diff --git "a/analyze_results.ipynb" "b/analyze_results.ipynb" --- "a/analyze_results.ipynb" +++ "b/analyze_results.ipynb" @@ -2,15 +2,22 @@ "cells": [ { "cell_type": "code", - "execution_count": 65, + "execution_count": 92, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Fetching datafiles...: 100%|██████████| 83/83 [00:01<00:00, 58.08it/s]\n", - "Loading evals data...: 100%|██████████| 2407/2407 [00:05<00:00, 422.65it/s]\n" + "Fetching datafiles...: 0%| | 0/83 [00:00" ] @@ -472,7 +479,7 @@ "plt.bar(step_14000_data['runname'], step_14000_data['average_rank'], color=colors, edgecolor='black') # Added edgecolor for outline\n", "plt.title(f\"Comparison of Multilingual Datasets\\n({', '.join([x for x in considered_langs])})\")\n", "plt.xlabel('Dataset')\n", - "plt.ylabel('Average Rank (lower is better)')\n", + "plt.ylabel('Average Rank (1=best, 7=worst)')\n", "plt.xticks(rotation=45, ha='right')\n", "plt.tight_layout()\n", "\n", @@ -768,27 +775,27 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 86, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Fetching datafiles...: 100%|██████████| 96/96 [00:01<00:00, 60.22it/s]\n", - "Loading evals data...: 100%|██████████| 2784/2784 [00:05<00:00, 503.69it/s]\n" + "Fetching datafiles...: 100%|██████████| 97/97 [00:01<00:00, 61.02it/s]\n", + "Loading evals data...: 100%|██████████| 2813/2813 [00:05<00:00, 480.55it/s]\n" ] } ], "source": [ "\n", - "ALL_RUNS_REGEX = \"(1p46G-gemma-(commoncrawl|defi-rehydrfix|hplt|mc4|cultura|cc-100|arabicweb|101b_arabic|croissant|omnica|odaigen|sangr|sea-common|vngrs|mapcc|tigerbot|mnbvc).*-29BT-.*)|(1p46G-gemma-defi-rehydrto-sw-29BT-seed-6)\"\n", + "ALL_RUNS_REGEX = \"(1p46G-gemma-(commoncrawl|defi-rehydrfix|hplt|mc4|cultura|cc-100|arabicweb|101b_arabic|croissant|omnica|odaigen|sangr|sea-common|vngrs|mapcc|tigerbot|mnbvc).*-29BT-.*)|(1p46G-gemma-defi-rehydrto-sw-29BT-seed-6|1p46G-gemma-defi-extract-sw-29BT-seed-6)\"\n", "df = load_data(results_uri, ALL_RUNS_REGEX)\n" ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 87, "metadata": {}, "outputs": [ { @@ -987,162 +994,62 @@ }, { "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'ar': {'avg_spearman': 0.9562671045429665,\n", - " 'avg_kendall_tau_a': 0.8730158730158731,\n", - " 'max_std': 0.21982751039230422,\n", - " 'max_std_step': 13500.0,\n", - " 'min_std': 0.13405077680143612,\n", - " 'min_std_step': 14000.0,\n", - " 'mean_std': 0.17186309872416877,\n", - " 'avg_snr': 0.42741226992828113,\n", - " 'max_n_std': nan},\n", - " 'fr': {'avg_spearman': 0.9828817733990145,\n", - " 'avg_kendall_tau_a': 0.8826530612244898,\n", - " 'max_std': 0.30511512263227314,\n", - " 'max_std_step': 14000.0,\n", - " 'min_std': 0.24671287236650738,\n", - " 'min_std_step': 13000.0,\n", - " 'mean_std': 0.2772310962941867,\n", - " 'avg_snr': 2.6373153163708167,\n", - " 'max_n_std': nan},\n", - " 'ru': {'avg_spearman': 0.9881773399014775,\n", - " 'avg_kendall_tau_a': 0.9081632653061223,\n", - " 'max_std': 0.11284960953987701,\n", - " 'max_std_step': 12000.0,\n", - " 'min_std': 0.0487668766065255,\n", - " 'min_std_step': 13000.0,\n", - " 'mean_std': 0.08579819620166992,\n", - " 'avg_snr': 0.06893170634206541,\n", - " 'max_n_std': nan},\n", - " 'th': {'avg_spearman': 0.9288793103448274,\n", - " 'avg_kendall_tau_a': 0.913265306122449,\n", - " 'max_std': 0.3831718947659415,\n", - " 'max_std_step': 12500.0,\n", - " 'min_std': 0.29525076368653597,\n", - " 'min_std_step': 14000.0,\n", - " 'mean_std': 0.34703451330088,\n", - " 'avg_snr': 0.4720416186966728,\n", - " 