diff --git a/lab_pokemon.ipynb b/lab_pokemon.ipynb index 01594f2..b4dc7c3 100644 --- a/lab_pokemon.ipynb +++ b/lab_pokemon.ipynb @@ -16,17 +16,19 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "id": "5923b0d7-c0e0-48fa-b765-4aa6002c2d4f", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { "text/plain": [ - "3" + "22" ] }, - "execution_count": 1, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -37,7 +39,7 @@ "# printed below the cell. \n", "# Then try changing the Python code and re-run it.\n", "\n", - "1+1+1" + "20+1+1" ] }, { @@ -52,32 +54,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "id": "0e2a2927-f6d1-4b13-97ae-ff97416723e9", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "10" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Cell A\n", - "x = 10\n", - "x" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "69dd7908-b213-4d0f-8016-e46a4a491961", - "metadata": {}, "outputs": [ { "data": { @@ -85,7 +64,30 @@ "20" ] }, - "execution_count": 3, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Cell A\n", + "x = 20\n", + "x" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "69dd7908-b213-4d0f-8016-e46a4a491961", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "40" + ] + }, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -130,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "id": "ba09a0f8-27d9-456f-aeff-3980e3362d5b", "metadata": {}, "outputs": [], @@ -140,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "id": "a29d508a-2d9a-4d62-9ff6-7a0ecfd5eba4", "metadata": {}, "outputs": [], @@ -163,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "id": "579d8dda-ca39-48b1-8819-b17651029729", "metadata": {}, "outputs": [ @@ -403,7 +405,7 @@ "[800 rows x 12 columns]" ] }, - "execution_count": 6, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -426,12 +428,241 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "id": "c9e5e4ec-b197-450c-ae2d-318006fa0a2f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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agesexincomeeducationsexual_orientationheightweighthealthno_doctorexercisesleep
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" + ], + "text/plain": [ + " 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", + " 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", + "[166425 rows x 11 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here" + "people" ] }, { @@ -448,30 +679,35 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 14, "id": "9afca362-9edc-423c-981b-dc42107d5de0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "generation\n", - "1 166\n", - "5 165\n", - "3 160\n", - "4 121\n", - "2 106\n", - "6 82\n", - "Name: count, dtype: int64" + "speed\n", + "50 46\n", + "60 44\n", + "70 37\n", + "65 36\n", + "30 35\n", + " ..\n", + "39 1\n", + "24 1\n", + "82 1\n", + "113 1\n", + "123 1\n", + "Name: count, Length: 108, dtype: int64" ] }, - "execution_count": 8, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pokemon.generation.value_counts()" + "pokemon.speed.value_counts()" ] }, { @@ -484,7 +720,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 15, "id": "5fe580d0-5939-4152-9f8c-4c32d35a4479", "metadata": {}, "outputs": [ @@ -498,7 +734,7 @@ "dtype: float64" ] }, - "execution_count": 9, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -517,7 +753,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 16, "id": "dc69ef53-70cd-4ae0-80e7-c9c8e28de76f", "metadata": {}, "outputs": [ @@ -527,7 +763,7 @@ "np.float64(0.08125)" ] }, - "execution_count": 10, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -556,12 +792,23 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 19, "id": "8fbcc766-8399-4f93-a6c8-e0607250a72a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(48.76603274748385)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here" + "people.age.mean()" ] }, { @@ -574,12 +821,25 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 23, "id": "b7f910c8-3d40-49ae-b270-678734c04100", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "height 1.705082\n", + "weight 83.053588\n", + "dtype: float64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here" + "people[[\"height\",\"weight\"]].mean()" ] }, { @@ -592,12 +852,23 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 25, "id": "f3891188-a85f-4089-8388-d4d81c7438ad", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.7858014120474688)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here" + "people.exercise.mean()" ] }, { @@ -615,7 +886,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 26, "id": "12c0c6c9-c07b-4183-82f6-5e346c74aac9", "metadata": {}, "outputs": [ @@ -855,7 +1126,7 @@ "[65 rows x 12 columns]" ] }, - "execution_count": 14, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -875,7 +1146,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 27, "id": "5d089acf-7b76-4f91-8803-42a4a9a11e3e", "metadata": {}, "outputs": [ @@ -896,7 +1167,7 @@ "Name: type, Length: 800, dtype: bool" ] }, - 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" + ], + "text/plain": [ + " age sex income education sexual_orientation height weight \\\n", + "2 35 female 8 4 heterosexual 1.65 77.11 \n", + "24 35 male 8 3 heterosexual 1.73 94.35 \n", + "50 35 female 4 2 heterosexual 1.78 81.65 \n", + "66 45 female 6 4 heterosexual 1.57 72.57 \n", + "73 65 male 7 4 heterosexual 1.65 73.48 \n", + "... ... ... ... ... ... ... ... \n", + "166407 18 male 5 2 heterosexual 1.68 68.04 \n", + "166409 25 male 6 2 heterosexual 1.57 58.51 \n", + "166414 55 female 8 3 heterosexual 1.63 88.45 \n", + "166416 65 female 5 2 heterosexual 1.50 55.34 \n", + "166423 35 female 5 4 heterosexual 1.60 68.04 \n", + "\n", + " health no_doctor exercise sleep \n", + "2 4 True True 7 \n", + "24 4 True False 8 \n", + "50 4 True False 10 \n", + "66 4 True True 7 \n", + "73 3 True True 7 \n", + "... ... ... ... ... \n", + "166407 3 True True 8 \n", + "166409 4 True False 7 \n", + "166414 3 True False 6 \n", + "166416 3 True False 6 \n", + "166423 4 True True 6 \n", + "\n", + "[9860 rows x 11 columns]" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# YOUR CODE HERE" + "no_doctor = people[(people.no_doctor) & (people.health >= 3)]\n", + "no_doctor" ] }, { @@ -1643,12 +2374,242 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 42, "id": "315682ae-7d54-4d78-9a63-d23c83ba1576", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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agesexincomeeducationsexual_orientationheightweighthealthno_doctorexercisesleep
235female84heterosexual1.6577.114TrueTrue7
6645female64heterosexual1.5772.574TrueTrue7
13555female64heterosexual1.7081.654TrueTrue7
14665female54heterosexual1.5572.575TrueTrue7
25965female44heterosexual1.5756.703TrueTrue6
....................................
