diff --git a/your-code/challenge-1.ipynb b/your-code/challenge-1.ipynb old mode 100755 new mode 100644 index c1bb43d..e7fefed --- a/your-code/challenge-1.ipynb +++ b/your-code/challenge-1.ipynb @@ -19,12 +19,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Import libraries\n", - "import pandas as pd" + "import pandas as pd\n", + "import scipy.stats as st" ] }, { @@ -38,11 +39,266 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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#NameType 1Type 2TotalHPAttackDefenseSp. AtkSp. DefSpeedGenerationLegendary
01BulbasaurGrassPoison3184549496565451False
12IvysaurGrassPoison4056062638080601False
23VenusaurGrassPoison525808283100100801False
33VenusaurMega VenusaurGrassPoison62580100123122120801False
44CharmanderFireNaN3093952436050651False
..........................................
795719DiancieRockFairy60050100150100150506True
796719DiancieMega DiancieRockFairy700501601101601101106True
797720HoopaHoopa ConfinedPsychicGhost6008011060150130706True
798720HoopaHoopa UnboundPsychicDark6808016060170130806True
799721VolcanionFireWater6008011012013090706True
\n", + "

800 rows × 13 columns

\n", + "
" + ], + "text/plain": [ + " # Name Type 1 Type 2 Total HP Attack Defense \\\n", + "0 1 Bulbasaur Grass Poison 318 45 49 49 \n", + "1 2 Ivysaur Grass Poison 405 60 62 63 \n", + "2 3 Venusaur Grass Poison 525 80 82 83 \n", + "3 3 VenusaurMega Venusaur Grass Poison 625 80 100 123 \n", + "4 4 Charmander Fire NaN 309 39 52 43 \n", + ".. ... ... ... ... ... .. ... ... \n", + "795 719 Diancie Rock Fairy 600 50 100 150 \n", + "796 719 DiancieMega Diancie Rock Fairy 700 50 160 110 \n", + "797 720 HoopaHoopa Confined Psychic Ghost 600 80 110 60 \n", + "798 720 HoopaHoopa Unbound Psychic Dark 680 80 160 60 \n", + "799 721 Volcanion Fire Water 600 80 110 120 \n", + "\n", + " Sp. Atk Sp. Def Speed Generation Legendary \n", + "0 65 65 45 1 False \n", + "1 80 80 60 1 False \n", + "2 100 100 80 1 False \n", + "3 122 120 80 1 False \n", + "4 60 50 65 1 False \n", + ".. ... ... ... ... ... \n", + "795 100 150 50 6 True \n", + "796 160 110 110 6 True \n", + "797 150 130 70 6 True \n", + "798 170 130 80 6 True \n", + "799 130 90 70 6 True \n", + "\n", + "[800 rows x 13 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here:\n" + "# Your code here:\n", + "data = pd.read_csv('Pokemon.csv')\n", + "data" ] }, { @@ -58,11 +314,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def t_test_features(s1, s2, features=['HP', 'Attack', 'Defense', 'Sp. Atk', 'Sp. Def', 'Speed', 'Total']):\n", + " \n", " \"\"\"Test means of a feature set of two samples\n", " \n", " Args:\n", @@ -76,7 +333,10 @@ " results = {}\n", "\n", " # Your code here\n", - " \n", + " for feature in features:\n", + " t_stat, p_value = st.ttest_ind(s1[feature], s2[feature], equal_var=False)\n", + "\n", + " results[feature] = {'t_statistic': t_stat, 'p_value': p_value}\n", " return results" ] }, @@ -101,11 +361,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([False, True])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here\n" + "data[\"Legendary\"].