Let’s read the budget.xlsx file into a DataFrame:
import pandas as pd\n\nbudget = pd.read_excel("budget.xlsx")\n\nbudgetOutput:
\nNote that we have a numeric index in our DataFrame.
\nLet’s say we want to delete the row with index number 0. We can use the drop() method, which by default, deletes rows.
budget.drop(labels=0, inplace=True)\n\nbudgetOutput:
\nWhat if we want to delete columns?
\nAll we need to do is to pass axis=1 to the drop() method.
\n\naxis=1 represents columns. We can also pass
\naxis=”columns”.
Let’s delete the City column:
budget.drop(labels="City", axis="columns", inplace=True)\n\nbudgetOutput:
\nSimilarly, we can delete more than one column by passing the column labels to a list, like the way we did for rows.
\nFor deleting columns, we have access to another method named pop(), which removes the column from the DataFrame and also returns it.
budget.pop("LTL Flag")Output:
\nNote that we can’t delete multiple columns with the pop() method.
Another way we can delete a column is by using the del keyword:
del budget["July'19 Achievement"]\n\nbudgetOutput:
\n