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Tension-Board-2-Analysis/notebooks/01_data_overview_and_climbing_statistics.ipynb
Pawel Sarkowicz ba8e43263a database
2026-03-22 12:19:01 -04:00

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{
"cells": [
{
"cell_type": "markdown",
"id": "37e8cfe9",
"metadata": {},
"source": [
"# Tension Board 2: Climbing Statistics\n",
"This data was downloaded using [Boardlib](https://github.com/lemeryfertitta/BoardLib). It was downloaded in March 2026, but (most of) the data was actually updated 2026-01-22 (as can be seen in the `kits` table of the database). As such, we only take data into account up to this date. Moreover, we only have data about the climbs, with no specific user data. That said, we can still use climb features like `ascensionist_count` and `fa_at` (the first ascent date for the climb) to understand climbing trends.\n",
"\n",
"We've explored the database using SQL in [sql/01_data_exploration.sql](../sql/01_data_exploration.sql). We'll now use some of our findings to visualize some data and give some statistics. We'll begin by loading the necessary data. We will also include ways to do similary queries in SQL vs pandas (although, we still do our initial query in SQL); these can be found in [sql/02_alternative_queries.sql](../sql/02_alternative_queries.sql).\n",
"\n",
"\n",
"## Understanding the data\n",
"\n",
"## Notebook Structure\n",
"1. [Setup and Imports](#setup-and-imports)\n",
"2. [Popularity](#popularity)\n",
"3. [Climbing Statistics: Grades, Angles, Quality, and \"Match\" vs. \"No Match\"](#climbing-statistics-grades-angles-quality-and-matching)\n",
"4. [Prolific Statistics](#prolific-statistics)\n",
"5. [Conclusion](#conclusion)\n"
]
},
{
"cell_type": "markdown",
"id": "898cad20",
"metadata": {},
"source": [
"## Setup and Imports"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "e48e2d25",
"metadata": {},
"outputs": [],
"source": [
"### Setup\n",
"\n",
"# Imports\n",
"import pandas as pd\n",
"import sqlite3\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import numpy as np\n",
"\n",
"import matplotlib.patches as mpatches\n",
"import sqlite3\n",
"\n",
"\n",
"# Set some display options\n",
"pd.set_option('display.max_columns', None)\n",
"pd.set_option('display.max_rows', 100)\n",
"\n",
"# Set style\n",
"palette=['steelblue', 'coral', 'seagreen'] #(for multi-bar graphs)\n",
"\n",
"# Connect to the database\n",
"DB_PATH=\"../data/tb2.db\"\n",
"conn = sqlite3.connect(DB_PATH)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2e7b5862",
"metadata": {},
"outputs": [],
"source": [
"### Query our data from the DB\n",
"\n",
"# Query climb data\n",
"climbs_query = \"\"\"\n",
"SELECT\n",
" c.uuid,\n",
" c.name AS climb_name,\n",
" c.setter_username,\n",
" c.layout_id AS layout_id,\n",
" c.description,\n",
" c.is_nomatch,\n",
" c.is_listed,\n",
" l.name AS layout_name,\n",
" p.name AS board_name,\n",
" c.frames,\n",
" cs.angle,\n",
" cs.display_difficulty,\n",
" dg.boulder_name AS boulder_grade,\n",
" cs.ascensionist_count,\n",
" cs.quality_average,\n",
" cs.fa_at\n",
" \n",
"FROM climbs c\n",
"JOIN layouts l ON c.layout_id = l.id\n",
"JOIN products p ON l.product_id = p.id\n",
"JOIN climb_stats cs ON c.uuid = cs.climb_uuid\n",
"JOIN difficulty_grades dg ON ROUND(cs.display_difficulty) = dg.difficulty\n",
"WHERE cs.display_difficulty IS NOT NULL AND c.is_listed=1;\n",
"\"\"\"\n",
"\n",
"# Load it into a DataFrame\n",
"df = pd.read_sql_query(climbs_query, conn)"
]
},
{
"cell_type": "markdown",
"id": "70a88be4",
"metadata": {},
"source": [
"The above query will allow us to gather basically anything we need to in order to analyze climbing statistics. We leave out information about climging holds and things like this, because they will be analyzed in a different notebook. Let's see what our DataFrame looks like."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8b0057f5",
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uuid</th>\n",
" <th>climb_name</th>\n",
" <th>setter_username</th>\n",
" <th>layout_id</th>\n",
" <th>description</th>\n",
" <th>is_nomatch</th>\n",
" <th>is_listed</th>\n",
" <th>layout_name</th>\n",
" <th>board_name</th>\n",
" <th>frames</th>\n",
" <th>angle</th>\n",
" <th>display_difficulty</th>\n",
" <th>boulder_grade</th>\n",
" <th>ascensionist_count</th>\n",
" <th>quality_average</th>\n",
" <th>fa_at</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>00163801596af1064d549ad75b684539</td>\n",
" <td>Duroxmanie 2.0</td>\n",
" <td>vinsewah</td>\n",
" <td>9</td>\n",
" <td>No matching</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Original Layout</td>\n",
" <td>Tension Board</td>\n",
" <td>p3r4p29r2p59r1p65r2p75r3p89r2p157r4p158r4</td>\n",
" <td>30</td>\n",
" <td>16.3333</td>\n",
" <td>6a/V3</td>\n",
" <td>3</td>\n",
" <td>3.00000</td>\n",
" <td>2024-07-21 01:37:45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>00163801596af1064d549ad75b684539</td>\n",
" <td>Duroxmanie 2.0</td>\n",
" <td>vinsewah</td>\n",
" <td>9</td>\n",
" <td>No matching</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Original Layout</td>\n",
" <td>Tension Board</td>\n",
" <td>p3r4p29r2p59r1p65r2p75r3p89r2p157r4p158r4</td>\n",
" <td>35</td>\n",
" <td>16.3333</td>\n",
" <td>6a/V3</td>\n",
" <td>3</td>\n",
" <td>2.00000</td>\n",
" <td>2021-02-17 02:31:28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>001945feb7509ce231c9d8b237082a39</td>\n",
" <td>1 am</td>\n",
" <td>ssssss</td>\n",
" <td>9</td>\n",
" <td></td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Original Layout</td>\n",
" <td>Tension Board</td>\n",
" <td>p18r1p22r1p57r2p70r2p149r3p161r4p162r4</td>\n",
" <td>20</td>\n",
" <td>13.0000</td>\n",
" <td>5a/V1</td>\n",
" <td>1</td>\n",
" <td>2.00000</td>\n",
" <td>2021-01-30 21:46:16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>001945feb7509ce231c9d8b237082a39</td>\n",
" <td>1 am</td>\n",
" <td>ssssss</td>\n",
" <td>9</td>\n",
" <td></td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Original Layout</td>\n",
" <td>Tension Board</td>\n",
" <td>p18r1p22r1p57r2p70r2p149r3p161r4p162r4</td>\n",
" <td>30</td>\n",
" <td>14.6250</td>\n",
" <td>5c/V2</td>\n",
" <td>8</td>\n",
" <td>2.87500</td>\n",
" <td>2021-04-05 01:29:07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>001945feb7509ce231c9d8b237082a39</td>\n",
" <td>1 am</td>\n",
" <td>ssssss</td>\n",
" <td>9</td>\n",
" <td></td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Original Layout</td>\n",
" <td>Tension Board</td>\n",
