Chaining Notation

What is Chaining?

Attribution

Up until now, when we perform multiple actions on an object, we have been saving the results with the = operator after each line.

Chaining allows us to do multiple actions in a single line of code without the need to save each action in an intermediate object.

You can imagine that we are linking verbs together with a chain.

cereal = pd.read_csv('data/cereal.csv')

manufacturer_column = cereal['mfr']
manufacturer_column.value_counts()

mfr
K    23
G    22
P     9
     ..
Q     8
N     6
A     1
Name: count, Length: 7, dtype: int64

cereal['mfr'].value_counts()

mfr
K    23
G    22
P     9
     ..
Q     8
N     6
A     1
Name: count, Length: 7, dtype: int64

When we made our frequency table in Module 1, we first saved the single column as an object before we used value_counts() like we show you here.

Instead of saving the column as an intermediate value, we can skip this step and make the frequency table in one line, with chaining.

The convenience doesn’t stop there either.

mfr_k = cereal[cereal['mfr'] == 'K']
csr_df = mfr_k.loc[:, ["calories", "sugars", "rating"]]
cereal_mean = csr_df.mean()
cereal_mean

calories    108.695652
sugars        7.565217
rating       44.038462
dtype: float64

cereal_mean = cereal[cereal['mfr'] == 'K'].loc[:, ["calories", "sugars", "rating"]].mean()
cereal_mean

calories    108.695652
sugars        7.565217
rating       44.038462
dtype: float64

Let’s say we want to perform three actions:

1. Filter the dataframe for cereals only from manufacturer “K”.

2. Select the columns calories, sugars and rating using the verb loc.

3. Find the mean of each column using .mean().

Previous we would need 3 different lines to code this.

Instead we can chain them, as shown here.

This chain avoided the use of the intermediate objects; mfr_k and csr_df.

We cut out creating intermediate variables which is great but now we have a really long line of code and it’s a bit hard to read.

How can we make this easier to read?

cereal_mean = cereal[cereal['mfr'] == 'K'].loc[:, ["calories", "sugars", "rating"]].mean()

cereal_mean

calories    108.695652
sugars        7.565217
rating       44.038462
dtype: float64

cereal_mean = (cereal[cereal['mfr'] == 'K'].loc[:, ["calories", "sugars", "rating"]]
                                           .mean()
              )
              
cereal_mean.head()

calories    108.695652
sugars        7.565217
rating       44.038462
dtype: float64

In this course, we suggest using a new line for each verb.

We can do this by wrapping our all code (to the right of the equals sign) in parentheses and inserting a new line before each period (.).

It’s a good habit to indent and have the verbs lined up for additional clarity.

Coding Preferences

• Chaining has advantages and disadvantages.
• Increases the readability of our code.
• Comments are extremely important with of without chaining.

Although we have seen how chaining has advantages, it’s a coding style that is adopted by the person writing the code.

Someone else (or more often, future you) must be able to understand what is being accomplished.

This is why comments (#) are so important. If a lot is going on in your code, it’s a good habit to explain it whether it’s with chaining, or without.

Let’s apply what we learned!