrame(data)\n\n# Display the original DataFrame\nprint('Original DataFrame:\\n', df)\n\n# Convert the 'Gender' column to categorical data type\ndf["Gender"] = df["Gender"].astype('category')\n\n# Display the new DataFrame. \nprint('Updated DataFrame:\\n',df)<\/pre><\/div>\n\n\n\nOriginal DataFrame:\n Name Gender Age\n0 John Male 25\n1 Emma Female 30\n2 Michael Male 35\n\nUpdated DataFrame:\n Name Gender Age\n0 John Male 25\n1 Emma Female 30\n2 Michael Male 35<\/code><\/pre>\n\n\n\nHandling Missing Values <\/strong><\/h3>\n\n\n\n