" <td>p18r1p22r1p57r2p70r2p149r3p161r4p162r4</td>\n",
" <td>35</td>\n",
" <td>15.0000</td>\n",
" <td>5c/V2</td>\n",
" <td>7</td>\n",
" <td>2.71429</td>\n",
" <td>2020-12-15 23:24:57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>143196</th>\n",
" <td>9A09A2F4F82C46EFA8F8309DF263E878</td>\n",
" <td>nemesis 1</td>\n",
" <td>Hibiki0108</td>\n",
" <td>10</td>\n",
" <td>No matching.</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Tension Board 2 Mirror</td>\n",
" <td>Tension Board 2</td>\n",
" <td>p339r6p347r6p412r6p583r5p664r7p713r8</td>\n",
" <td>50</td>\n",
" <td>24.0000</td>\n",
" <td>7b/V8</td>\n",
" <td>1</td>\n",
" <td>3.00000</td>\n",
" <td>2026-01-31 00:50:26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>143197</th>\n",
" <td>EAEED5ED9B4A43ECA6EE71D602A431B4</td>\n",
" <td>So Tuff</td>\n",
" <td>canardo</td>\n",
" <td>11</td>\n",
" <td>No matching.</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Tension Board 2 Spray</td>\n",
" <td>Tension Board 2</td>\n",
" <td>p822r8p918r8p961r8p1003r8p1069r6p1078r7p1125r6...</td>\n",
" <td>45</td>\n",
" <td>18.5000</td>\n",
" <td>6b+/V4</td>\n",
" <td>2</td>\n",
" <td>3.00000</td>\n",
" <td>2026-01-31 00:45:30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>143198</th>\n",
" <td>98DE5842AC624739926D561DD68540AA</td>\n",
" <td>Ate That Up</td>\n",
" <td>canardo</td>\n",
" <td>11</td>\n",
" <td>No matching.</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Tension Board 2 Spray</td>\n",
" <td>Tension Board 2</td>\n",
" <td>p992r7p1088r8p1091r6p1121r8p1122r5p1124r6p1155...</td>\n",
" <td>45</td>\n",
" <td>20.0000</td>\n",
" <td>6c/V5</td>\n",
" <td>1</td>\n",
" <td>3.00000</td>\n",
" <td>2026-01-31 00:49:18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>143199</th>\n",
" <td>8FD9E8A483C44D4C88ECD014034A1688</td>\n",
" <td>Surprise sur Prise</td>\n",
" <td>canardo</td>\n",
" <td>11</td>\n",
" <td>No matching.</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Tension Board 2 Spray</td>\n",
" <td>Tension Board 2</td>\n",
" <td>p807r8p824r7p836r6p905r6p909r6p918r8p938r5p941...</td>\n",
" <td>45</td>\n",
" <td>24.0000</td>\n",
" <td>7b/V8</td>\n",
" <td>1</td>\n",
" <td>3.00000</td>\n",
" <td>2026-01-31 01:05:44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>143200</th>\n",
" <td>4D8849CDCC1745C9AD02CD02B5C4DEFF</td>\n",
" <td>THE BIG K</td>\n",
" <td>EnzoB</td>\n",
" <td>9</td>\n",
" <td></td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Original Layout</td>\n",
" <td>Tension Board</td>\n",
" <td>p2r3p13r2p16r2p28r2p30r2p32r4p47r4p57r2p59r1p6...</td>\n",
" <td>30</td>\n",
" <td>16.0000</td>\n",
" <td>6a/V3</td>\n",
" <td>1</td>\n",
" <td>3.00000</td>\n",
" <td>2026-01-31 01:07:46</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>143201 rows × 16 columns</p>\n",
"</div>"
],
"text/plain": [
" uuid climb_name setter_username \\\n",
"0 00163801596af1064d549ad75b684539 Duroxmanie 2.0 vinsewah \n",
"1 00163801596af1064d549ad75b684539 Duroxmanie 2.0 vinsewah \n",
"2 001945feb7509ce231c9d8b237082a39 1 am ssssss \n",
"3 001945feb7509ce231c9d8b237082a39 1 am ssssss \n",
"4 001945feb7509ce231c9d8b237082a39 1 am ssssss \n",
"... ... ... ... \n",
"143196 9A09A2F4F82C46EFA8F8309DF263E878 nemesis 1 Hibiki0108 \n",
"143197 EAEED5ED9B4A43ECA6EE71D602A431B4 So Tuff canardo \n",
"143198 98DE5842AC624739926D561DD68540AA Ate That Up canardo \n",
"143199 8FD9E8A483C44D4C88ECD014034A1688 Surprise sur Prise canardo \n",
"143200 4D8849CDCC1745C9AD02CD02B5C4DEFF THE BIG K EnzoB \n",
"\n",
" layout_id description is_nomatch is_listed \\\n",
"0 9 No matching 1 1 \n",
"1 9 No matching 1 1 \n",
"2 9 0 1 \n",
"3 9 0 1 \n",
"4 9 0 1 \n",
"... ... ... ... ... \n",
"143196 10 No matching. 1 1 \n",
"143197 11 No matching. 1 1 \n",
"143198 11 No matching. 1 1 \n",
"143199 11 No matching. 1 1 \n",
"143200 9 0 1 \n",
"\n",
" layout_name board_name \\\n",
"0 Original Layout Tension Board \n",
"1 Original Layout Tension Board \n",
"2 Original Layout Tension Board \n",
"3 Original Layout Tension Board \n",
"4 Original Layout Tension Board \n",
"... ... ... \n",
"143196 Tension Board 2 Mirror Tension Board 2 \n",
"143197 Tension Board 2 Spray Tension Board 2 \n",
"143198 Tension Board 2 Spray Tension Board 2 \n",
"143199 Tension Board 2 Spray Tension Board 2 \n",
"143200 Original Layout Tension Board \n",
"\n",
" frames angle \\\n",
"0 p3r4p29r2p59r1p65r2p75r3p89r2p157r4p158r4 30 \n",
"1 p3r4p29r2p59r1p65r2p75r3p89r2p157r4p158r4 35 \n",
"2 p18r1p22r1p57r2p70r2p149r3p161r4p162r4 20 \n",
"3 p18r1p22r1p57r2p70r2p149r3p161r4p162r4 30 \n",
"4 p18r1p22r1p57r2p70r2p149r3p161r4p162r4 35 \n",
"... ... ... \n",
"143196 p339r6p347r6p412r6p583r5p664r7p713r8 50 \n",
"143197 p822r8p918r8p961r8p1003r8p1069r6p1078r7p1125r6... 45 \n",
"143198 p992r7p1088r8p1091r6p1121r8p1122r5p1124r6p1155... 45 \n",
"143199 p807r8p824r7p836r6p905r6p909r6p918r8p938r5p941... 45 \n",
"143200 p2r3p13r2p16r2p28r2p30r2p32r4p47r4p57r2p59r1p6... 30 \n",
"\n",
" display_difficulty boulder_grade ascensionist_count quality_average \\\n",
"0 16.3333 6a/V3 3 3.00000 \n",
"1 16.3333 6a/V3 3 2.00000 \n",
"2 13.0000 5a/V1 1 2.00000 \n",
"3 14.6250 5c/V2 8 2.87500 \n",
"4 15.0000 5c/V2 7 2.71429 \n",
"... ... ... ... ... \n",
"143196 24.0000 7b/V8 1 3.00000 \n",
"143197 18.5000 6b+/V4 2 3.00000 \n",
"143198 20.0000 6c/V5 1 3.00000 \n",
"143199 24.0000 7b/V8 1 3.00000 \n",
"143200 16.0000 6a/V3 1 3.00000 \n",
"\n",
" fa_at \n",
"0 2024-07-21 01:37:45 \n",
"1 2021-02-17 02:31:28 \n",
"2 2021-01-30 21:46:16 \n",
"3 2021-04-05 01:29:07 \n",
"4 2020-12-15 23:24:57 \n",
"... ... \n",
"143196 2026-01-31 00:50:26 \n",
"143197 2026-01-31 00:45:30 \n",
"143198 2026-01-31 00:49:18 \n",
"143199 2026-01-31 01:05:44 \n",
"143200 2026-01-31 01:07:46 \n",
"\n",
"[143201 rows x 16 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"id": "e7eb46cc",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"id": "ba4fc956",
"metadata": {},
"source": [
"# Popularity\n",
"\n",
"## Popularity of Tension Board\n",
"\n",
"Since we do not have access to user data, we will examine the popular of the Tension Boards by counting first ascents and unique setters by year. Often it's the case that the first ascensionist is the also the setter of the climb, but not always. None the less, we group up first ascensionists by year, with an extra tidbit about how many unique setters there were. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "3dff1bd7",
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 1200x600 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### Popular of tension board by year. \n",
