Скачать или смотреть 12) How to remove the Multiple column in Python

Hello, everyone! Welcome back to My Channel Learn Now from your learning partner.In this Video, we will be learning on how to delete an Multiply column from an existing data file in Pandas and then save it as a new file in Excel. Before we start if your are new to Python don't worry, We will explain everything you need to know so please watch our playlist mention above, and don't forget to subscribe to our channel so that you don’t miss out your learning.
To access our Excel file, simply double-click on it and it will automatically open with Microsoft Office. As you can observe, the file is already open and displays data where we have and Multiple column and we will be deleting department and gender column from our existing data. So let start with python
First, let's open the data file in Pandas. We can do this by using the read CSV method. We will need to specify the file path and the name of the file
import pandas as pd
df = pd.read_csv('file_path/file_name.csv', sheet_name='Sheet1')
To delete a column from our data frame, we can use the `drop` method and specify the name of the column we want to delete. the formula is df is equal to df dot round bracket open then square bracket open then we will write the Column Name which we want to delete and then comma and then we will add another column name and axis is equal to 1. In our example the column name will be Age and Gender
df = df.drop(['Age', 'Gender'], axis=1)
Now that we have deleted the column, we can save the updated data frame as a new Excel file using the `to_csv` method. We will need to specify the file path and name for the new file.
After entering the formula we need to run our code and we will see the code is process and finished
output = 'File path'+'modified_filename+'.csv'
df.to_csv(output.csv, index=False)
Hence we will open the CSV file from the above path and will check the result, Hence we can see the 2 column are deleted from our datbase. Congratulations! You have successfully learned how to delete and multiple columns from existing data in Pandas, and save it as a new Excel file. In the next lecture, we will focus on renaming column names.
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