Business Analytics for Beginners: How to create a new column based on conditions in Python

How to create a new column based on conditions in Python

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Creating a new column based on conditions in Python is a simple process that can be done using the pandas library. In this article, we will go over the steps needed to create a new column based on conditions in Python.

The first step is to import the pandas library and load the dataset into a dataframe. This can be done by using the “pandas.read_csv()” function to load data from a CSV file or by using the “pandas.read_excel()” function to load data from an Excel file. Once the data is loaded into a dataframe, it’s important to make sure that the variables are in the correct format, such as numeric for continuous variables and string for categorical variables.

The next step is to create a new column based on conditions. This can be done by using the “pandas.DataFrame.assign()” function to create a new column and the “pandas.Series.where()” function to apply the conditions. The “pandas.DataFrame.assign()” function takes the name of the new column and the “pandas.Series.where()” function takes the conditions as its arguments.

For example, if you want to create a new column called “grade” based on the values of the “score” column, you would use the following code:

df = df.assign(grade = df[“score”].where(df[“score”] >= 90, “A”))

This code creates a new column called “grade” and assigns the value “A” to all the rows where the value of the “score” column is greater than or equal to 90.

It’s important to note that when creating a new column based on conditions, it’s important to make sure that the conditions are clearly defined and that the new column is in the correct format. Additionally, it’s important to keep in mind that the new column will not be added permanently to the dataframe unless you overwrite the dataframe or use the inplace=True argument in the assign() function.

In conclusion, creating a new column based on conditions in Python is a simple process that can be done using the pandas library. The first step is to import the pandas library and load the dataset into a dataframe. The next step is to use the “pandas.DataFrame.assign()” function to create a new column and the “pandas.Series.where()” function to apply the conditions to the new column. It is important to make sure that the conditions are clearly defined and that the new column is in the correct format. Additionally, keep in mind that the new column will not be added permanently to the dataframe unless you overwrite the dataframe or use the inplace=True argument in the assign() function. This is a powerful technique to manipulate the data and make it ready for further analysis or modeling.

 

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