18.2. Wrangling and Transforming#

We begin by taking a peek at the contents of our datafile. To do this, we open the file and examine the first few rows (Chapter 8):

from pathlib import Path

# Create a Path pointing to our data file
insp_path = Path('data/donkeys.csv')

with insp_path.open() as f:
    # Display first five lines of file
    for _ in range(5):
        print(f.readline(), end='')

Since the file is CSV formatted, we can easily read it into a dataframe:

donkeys = pd.read_csv("data/donkeys.csv")
BCS Age Sex Length Girth Height Weight WeightAlt
0 3.0 <2 stallion 78 90 90 77 NaN
1 2.5 <2 stallion 91 97 94 100 NaN
2 1.5 <2 stallion 74 93 95 74 NaN
... ... ... ... ... ... ... ... ...
541 2.5 10-15 stallion 103 118 103 174 NaN
542 3.0 2-5 stallion 91 112 100 139 NaN
543 3.0 5-10 stallion 104 124 110 189 NaN

544 rows × 8 columns

Over 500 donkeys participated in the survey, and eight measurements were made on each donkey. According to the documentation, the granularity is a single donkey (Chapter 9). Table 18.1 provides descriptions of the eight features.

Table 18.1 Donkey study codebook#


Data type

Feature type





Body condition score: from 1 (emaciated) to 3 (healthy) to 5 (obese) in increments of 0.5.




Age in years, under 2, 2–5, 5–10, 10–15, 15–20, and over 20 years




Sex categories: stallion, gelding, female




Body length (cm) from front leg elbow to back of pelvis




Body circumference (cm), measured just behind front legs




Body height (cm) up to point where neck connects to back




Weight (kilogram)




Second weight measurement taken on a subset of donkeys

Figure 18.1 is a stylized representation of a donkey as a cylinder with neck and legs appended. Height is measured from the ground to the base of the neck above the shoulders; girth is around the body, just behind the legs; and length is from the front elbow to the back of the pelvis.


Fig. 18.1 Diagram of a donkey’s girth, length, and height, characterized as measurements on a cylinder#

Our next step is to perform some quality checks on the data. In the previous section, we listed a few potential quality concerns based on scope. Next, we check the quality of the measurements and their distributions.

Let’s start by comparing the two weight measurements made on the subset of donkeys to check on the consistency of the scale. We make a histogram of the difference between these two measurements for the 31 donkeys that were weighed twice:

donkeys = donkeys.assign(difference=donkeys["WeightAlt"] - donkeys["Weight"])

px.histogram(donkeys, x="difference", nbins=15,
        difference="Differences of two weighings (kg)<br>on the same donkey"
    width=350, height=250,

The measurements are all within 1 kg of each other, and the majority are exactly the same (to the nearest kilogram). This gives us confidence in the accuracy of the measurements.

Next, we look for unusual values in the body condition score:

3.0    307
2.5    135
3.5     55
1.5      5
4.5      1
1.0      1
Name: count, Length: 8, dtype: int64

From this output, we see that there’s only one emaciated (BCS = 1) and one obese (BCS = 4.5) donkey. Let’s look at the complete records for these two donkeys:

donkeys[(donkeys['BCS'] == 1.0) | (donkeys['BCS'] == 4.5)]
BCS Age Sex Length Girth Height Weight WeightAlt
291 4.5 10-15 female 107 130 106 227 NaN
445 1.0 >20 female 97 109 102 115 NaN

Since these BCS values are extreme, we want to be cautious about including these two donkeys in our analysis. Since we have only one donkey in each of these extreme categories, our model might well not extend to donkeys with a BCS of 1 or 4.5. So we remove these two records from the dataframe and note that our analysis may not extend to emaciated or obese donkeys. In general, we exercise caution in dropping records from a dataframe. Later, we may also decide to remove the five donkeys with a score of 1.5 if they appear anomalous in our analysis, but for now, we keep them in our dataframe. We need good reasons for their exclusion, document our action, and keep the number of excluded observations low so that we avoid overfitting a model because we dropped any record that disagreed with the model.

We remove these two outliers next:

def remove_bcs_outliers(donkeys):
    return donkeys[(donkeys['BCS'] >= 1.5) & (donkeys['BCS'] <= 4)] 

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

Now, we examine the distribution of values for weight to see if there are any issues with quality:

px.histogram(donkeys, x='Weight', nbins=40, width=350, height=250,
            labels={'Weight':'Weight (kg)'})

It appears there is one very light donkey weighing less than 30 kg. Next, we check the relationship between weight and height to assess the quality of the data for analysis:

px.scatter(donkeys, x='Height', y='Weight', width=350, height=250,
          labels={'Weight':'Weight (kg)', 'Height':'Height (cm)'})

The small donkey is far from the main concentration of donkeys and would overly influence our models. For this reason, we exclude it. Again, we keep in mind that we may also want to exclude the one or two heavy donkeys if they appear to overly influence our future model fitting:

def remove_weight_outliers(donkeys):
    return donkeys[(donkeys['Weight'] >= 40)]

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

(541, 8)

In summary, based on our cleaning and quality checks, we removed three anomalous observations from the dataframe. Now we’re nearly ready to begin our exploratory analysis. Before we proceed, we set aside some of our data as a test set.

We talked about why it’s important to separate out a test set from the train set in Chapter 16. A best practice is to separate out a test set early in the analysis, before we explore the data in detail, because in EDA, we begin to make decisions about what kinds of models to fit and what variables to use in the model. It’s important that our test set isn’t involved in these decisions so that it imitates how our model would perform with entirely new data.

We divide our data into an 80/20 split, where we use 80% of the data to explore and build a model. Then we evaluate the model with the 20% that has been set aside. We use a simple random sample to split the dataframe into the test and train sets. To begin, we randomly shuffle the indices of the dataframe:

n = len(donkeys)
indices = np.arange(n)
n_train = int(np.round((0.8 * n)))

Next, we assign the first 80% of the dataframe to the train set and the remaining 20% to the test set:

train_set = donkeys.iloc[indices[:n_train]]
test_set = donkeys.iloc[indices[n_train:]]

Now we’re ready to explore the training data and look for useful relationships and distributions that inform our modeling.