Given a Pandas DataFrame, let’s see how to rename column names.
About Pandas DataFrame:
Pandas DataFrame are rectangular grids which are used to store data. It is easy to visualize and work with data when stored in dataFrame.
- It consists of rows and columns.
- Each row is a measurement of some instance while column is a vector which contains data for some specific attribute/variable.
- Each dataframe column has a homogeneous data throughout any specific column but dataframe rows can contain homogeneous or heterogeneous data throughout any specific row.
- Unlike two dimensional array, pandas dataframe axes are labeled.
Method #1: Using
One way of renaming the columns in a Pandas dataframe is by using the
rename() function. This method is quite useful when we need to rename some selected columns because we need to specify information only for the columns which are to be renamed.
Rename a single column.
Rename multiple column.
Method #2: By assigning a list of new column names
The columns can also be renamed by directly assigning a list containing the new names to the
columns attribute of the dataframe object for which we want to rename the columns. The disadvantage with this method is that we need to provide new names for all the columns even if want to rename only some of the columns.
- Python | Pandas Dataframe.rename()
- Python | Pandas DataFrame.columns
- Difference of two columns in Pandas dataframe
- How to select multiple columns in a pandas dataframe
- Dealing with Rows and Columns in Pandas DataFrame
- How to drop one or multiple columns in Pandas Dataframe
- Getting frequency counts of a columns in Pandas DataFrame
- Iterating over rows and columns in Pandas DataFrame
- Conditional operation on Pandas DataFrame columns
- Split a text column into two columns in Pandas DataFrame
- Change Data Type for one or more columns in Pandas Dataframe
- Split a String into columns using regex in pandas DataFrame
- Using dictionary to remap values in Pandas DataFrame columns
- Create a new column in Pandas DataFrame based on the existing columns
- Python | Delete rows/columns from DataFrame using Pandas.drop()
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