"### First ascents by year + unique setters by year\n",
"\n",
"# Convert df['fa_at'] to datetime format. (For some reason, it does not register as such)\n",
"df['fa_at'] = pd.to_datetime(df['fa_at'])\n",
"\n",
"# Add a new column for the year\n",
"df['fa_year'] = df['fa_at'].dt.year\n",
"\n",
"# Make a new DataFrame with year, first_ascents, and unique_setters\n",
"df_growth = df.groupby('fa_year').agg(\n",
" first_ascents=('uuid', 'count'),\n",
" unique_setters=('setter_username', 'nunique')\n",
").reset_index()\n",
"\n",
"# Disregard the year 2026 since the data only goes one month in. \n",
"df_growth = df_growth[df_growth['fa_year'] < 2026]\n",
"\n",
"## Plot\n",
"# Dual index plotting\n",
"\n",
"fig, ax1 = plt.subplots(figsize=(12,6))\n",
"\n",
"# Bar chart for first ascents\n",
"ax1.bar(df_growth['fa_year'], df_growth['first_ascents'], label='First Ascents', color='coral')\n",
"ax1.set_xlabel('Year')\n",
"ax1.set_ylabel('First Ascents')\n",
"ax1.set_title('TB First Ascents & Unique Setters over Time')\n",
"#ax1.tick_params(axis='y')\n",
"\n",
"# Line chart for unique setters (secondary axis)\n",
"ax2 = ax1.twinx()\n",
"ax2.plot(df_growth['fa_year'], df_growth['unique_setters'], color='steelblue', marker='o', label='Unique Setters')\n",
"ax2.set_ylabel('Unique Setters', color='steelblue')\n",
"ax2.tick_params(axis='y', labelcolor='steelblue')\n",
"\n",
"# Other stuff\n",
"fig.legend(loc='upper left', bbox_to_anchor=(0.15,0.85))\n",
"\n",
"plt.xticks()\n",
"plt.savefig('../images/stats/first_ascents_by_year.png')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "e2bca15a",
"metadata": {},
"source": [
"## Seasonal analysis\n",
"\n",
"Next, we examine when the Tension board is most popular. Again, we will work with what we have and use first ascent data. We will plot first ascents by month, combing all years. We exclude the year 2026 because this can skew the analysis as some of the month of January has data (and clearly, 2026 is when the TB2 is the most popular, so this can actually add quite a bit bias). "
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "8912306b",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1200x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### Season analysis: first ascents by month\n",
"\n",
"# First let us add a column for the month to our data\n",
"df['fa_month'] = df['fa_at'].dt.month\n",
"\n",
"# Filter to years < 2026 since the data only goes one month in\n",
"df_filter = df[df['fa_year'] < 2026]\n",
"\n",
"# Make a new DataFrame with month and first ascents\n",
"df_season = df_filter.groupby('fa_month').agg(\n",
" first_ascents=('uuid', 'count'),\n",
").reset_index()\n",
"\n",
"# We also add a column for the month name. \n",
"month_names = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', \n",
" 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
"df_season['fa_month_name'] = df_season['fa_month'].apply(lambda x: month_names[x-1])\n",
"\n",
"# Plot the data\n",
"fig,ax = plt.subplots(figsize=(12,6))\n",
"ax.bar(df_season['fa_month_name'], df_season['first_ascents'], color='coral')\n",
"ax.set_title('First Ascents by Month (All Years Combined)')\n",
"ax.set_xlabel('Month')\n",
"ax.set_ylabel('Total First Ascents')\n",
"\n",
"# Save the file\n",
"plt.savefig('../images/stats/first_ascents_by_month.png')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "bb802d74",
"metadata": {},
"source": [
"This should be what we expect: that the winter months (Dec/Jan) see the most traffic. This is probably when the outdoor climbers are hitting the boards because they're stuck inside. The warmer months see the least number of first ascents since the strong climbers are probably outdoors.\n",
"\n",
"We can plot the number of first ascents by day of week. Removing the 2026 data shouldn't make a difference here, so we opt to keep it."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "071cfd5f",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1200x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### Day of Week analysis\n",
"\n",
"# Let us add a column in our DataFrame for the day of the week.\n",
"# Note that df.dt.day_of_week will have Monday be 0 and Sunday be 6. \n",
"\n",
"df['fa_day_of_week'] = df['fa_at'].dt.day_of_week\n",
"\n",
"\n",
"# Make a new DataFrame with month and first ascents\n",
"df_days = df.groupby('fa_day_of_week').agg(\n",
" first_ascents=('uuid', 'count'),\n",
").reset_index()\n",
"\n",
"# We also add a column for the month name. \n",
"day_names = ['Mon', 'Tues', 'Wed', 'Thurs', 'Fri', 'Sat', 'Sun']\n",
"df_days['fa_day_name'] = df_days['fa_day_of_week'].apply(lambda x: day_names[x])\n",
"\n",
"# Plot the data\n",
"fig,ax = plt.subplots(figsize=(12,6))\n",
"ax.bar(df_days['fa_day_name'], df_days['first_ascents'], color='coral')\n",
"ax.set_title('First Ascents by Day of Week (All Years Combined)')\n",
"ax.set_xlabel('Day')\n",
"ax.set_ylabel('Total First Ascents')\n",
"\n",
"# Save the file\n",
"plt.savefig('../images/stats/first_ascents_by_day_of_week.png')\n",
"plt.show()\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "9961e92e",
"metadata": {},
"source": [
"Interesting, Tuesday and Wednesday have the most traffic, while Monday is the least popular."
]
},
{
"cell_type": "markdown",
"id": "5ddc49b3",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"id": "533dc4a2",
"metadata": {},
"source": [
"# Climbing Statistics: Grades, Angles, Quality, and Matching\n",
"\n",
"We will visualize the climbing grade distribution. Recall that we have the following table of grades (with some other unlisted grades).\n",
"\n",
"|difficulty|boulder_name|route_name|\n",
"|----------|------------|----------|\n",
"| 10|4a/V0 |5b/5.9 |\n",
"| 11|4b/V0 |5c/5.10a |\n",
"| 12|4c/V0 |6a/5.10b |\n",
"| 13|5a/V1 |6a+/5.10c |\n",
"| 14|5b/V1 |6b/5.10d |\n",
"| 15|5c/V2 |6b+/5.11a |\n",
"| 16|6a/V3 |6c/5.11b |\n",
"| 17|6a+/V3 |6c+/5.11c |\n",
"| 18|6b/V4 |7a/5.11d |\n",
"| 19|6b+/V4 |7a+/5.12a |\n",
"| 20|6c/V5 |7b/5.12b |\n",
"| 21|6c+/V5 |7b+/5.12c |\n",
"| 22|7a/V6 |7c/5.12d |\n",
"| 23|7a+/V7 |7c+/5.13a |\n",
"| 24|7b/V8 |8a/5.13b |\n",
"| 25|7b+/V8 |8a+/5.13c |\n",
"| 26|7c/V9 |8b/5.13d |\n",
"| 27|7c+/V10 |8b+/5.14a |\n",
"| 28|8a/V11 |8c/5.14b |\n",
"| 29|8a+/V12 |8c+/5.14c |\n",
"| 30|8b/V13 |9a/5.14d |\n",
"| 31|8b+/V14 |9a+/5.15a |\n",
"| 32|8c/V15 |9b/5.15b |\n",
"| 33|8c+/V16 |9b+/5.15c |\n",
"\n",
"We will use the actual difficulty in our work, and then unpack translations into boulder_name."
