Follow the transformation
Select columns by name when the variables are conceptually known.
Use a boolean condition to select rows that satisfy an analytical rule.
Use .loc for label/condition-based row-and-column selection.
Python & pandas Essentials · Lesson 9
Selecting Rows and Columns
Selecting rows and columns defines the subset of a DataFrame that an analysis will use.
Selecting Rows and Columns
Selecting rows and columns defines the subset of a DataFrame that an analysis will use. Column selection answers “which variables?”, while row filtering/indexing answers “which observations?”. pandas provides label-based .loc and position-based .iloc so the selection rule can be explicit rather than relying on accidental row order.
Learning goal: explain why Selecting Rows and Columns behaves this way, apply it to a small example, and verify the result independently. Begin by being able to justify this first step: Select columns by name when the variables are conceptually known.
Deeper walkthrough
Read Selecting Rows and Columns as a mechanism, not a recipe
Treat this as a sequence of observable decisions rather than one opaque command. Stage 1: Select columns by name when the variables are conceptually known. Stage 2: Use a boolean condition to select rows that satisfy an analytical rule. Stage 3: Use .loc for label/condition-based row-and-column selection. Final checkpoint: Check the resulting shape and a few records before computing summaries.
Know what would convince you
- Recompute one result from a handful of source rows or an independent formula.
- Check row counts, group totals and units before interpreting differences.
Useful distinctionDefinition: The exact metric/selection/comparison being computed.
Click a stage to inspect what happens, what changes, and what should be checked before moving on.
Stage 1
Select columns by name when the…
Select columns by name when the variables are conceptually known. This is an input-preparation stage for Selecting Rows and Columns. Verify the relevant type, shape, units, keys, missingness or assumptions before later steps depend on them.
Input focus: confirm the data/object, units, type, shape and assumptions before the next operation depends on them.
Trace the mechanism step by step
- Select columns by name when the variables are conceptually known.
- Use a boolean condition to select rows that satisfy an analytical rule.
- Use .loc for label/condition-based row-and-column selection.
- Use .iloc only when positional selection is genuinely intended.
- Check the resulting shape and a few records before computing summaries.
Explicit table subset
# Step 1 — Import the module so its functions/classes are available to the rest of this example.
import pandas as pd
# Step 2 — Construct `df` as a tabular object with named columns for inspectable analysis.
df = pd.DataFrame({"region":["E","W","E"],"sales":[10,20,30]})
# Step 3 — Execute this statement and inspect how it changes the current value, object or program state.
subset = df.loc[df["region"] == "E", ["sales"]]
# Step 4 — Display the current value explicitly so the result/state can be inspected during execution.
print(subset["sales"].tolist())Expected / illustrative result
Only East rows remain, producing sales values [10, 30].
Interpret the result.
For Selecting Rows and Columns, trace representative input values into the result and verify shape, dtype, row grain, axis or key behaviour that the operation can change.
Place the concept correctly
DefinitionThe exact metric/selection/comparison being computed.EvidenceTable, formula or visual that answers the question.AuditIndependent count/total/rule check that can reveal an error.When it is appropriate
Use Selecting Rows and Columns when it answers a defined question in Python & pandas Essentials and its inputs/assumptions match the current data or program state.
When to stop or reconsider
Reconsider Selecting Rows and Columns when the required information is unavailable, the operation would violate a validation/data boundary, or a simpler operation answers the question more transparently.
Failure modes to recognise
- Changing the population/grain without noticing it.
- Using an undefined denominator, time window, unit or category rule.
- Presenting a number/plot without reconciling it to source counts or totals.
How to check the result
- Recompute one result from a handful of source rows or an independent formula.
- Check row counts, group totals and units before interpreting differences.
- Change one source value and predict which reported value/mark should change.
Demonstrate understanding
Try this:
Construct a tiny example of Selecting Rows and Columns. First select columns by name when the variables are conceptually known. Then use a boolean condition to select rows that satisfy an analytical rule. Predict the result before execution and explain one boundary or failure case.
Use 4–8 rows containing the exact key/category/missing-value pattern. Trace one row or group from input to output.
Check reasoning, not memorisation
Which approach best demonstrates understanding of Selecting Rows and Columns?
Remember the logic
Step 1Select columns by name when the variables are conceptually known.Step 2Use a boolean condition to select rows that satisfy an analytical rule.Step 3Use .loc for label/condition-based row-and-column selection.Step 4Use .iloc only when positional selection is genuinely intended.What to remember
- Selecting rows and columns defines the subset of a DataFrame that an analysis will use. Column selection answers “which variables?”, while row filtering/indexing answers “which observations?”. pandas provides label-based .loc and position-based .iloc so the selection rule can be explicit rather than relying on accidental row order.
- Select columns by name when the variables are conceptually known.
- Changing the population/grain without noticing it.
- Recompute one result from a handful of source rows or an independent formula.