Rename pandas dataframe columns using df.rename() | Image by AuthorĪs you can see, I passed dictionary in the parameter columns in df.rename(), where keys are Status and Quantity which are old column names. And values are Order_Status and Order_Quantity which are new column names. ? Note: df.rename() consists an inplace parameter which is False by default. ![]() In order to retain the changes in the column names, you need to make inplace = True.Īs I did not wanted to retain the changed column names I used. All you need to do is simply pass the list of column names to the. head() method to only see how it looks with changed column name. setaxis () function and specify axis 1 to rename columns, like below. This is how you can change the column names for all or some of the columns. Here also one has to consider all the points which I mentioned in the previous method. ? Note: Before making inplace = True in any function, it is always good idea to use. The next methods is a slight variation of. Renaming the columns through a list in pandas requires knowing the shape of the dataset you have. Just like the first method above, we will still use the parameter columns in the. But instead of passing the old name - new name key-value pairs, we can also pass a function to columns parameter.įor example, converting all column names to upper case is quite simple using this trick, like below df.rename(columns= str.upper).head()Ĭhanging all column names at once using df.columns | Image by AuthorĪs you can see, I assigned list of new column names to df.columns and names of all columns are changed accordingly. The length of this names list must be exactly equal to the total number of columns in the DataFrame.Īnd without any other options like inplace, the column names are changed directly and permanently, this method is a bit risky take.⚠️ ? Note: You need to pass the names of all the columns. So, I would suggest to use it only when you are 100% sure that you want to change the column names. TLDR Pandas performance is slowed by dataframe copying under the hood. ? Note: The sequence of the column names list should be same in which you have columns in the DataFrame, otherwise the column names can be assigned incorrectly. I show that this leads to a nearly 4x performance slowdown compared with the most performant method. Renaming Columns in Pandas We will accomplish two things: Show two ways to rename columns in pandas Show the most perfomant method. When all above points kept in mind, this is the best method to change all columns in one go. This method is originally used to set labels to DataFrame’s axis i.e. ![]() This method can be used to label columns as well as rows.Īll you need to do is simply pass the list of column names to the. But if you don’t want to change the entire column, and simply want to add a new sub string to it, then using the add_suffix() or add_prefix() are the best choice.Set_axis() function and specify axis = 1 to rename columns, like below ? df.set_axis(, axis=1). But the most commonly used technique is the rename() method. After modifying second column, we simply displayed the overall updated DataFrame using the print().Īll these techniques are important and have their own significance. 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. Pandas is one of the most common libraries for data analysis.
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