diff --git a/lab_pokemon.ipynb b/lab_pokemon.ipynb index 8b15f23..4e5b5df 100644 --- a/lab_pokemon.ipynb +++ b/lab_pokemon.ipynb @@ -16,17 +16,17 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 13, "id": "5923b0d7-c0e0-48fa-b765-4aa6002c2d4f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "4" + "5" ] }, - "execution_count": 6, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -37,7 +37,7 @@ "# printed below the cell. \n", "# Then try changing the Python code and re-run it.\n", "\n", - "1+1+1+1" + "1+1+1+2" ] }, { @@ -128,21 +128,13 @@ "First, we'll import pandas (using the conventional variable name `pd`) and load the two datasets. *Run these cells and every code cell you encounter in this notebook.*" ] }, - { - "cell_type": "code", - "execution_count": 4, - "id": "ba09a0f8-27d9-456f-aeff-3980e3362d5b", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, { "cell_type": "markdown", - "id": "0abf30ad-890b-4e89-ab86-b2d155de8bd1", + "id": "f60aa4b0-7050-4e43-9619-5f8500770cb0", "metadata": {}, "source": [ + "import pandas as pd\n", + "\n", "pokemon = pd.read_csv(\"pokemon.csv\")\n", "people = pd.read_csv(\"brfss_2020.csv\")" ] @@ -161,9 +153,33 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 22, "id": "579d8dda-ca39-48b1-8819-b17651029729", "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "pokemon = pd.read_csv(\"pokemon.csv\")\n", + "people = pd.read_csv(\"brfss_2020.csv\")" + ] + }, + { + "cell_type": "markdown", + "id": "ee8b0718-56f9-4fc8-bd35-fa0ccb445179", + "metadata": {}, + "source": [ + "OK, 800 Pokémon, with 12 columns for each. And you can see all the columns. Not all the data is shown in this preview, of course. If there were more columns than could be displayed, you could see them all by typing `pokemon.columns`. \n", + "\n", + "#### Your turn\n", + "\n", + "Now do the same for your data set, `people`." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "c9e5e4ec-b197-450c-ae2d-318006fa0a2f", + "metadata": {}, "outputs": [ { "data": { @@ -186,95 +202,89 @@ " \n", " \n", " \n", - " name\n", - " type\n", - " subtype\n", - " total\n", - " hp\n", - " attack\n", - " defense\n", - " special_attack\n", - " special_defense\n", - " speed\n", - " generation\n", - " legendary\n", + " age\n", + " sex\n", + " income\n", + " education\n", + " sexual_orientation\n", + " height\n", + " weight\n", + " health\n", + " no_doctor\n", + " exercise\n", + " sleep\n", " \n", " \n", " \n", " \n", " 0\n", - " Bulbasaur\n", - " Grass\n", - " Poison\n", - " 318\n", - " 45\n", - " 49\n", - " 49\n", - " 65\n", - " 65\n", - " 45\n", - " 1\n", - " False\n", + " 55\n", + " female\n", + " 5\n", + " 2\n", + " other\n", + " 1.55\n", + " 83.01\n", + " 2\n", + " True\n", + " True\n", + " 7\n", " \n", " \n", " 1\n", - " Ivysaur\n", - " Grass\n", - " Poison\n", - " 405\n", - " 60\n", - " 62\n", - " 63\n", - " 80\n", - " 80\n", - " 60\n", + " 65\n", + " female\n", + " 8\n", " 1\n", + " heterosexual\n", + " 1.65\n", + " 78.02\n", + " 3\n", " False\n", + " False\n", + " 8\n", " \n", " \n", " 2\n", - " Venusaur\n", - " Grass\n", - " Poison\n", - " 525\n", - " 80\n", - " 82\n", - " 83\n", - " 100\n", - " 100\n", - " 80\n", - " 1\n", - " False\n", + " 35\n", + " female\n", + " 8\n", + " 4\n", + " heterosexual\n", + " 1.65\n", + " 77.11\n", + " 4\n", + " True\n", + " True\n", + " 7\n", " \n", " \n", " 3\n", - " VenusaurMega Venusaur\n", - " Grass\n", - " Poison\n", - " 625\n", - " 80\n", - " 100\n", - " 123\n", - " 122\n", - " 120\n", - " 80\n", - " 1\n", + " 55\n", + " male\n", + " 8\n", + " 4\n", + " heterosexual\n", + " 1.83\n", + " 81.65\n", + " 5\n", " False\n", + " True\n", + " 8\n", " \n", " \n", " 4\n", - " Charmander\n", - " Fire\n", - " NaN\n", - " 309\n", - " 39\n", - " 52\n", - " 43\n", - " 60\n", - " 50\n", - " 65\n", - " 1\n", + " 55\n", + " female\n", + " 8\n", + " 4\n", + " heterosexual\n", + " 1.80\n", + " 76.66\n", + " 4\n", " False\n", + " True\n", + " 8\n", " \n", " \n", " ...\n", @@ -289,159 +299,119 @@ " ...\n", " ...\n", " ...\n", - " ...