4 · Data Cleaning & Missing Data · Data Preprocessing

Missing values

Impute using statistics or learned models fitted on training data only. Missingness indicators can be useful when absence itself is informative. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.

Reference lessonPython exampleVisual explanation

Intuition first

What this concept means in practice

The practical value of Missing values comes from understanding both the transformation and the boundary around it: what information is allowed to enter, what assumption is being made, and how you know the result is still valid after the transformation.

A beginner-friendly way to reason about it is to start with a tiny case where the correct result can be checked independently. Once the mechanism is clear, scale the exact same reasoning to larger tables, pipelines or models.

PurposeUse whenever missing values occur and the chosen model cannot safely handle them natively.

MechanismChoose an explicit representation for missingness: deletion, simple statistics, model-based imputation or native missing-value handling. Fit imputation only on training data.

EvidenceInspect intermediate and final output; compare with an independent expectation.

Main cautionGlobal imputation before CV leaks distribution information; missingness can also be informative rather than random.

Mechanism

Trace the operation from input to decision

Choose an explicit representation for missingness: deletion, simple statistics, model-based imputation or native missing-value handling. Fit imputation only on training data.

1Input→

2Apply rule→

3Inspect state→

4Validate→

5Use result

Key rule

fit imputer on train only

Visual explanation

Make the structure visible

The interactive view uses a concept-specific plot when the topic maps naturally to one; otherwise it uses a workflow view instead of leaving a broken placeholder.

Loading visual…

Practical example

Where you would use it

Median-impute age within each training fold and optionally add an “age_missing” indicator.

Use when

Use whenever missing values occur and the chosen model cannot safely handle them natively.

Pitfall

What can make the result misleading

Watch out

Global imputation before CV leaks distribution information; missingness can also be informative rather than random.

A useful diagnostic question is: Could the same code still run successfully if the analytical assumption were wrong? If yes, add an explicit validation check rather than relying on execution success.

Implementation

Miniature Python example

Keep the example small enough that you can inspect each stage manually.

Python

# Purpose: demonstrate Missing values with a small, inspectable example.
# Follow the comments and printed stages to connect each operation with its result.
# Import the library or helper used in this example.
# Step 1 — Import the module so its functions/classes are available to the rest of this example.
import numpy as np
# Import the library or helper used in this example.
# Step 2 — Import the module so its functions/classes are available to the rest of this example.
import pandas as pd
# Import the library or helper used in this example.
# Step 3 — Import only the named objects needed by the following steps, keeping dependencies explicit.
from sklearn.compose import ColumnTransformer
# Import the library or helper used in this example.
# Step 4 — Import only the named objects needed by the following steps, keeping dependencies explicit.
from sklearn.impute import SimpleImputer
# Import the library or helper used in this example.
# Step 5 — Import only the named objects needed by the following steps, keeping dependencies explicit.
from sklearn.preprocessing import OneHotEncoder

# Create a small labelled dataset that is easy to inspect by eye.
# Step 6 — Construct `X` as a tabular object with named columns for inspectable analysis.
X = pd.DataFrame({
    "age": [22,25,np.nan,31,35,38,41,44,48,np.nan,56,60],
    "income": [42,45,48,np.nan,55,59,62,66,70,74,np.nan,82],
    "city": ["A","B","A","B",None,"C","A","C","B","A","C",None]
})
# Print this intermediate result so you can verify the workflow step by step.
# Step 7 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 1 · Shape:", X.shape)
# Print this intermediate result so you can verify the workflow step by step.
# Step 8 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 1 · Missing counts:", X.isna().sum().to_dict())
# Store this intermediate value with a descriptive name for the next step.
# Step 9 — Compute the right-hand expression and store its result in `pre` for the next step.
pre = ColumnTransformer([
    ("num", SimpleImputer(strategy="median"), ["age","income"]),
    ("cat", SimpleImputer(strategy="most_frequent"), ["city"])
], verbose_feature_names_out=False)
# Learn the transformation from this data and apply it in one step.
# Step 10 — Fit the transformation on the training input and immediately transform that same input.
Xt = pre.fit_transform(X)
# Print this intermediate result so you can verify the workflow step by step.
# Step 11 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 2 · Missing after imputation:", int(pd.isna(Xt).sum()))
# Print this intermediate result so you can verify the workflow step by step.
# Step 12 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 3 · First four transformed rows:")
# Print this intermediate result so you can verify the workflow step by step.
# Step 13 — Display the current value explicitly so the result/state can be inspected during execution.
print(Xt[:4])

Expected / illustrative output

STEP 1 · Shape: (12, 3)
STEP 1 · Missing counts: {'age': 2, 'income': 2, 'city': 2}
STEP 2 · Missing after imputation: 2
STEP 3 · First four transformed rows:
[[22.0 42.0 'A']
 [25.0 45.0 'B']
 [39.5 48.0 'A']
 [31.0 60.5 'B']]

Implementation checklist

Before you move on

Can you state what data or object enters the operation?
Can you explain what changes and what must remain invariant?
Have you checked the result on a tiny case you can verify independently?
Have you considered the main failure mode described above?
Can the operation be reproduced from code/formulas and documented assumptions?