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'HP': {'t_statistic': 8.981370483625046, 'p_value': 1.0026911708035284e-13}, 'Attack': {'t_statistic': 10.438133539322203, 'p_value': 2.520372449236646e-16}, 'Defense': {'t_statistic': 7.637078164784618, 'p_value': 4.826998494919331e-11}, 'Sp. Atk': {'t_statistic': 13.417449984138461, 'p_value': 1.5514614112239816e-21}, 'Sp. Def': {'t_statistic': 10.015696613114878, 'p_value': 2.2949327864052826e-15}, 'Speed': {'t_statistic': 11.47504444631443, 'p_value': 1.0490163118824507e-18}, 'Total': {'t_statistic': 25.8335743895517, 'p_value': 9.357954335957444e-47}}\n" + ] + } + ], + "source": [ + "# Your code here\n", + "legendary = data[data['Legendary'] == True]\n", + "non_legendary = data[data['Legendary'] == False]\n", + "\n", + "t_test_results = t_test_features(legendary, non_legendary, features=[\"HP\", \"Attack\", \"Defense\", \"Sp. Atk\", \"Sp. Def\", \"Speed\", \"Total\"])\n", + "print(t_test_results)" ] }, { @@ -117,11 +410,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "# Your comment here" + "# Your comment here\n", + "#The p-value is significantly smaller than 0.05. \n", + "#Reject the null hypothesis for each feature. \n", + "#There is a significant difference in the stats between Legendary and non-Legendary Pokémon." ] }, { @@ -133,11 +429,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'HP': {'t_statistic': -1.4609700002846653, 'p_value': 0.14551697834219626}, 'Attack': {'t_statistic': 1.1603052805533747, 'p_value': 0.24721958967217725}, 'Defense': {'t_statistic': -0.5724173235153119, 'p_value': 0.5677711011725426}, 'Sp. Atk': {'t_statistic': 1.54608675231508, 'p_value': 0.12332165977104388}, 'Sp. Def': {'t_statistic': -1.3203746053318755, 'p_value': 0.18829872292645752}, 'Speed': {'t_statistic': 3.069594374071931, 'p_value': 0.00239265937312135}, 'Total': {'t_statistic': 0.579073329450271, 'p_value': 0.5631377907941676}}\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "generation_1 = data[data['Generation'] == 1]\n", + "generation_2 = data[data['Generation'] == 2]\n", + "\n", + "t_test_results = t_test_features(generation_1, generation_2, features=[\"HP\", \"Attack\", \"Defense\", \"Sp. Atk\", \"Sp. Def\", \"Speed\", \"Total\"])\n", + "print(t_test_results)" ] }, { @@ -149,11 +458,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "# Your comment here" + "# Your comment here\n", + "#The p-value is higher than 0.05. \n", + "#Don't reject the null hypothesis for each feature. \n", + "#There isn't a significant difference in the stats between Generation1 and Generation 2 Pokemons." ] }, { @@ -165,11 +477,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'HP': {'t_statistic': -1.586088850338319, 'p_value': 0.11314389855379413}, 'Attack': {'t_statistic': -3.810556219950897, 'p_value': 0.00014932578145948305}, 'Defense': {'t_statistic': -5.60979416640793, 'p_value': 2.7978540411514693e-08}, 'Sp. Atk': {'t_statistic': -3.828976815384819, 'p_value': 0.00013876216585667907}, 'Sp. Def': {'t_statistic': -3.892991138685155, 'p_value': 0.00010730610934512779}, 'Speed': {'t_statistic': -2.258014040079978, 'p_value': 0.02421703281819093}, 'Total': {'t_statistic': -5.355678438759113, 'p_value': 1.1157056505229964e-07}}\n" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "data['Type Count'] = (data['Type 2'].notnull()).astype(int)\n", + "\n", + "single_type = data[data['Type Count'] == 0] # um tipo, ou seja, não tem o tipo 2\n", + "two_types = data[data['Type Count'] == 1] #dois tipos, tem o tipo 1 e o tipo 2\n", + "\n", + "t_test_results = t_test_features(single_type, two_types, features=[\"HP\", \"Attack\", \"Defense\", \"Sp. Atk\", \"Sp. Def\", \"Speed\", \"Total\"])\n", + "print(t_test_results)\n" ] }, { @@ -181,11 +508,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "# Your comment here" + "# Your comment here\n", + "#The p-value is smaller than 0.05, in almost all features, except HP. \n", + "#But we reject the null hypothesis for almost every feature, except HP. \n", + "#There is a significant difference in the stats between Single Types and Two Types." ] }, { @@ -199,11 +529,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "TtestResult(statistic=4.325566393330478, pvalue=1.7140303479358558e-05, df=799)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "from scipy.stats import ttest_rel\n", + "#H0: There is no significant difference between the Attack and Defense stats.