]
},
{
"cell_type": "markdown",
"id": "4f986836",
"metadata": {},
"source": [
"## Grade distribution"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "5fa594ba",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1600x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### Difficulty distribution by layout (with total)\n",
"\n",
"grade_counts = df['boulder_grade'].value_counts()\n",
"grade_order = df.groupby('boulder_grade')['display_difficulty'].mean().sort_values().index.tolist()\n",
"grade_counts = grade_counts.reindex(grade_order)\n",
"\n",
"# Prepare data in long format\n",
"df_long = df.groupby(['boulder_grade', 'layout_name']).size().reset_index(name='count')\n",
"\n",
"# Calculate total for background\n",
"df_total = df.groupby('boulder_grade').size().reset_index(name='count')\n",
"df_total['layout_name'] = 'All Layouts'\n",
"\n",
"# Reindex to correct grade order\n",
"df_long['grade_order'] = df_long['boulder_grade'].map(\n",
" {g: i for i, g in enumerate(grade_order)}\n",
")\n",
"df_long = df_long.sort_values('grade_order')\n",
"\n",
"df_total['grade_order'] = df_total['boulder_grade'].map(\n",
" {g: i for i, g in enumerate(grade_order)}\n",
")\n",
"df_total = df_total.sort_values('grade_order')\n",
"\n",
"# Plot\n",
"fig, ax = plt.subplots(figsize=(16, 8))\n",
"\n",
"# Plot \"All Layouts\" behind (light gray)\n",
"sns.barplot(\n",
" data=df_total,\n",
" x='boulder_grade',\n",
" y='count',\n",
" color='lightgray',\n",
" ax=ax,\n",
" zorder=1,\n",
" width=0.6,\n",
" order=grade_order\n",
")\n",
"\n",
"# Plot individual layouts (grouped) in front\n",
"sns.barplot(\n",
" data=df_long,\n",
" x='boulder_grade',\n",
" y='count',\n",
" hue='layout_name',\n",
" palette=['steelblue', 'coral', 'seagreen'],\n",
" ax=ax,\n",
" zorder=2,\n",
" order=grade_order\n",
")\n",
"\n",
"# Create custom legend with \"All Layouts\" included\n",
"handles, labels = ax.get_legend_handles_labels()\n",
"all_layouts_patch = mpatches.Patch(color='lightgray', label='All Layouts')\n",
"handles.insert(0, all_layouts_patch)\n",
"ax.legend(handles=handles, title='Layout', fontsize=10)\n",
"\n",
"ax.set_xlabel('Grade', fontsize=11)\n",
"ax.set_ylabel('Number of Climbs', fontsize=11)\n",
"ax.set_title('Difficulty Distribution by Board Layout', fontsize=14)\n",
"ax.tick_params(axis='x', rotation=45)\n",
"ax.grid(axis='y', alpha=0.3)\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig('../images/stats/difficulty_distribution_by_layout_with_total.png', dpi=150, bbox_inches='tight')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "871bb45d",
"metadata": {},
"source": [
"As a climber in North America, I tend to just use the V-grade and not look at the French grade. So let us group the V-grades together and show the distribution like that. We'll usually just stick the boulder_grade (e.g., 5c/V2) instead of grouping the V-grades though. "
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d7f0b911",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1600x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# V-Grade distribution by layout (with total)\n",
"\n",
"# Let's add a v_grade column and v_grade_counts\n",
"df['v_grade'] = df['boulder_grade'].str.split('/').str[1]\n",
"v_grade_counts = df['v_grade'].value_counts()\n",
"v_grade_order = df.groupby('v_grade')['display_difficulty'].mean().sort_values().index.tolist()\n",
"v_grade_counts = grade_counts.reindex(v_grade_order)\n",
"\n",
"# Prepare data in long format\n",
"df_long = df.groupby(['v_grade', 'layout_name']).size().reset_index(name='count')\n",
"\n",
"# Calculate total for background\n",
"df_total = df.groupby('v_grade').size().reset_index(name='count')\n",
"df_total['layout_name'] = 'All Layouts'\n",
"\n",
"# Reindex to correct grade order\n",
"df_long['v_grade_order'] = df_long['v_grade'].map(\n",
" {g: i for i, g in enumerate(v_grade_order)}\n",
")\n",
"df_long = df_long.sort_values('v_grade_order')\n",
"\n",
"df_total['v_grade_order'] = df_total['v_grade'].map(\n",
" {g: i for i, g in enumerate(v_grade_order)}\n",
")\n",
"df_total = df_total.sort_values('v_grade_order')\n",
"\n",
"# Plot\n",
"fig, ax = plt.subplots(figsize=(16, 8))\n",
"\n",
"# Plot \"All Layouts\" behind (light gray)\n",
"sns.barplot(\n",
" data=df_total,\n",
" x='v_grade',\n",
" y='count',\n",
" color='lightgray',\n",
" ax=ax,\n",
" zorder=1,\n",
" width=0.6,\n",
" order=v_grade_order\n",
")\n",
"\n",
"# Plot individual layouts (grouped) in front\n",
"sns.barplot(\n",
" data=df_long,\n",
" x='v_grade',\n",
" y='count',\n",
" hue='layout_name',\n",
" palette=palette,\n",
" ax=ax,\n",
" zorder=2,\n",
" order=v_grade_order\n",
")\n",
"\n",
"# Create legend with \"All Layouts\" included\n",
"handles, labels = ax.get_legend_handles_labels()\n",
"all_layouts_patch = mpatches.Patch(color='lightgray', label='All Layouts')\n",
"handles.insert(0, all_layouts_patch)\n",
"ax.legend(handles=handles, title='Layout', fontsize=10)\n",
"\n",
"ax.set_xlabel('Grade', fontsize=11)\n",
"ax.set_ylabel('Number of Climbs', fontsize=11)\n",
"ax.set_title('Difficulty Distribution by Board Layout', fontsize=14)\n",
"ax.tick_params(axis='x', rotation=45)\n",
"ax.grid(axis='y', alpha=0.3)\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig('../images/stats/v_grade_distribution_by_layout_with_total.png', dpi=150, bbox_inches='tight')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "e27f4277",
"metadata": {},
"source": [
"So the grade distribution actually varies quite a bit from board to board. Some key differences in grades are the angle at which the climb is. Note that climbs can be done at different angles."
]
},
{
"cell_type": "markdown",
"id": "e6a34766",
"metadata": {},
"source": [
"## Angle Distribution\n",
"\n",
"What about the angle distribution? Since the TB1 goes from 0 to 50 and the TB2 goes from 0 to 65 (although my local board only goes to 60?), let's do an analysis on each."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "7d65b6cd",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1600x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### Angle distribution.\n",
"\n",
"# TB1 goes up to 50 degrees, TB2 up to 65. (Although my local TB2 only goes up to 60 -- brutal climbing)\n",
"\n",
"# Prepare data in long format\n",
"df_long = df.groupby(['angle', 'layout_name']).size().reset_index(name='count')\n",
"\n",
"# Calculate total for background\n",
"df_total = df.groupby('angle').size().reset_index(name='count')\n",
"df_total['layout_name'] = 'All Layouts'\n",
"\n",
"# Reindex to correct order\n",
"angle_order = sorted(df['angle'].unique())\n",
"\n",
"# Plot\n",
"fix, ax = plt.subplots(figsize=(16,8))\n",
"\n",
"# Plot All Layouts\n",
"sns.barplot(\n",
" data=df_total,\n",
" x='angle',\n",
" y='count',\n",
" color='lightgray',\n",
" ax=ax,\n",
" zorder=1,\n",
" width=0.6,\n",
" order=angle_order\n",
")\n",
"\n",
"# Plt indivudual layouts\n",
"sns.barplot(\n",
" data=df_long,\n",
" x='angle',\n",
" y='count',\n",
" hue='layout_name',\n",
" palette=palette,\n",
" order=angle_order,\n",
" ax=ax,\n",
" zorder=2\n",
")\n",
"\n",
"handles,labels = ax.get_legend_handles_labels()\n",
"all_layouts_patch = mpatches.Patch(color='lightgray', label='All Layouts')\n",
"handles.insert(0, all_layouts_patch)\n",
"ax.legend(handles=handles, title='Layout')\n",
"\n",
"ax.set_xlabel('Angle')\n",
"ax.set_ylabel('Number of Climbs')\n",
"ax.set_title('Angle Distribution by Board Layout')\n",
"ax.grid(axis='y', alpha=0.3)\n",
"\n",
"\n",
"plt.suptitle('Angle Distribution by Board Layout')\n",
"plt.savefig('../images/stats/angle_distribution_by_layout.png')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "7a00ee20",
"metadata": {},
"source": [
"We see that amongst all the boards, 40 degrees is the most common angle."
