![]() ![]() If needed, you might want to remove unnamed columns from your DataFrame. For example: hr.columns = Drop unnamed columns off your DataFrame This will return the column index: Index(, dtype='object') Assign new column names to the DataFrameĪn additional possibility, probably less elegant, is to directly assign into the Dataframe column index using a list. To find out the updated columns names, use the df.columns attribute. We will use the DataFrame rename() method and pass a Python mapping dictionary containing one or more columns to be renamed: hr.rename(columns =, inplace = True) Hr = pd.read_clipboard() Rename an unnamed column Import the pandas library and use the pd.read_clipboard() method to create a simple DataFrame in your Jupyter or Colab notebook (or any other Python environment you might be using for Data Analysis.Īs mentioned above, feel free to copy the table then use the following snippet to construct your example HR DataFrame: import pandas as pd ![]() You can copy the table posted below to follow up with this tutorial. You would like to tidy your data by either maintaining the relevant columns, or simply delete the unnecessary ones. ![]() The data contains columns that are unnamed which is obviously confusing. To mass rename columns without name in pandas use the following code: your_df.rename (columns = ('col_to_be_renamed':'new_name'), inplace=True) Understanding the use caseĪssume that you have acquired some data into your DataFrame from a CSV or Excel file. ![]()
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