\n", " \n", " \n", - " 795\n", - " Diancie\n", - " Rock\n", - " Fairy\n", - " 600\n", - " 50\n", - " 100\n", - " 150\n", - " 100\n", - " 150\n", - " 50\n", + " 166420\n", + " 45\n", + " female\n", + " 8\n", + " 3\n", + " heterosexual\n", + " 1.63\n", + " 86.18\n", + " 1\n", + " False\n", + " False\n", " 6\n", - " True\n", " \n", " \n", - " 796\n", - " DiancieMega Diancie\n", - " Rock\n", - " Fairy\n", - " 700\n", - " 50\n", - " 160\n", - " 110\n", - " 160\n", - " 110\n", - " 110\n", - " 6\n", + " 166421\n", + " 25\n", + " male\n", + " 7\n", + " 2\n", + " heterosexual\n", + " 1.78\n", + " 86.18\n", + " 4\n", + " False\n", " True\n", + " 6\n", " \n", " \n", - " 797\n", - " HoopaHoopa Confined\n", - " Psychic\n", - " Ghost\n", - " 600\n", - " 80\n", - " 110\n", - " 60\n", - " 150\n", - " 130\n", - " 70\n", - " 6\n", - " True\n", + " 166422\n", + " 25\n", + " female\n", + " 1\n", + " 2\n", + " heterosexual\n", + " 1.91\n", + " 45.36\n", + " 1\n", + " False\n", + " False\n", + " 8\n", " \n", " \n", - " 798\n", - " HoopaHoopa Unbound\n", - " Psychic\n", - " Dark\n", - " 680\n", - " 80\n", - " 160\n", - " 60\n", - " 170\n", - " 130\n", - " 80\n", - " 6\n", + " 166423\n", + " 35\n", + " female\n", + " 5\n", + " 4\n", + " heterosexual\n", + " 1.60\n", + " 68.04\n", + " 4\n", " True\n", + " True\n", + " 6\n", " \n", " \n", - " 799\n", - " Volcanion\n", - " Fire\n", - " Water\n", - " 600\n", - " 80\n", - " 110\n", - " 120\n", - " 130\n", - " 90\n", - " 70\n", - " 6\n", - " True\n", + " 166424\n", + " 35\n", + " male\n", + " 7\n", + " 2\n", + " heterosexual\n", + " 1.75\n", + " 86.18\n", + " 3\n", + " False\n", + " False\n", + " 8\n", " \n", " \n", "\n", - "

800 rows × 12 columns

\n", + "

166425 rows × 11 columns

\n", "" ], "text/plain": [ - " name type subtype total hp attack defense \\\n", - "0 Bulbasaur Grass Poison 318 45 49 49 \n", - "1 Ivysaur Grass Poison 405 60 62 63 \n", - "2 Venusaur Grass Poison 525 80 82 83 \n", - "3 VenusaurMega Venusaur Grass Poison 625 80 100 123 \n", - "4 Charmander Fire NaN 309 39 52 43 \n", - ".. ... ... ... ... .. ... ... \n", - "795 Diancie Rock Fairy 600 50 100 150 \n", - "796 DiancieMega Diancie Rock Fairy 700 50 160 110 \n", - "797 HoopaHoopa Confined Psychic Ghost 600 80 110 60 \n", - "798 HoopaHoopa Unbound Psychic Dark 680 80 160 60 \n", - "799 Volcanion Fire Water 600 80 110 120 \n", + " age sex income education sexual_orientation height weight \\\n", + "0 55 female 5 2 other 1.55 83.01 \n", + "1 65 female 8 1 heterosexual 1.65 78.02 \n", + "2 35 female 8 4 heterosexual 1.65 77.11 \n", + "3 55 male 8 4 heterosexual 1.83 81.65 \n", + "4 55 female 8 4 heterosexual 1.80 76.66 \n", + "... ... ... ... ... ... ... ... \n", + "166420 45 female 8 3 heterosexual 1.63 86.18 \n", + "166421 25 male 7 2 heterosexual 1.78 86.18 \n", + "166422 25 female 1 2 heterosexual 1.91 45.36 \n", + "166423 35 female 5 4 heterosexual 1.60 68.04 \n", + "166424 35 male 7 2 heterosexual 1.75 86.18 \n", "\n", - " special_attack special_defense 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", + " health no_doctor exercise sleep \n", + "0 2 True True 7 \n", + "1 3 False False 8 \n", + "2 4 True True 7 \n", + "3 5 False True 8 \n", + "4 4 False True 8 \n", + "... ... ... ... ... \n", + "166420 1 False False 6 \n", + "166421 4 False True 6 \n", + "166422 1 False False 8 \n", + "166423 4 True True 6 \n", + "166424 3 False False 8 \n", "\n", - "[800 rows x 12 columns]" + "[166425 rows x 11 columns]" ] }, - "execution_count": 6, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pokemon" - ] - }, - { - "cell_type": "markdown", - "id": "ee8b0718-56f9-4fc8-bd35-fa0ccb445179", - "metadata": {}, - "source": [ - "OK, 800 Pokémon, with 12 columns for each. And you can see all the columns. Not all the data is shown in this preview, of course. If there were more columns than could be displayed, you could see them all by typing `pokemon.columns`. \n", - "\n", - "#### Your turn\n", - "\n", - "Now do the same for your data set, `people`." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "c9e5e4ec-b197-450c-ae2d-318006fa0a2f", - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'people' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mpeople\u001b[49m\u001b[38;5;241m.\u001b[39mcolumns\n", - "\u001b[0;31mNameError\u001b[0m: name 'people' is not defined" - ] - } - ], - "source": [ - "people.columns\n" + "people\n" ] }, { @@ -458,7 +428,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 25, "id": "9afca362-9edc-423c-981b-dc42107d5de0", "metadata": {}, "outputs": [ @@ -469,14 +439,14 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/tmp/ipykernel_1131/2599509385.