]
},
{
"cell_type": "markdown",
"id": "bc164cee",
"metadata": {},
"source": [
"## Angle vs grade\n",
"\n",
"How is the distribution between angles and grades? Let's do this with a heatmap."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "2023cb49",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1600x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### Angle vs grade\n",
"\n",
"fig, ax = plt.subplots(figsize=(16, 8))\n",
"\n",
"# Create mapping from difficulty to V-grade\n",
"grade_mapping = df.groupby('display_difficulty')['boulder_grade'].first().to_dict()\n",
"\n",
"# Plot \"All Layouts\" as faint background boxes\n",
"sns.boxplot(\n",
" data=df,\n",
" x='angle',\n",
" y='display_difficulty',\n",
" color='lightgray',\n",
" order=angle_order,\n",
" showfliers=False,\n",
" width=0.6,\n",
" ax=ax,\n",
" zorder=1\n",
")\n",
"\n",
"# Plot individual layouts in front\n",
"sns.boxplot(\n",
" data=df,\n",
" x='angle',\n",
" y='display_difficulty',\n",
" hue='layout_name',\n",
" palette=['steelblue', 'coral', 'seagreen'],\n",
" order=angle_order,\n",
" showfliers=False,\n",
" ax=ax,\n",
" width=0.5,\n",
" zorder=2\n",
")\n",
"\n",
"# Relabel y-axis with V-grades\n",
"yticks_rounded = sorted(set(int(round(t)) for t in df['display_difficulty'].unique() if not pd.isna(t)))\n",
"ylabels = [grade_mapping.get(t, '') for t in yticks_rounded]\n",
"ax.set_yticks(yticks_rounded)\n",
"ax.set_yticklabels(ylabels)\n",
"\n",
"# Custom legend with \"All Layouts\"\n",
"handles, labels = ax.get_legend_handles_labels()\n",
"all_patch = mpatches.Patch(color='lightgray', label='All Layouts')\n",
"handles.insert(0, all_patch)\n",
"ax.legend(handles=handles, title='Layout', fontsize=10)\n",
"\n",
"ax.set_xlabel('Angle (degrees)', fontsize=11)\n",
"ax.set_ylabel('V-Grade', fontsize=11)\n",
"ax.set_title('Difficulty Distribution by Angle and Layout', fontsize=14)\n",
"ax.grid(axis='y', alpha=0.3)\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig('../images/stats/difficulty_by_angle_boxplot_by_layout.png', dpi=150, bbox_inches='tight')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "c7a5973a",
"metadata": {},
"source": [
"## The Quality of a climb\n",
"\n",
"Next we examine the quality of a climb. First we look at how quality relates to the number of ascents."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "bf5bd013",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 500x500 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### Climb quality vs popularity\n",
"\n",
"# Filter to climbs with quality ratings\n",
"df_quality = df[(df['quality_average'].notna()) & (df['quality_average'] > 0)]\n",
"\n",
"# Sample for performance\n",
"df_sample = df_quality.sample(min(2000, len(df_quality)))\n",
"\n",
"g = sns.jointplot(\n",
" data=df_sample,\n",
" x='quality_average',\n",
" y='ascensionist_count',\n",
" kind='scatter',\n",
" color='teal',\n",
" height=5\n",
")\n",
"\n",
"g.ax_joint.set_xlabel('Quality Rating')\n",
"g.ax_joint.set_ylabel('Ascensionist Count')\n",
"g.fig.suptitle('Quality vs Popularity')\n",
"\n",
"plt.savefig('../images/stats/quality_popularity.png', dpi=150, bbox_inches='tight')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "bf0ca290",
"metadata": {},
"source": [
"Next we visualize the average quality vs the angle and grade, by means of a heatmap. Keep in mind that the harder the climb and steeper the angle, the less people will be doing it. So harder climbs are skewed towards people who can actually do it. The point is that, on boards, the climb quality isn't always the best metric. As such, we won't spend too much time on the quality and will only do a heatmap which takes into account all layouts."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "af7282b9",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1600x800 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### Average quality by angle and grade\n",
"\n",
"# Filter to climbs with quality ratings\n",
"df_quality = df[(df['quality_average'].notna()) & (df['quality_average'] > 0)]\n",
"\n",
"\n",
"# Create pivot table\n",
"quality_pivot = df_quality.pivot_table(\n",
" index='boulder_grade',\n",
" columns='angle',\n",
" values='quality_average',\n",
" aggfunc='mean'\n",
")\n",
"quality_pivot = quality_pivot.reindex(grade_order)\n",
"quality_pivot = quality_pivot.reindex(columns=[a for a in angle_order if a in quality_pivot.columns])\n",
"\n",
"# Plot\n",
"fig, ax = plt.subplots(figsize=(16, 8))\n",
"\n",
"sns.heatmap(\n",
" quality_pivot,\n",
" cmap='RdYlGn',\n",
" cbar_kws={'label': 'Avg Quality Rating'},\n",
" ax=ax\n",
")\n",
"\n",
"ax.set_xlabel('Angle (°)')\n",
"ax.set_ylabel('Grade')\n",
"ax.invert_yaxis()\n",
"ax.set_title('Average Quality Rating by Grade and Angle (All Layouts)')\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig('../images/stats/quality_heatmap_all_layouts.png', dpi=150, bbox_inches='tight')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "3630b077",
"metadata": {},
"source": [
"## \"Match\" vs. \"No Match\"\n",
"\n",
"Some setters opt to put the \"no match\" tag onto their climbs. This means that the climber is not allowed to match their hands on any hold. Let's do an analysis of the differences."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "078cd6b6",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1800x600 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### Match vs No Match analysis. \n",
"\n",
"# Create status column (Match vs No Match)\n",
"df['status'] = df.apply(\n",
" lambda x: 'No Match' if (\n",
" pd.notna(x['description']) and 'No matching' in str(x['description'])\n",
" ) or x.get('is_nomatch', 0) == 1 else 'Matched',\n",
" axis=1\n",
")\n",
"\n",
"# Aggregate by layout and status\n",
"df_agg = df.groupby(['layout_name', 'status']).agg(\n",
" count=('uuid', 'count'),\n",
" avg_ascensionists=('ascensionist_count', 'mean'),\n",
" avg_difficulty=('display_difficulty', 'mean')\n",
").reset_index()\n",
"\n",
"# Calculate All Layouts totals\n",
"df_all = df.groupby('status').agg(\n",
" count=('uuid', 'count'),\n",
" avg_ascensionists=('ascensionist_count', 'mean'),\n",
" avg_difficulty=('display_difficulty', 'mean')\n",
").reset_index()\n",
"df_all['layout_name'] = 'All Layouts'\n",
"\n",
"# Combine\n",
"df_combined = pd.concat([df_agg, df_all], ignore_index=True)\n",
"\n",
"# Order\n",
"status_order = ['Matched', 'No Match']\n",
"\n",
"\n",
"# Plot\n",
"fig, axes = plt.subplots(1, 3, figsize=(18, 6))\n",
"\n",
"for ax, metric, title in zip(axes, ['count', 'avg_difficulty', 'avg_ascensionists'], \n",
" ['Total Climbs', 'Average Difficulty', 'Avg Ascensionists']):\n",
" \n",
" # All Layouts as background (separate for each status)\n",
" for i, status in enumerate(status_order):\n",
" df_bg = df_combined[(df_combined['layout_name'] == 'All Layouts') & \n",
" (df_combined['status'] == status)]\n",
" \n",
" if not df_bg.empty:\n",
" # Position for status\n",
" x_pos = i\n",
" ax.bar(\n",
" x_pos,\n",
" df_bg[metric].values[0],\n",
" width=0.7,\n",
" color='lightgray',\n",
" zorder=1\n",
" )\n",
" \n",
" # Individual layouts in front\n",
" df_plot = df_combined[df_combined['layout_name'] != 'All Layouts']\n",
" \n",
" sns.barplot(\n",
" data=df_plot,\n",
" x='status',\n",
" y=metric,\n",
" hue='layout_name',\n",
" palette=palette,\n",
" order=status_order,\n",
" ax=ax,\n",
" zorder=2\n",
" )\n",
" \n",
" ax.set_title(title, fontsize=12)\n",
" ax.set_xlabel('')\n",
" ax.set_ylabel(title if metric == 'count' else ('Grade' if metric == 'avg_difficulty' else 'Count'), fontsize=11)\n",
" ax.legend_.remove()\n",
" ax.grid(axis='y', alpha=0.3)\n",
"\n",
"# Y-axis labels for difficulty plot\n",
"yticks = [11, 13, 15, 17, 19, 21, 23]\n",
"ylabels = [grade_mapping.get(t, f\"V{t-10}\") for t in yticks]\n",
"axes[1].set_yticks(yticks)\n",
"axes[1].set_yticklabels(ylabels)\n",
"axes[1].set_ylim(bottom=10)\n",
"\n",
"# Add V-grade annotations on difficulty bars\n",
"for i, status in enumerate(status_order):\n",
" for j, layout in enumerate(df['layout_name'].unique()):\n",
" row = df_combined[(df_combined['layout_name'] == layout) & (df_combined['status'] == status)]\n",
" if not row.empty:\n",
" diff = row['avg_difficulty'].values[0]\n",
" v_grade = grade_mapping.get(round(diff), '')\n",
" # Position: x = status index + offset for layout\n",
" x_pos = i + (j - 1) * 0.27\n",
" axes[1].text(x_pos, diff + 0.3, v_grade, ha='center', fontsize=8, fontweight='bold')\n",
"\n",
"\n",
"# Custom Legend\n",
"handles,labels = ax.get_legend_handles_labels()\n",
"all_layouts_patch = mpatches.Patch(color='lightgray', label='All Layouts')\n",
"handles.insert(0, all_layouts_patch)\n",
"fig.legend(handles=handles, title='Layout', bbox_to_anchor=(1.08, 0.9))\n",
"\n",
"plt.suptitle('Match vs No Match Climbs by Layout', fontsize=14, y=1.02)\n",
"plt.tight_layout()\n",
"plt.savefig('../images/stats/match_vs_nomatch_by_layout.png', dpi=150, bbox_inches='tight')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "6f51156a",
"metadata": {},
"source": [
"So we gather the following about \"no match\" climbs:\n",
"\n",
"- they are far fewer than \"match\" climbs,\n",
"- they are on average hard than \"match\" climbs,\n",
"- and that they tend to have a bit more ascensionists on the TB2, and less on the TB1 (although, much more overall)."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "21e12faa",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead tr th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe thead tr:last-of-type th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"2\" halign=\"left\">avg_ascensionists</th>\n",
" <th colspan=\"2\" halign=\"left\">avg_difficulty</th>\n",
" <th colspan=\"2\" halign=\"left\">count</th>\n",
" </tr>\n",
" <tr>\n",
" <th>status</th>\n",
" <th>Matched</th>\n",
" <th>No Match</th>\n",
" <th>Matched</th>\n",
" <th>No Match</th>\n",
" <th>Matched</th>\n",
" <th>No Match</th>\n",
" </tr>\n",
" <tr>\n",
" <th>layout_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>All Layouts</th>\n",
" <td>22.66</td>\n",
" <td>31.89</td>\n",
" <td>18.16</td>\n",
" <td>20.15</td>\n",
" <td>82923.0</td>\n",
" <td>60278.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Original Layout</th>\n",
" <td>22.68</td>\n",
" <td>21.66</td>\n",
" <td>17.35</td>\n",
" <td>19.61</td>\n",
" <td>48123.0</td>\n",
" <td>26595.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tension Board 2 Mirror</th>\n",
" <td>20.71</td>\n",
" <td>46.15</td>\n",
" <td>19.20</td>\n",
" <td>20.50</td>\n",
" <td>20752.0</td>\n",
" <td>22688.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tension Board 2 Spray</th>\n",
" <td>25.48</td>\n",
" <td>27.22</td>\n",
" <td>19.42</td>\n",
" <td>20.71</td>\n",
" <td>14048.0</td>\n",
" <td>10995.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" avg_ascensionists avg_difficulty \\\n",
"status Matched No Match Matched No Match \n",
"layout_name \n",
"All Layouts 22.66 31.89 18.16 20.15 \n",
"Original Layout 22.68 21.66 17.35 19.61 \n",
"Tension Board 2 Mirror 20.71 46.15 19.20 20.50 \n",
"Tension Board 2 Spray 25.48 27.22 19.42 20.71 \n",
"\n",
" count \n",
"status Matched No Match \n",
"layout_name \n",
"All Layouts 82923.0 60278.0 \n",
"Original Layout 48123.0 26595.0 \n",
"Tension Board 2 Mirror 20752.0 22688.0 \n",
"Tension Board 2 Spray 14048.0 10995.0 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### Match vs No Match Summary\n",
"summary = df_combined.pivot_table(\n",
" index='layout_name',\n",
" columns='status',\n",
" values=['count', 'avg_difficulty', 'avg_ascensionists']\n",
").round(2)\n",
"\n",
"summary"
]
},
{
"cell_type": "markdown",
"id": "de8eb20e",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"id": "754abf39",
"metadata": {},
"source": [
"# Prolific statistics\n",
"\n",
"Here we will take note of some prolific statistics: what are the most popular climbs and who are the most popular setters?"
