![]() DataFrame() Here we will create three columns with the names A, B, and C. We will create some dummy data to illustrate the various techniques. The first steps involve importing the pandas library and creating some dummy data that we can use to illustrate the process of column renaming. If you are interested in learning about other popular Python libraries then you may be interested in this article. These tables (dataframes) can be manipulated, analyzed, and visualized using a variety of functions that are available within pandas. It allows data to be loaded in from a number file formats (CSV, XLS, XLSX, Pickle, etc.) and stored within table-like structures. According to Wikipedia, the name originates from the term “panel data”. The Pandas name itself stands for “Python Data Analysis Library”. In this short article, we will cover a number of ways to rename columns in a pandas dataframe.īut first, what is Pandas? Pandas is a powerful, fast, and commonly used python library for carrying out data analytics. Revenue_df.columns = revenue_df. short guide on multiple options for renaming columns in a pandas dataframeĮnsuring that dataframe columns are appropriately named is essential to understanding what data is contained within, especially when we pass our data on to others. If you happen to have mixed lower / upper case and want to standardize your column names look and feel you can use the pandas str accessor and convert to lower case, upper case or capitalize your column names. Now we are able to easily set the index naming: revenue_df.index.name = 'area' We’ll first set a column as an index: revenue_df.set_index('domain', inplace=True) We can then pass a list to define the df column index: new_rev_df = pd.DataFrame(columns = new_cols) Rename the DataFrame index We can easidly create an empty DataFrame object using the pd.DataFrame constructor. Values of the DataFrame are replaced with other values dynamically. Revenue_df.rename (columns =, inplace=True) Set column names on empty DataFrame DataFrame.replace(toreplaceNone, valueNoDefault.nodefault,, inplaceFalse, limitNone, regexFalse, methodNoDefault.nodefault) source Replace values given in toreplace with value. ![]() If we are interested to use the column index position to modify our column names we can use the rename DataFrame method as shown in the following code: Here’s the new column index: Index(, dtype='object') Change column names by index position # apply the new column names to the DataFrame: Specifically we want to get rid of the biz_ prefix on each column name. Next, we will go ahead and rename the columns with values in a Python list. Print (df_cols) Set DataFrame column names from list We can easily access the column index using the DataFrame columns property: #will return the column index Feel free to use the data to follow along this tutorial. We will first import the pandas library into your Python development environment and create a very simple DataFrame that we can use to manipulate its column names. ![]() Example DataFrame for setting column names In this tutorial we’ll explain the mechanics of change column names in pandas DataFrames so that you can troubleshoot most issues you might encounter when defining new column header for your data after loading data into a DataFrame from a text or csv file or other sources.
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