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpeople\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeneration\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/tmp/ipykernel_1039/3985783049.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpeople\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeneration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/.cache/pypoetry/virtualenvs/lab-pokemon-MIddldub-py3.12/lib/python3.12/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 6295\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6296\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6297\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6298\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6299\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'generation'" ] } ], "source": [ - "people.generation()\n" + "people.generation\n" ] }, { @@ -489,7 +459,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 26, "id": "5fe580d0-5939-4152-9f8c-4c32d35a4479", "metadata": {}, "outputs": [ @@ -503,7 +473,7 @@ "dtype: float64" ] }, - "execution_count": 9, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -522,7 +492,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "id": "dc69ef53-70cd-4ae0-80e7-c9c8e28de76f", "metadata": {}, "outputs": [ @@ -532,7 +502,7 @@ "np.float64(0.08125)" ] }, - "execution_count": 28, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -561,7 +531,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 28, "id": "8fbcc766-8399-4f93-a6c8-e0607250a72a", "metadata": {}, "outputs": [ @@ -571,7 +541,7 @@ "np.float64(48.76603274748385)" ] }, - "execution_count": 31, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1223,10 +1193,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "bbbeeeef-3490-48f1-aadf-c39c31c6c41b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'high_speed' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[29], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mhigh_speed\u001b[49m\n", + "\u001b[0;31mNameError\u001b[0m: name 'high_speed' is not defined" + ] + } + ], "source": [ "high_speed" ] @@ -1251,7 +1233,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 30, "id": "198cb0c6-3f43-43c2-9eee-3939c12ea537", "metadata": {}, "outputs": [ @@ -1272,7 +1254,7 @@ "Name: no_doctor, Length: 166425, dtype: bool" ] }, - "execution_count": 41, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1299,20 +1281,30 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 31, "id": "8a8c1ad6-4c1e-4996-ab5e-5212dadb1851", "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'people' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mpeople\u001b[49m\u001b[38;5;241m.\u001b[39mhealth\n", - "\u001b[0;31mNameError\u001b[0m: name 'people' is not defined" - ] + "data": { + "text/plain": [ + "0 2\n", + "1 3\n", + "2 4\n", + "3 5\n", + "4 4\n", + " ..\n", + "166420 1\n", + "166421 4\n", + "166422 1\n", + "166423 4\n", + "166424 3\n", + "Name: health, Length: 166425, dtype: int64" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -1714,24 +1706,24 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 33, "id": "75c1ac4f-3914-4c0a-a156-2e084002df66", "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'people' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mpeople\u001b[49m\u001b[38;5;241m.\u001b[39msleep\n", - "\u001b[0;31mNameError\u001b[0m: name 'people' is not defined" - ] + "data": { + "text/plain": [ + "np.float64(7.068553402433529)" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "people.sleep\n" + "people.sleep.mean()\n", + "\n" ] }, { @@ -2114,12 +2106,55 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 35, "id": "3b268a30-42ff-4ab8-b2cd-c58a76121f9c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'histplot' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[35], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mhistplot\u001b[49m(data\u001b[38;5;241m=\u001b[39mpeople, x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mheight\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mNameError\u001b[0m: name 'histplot' is not defined" + ] + } + ], "source": [ - "# Your code here" + "histplot(data=people, x=\"height\")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "f145ae04-2796-4420-8d98-ce74d5bd4c83", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.histplot(data=pokemon, x=\"attack\")" ] }, {