]
},
{
"cell_type": "markdown",
"id": "935c0ea3",
"metadata": {},
"source": [
"## Most popular climbs"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "42f93d59",
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 1600x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### Most popular climbs\n",
"# The ascensionist_count column will allow us to easily deduce the top 15 climbs. \n",
"\n",
"# Create a DataFrame with the top 15 climbs\n",
"df_popular_climbs = df.sort_values(by='ascensionist_count', ascending=False).head(15)[::-1]\n",
"\n",
"# We want the y-axis to say \"Climb @ angle\". Let's just create a new column for this. \n",
"df_popular_climbs['y_label'] = df_popular_climbs.apply(\n",
" lambda row: f\"{row['climb_name']} @ {row['angle']}°\", axis=1\n",
")\n",
"\n",
"# Plot it\n",
"fig, ax = plt.subplots(figsize=(16,8))\n",
"bars = ax.barh(df_popular_climbs['y_label'], df_popular_climbs['ascensionist_count'], color='teal')\n",
"ax.set_xlabel('Ascensionist Count')\n",
"ax.set_title('Top 15 Most Popular Climbs (at a specific angle)')\n",
"\n",
"# Add grade and angle labels\n",
"for bar, (_, row) in zip(bars, df_popular_climbs.iterrows()):\n",
" label = f\"{row['boulder_grade']} on {row['layout_name']}\"\n",
" ax.text(100,\n",
" bar.get_y() + bar.get_height()/2,\n",
" label,\n",
" va='center',\n",
" ha='left',\n",
" color='white')\n",
"\n",
"\n",
"plt.savefig('../images/stats/top_15_climbs.png', bbox_inches='tight')\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"id": "9d70aa42",
"metadata": {},
"source": [
"It's unsuprising that every one of these climbs is at 40° given that 40° is the most popular angle, by a long shot.\n",
"\n",
"What about an angle-agnostic analysis? What are the top climbs amonst all angles?"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "404c67aa",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1600x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### Top 15 most popular climbs (angle agnostic)\n",
"\n",
"# Aggregate by climb_name (sum counts across all angles)\n",
"df_agg = df.groupby(['climb_name', 'layout_name']).agg(\n",
" total_ascensionists=('ascensionist_count', 'sum'),\n",
" avg_difficulty=('display_difficulty', 'mean')\n",
").reset_index()\n",
"\n",
"\n",
"# Sort and select top 15\n",
"df_popular_climbs_aa = df_agg.sort_values(by='total_ascensionists', ascending=False).head(15)\n",
"\n",
"df_popular_climbs_aa\n",
"\n",
"# Plot\n",
"fig, ax = plt.subplots(figsize=(16, 8))\n",
"bars = ax.barh(df_popular_climbs_aa['climb_name'], df_popular_climbs_aa['total_ascensionists'], color='teal')\n",
"ax.set_xlabel('Total Ascensionist Count (All Angles)')\n",
"ax.set_title('Top 15 Most Popular Climbs (Angle Agnostic)')\n",
"ax.invert_yaxis()\n",
"\n",
"# Add grade labels inside bars\n",
"for bar, (_, row) in zip(bars, df_popular_climbs.iterrows()):\n",
" # Create a label like \"(Tension Board 2 Mirror)\"\n",
" label = f\"{row['layout_name']}\" \n",
" \n",
" ax.text(\n",
" 100, # Position inside bar\n",
" bar.get_y() + bar.get_height() / 2,\n",
" label,\n",
" va='center',\n",
" ha='left',\n",
" color='white'\n",
" )\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig('../images/stats/top_15_climbs_angle_agnostic.png', bbox_inches='tight')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "ff455a15",
"metadata": {},
"source": [
"What about the top climbs (with/without angles) for each of the board layouts? Let's do with angles first. "
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "1d50a209",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Original Layout\n",
"\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Angle</th>\n",
" <th>Grade</th>\n",
" <th>Ascensionists</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Pre-game</td>\n",
" <td>40°</td>\n",
" <td>4a/V0</td>\n",
" <td>10378</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Laser Camera</td>\n",
" <td>40°</td>\n",
" <td>6a/V3</td>\n",
" <td>8457</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>lefty loosy</td>\n",
" <td>40°</td>\n",
" <td>5a/V1</td>\n",
" <td>7248</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Riopio</td>\n",
" <td>40°</td>\n",
" <td>4a/V0</td>\n",
" <td>6961</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Sea Freight</td>\n",
" <td>40°</td>\n",
" <td>5c/V2</td>\n",
" <td>6953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Pre-game</td>\n",
" <td>30°</td>\n",
" <td>4a/V0</td>\n",
" <td>6863</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Bubbles</td>\n",
" <td>40°</td>\n",
" <td>6b+/V4</td>\n",
" <td>6653</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Laser Camera</td>\n",
" <td>30°</td>\n",
" <td>5a/V1</td>\n",
" <td>6409</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Holler</td>\n",
" <td>40°</td>\n",
" <td>6a/V3</td>\n",
" <td>6288</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Jiggles</td>\n",
" <td>40°</td>\n",
" <td>6b/V4</td>\n",
" <td>5568</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Shimmy</td>\n",
" <td>40°</td>\n",
" <td>6c/V5</td>\n",
" <td>5489</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Bubbles</td>\n",
" <td>30°</td>\n",
" <td>6a/V3</td>\n",
" <td>5473</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Gunter</td>\n",
" <td>40°</td>\n",
" <td>4a/V0</td>\n",
" <td>5461</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>It's Alive!</td>\n",
" <td>30°</td>\n",
" <td>5c/V2</td>\n",
" <td>5357</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>It's Alive!</td>\n",
" <td>40°</td>\n",
" <td>5b/V1</td>\n",
" <td>5301</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Angle Grade Ascensionists\n",
"1 Pre-game 40° 4a/V0 10378\n",
"2 Laser Camera 40° 6a/V3 8457\n",
"3 lefty loosy 40° 5a/V1 7248\n",
"4 Riopio 40° 4a/V0 6961\n",
"5 Sea Freight 40° 5c/V2 6953\n",
"6 Pre-game 30° 4a/V0 6863\n",
"7 Bubbles 40° 6b+/V4 6653\n",
"8 Laser Camera 30° 5a/V1 6409\n",
"9 Holler 40° 6a/V3 6288\n",
"10 Jiggles 40° 6b/V4 5568\n",
"11 Shimmy 40° 6c/V5 5489\n",
"12 Bubbles 30° 6a/V3 5473\n",
"13 Gunter 40° 4a/V0 5461\n",
"14 It's Alive! 30° 5c/V2 5357\n",
"15 It's Alive! 40° 5b/V1 5301"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Tension Board 2 Mirror\n",
"\n"
]
},
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Angle</th>\n",
" <th>Grade</th>\n",
" <th>Ascensionists</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Masquerade</td>\n",
" <td>40°</td>\n",
" <td>6a/V3</td>\n",
" <td>12123</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Drain from the Brain</td>\n",
" <td>40°</td>\n",
" <td>6c/V5</td>\n",
" <td>10242</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Compliments To The Climber</td>\n",
" <td>40°</td>\n",
" <td>6b/V4</td>\n",
" <td>10204</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Putty</td>\n",
" <td>40°</td>\n",
" <td>5c/V2</td>\n",
" <td>10162</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Propagation</td>\n",
" <td>40°</td>\n",
" <td>6b/V4</td>\n",
" <td>9640</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Thunderstruck</td>\n",
" <td>40°</td>\n",
" <td>6b/V4</td>\n",
" <td>9352</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>All Plastic</td>\n",
" <td>40°</td>\n",
" <td>5a/V1</td>\n",
" <td>9052</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>You Look Great Today</td>\n",
" <td>40°</td>\n",
" <td>6a/V3</td>\n",
" <td>8881</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>love is an open door.</td>\n",
" <td>40°</td>\n",
" <td>6c/V5</td>\n",
" <td>8693</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Sherman</td>\n",
" <td>40°</td>\n",
" <td>6a/V3</td>\n",
" <td>8212</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>hop scotch</td>\n",
" <td>40°</td>\n",
" <td>6b/V4</td>\n",
" <td>7936</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Dear John</td>\n",
" <td>40°</td>\n",
" <td>5c/V2</td>\n",
" <td>7821</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Dancing in the Moonlight</td>\n",
" <td>40°</td>\n",
" <td>6b/V4</td>\n",
" <td>7109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Doomscroll</td>\n",
" <td>40°</td>\n",
" <td>4c/V0</td>\n",
" <td>7107</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Poseidon</td>\n",
" <td>40°</td>\n",
" <td>6c/V5</td>\n",
" <td>7078</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Angle Grade Ascensionists\n",
"1 Masquerade 40° 6a/V3 12123\n",
"2 Drain from the Brain 40° 6c/V5 10242\n",
