Column renaming and column dropping

candy = pd.read_csv('data/candybars.csv')
candy

	name	weight	chocolate	peanuts	caramel	nougat	cookie_wafer_rice	coconut	white_chocolate	multi	available_canada_america
0	Coffee Crisp	50	1	0	0	0	1	0	0	0	Canada
1	Butterfinger	184	1	1	1	0	0	0	0	0	America
2	Skor	39	1	0	1	0	0	0	0	0	Both
...	...	...	...	...	...	...	...	...	...	...	...
22	Almond Joy	46	1	0	0	0	0	1	0	0	America
23	Oh Henry	51	1	1	1	0	0	0	0	0	Both
24	Cookies and Cream	43	0	0	0	0	1	0	1	0	Both

25 rows × 11 columns

Remember our candybars.csv dataframe?

Let’s bring it back and save it as object named candy.

Column Renaming

candy = candy.rename(columns={'available_canada_america':'availability'})
candy

	name	weight	chocolate	peanuts	caramel	nougat	cookie_wafer_rice	coconut	white_chocolate	multi	availability
0	Coffee Crisp	50	1	0	0	0	1	0	0	0	Canada
1	Butterfinger	184	1	1	1	0	0	0	0	0	America
2	Skor	39	1	0	1	0	0	0	0	0	Both
...	...	...	...	...	...	...	...	...	...	...	...
22	Almond Joy	46	1	0	0	0	0	1	0	0	America
23	Oh Henry	51	1	1	1	0	0	0	0	0	Both
24	Cookies and Cream	43	0	0	0	0	1	0	1	0	Both

25 rows × 11 columns

 columns={'old column name':'new column name'}

There will be times where you are unsatisfied with the column names and you may want to change them.

The proper syntax to do that is with .rename().

The column name available_canada_america is a bit long.

Perhaps it would be a good idea to change it to something shorter like availability.

Here is how we can accomplish that.

This code uses something we’ve never seen before - {} curly braces, also called curly brackets.

These have a special meaning but for now, you only need to concentrate your attention on the fact that the argument columns needs to have the format shown on the slide.

candy = candy.rename(columns={'available_canada_america':'availability',
                        'weight':'weight_g'})
candy.head()

	name	weight_g	chocolate	peanuts	caramel	nougat	cookie_wafer_rice	coconut	white_chocolate	multi	availability
0	Coffee Crisp	50	1	0	0	0	1	0	0	0	Canada
1	Butterfinger	184	1	1	1	0	0	0	0	0	America
2	Skor	39	1	0	1	0	0	0	0	0	Both
3	Smarties	45	1	0	0	0	0	0	0	1	Canada
4	Twix	58	1	0	1	0	1	0	0	1	Both

You can also rename multiple columns at once by adding a comma between the new and old column pairs within the curly brackets.

It’s important that we always save the dataframe to an object when making column changes or the changes will not be saved in our dataframe.

Column Dropping

candy.drop(columns='coconut')

	name	weight_g	chocolate	peanuts	caramel	nougat	cookie_wafer_rice	white_chocolate	multi	availability
0	Coffee Crisp	50	1	0	0	0	1	0	0	Canada
1	Butterfinger	184	1	1	1	0	0	0	0	America
2	Skor	39	1	0	1	0	0	0	0	Both
...	...	...	...	...	...	...	...	...	...	...
22	Almond Joy	46	1	0	0	0	0	0	0	America
23	Oh Henry	51	1	1	1	0	0	0	0	Both
24	Cookies and Cream	43	0	0	0	0	1	1	0	Both

25 rows × 10 columns

.drop() is the verb we use to delete columns in a dataframe.

Let’s delete the column coconut by specifying it in the columns argument of the drop verb.

candy.drop(columns='coconut')

candy.head()

	name	weight_g	chocolate	peanuts	caramel	nougat	cookie_wafer_rice	coconut	white_chocolate	multi	availability
0	Coffee Crisp	50	1	0	0	0	1	0	0	0	Canada
1	Butterfinger	184	1	1	1	0	0	0	0	0	America
2	Skor	39	1	0	1	0	0	0	0	0	Both
3	Smarties	45	1	0	0	0	0	0	0	1	Canada
4	Twix	58	1	0	1	0	1	0	0	1	Both

candy = candy.drop(columns=['nougat', 'coconut'])
candy.head()

	name	weight_g	chocolate	peanuts	caramel	cookie_wafer_rice	white_chocolate	multi	availability
0	Coffee Crisp	50	1	0	0	1	0	0	Canada
1	Butterfinger	184	1	1	1	0	0	0	America
2	Skor	39	1	0	1	0	0	0	Both
3	Smarties	45	1	0	0	0	0	1	Canada
4	Twix	58	1	0	1	1	0	1	Both

If you look again at the code we just wrote you’ll notice we didn’t save over the dataframe object, so the dataframe candy still will contain the coconut column.

Let’s overwrite the dataframe and remove multiple columns at the same time.

Let’s drop nougat and coconut together.

We put the columns we want to drop in square brackets and this time we will remember to overwrite over the candy object.

Now when we call candy.head() it reflects the dropped columns. They’re no longer there.

Let’s apply what we learned!