Introductory Data Analysis

Start with a question and a data contract

Introductory analysis begins before a calculation. State a narrow question, identify what one row represents, and name the expected fields and units. For example, a study log might have one row per week and a minutes field measured in whole minutes. A CSV file is a common way to exchange such a table. Python's csv module can read tabular data, but CSV is a family of conventions rather than a guarantee that every file uses identical quoting or delimiters. Inspect a small input instead of assuming it is clean. Ask whether headers are present, whether a field that should be numeric contains text, and whether repeated or missing rows have a defined meaning. The goal is not to make every dataset perfect. It is to establish what the program will accept and what it will report as excluded.

This lesson deliberately uses only the standard library. That keeps each step visible and removes any requirement to install a third-party package. Larger projects may choose specialized tools, but the reasoning remains: define the unit, inspect the values, state rules, and preserve the path from input to output.

Clean by applying recorded rules

Cleaning is a transformation from raw entries to analysis-ready values. It is not permission to quietly change inconvenient observations. Suppose the minutes column contains '30', '45', an empty string, and '90'. If the question concerns recorded numeric minutes, an explicit rule can be: strip surrounding whitespace; exclude a blank value; convert the remaining strings with int; and count the usable records. A different question might require treating a blank as zero, but that is a substantive choice and should be justified rather than hidden in code. Values that cannot be converted should likewise be reported, corrected from a source record when possible, or excluded under a stated rule.

Keep raw data separate from the cleaned list. That makes it possible to check whether the rule was applied as intended and to rerun the analysis if the rule changes. For a small example, printing the cleaned values and their count is a useful audit trail. Do not silently remove rows and then present the result as though it summarizes every original row.

Describe, then limit the conclusion

For numeric data, the statistics module supplies common descriptive functions. mean(values) calculates the arithmetic mean. median(values) gives a middle value after ordering; with an even number of values it averages the two central values. These describe center, but neither replaces looking at the individual values, the count, and the spread. With [30, 45, 90], the mean is 55 while the median is 45. The larger 90-minute value pulls the mean upward, so reporting both reveals more than a single number. min and max add a simple range check.

A reproducible result includes the input or its stable location, the cleaning rule, executable code, and the output with a date or version when relevant. Reproducibility does not promise that the conclusion is universally true; it lets another person rerun the same stated procedure. Finally, a pattern is not a causal explanation. If weeks with more recorded study minutes also have higher scores, many possibilities remain: prior preparation, assignment difficulty, who chose to record, or chance. The records can support a careful description—'these values varied together in this small dataset'—but establishing cause requires a design and evidence beyond that pattern.

A compact standard-library workflow

A practical sequence is: preserve the raw rows; validate or clean under a written rule; calculate summaries only from the resulting values; print enough intermediate information to audit the result; and write a conclusion whose strength matches the data. This workflow is intentionally modest. It does not estimate a population parameter, prove a hypothesis, or decide whether a program caused an outcome. Its value is that it makes simple claims accurate and reviewable. When the dataset or question grows, the same habits—documented assumptions, visible transformations, and limited conclusions—scale better than a one-line calculation with no context.