"3 Compliments To The Climber 40° 6b/V4 10204\n",
"4 Putty 40° 5c/V2 10162\n",
"5 Propagation 40° 6b/V4 9640\n",
"6 Thunderstruck 40° 6b/V4 9352\n",
"7 All Plastic 40° 5a/V1 9052\n",
"8 You Look Great Today 40° 6a/V3 8881\n",
"9 love is an open door. 40° 6c/V5 8693\n",
"10 Sherman 40° 6a/V3 8212\n",
"11 hop scotch 40° 6b/V4 7936\n",
"12 Dear John 40° 5c/V2 7821\n",
"13 Dancing in the Moonlight 40° 6b/V4 7109\n",
"14 Doomscroll 40° 4c/V0 7107\n",
"15 Poseidon 40° 6c/V5 7078"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Tension Board 2 Spray\n",
"\n"
]
},
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Angle</th>\n",
" <th>Grade</th>\n",
" <th>Ascensionists</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Pizza Box</td>\n",
" <td>40°</td>\n",
" <td>6a/V3</td>\n",
" <td>6649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Aw, shoot</td>\n",
" <td>40°</td>\n",
" <td>6b/V4</td>\n",
" <td>5453</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Write This Way</td>\n",
" <td>40°</td>\n",
" <td>6a/V3</td>\n",
" <td>5145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Shoulder Rust</td>\n",
" <td>40°</td>\n",
" <td>5a/V1</td>\n",
" <td>5018</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Nacho Mango</td>\n",
" <td>40°</td>\n",
" <td>5c/V2</td>\n",
" <td>4841</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Frictionless</td>\n",
" <td>40°</td>\n",
" <td>6c/V5</td>\n",
" <td>4536</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Put It Up</td>\n",
" <td>40°</td>\n",
" <td>6b/V4</td>\n",
" <td>4499</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Pour Cece</td>\n",
" <td>40°</td>\n",
" <td>6a/V3</td>\n",
" <td>4225</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Authorized</td>\n",
" <td>40°</td>\n",
" <td>6c/V5</td>\n",
" <td>4207</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>It’s A Mid-West Alcohol</td>\n",
" <td>40°</td>\n",
" <td>6b/V4</td>\n",
" <td>4081</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Center Left</td>\n",
" <td>40°</td>\n",
" <td>6c/V5</td>\n",
" <td>4008</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Lost Glove</td>\n",
" <td>40°</td>\n",
" <td>6b/V4</td>\n",
" <td>3563</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Perfect Pizza Party</td>\n",
" <td>40°</td>\n",
" <td>6a/V3</td>\n",
" <td>3420</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Lightyears</td>\n",
" <td>40°</td>\n",
" <td>5c/V2</td>\n",
" <td>3379</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>The Where</td>\n",
" <td>40°</td>\n",
" <td>6b/V4</td>\n",
" <td>3310</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Angle Grade Ascensionists\n",
"1 Pizza Box 40° 6a/V3 6649\n",
"2 Aw, shoot 40° 6b/V4 5453\n",
"3 Write This Way 40° 6a/V3 5145\n",
"4 Shoulder Rust 40° 5a/V1 5018\n",
"5 Nacho Mango 40° 5c/V2 4841\n",
"6 Frictionless 40° 6c/V5 4536\n",
"7 Put It Up 40° 6b/V4 4499\n",
"8 Pour Cece 40° 6a/V3 4225\n",
"9 Authorized 40° 6c/V5 4207\n",
"10 It’s A Mid-West Alcohol 40° 6b/V4 4081\n",
"11 Center Left 40° 6c/V5 4008\n",
"12 Lost Glove 40° 6b/V4 3563\n",
"13 Perfect Pizza Party 40° 6a/V3 3420\n",
"14 Lightyears 40° 5c/V2 3379\n",
"15 The Where 40° 6b/V4 3310"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### Top 15 most popular climbs by layout\n",
"\n",
"layouts = df['layout_name'].unique()\n",
"\n",
"for layout in layouts:\n",
" # Filter data for this layout\n",
" df_layout = df[df['layout_name'] == layout]\n",
" \n",
" # Sort by popularity and take top 15\n",
" df_top = df_layout.sort_values(by='ascensionist_count', ascending=False).head(15).reset_index(drop=True)\n",
" \n",
" # Select desired columns\n",
" df_display = df_top[['climb_name', 'angle', 'boulder_grade', 'ascensionist_count']].copy()\n",
" \n",
" # Rename columns for display\n",
" df_display.columns = ['Name', 'Angle', 'Grade', 'Ascensionists']\n",
" \n",
" # Format angle as string with degree symbol\n",
" df_display['Angle'] = df_display['Angle'].astype(int).astype(str) + '°'\n",
" \n",
" # Reset index to show rank 1-15\n",
" df_display.index = df_display.index + 1\n",
" \n",
" print(f\"\\n{layout}\\n\")\n",
" display(df_display)"
]
},
{
"cell_type": "markdown",
"id": "e60b4705",
"metadata": {},
"source": [
"On the TB2, it looks like 40 degrees constantly wins out. It is cool to see that, on the TB1, at least one climb made it to the top 15 twice, at two different angles! Congrats \"It's Alive.\"\n",
"\n",
"Now let us do a per-board angle agnostic analysis."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "6f99580f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"### Original Layout\n",
"\n"
]
},
{
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" <th></th>\n",
" <th>Name</th>\n",
" <th>Total Ascensionists</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Pre-game</td>\n",
" <td>27468</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Laser Camera</td>\n",
" <td>20968</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Bubbles</td>\n",
" <td>16161</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Sea Freight</td>\n",
" <td>15264</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Holler</td>\n",
" <td>14275</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>It's Alive!</td>\n",
" <td>13545</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Dull Scissors</td>\n",
" <td>11488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>lefty loosy</td>\n",
" <td>11275</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Getting By</td>\n",
" <td>11116</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Go Figure</td>\n",
" <td>10483</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Intro To Power</td>\n",
" <td>10377</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Switching Places</td>\n",
" <td>9748</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Shimmy</td>\n",
" <td>9427</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Foles Gold</td>\n",
" <td>9339</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Big Pinch Pinchin'</td>\n",
" <td>8823</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Total Ascensionists\n",
"1 Pre-game 27468\n",
"2 Laser Camera 20968\n",
"3 Bubbles 16161\n",
"4 Sea Freight 15264\n",
"5 Holler 14275\n",
"6 It's Alive! 13545\n",
"7 Dull Scissors 11488\n",
"8 lefty loosy 11275\n",
"9 Getting By 11116\n",
"10 Go Figure 10483\n",
"11 Intro To Power 10377\n",
"12 Switching Places 9748\n",
"13 Shimmy 9427\n",
"14 Foles Gold 9339\n",
"15 Big Pinch Pinchin' 8823"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"### Tension Board 2 Mirror\n",
"\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Total Ascensionists</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Masquerade</td>\n",
" <td>19655</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Putty</td>\n",
" <td>19445</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Compliments To The Climber</td>\n",
" <td>16844</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Thunderstruck</td>\n",
" <td>16121</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Further</td>\n",
" <td>15981</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Propagation</td>\n",
" <td>15606</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Poseidon</td>\n",
" <td>15549</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>All Plastic</td>\n",
" <td>15135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>You Look Great Today</td>\n",
" <td>14607</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Doomscroll</td>\n",
" <td>14150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Drain from the Brain</td>\n",
" <td>12910</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Prime</td>\n",
" <td>11384</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Endearing</td>\n",
" <td>10946</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Dancing in the Moonlight</td>\n",
" <td>10855</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Guided By Angels</td>\n",
" <td>10757</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Total Ascensionists\n",
"1 Masquerade 19655\n",
"2 Putty 19445\n",
"3 Compliments To The Climber 16844\n",
"4 Thunderstruck 16121\n",
"5 Further 15981\n",
"6 Propagation 15606\n",
"7 Poseidon 15549\n",
"8 All Plastic 15135\n",
"9 You Look Great Today 14607\n",
"10 Doomscroll 14150\n",
"11 Drain from the Brain 12910\n",
"12 Prime 11384\n",
"13 Endearing 10946\n",
