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Practice Excel With Pokémon: Four Data Projects to Build

Use Pokémon data to build four Excel projects, from a type-distribution chart to base-stat rankings, generation summaries, and matchup comparisons.
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Use Pokémon data to practice Excel with four concrete questions: which types are most common, how do base stats compare, how do generations differ, and which matchup multipliers shape a Pokémon’s defensive profile? Each project ends with a table, summary, or chart—and makes you choose clear rules for forms and dual types rather than treating a spreadsheet as a pile of rows.

Choose a dataset and set your scope

The Complete Pokédex Dataset repository describes a CSV with 55 columns, including species identifiers, primary and secondary types, six base stats, 18 type-matchup multiplier fields, evolution and form information, and Pokédex descriptions. Its metadata describes 1,025 Pokémon from Gen I–IX; treat that as the scope of this particular community-maintained dataset, not an official download or a permanent guarantee. The repository says its material draws on Serebii for stats and breeding or legendary status, Pokémon.com for Pokédex descriptions, and Bulbapedia for artwork. Check the dataset repository’s fields, files, and license before relying on them.

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For the steps below, use the repository’s pokemon_utf8.csv, described as its recommended UTF-8 comma-separated file. It also describes an original UTF-16 tab-separated file and a long-format stats file; those alternatives may import or organize data differently. In Excel, use Data > From Text/CSV, select the UTF-8 CSV, inspect the preview, and choose Load. Confirm that each field appears in its own column and that names and punctuation display correctly.

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If you prefer a smaller, configurable selection, Pokémon Database’s text-list generator can filter by form, generation, or type and lets you choose attributes such as number, name, form, type, and base stats. Select its tab-separated output, then copy and paste into Excel or LibreOffice. Your export may include fewer records or columns than the full CSV because the filters and selected attributes determine its contents. Open the Pokémon Database text-list generator.

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Before analysis, decide which records count. A dataset’s form handling can change totals and comparisons, and dual-type Pokémon require an explicit counting rule. Write that rule near the finished chart or table so readers know what it represents. The repository identifies its dataset license as CC-BY-NC-SA-4.0; check the current license and reuse terms for the specific files and any artwork before redistributing them.

1. Chart Pokémon by type

Question to answer

Which types appear most often in the dataset? The answer depends on whether you count only the primary type or count every type a Pokémon has. The first method gives each record one type; the second counts dual-type Pokémon under both types, so the totals can exceed the number of records.

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Build the summary

  1. Choose one rule: count each Pokémon’s primary type only, or count it once for every type listed. Record whether alternate forms are included.
  2. For a primary-type count, create a PivotTable from the data: select a cell in the imported table, choose Insert > PivotTable, and place Primary Type in Rows and a nonblank identifier such as the Pokémon name or number in Values. Set the value calculation to Count if Excel does not choose it automatically.
  3. For counts that include secondary types, first reshape the type data so each Pokémon-type pairing is a separate row. Include the primary type and, when present, the secondary type; then count the resulting type column in a PivotTable. Do not simply add primary- and secondary-type counts without accounting for blanks or duplicates.
  4. Sort the count from largest to smallest and insert a bar or column chart. Label it with the inclusion rule, for example, “Primary type only” or “Each listed type counted.”

The Complete Pokédex Dataset documents separate primary and secondary type fields, and its companion exploratory notebook includes type-distribution analysis. Review the dataset schema and the documented exploratory analyses before adapting the exercise to a changed file layout.

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2. Rank base stats and compare profiles

Question to answer

Which records rank highest for one selected base stat, and how do a few Pokémon compare across all six stats? Keeping those questions separate makes the result easier to read: a ranked list identifies leaders for one measure, while a profile chart shows each selected record’s shape.

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Build the leaderboard and chart

  1. Choose one of the six base-stat columns, such as Speed or HP. Confirm the exact column labels in your imported file rather than assuming another version uses the same names.
  2. Filter out blank values if necessary, then sort the chosen stat from largest to smallest. Add a rank column if you want an explicit position; ties should share a rank or be clearly handled by your chosen ranking method.
  3. Select a small group of records for a profile comparison. Keep the same six stat columns for every record, and use the names as series labels.
  4. Insert a clustered column or radar chart, then check that the six stat categories and Pokémon names are legible. A table beside the chart preserves the exact values when visual shapes are close.

Describe what your file calls a base stat and which forms it includes; do not imply the values cover every game mechanic or alternate form. The dataset repository documents six base stats and also provides a long-format stats file. Its exploratory notebook includes stat distributions and correlations, which can inspire a follow-up question but do not establish that a stat causes another stat to change. Check the repository’s current field descriptions and its example analyses.

3. Compare generations

Question to answer

How many records does this dataset assign to each generation, or how does one chosen base stat vary across those assignments? A generation field is a dataset label; it should not automatically be read as a complete game-release chronology.

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Build a generation summary

  1. Choose whether you want to compare record counts or a stat. If comparing a stat, choose one measure and a consistent treatment of forms and missing values.
  2. Create a PivotTable with Generation in Rows. For counts, add a Pokémon identifier to Values and summarize by Count. For a stat comparison, add that stat to Values and select an appropriate summary such as Average or Median if available.
  3. Sort generations in numerical order, not by the displayed text if that puts labels out of sequence. Add a chart with a title that names the measure and summary.
  4. Check the dataset notes for unusual assignments before interpreting the result. The metadata notes that certain Legends: Arceus additions are assigned to Gen VIII.

The generation comparison describes the dataset’s assignments and inclusion rules; it is not, by itself, a claim about when every Pokémon first appeared in a particular release. Consult the repository metadata for its stated scope and generation notes.

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4. Compare type-defence profiles

Question to answer

How do the dataset’s recorded matchup multipliers differ across Pokémon or types? The 18 against_* fields are matchup values supplied by the dataset. Inspect their definitions and the source’s assumptions before treating a value as a complete description of game behavior.

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Build a lookup or comparison

  1. Choose the comparison: one Pokémon against all listed attack types, or a small set of Pokémon compared across selected matchup columns.
  2. Filter to the records of interest, then keep the identifier and relevant against_* columns. For a type-level comparison, summarize only after deciding how to group records and handle forms; state that rule with the result.
  3. Use conditional formatting to highlight different multiplier values, or chart a selected subset of columns. Keep the column names visible so each value remains tied to the matchup it represents.
  4. Check a few values against the dataset’s field definitions before drawing conclusions, especially if the CSV version or source has changed.

The dataset schema lists 18 matchup multiplier fields, and its example notebook includes a type-defence profile analysis. Those establish a workable exercise, not a universal guarantee that every game, form, or mechanic is represented identically. Review the field definitions in the dataset and see the documented type-defence example.

Turn one project into deliberate Excel practice

For each project, save the question, inclusion rule, source file, summary table, and chart together in the workbook. That small record makes the result understandable when filters, forms, or data versions change. The four exercises naturally practice importing tabular data, filtering, sorting, counting, summarizing, and charting; they are proposed practice projects, not measured claims about learning outcomes.

A separate repository, lgreski/pokemonData, stated in a June 2026 update that its National Pokédex contained 1,025 Pokémon and identifies Pokémon Database’s generation-by-generation stats pages as its statistics source. That is a dated statement about that repository, not an official count or a guarantee for other files. Its README says its original data is CC0 1.0; that license does not apply automatically to unrelated datasets or their artwork. Check that repository’s current files, provenance, and terms if you choose it instead.

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