![]() Now, with that DataFrame object, we have used the rename() method and within the column parameter, we will create a lambda expression that will add the ‘New’ because of the re.sub() method which adds a subscript to all the previously expositing column names. We then printed the DataFrame using the print() function. We then use the pd.DataFrame() and used the dictionary as the DataFrame. Next, we create a basic dictionary, which has a list nesting it. Also, we have to import re (regular expression). Profile_pd = profile_pd.rename(columns=lambda x: re.sub('New','',x))įirst we will have to import the module Pandas and alias it with a name (here pd). It takes the replaced value in the form of a key:value pair within a dictionary. Here we need to define the specific information related to the columns that we want to rename. The very common and usual technique of renaming the DataFrame columns is by calling the rename() method. That is where data analysts use the following methods or techniques to rename the DataFrame columns. Many a time, it is essential to fetch a cluster of data from one DataFrame and place it in a new DataFrame and adjust the column name according to the data. This process is called renaming the DataFrame column. It is always possible to rename the label of a column in DataFrame. ![]() What do you mean by renaming a DataFrame column? In this article, you will learn how to rename a DataFrame column in Python. The DataFrame is the most commonly used data structure, and renaming its column is another essential technique that most data analysts have to do frequently. These data structures help in defining the data in a specific order and structure. It has different data structures: Series, DataFrames, and Panels. Print(Core_Dataframe.Pandas is one of the most common libraries for data analysis. Print(" THE CORE DATAFRAME AFTER RENAME OPERATION ") Print(" THE CORE DATAFRAME BEFORE RENAME OPERATION ") to achieve this capability to flexibly travel over a dataframe the axis value is framed on below means ,) The value specified in this argument represents either a column position or a row position in the dataframe. This argument represents the column or the axis upon which the Rename() function needs to be applied on. So every rename values which are mentioned here will be applied to the column names of the dataframe. ![]() This is again an another alternative to the argument axis ( mapper, axis=1 ), Here columns as the name suggest it represents the columns of the dataframe. So every rename values which are mentioned here will be applied to the rows of the dataframe. This is an alternative to the argument axis ( mapper, axis=0 ), Here Index represents the rows of the dataframe. The description of this argument is explained below separately. The axis argument here mentions whether the change is for the column or the index. ![]() When using the Mapper argument it needs to be combined with the axis argument. The above dictionary when passed in the rename function it implies that the value ‘A’ in either the column or the index needs to be replaced as Header1 and similarly the column or index with value ‘B’ needs to be replaced with the new value ‘Header2’. The mapper argument usually takes values in the form of a dictionary. The mapper holds the values which needs to be replaced and their replacement values, So the old value and the corresponding new value which needs to be replaced will be specified here. Web development, programming languages, Software testing & others DataFrame.rename(self, mapper=None, index=None, columns=None, axis=None, copy=True, inplace=False, level=None, errors='ignore') Parameter & Description of Pandas DataFrame.rename()īelow are the parameters of Pandas DataFrame.rename() in Python: Parameter Start Your Free Software Development Course ![]()
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