"14 Dancing in the Moonlight 10855\n",
"15 Guided By Angels 10757"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"### Tension Board 2 Spray\n",
"\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Total Ascensionists</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Pizza Box</td>\n",
" <td>11884</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Nacho Mango</td>\n",
" <td>10325</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Write This Way</td>\n",
" <td>9299</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Shoulder Rust</td>\n",
" <td>8749</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Marshmallow Dragon</td>\n",
" <td>8001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Authorized</td>\n",
" <td>7387</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>The Where</td>\n",
" <td>7325</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Aw, shoot</td>\n",
" <td>7006</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Perfect Pizza Party</td>\n",
" <td>6575</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Frictionless</td>\n",
" <td>6536</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>New Gold</td>\n",
" <td>6529</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Pour Cece</td>\n",
" <td>6022</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Put It Up</td>\n",
" <td>6012</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>It’s A Mid-West Alcohol</td>\n",
" <td>5863</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Bring The Ruckus</td>\n",
" <td>5689</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Total Ascensionists\n",
"1 Pizza Box 11884\n",
"2 Nacho Mango 10325\n",
"3 Write This Way 9299\n",
"4 Shoulder Rust 8749\n",
"5 Marshmallow Dragon 8001\n",
"6 Authorized 7387\n",
"7 The Where 7325\n",
"8 Aw, shoot 7006\n",
"9 Perfect Pizza Party 6575\n",
"10 Frictionless 6536\n",
"11 New Gold 6529\n",
"12 Pour Cece 6022\n",
"13 Put It Up 6012\n",
"14 It’s A Mid-West Alcohol 5863\n",
"15 Bring The Ruckus 5689"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### Top 15 climbs by layout (angle agnostic)\n",
"\n",
"layouts = df['layout_name'].unique()\n",
"\n",
"# Aggregate counts and collect angles\n",
"df_agg = df.groupby(['climb_name', 'layout_name']).agg(\n",
" total_ascensionists=('ascensionist_count', 'sum')\n",
").reset_index()\n",
"\n",
"for layout in layouts:\n",
" df_layout = df_agg[df_agg['layout_name'] == layout]\n",
" df_top = df_layout.sort_values(by='total_ascensionists', ascending=False).head(15).reset_index(drop=True)\n",
" \n",
" df_display = df_top[['climb_name', 'total_ascensionists']].copy()\n",
" df_display.columns = ['Name', 'Total Ascensionists']\n",
" \n",
" # Appropriate index\n",
" df_display.index = df_display.index + 1\n",
" \n",
" print(f\"\\n### {layout}\\n\")\n",
" display(df_display)"
]
},
{
"cell_type": "markdown",
"id": "cab87caa",
"metadata": {},
"source": [
"## Prolific setters\n",
"\n",
"Next, we will make a simple table of the most prolific setters by board."
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "3053ddc3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Original Layout\n",
"\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Username</th>\n",
" <th>Climbs</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>adamf1234</td>\n",
" <td>420</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>jonlackman</td>\n",
" <td>383</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>willanglin</td>\n",
" <td>332</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>kylejosephharding</td>\n",
" <td>326</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>tmon</td>\n",
" <td>246</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>gibbs</td>\n",
" <td>240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>str8_crimpin</td>\n",
" <td>238</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>senderone</td>\n",
" <td>223</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>topoutclimbinggym</td>\n",
" <td>216</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>memphisben</td>\n",
" <td>198</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Username Climbs\n",
"1 adamf1234 420\n",
"2 jonlackman 383\n",
"3 willanglin 332\n",
"4 kylejosephharding 326\n",
"5 tmon 246\n",
"6 gibbs 240\n",
"7 str8_crimpin 238\n",
"8 senderone 223\n",
"9 topoutclimbinggym 216\n",
"10 memphisben 198"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Tension Board 2 Mirror\n",
"\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Username</th>\n",
" <th>Climbs</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>tensionclimbing</td>\n",
" <td>506</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>SocksonBlocs</td>\n",
" <td>370</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>jaketiger111</td>\n",
" <td>315</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>mo3_3az</td>\n",
" <td>305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>limberlimb</td>\n",
" <td>299</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>lijahl</td>\n",
" <td>261</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>iansutherland</td>\n",
" <td>215</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>theruz</td>\n",
" <td>202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>AlexK</td>\n",
" <td>200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>nicholson.brendan</td>\n",
" <td>174</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Username Climbs\n",
"1 tensionclimbing 506\n",
"2 SocksonBlocs 370\n",
"3 jaketiger111 315\n",
"4 mo3_3az 305\n",
"5 limberlimb 299\n",
"6 lijahl 261\n",
"7 iansutherland 215\n",
"8 theruz 202\n",
"9 AlexK 200\n",
"10 nicholson.brendan 174"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Tension Board 2 Spray\n",
"\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Username</th>\n",
" <th>Climbs</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>MaxClark</td>\n",
" <td>239</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>ianmek</td>\n",
" <td>216</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Jeremy_Fullerton</td>\n",
" <td>171</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>tensionclimbing</td>\n",
" <td>170</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>danielwoodseyetattoo</td>\n",
" <td>148</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Fatbeninco</td>\n",
" <td>136</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>rprops</td>\n",
" <td>128</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>SPIGGOTT</td>\n",
" <td>119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>cotton125</td>\n",
" <td>115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>graydawgggg</td>\n",
" <td>107</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Username Climbs\n",
"1 MaxClark 239\n",
"2 ianmek 216\n",
"3 Jeremy_Fullerton 171\n",
"4 tensionclimbing 170\n",
"5 danielwoodseyetattoo 148\n",
"6 Fatbeninco 136\n",
"7 rprops 128\n",
"8 SPIGGOTT 119\n",
"9 cotton125 115\n",
"10 graydawgggg 107"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### Top 10 setters by layout\n",
"\n",
"# Make a DataFrame for the setters\n",
"df_setters = df.groupby(['setter_username', 'layout_name']).agg(\n",
" climb_count=('uuid', 'nunique')\n",
").reset_index()\n",
"\n",
"layouts = df['layout_name'].unique()\n",
"\n",
"for layout in layouts:\n",
" df_layout = df_setters[df_setters['layout_name'] == layout]\n",
" df_top = df_layout.sort_values(by='climb_count', ascending=False).head(10).reset_index(drop=True)\n",
"\n",
" df_display = df_top[['setter_username', 'climb_count']].copy()\n",
" df_display.columns = ['Username', 'Climbs']\n",
"\n",
" # Reset index to show rank\n",
" df_display.index = df_display.index + 1\n",
"\n",
" # Display\n",
" print(f\"\\n{layout}\\n\")\n",
" display(df_display)\n",
" \n"
]
},
{
"cell_type": "markdown",
"id": "19dd4fad",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"id": "085c99ea",
"metadata": {},
"source": [
"\n",
"# Conclusion\n",
"\n",
"With that, we will move on to analyzing specific holds on the boards. We will do this in [02_hold_analysis_and_board_heatmap.ipynb](02_hold_analysis_and_board_heatmap.ipynb)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
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"file_extension": ".py",
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"pygments_lexer": "ipython3",
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