'max_n_std': nan},\n", - " 'tr': {'avg_spearman': 0.9828817733990145,\n", - " 'avg_kendall_tau_a': 0.9030612244897959,\n", - " 'max_std': 0.3172063039272791,\n", - " 'max_std_step': 14000.0,\n", - " 'min_std': 0.18382219089983276,\n", - " 'min_std_step': 13500.0,\n", - " 'mean_std': 0.2175352128271121,\n", - " 'avg_snr': 2.1893519023584744,\n", - " 'max_n_std': nan},\n", - " 'zh': {'avg_spearman': 0.8998029556650243,\n", - " 'avg_kendall_tau_a': 0.942857142857143,\n", - " 'max_std': 0.1907085447186011,\n", - " 'max_std_step': 12000.0,\n", - " 'min_std': 0.07019697700062395,\n", - " 'min_std_step': 13500.0,\n", - " 'mean_std': 0.1301938971533349,\n", - " 'avg_snr': -0.04891658418497957,\n", - " 'max_n_std': nan},\n", - " 'te': {'avg_spearman': 0.94987684729064,\n", - " 'avg_kendall_tau_a': 0.8571428571428571,\n", - " 'max_std': 0.3903127939144447,\n", - " 'max_std_step': 12000.0,\n", - " 'min_std': 0.20241732903100457,\n", - " 'min_std_step': 13000.0,\n", - " 'mean_std': 0.29060920502341175,\n", - " 'avg_snr': 7.147316103189745,\n", - " 'max_n_std': nan},\n", - " 'hi': {'avg_spearman': 0.9628430682617873,\n", - " 'avg_kendall_tau_a': 0.816326530612245,\n", - " 'max_std': 0.24871853007307024,\n", - " 'max_std_step': 12500.0,\n", - " 'min_std': 0.14875916783599552,\n", - " 'min_std_step': 14000.0,\n", - " 'mean_std': 0.21135759941121507,\n", - " 'avg_snr': 2.647790756067855,\n", - " 'max_n_std': nan},\n", - " 'sw': {'avg_spearman': 0.9144499178981936,\n", - " 'avg_kendall_tau_a': 1.0,\n", - " 'max_std': 0.4348520564519765,\n", - " 'max_std_step': 14000.0,\n", - " 'min_std': 0.1884788824475846,\n", - " 'min_std_step': 12000.0,\n", - " 'mean_std': 0.30204724488476403,\n", - " 'avg_snr': 3.632302712768627,\n", - " 'max_n_std': nan}}" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lang_stats" - ] - }, - { - "cell_type": "code", - "execution_count": 63, + "execution_count": 91, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -1164,49 +1071,49 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -1228,49 +1135,49 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -1292,49 +1199,49 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -1356,49 +1263,49 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -1420,61 +1327,61 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -1496,25 +1403,25 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -1536,43 +1443,43 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -1594,19 +1501,25 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " run_data[\"average_score\"] = run_data[\"average_score\"] - min_step_0\n", - "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/758710532.py:83: SettingWithCopyWarning: \n", + "/var/folders/ry/w953ycvs06bd2crg_z94yff00000gn/T/ipykernel_28119/3091101127.py:85: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -1616,7 +1529,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -1639,6 +1552,7 @@ " r'1p46G-gemma-culturax-29BT': 'CulturaX',\n", " r'1p46G-gemma-defi-rehydrfix-29BT': 'FineWeb 2 (Ours)',\n", " r'1p46G-gemma-defi-rehydrto-29BT': 'FineWeb 2 (Ours)',\n", + " r'1p46G-gemma-defi-extract-29BT': 'Fineweb 2 (No filtering)',\n", " r'1p46G-gemma-hplt-29BT': 'HPLT',\n", " r'1p46G-gemma-arabicweb24-29BT': 'ArabicWeb',\n", " r'1p46G-gemma-101b_arabicwords-29BT': 'Arabic-101B',\n", @@ -1670,6 +1584,7 @@ " 'ArabicWeb': 'brown',\n", " 'Omnica Russia': 'brown',\n", " 'Sangraha': 'brown',\n", + " 'Fineweb 2 (No filtering)': 'brown',\n", " 'Arabic-101B': 'pink',\n", " 'Odaigen': 'pink',\n", " 'MNBVC': 'pink',\n",