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Turn Pokémon data into four practical Excel projects: a type-distribution dashboard, a base-stat leaderboard, a generation comparison, and a type-defence analysis. Each starts with a question and ends with a table or chart you can explain—not just a worksheet full of copied data.
Choose a dataset and load it into Excel
The instructions below assume the Complete Pokédex Dataset’s recommended pokemon_utf8.csv file, a UTF-8 comma-separated file. Its repository metadata describes a dataset of 1,025 Pokémon from Generations I–IX and 55 columns, including primary and secondary types, six base stats, 18 type-matchup multiplier fields, evolution and form fields, and Pokédex descriptions. These are claims about that repository’s dataset, not an official Pokémon download or a guarantee that every game mechanic and alternate form is represented. Check the dataset repository and its current files before beginning.
- Download the CSV. Select
pokemon_utf8.csvfrom the repository’s available files. - Import it in Excel. Choose Data → From Text/CSV, select the file, confirm that the delimiter is a comma and the text encoding is UTF-8, then choose Load. If Excel opens the file with garbled characters or puts a whole row into one column, use the import dialog to correct the encoding or delimiter.
- Make the range usable. Click a cell in the imported data and choose Insert → Table (or press Ctrl+T on Windows). Confirm that the table has headers. Filters in the header row make it easier to limit projects by generation, type, or form.
- Inspect the headers first. Confirm the exact names and contents of fields you plan to use. Repository schemas can change, and similarly named fields may represent different things.
You can instead use Pokémon Database’s text-list generator, which supports filters such as form, generation, and type, lets you choose attributes such as number, name, form, type, and base stats, and provides tab-separated output for copying into Excel or LibreOffice. Because its filters and selected attributes are configurable, an export may contain only a subset of the full dataset.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallBoth are community-maintained sources, not official Pokémon downloads. The Complete Pokédex Dataset metadata identifies its data license as CC-BY-NC-SA-4.0; a separate repository, lgreski/pokemonData, says its original data is CC0 1.0. Those terms do not automatically apply to other files or artwork. Check the current license and reuse terms for the specific material you select before redistributing data or images.
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Project 1: Build a type-distribution dashboard
Start with a question
Which types appear most often in this dataset? Decide how dual-type Pokémon count before creating a chart: counting each Pokémon once under its primary type answers a different question from counting it once under every type it has.
Make the summary
- Find the primary-type field and, if you want the all-types version, the secondary-type field. Check how blank secondary-type cells are represented.
- For a primary-type count, create a PivotTable by selecting a cell in the Excel table and choosing Insert → PivotTable. Put the primary-type field in Rows and a nonblank identifier, such as the Pokémon name or number, in Values. Set the value calculation to Count.
- For an all-types count, create a separate analysis that includes both primary and nonblank secondary types. A PivotTable on the original wide table will not automatically treat those two columns as one category; copy or transform the type values into a single type column first, excluding blank secondary values.
- Sort the count from largest to smallest, then select the summary and choose Insert → Bar Chart or Insert → Column Chart.
Make the chart answer the question
Title it with the counting rule—for example, “Pokémon by primary type” or “Type appearances, counting dual types twice.” Label the count axis and retain the source table so a reader can verify the totals. The distinction matters: the all-types chart counts type appearances, not unique Pokémon.
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Project 2: Rank base stats and compare profiles
Start with a question
Which Pokémon rank highest for a chosen base stat, and how do a few Pokémon differ across all six stats? The dataset describes six base stats and also includes a long-format stats file; an example analysis in the repository explores stat distributions and correlations. Check the repository’s schema and example materials for the fields available in its current version.
Build the leaderboard
- Choose one stat column, such as Speed or Defense, and sort it from largest to smallest using the table header’s sort control.
- Keep the Pokémon identifier and any form field beside the stat. That makes the ranking interpretable if multiple records represent forms or if the dataset distinguishes them.
- For a rank formula, add a column to the table and use
=RANK.EQ([@[Speed]],[Speed],0), replacingSpeedwith the exact column name. The final0ranks higher values first. If structured references are unavailable, use ordinary cell references instead.
Compare six-stat profiles
Filter the table to a small group of Pokémon and select their six stat values. Choose Insert → Radar Chart to compare profiles, or use a clustered column chart if you prefer values that are easier to compare precisely. Keep the Pokémon names as series labels and the six stat names as categories. A radar chart can make overall shapes easy to scan, but closely spaced values may be hard to distinguish.
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Describe the result as a comparison of the selected dataset’s base-stat fields and records. State whether alternate forms are included; do not imply that the ranking covers every form or interprets battle performance.
Project 3: Compare Pokémon by generation
Start with a question
Do the records in this dataset differ by generation in number or in a chosen base stat? These are two separate questions: a count compares dataset records, while a stat summary compares values among the records assigned to each generation.
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Summarize counts or a stat
- Use the dataset’s generation field to create a PivotTable. Put generation in Rows and a Pokémon identifier in Values, set to Count, to compare record totals.
- To compare a stat instead, put the chosen stat in Values and change Value Field Settings to Average (or another clearly stated summary). Include the count as well if you want readers to see how many records contribute to each average.
- Insert a column chart for counts or a line chart for a generation-ordered trend. Label the chart with the measure used and keep the generation categories in order.
Interpret the assignment carefully
A dataset’s generation assignment is not necessarily a game-release chronology. The Complete Pokédex Dataset metadata notes that certain additions from Pokémon Legends: Arceus are assigned to Generation VIII. Treat the field as the repository’s classification, not as an independent release-date record. Consult the dataset metadata if that distinction affects your analysis.
Project 4: Compare type-defence profiles
Start with a question
How do the dataset’s defensive matchup multipliers differ across selected Pokémon or types? The Complete Pokédex Dataset lists 18 against_* fields, and an example notebook documents a type-defence profile analysis. The values are dataset-provided matchup multipliers: inspect the field definitions before interpreting them, and do not treat a spreadsheet comparison as a complete account of battle mechanics. Review the current field descriptions.
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Compare a few profiles
- Choose a small, explicit set of Pokémon or forms, and retain their names and form fields in the comparison.
- Select the 18 matchup multiplier columns for those records. Put matchup types on the category axis and each Pokémon’s values in a separate series.
- Use a clustered column chart for direct comparisons across matchups, or a table with conditional formatting if exact multiplier values matter more than chart shape.
- Use the field definitions to explain what a value means. Do not label a value a weakness, resistance, or immunity unless the documented definition supports that interpretation.
Keep the conclusion narrow
Write down which records and fields you compared and what the dataset’s definitions establish. A profile is a comparison of those selected records and documented multipliers, not a substitute for checking a particular game’s rules.
Make each project a useful Excel practice session
These exercises are designed to produce a concrete result while practicing filtering, sorting, PivotTables, formulas, and charts. The Pokédex repository’s documented exploratory notebook includes type and base-stat distributions, generation trends, stat correlations, and type-defence profiles, providing examples of analyses that can be explored with its data. See the repository README and notebook. Treat each exercise as a proposed project: its usefulness depends on the fields, records, and definitions in the file you actually download.
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