\n", + "#H1: There is a significant difference between the Attack and Defense stats.\n", + "\n", + "ttest_rel(data[\"Attack\"], data[\"Defense\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "TtestResult(statistic=0.853986188453353, pvalue=0.3933685997548122, df=799)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#H0: There is no significant difference between the Sp. Atk and Sp. Def stats.\n", + "#H1: There is a significant difference between the Sp. Atk and Sp. Def stats.\n", + "ttest_rel(data[\"Sp. Atk\"], data[\"Sp. Def\"])" ] }, { @@ -215,17 +583,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ - "# Your comment here" + "# Your comment here\n", + "#For Attack and Defense, we can see that p-value < 0.05, then we should reject and conclude that there is a significant difference between attack and defense.\n", + "##For Sp. Attack and Sp. Def, we can see that p-value > 0.05, then we should not reject and conclude that there is not a significant difference between sp. atk and sp. def." ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -239,7 +616,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/your-code/challenge-2.ipynb b/your-code/challenge-2.ipynb old mode 100755 new mode 100644 index 1f0e335..eec4151 --- a/your-code/challenge-2.ipynb +++ b/your-code/challenge-2.ipynb @@ -17,21 +17,279 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Import libraries\n", - "import pandas as pd" + "import pandas as pd\n", + "from scipy.stats import f_oneway" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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#NameType 1Type 2TotalHPAttackDefenseSp. AtkSp. DefSpeedGenerationLegendary
01BulbasaurGrassPoison3184549496565451False
12IvysaurGrassPoison4056062638080601False
23VenusaurGrassPoison525808283100100801False
33VenusaurMega VenusaurGrassPoison62580100123122120801False
44CharmanderFireNaN3093952436050651False
..........................................
795719DiancieRockFairy60050100150100150506True
796719DiancieMega DiancieRockFairy700501601101601101106True
797720HoopaHoopa ConfinedPsychicGhost6008011060150130706True
798720HoopaHoopa UnboundPsychicDark6808016060170130806True
799721VolcanionFireWater6008011012013090706True
\n", + "

800 rows × 13 columns

\n", + "
" + ], + "text/plain": [ + " # Name Type 1 Type 2 Total HP Attack Defense \\\n", + "0 1 Bulbasaur Grass Poison 318 45 49 49 \n", + "1 2 Ivysaur Grass Poison 405 60 62 63 \n", + "2 3 Venusaur Grass Poison 525 80 82 83 \n", + "3 3 VenusaurMega Venusaur Grass Poison 625 80 100 123 \n", + "4 4 Charmander Fire NaN 309 39 52 43 \n", + ".. ... ... ... ... ... .. ... ... \n", + "795 719 Diancie Rock Fairy 600 50 100 150 \n", + "796 719 DiancieMega Diancie Rock Fairy 700 50 160 110 \n", + "797 720 HoopaHoopa Confined Psychic Ghost 600 80 110 60 \n", + "798 720 HoopaHoopa Unbound Psychic Dark 680 80 160 60 \n", + "799 721 Volcanion Fire Water 600 80 110 120 \n", + "\n", + " Sp. Atk Sp. Def Speed Generation Legendary \n", + "0 65 65 45 1 False \n", + "1 80 80 60 1 False \n", + "2 100 100 80 1 False \n", + "3 122 120 80 1 False \n", + "4 60 50 65 1 False \n", + ".. ... ... ... ... ... \n", + "795 100 150 50 6 True \n", + "796 160 110 110 6 True \n", + "797 150 130 70 6 True \n", + "798 170 130 80 6 True \n", + "799 