Missing values are not a single universal token. In pandas and NumPy they can appear as `NaN`, `pd.NA`, `NaT` or domain-specific sentinel codes such as -999. The first job is to convert all legitimate missing representations into an explicit, typed missing value without accidentally treating valid values as absent.
Missing values are not a single universal token. In pandas and NumPy they can appear as `NaN`, `pd.NA`, `NaT` or domain-specific sentinel codes such as -999. The first job is to convert all legitimate missing representations into an explicit, typed missing value without accidentally treating valid values as absent.
The practical value of Missing-value representations 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.
Audit each column’s dtype and source-system conventions; standardise sentinels before computing missingness rates.
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.
A survey export uses blank strings for unanswered text, -99 for missing age and 9999 for an unavailable laboratory result. Convert each documented sentinel to a true missing value before analysis.
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.
Keep the example small enough that you can inspect each stage manually.
# Purpose: demonstrate Missing-value representations 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])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']]