![]() ![]() So if the old variable name is old_var and the new variable name is new_var, you would present to the columns parameter as key/value pairs, inside of a dictionary: columns =, which is basically saying change the column name 'gross_domestic_product' to 'GDP'. When you change column names using the rename method, you need to present the old column name and new column name inside of a Python dictionary. Let’s look carefully at how to use the columns parameter. ![]() Inside the parenthesis, you’ll use the columns parameter, which enables you to specify the columns that you want to change. You type the name of the dataframe, and then. When we use the rename method, we actually start with our dataframe. (The syntax for renaming columns and renaming rows labels is almost identical, but let’s just take it one step at a time.) Ok, let’s start with the syntax to rename columns. You can import pandas with the following code:Īnd if you need a refresher on Pandas dataframes and how to create them, you can read our tutorial on Pandas dataframes. A quick noteĮverything that I’m about to describe assumes that you’ve imported Pandas and that you already have a Pandas dataframe created. Index: Either a dictionary or a function to change the index names. Here, I’ll show you the syntax for how to rename Pandas columns, and also how to rename Pandas row labels. Parameters of the rename() function Mapper: Function dictionary to change the column names. Ok, now that I’ve explained what the Pandas rename method does, let’s look at the syntax. I’ll show you examples of both of these in the examples section.īut first, let’s take a look at the syntax. This technique is most often used to rename the columns of a dataframe (i.e., the variable names).īut again, it can also rename the row labels (i.e., the labels in the dataframe index). The Pandas rename method is fairly straight-forward: it enables you to rename the columns or rename the row labels of a Python dataframe. Let’s start with a quick introduction to the rename method. If you need something specific, you can click on any of the following links. I’ll explain what the technique does, how the syntax works, and I’ll show you clear examples of how to use it. Extra labels in the mapping don’t throw an error.In this tutorial, I’ll explain how to use the Pandas rename method to rename columns in a Python dataframe.If the new name mapping is not provided for some column label then it isn’t renamed.Set errors='ignore' to not throw any errors.Set errors='raised' to throws KeyError for the unknown columns.If yes, then use the errors parameter of DataFrame.rename(). Print(student_df.columns.values) Raise error while renaming a columnīy default, The DataFrame.rename() doesn’t throw any error if column names you tried to rename doesn’t exist in the dataset.ĭo you want to throw an error in such cases? Use the following syntax code to rename the column. Use the column parameter of DataFrame.rename() function and pass the columns to be renamed. Sometimes it is required to rename the single or specific column names only. Also, It raises KeyError If any of the labels are not found in the selected axis when errors='raise'.It returns a DataFrame with the renamed column and row labels or None if inplace=True.If ‘ignore’, existing keys will be renamed and extra keys will be ignored. If ‘raise’, raise a KeyError if the columns or index are not present. ![]() errors: It is either ‘ignore’ or ‘raise’.level: In the case of a multi-index DataFrame, only rename labels in the specified level.inplace: It is used to specify whether to return a new copy of a DataFrame or update existing ones.copy: It allows the copy of underlying data.Column axis represented as 1 or ‘columns‘. It is used to specify the axis to apply with the mapper. It takes to dictionary or function as input. columns: It is used to specify new names for columns.It takes a Python dictionary or function as input. mapper: It is used to specify new names for columns.Syntax: DataFrame.rename(mapper=None, columns=None, axis=None, copy=True, inplace=False, level=None, errors='ignore') Let’s see the syntax of it before moving to examples. This is the most widely used pandas function for renaming columns and row indexes. ![]()
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