130 90 70 6 True \n", + "\n", + "[800 rows x 13 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Load the data:\n" + "# Load the data:\n", + "data = pd.read_csv('Pokemon.csv')\n", + "data" ] }, { @@ -58,12 +316,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Grass' 'Fire' 'Water' 'Bug' 'Normal' 'Poison' 'Electric' 'Ground'\n", + " 'Fairy' 'Fighting' 'Psychic' 'Rock' 'Ghost' 'Ice' 'Dragon' 'Dark' 'Steel'\n", + " 'Flying' nan]\n" + ] + }, + { + "data": { + "text/plain": [ + "19" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Your code here\n", + "unique_types = pd.concat([data['Type 1'], data['Type 2']]).unique()\n", "\n", + "print(unique_types)\n", "\n", "len(unique_types) # you should see 19" ] @@ -85,13 +365,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "18" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ + "#Your code here:\n", + "\n", "pokemon_totals = []\n", "\n", - "# Your code here\n", + "melted_data = pd.melt(data, id_vars='Total', value_vars=['Type 1', 'Type 2'], var_name='Type')\n", + "\n", + "pokemon_totals = melted_data.dropna().groupby('value')['Total'].sum().tolist()\n", "\n", "len(pokemon_totals) # you should see 18" ] @@ -111,11 +406,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ValueError", + "evalue": "zero-dimensional arrays cannot be concatenated", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[49], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# Your code here\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mstats\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m f_oneway\n\u001b[1;32m----> 3\u001b[0m anova_result \u001b[38;5;241m=\u001b[39m f_oneway(\u001b[38;5;241m*\u001b[39mpokemon_totals)\n\u001b[0;32m 4\u001b[0m anova_result\n", + "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\scipy\\stats\\_stats_py.py:4127\u001b[0m, in \u001b[0;36mf_oneway\u001b[1;34m(axis, *samples)\u001b[0m\n\u001b[0;32m 4121\u001b[0m num_groups \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(samples)\n\u001b[0;32m 4123\u001b[0m \u001b[38;5;66;03m# We haven't explicitly validated axis, but if it is bad, this call of\u001b[39;00m\n\u001b[0;32m 4124\u001b[0m \u001b[38;5;66;03m# np.concatenate will raise np.AxisError. The call will raise ValueError\u001b[39;00m\n\u001b[0;32m 4125\u001b[0m \u001b[38;5;66;03m# if the dimensions of all the arrays, except the axis dimension, are not\u001b[39;00m\n\u001b[0;32m 4126\u001b[0m \u001b[38;5;66;03m# the same.\u001b[39;00m\n\u001b[1;32m-> 4127\u001b[0m alldata \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mconcatenate(samples, axis\u001b[38;5;241m=\u001b[39maxis)\n\u001b[0;32m 4128\u001b[0m bign \u001b[38;5;241m=\u001b[39m alldata\u001b[38;5;241m.\u001b[39mshape[axis]\n\u001b[0;32m 4130\u001b[0m \u001b[38;5;66;03m# Check this after forming alldata, so shape errors are detected\u001b[39;00m\n\u001b[0;32m 4131\u001b[0m \u001b[38;5;66;03m# and reported before checking for 0 length inputs.\u001b[39;00m\n", + "File \u001b[1;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36mconcatenate\u001b[1;34m(*args, **kwargs)\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: zero-dimensional arrays cannot be concatenated" + ] + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "from scipy.stats import f_oneway\n", + "anova_result = f_oneway(*pokemon_totals)\n", + "anova_result" ] }, { @@ -127,17 +439,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ - "# Your comment here" + "# Your comment here\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -151,7 +470,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.11.5" } }, "nbformat": 4,