dummies = pd.get_dummies(df[['column_1']], drop_first=True)
df = pd.concat([df.drop(['column_1'],axis=1), dummies],axis=1)

note:for more that one coloum keep ading in the list 
dummies = pd.get_dummies(df[['column_1', 'column_2','column_3']], drop_first=True)
df = pd.concat([df.drop(['column_1', 'column_1'],axis=1), dummies],axis=1)

Here is what the above code is Doing:
1. We’re using the pd.get_dummies() method to create dummy variables from the column_1 column.
2. We’re setting the drop_first parameter to True to drop the first dummy variable.
3. We’re storing the dummy variables in a variable called dummies.
4. We’re using the pd.concat() method to concatenate the original DataFrame with the dummy variables.
5. We’re setting the axis parameter to 1 to indicate that we want to concatenate along the columns.
6. We’re setting the inplace parameter to True to make the changes to the DataFrame in place.
7. We’re dropping the column_1 column from the DataFrame.
8. We’re concatenating the dummy variables to the DataFrame.