16612135female84heterosexual1.65136.084TrueTrue5
16621955female54other1.5259.873TrueTrue5
16627225female84heterosexual1.5298.883TrueTrue8
16638145female54heterosexual1.5249.905TrueTrue6
16642335female54heterosexual1.6068.044TrueTrue6
\n", + "

2321 rows × 11 columns

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" + ], + "text/plain": [ + " age sex income education sexual_orientation height weight \\\n", + "2 35 female 8 4 heterosexual 1.65 77.11 \n", + "66 45 female 6 4 heterosexual 1.57 72.57 \n", + "135 55 female 6 4 heterosexual 1.70 81.65 \n", + "146 65 female 5 4 heterosexual 1.55 72.57 \n", + "259 65 female 4 4 heterosexual 1.57 56.70 \n", + "... ... ... ... ... ... ... ... \n", + "166121 35 female 8 4 heterosexual 1.65 136.08 \n", + "166219 55 female 5 4 other 1.52 59.87 \n", + "166272 25 female 8 4 heterosexual 1.52 98.88 \n", + "166381 45 female 5 4 heterosexual 1.52 49.90 \n", + "166423 35 female 5 4 heterosexual 1.60 68.04 \n", + "\n", + " health no_doctor exercise sleep \n", + "2 4 True True 7 \n", + "66 4 True True 7 \n", + "135 4 True True 7 \n", + "146 5 True True 7 \n", + "259 3 True True 6 \n", + "... ... ... ... ... \n", + "166121 4 True True 5 \n", + "166219 3 True True 5 \n", + "166272 3 True True 8 \n", + "166381 5 True True 6 \n", + "166423 4 True True 6 \n", + "\n", + "[2321 rows x 11 columns]" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# YOUR CODE HERE" + "no_doctor = people[(people.no_doctor) & (people.sex == \"female\") & (people.education ==4)]\n", + "no_doctor" ] }, { @@ -1667,7 +2628,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 43, "id": "069ea0ab-eff6-4985-9f46-db956fe1df91", "metadata": {}, "outputs": [ @@ -1696,7 +2657,7 @@ "Name: speed, dtype: float64" ] }, - "execution_count": 21, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -1715,7 +2676,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 44, "id": "5c420c0e-b5d2-49ae-ab98-3305ee076169", "metadata": {}, "outputs": [ @@ -1887,7 +2848,7 @@ "Dragon 83.312500 112.125000 86.375000" ] }, - "execution_count": 22, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1907,7 +2868,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 45, "id": "444a580d-e70c-48a1-bf87-77f98b8c9f85", "metadata": {}, "outputs": [ @@ -1934,7 +2895,7 @@ "Name: legendary, dtype: float64" ] }, - "execution_count": 23, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -1967,12 +2928,28 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 50, "id": "75c1ac4f-3914-4c0a-a156-2e084002df66", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ValueError", + "evalue": "'income' is both an index level and a column label, which is ambiguous.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/_7/n25613695311w6zcyvs4lqdh0000gn/T/ipykernel_71505/1155048095.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mdiff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpeople\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"income\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdiff\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"income\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"sleep\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"income\"\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~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-7YeDbseC-py3.13/lib/python3.13/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, by, axis, ascending, inplace, kind, na_position, ignore_index, key)\u001b[0m\n\u001b[1;32m 7185\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7186\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\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 7187\u001b[0m \u001b[0;31m# len(by) == 1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 7189\u001b[0;31m \u001b[0mk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label_or_level_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7191\u001b[0m \u001b[0;31m# need to rewrap column in Series to apply key function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7192\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-7YeDbseC-py3.13/lib/python3.13/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1902\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1903\u001b[0m \u001b[0mother_axes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0max\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0max\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_AXIS_LEN\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1904\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1905\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_label_reference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\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[0;32m-> 1906\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_label_or_level_ambiguity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mlevel_type\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m level and \u001b[0m\u001b[0;34m\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1866\u001b[0m \u001b[0;34mf\"\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mlabel_article\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m \u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mlabel_type\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m label, which is ambiguous.\u001b[0m\u001b[0;34m\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1867\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1868\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mValueError\u001b[0m: 'income' is both an index level and a column label, which is ambiguous." + ] + } + ], "source": [ - "# YOUR CODE HERE" + "diff = people.groupby(\"income\")\n", + "diff[[\"income\", \"sleep\"]].mean().sort_values(\"income\")" ] }, { @@ -1985,7 +2962,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 51, "id": "d46df8a1-bbc2-45a4-9be1-cee1858cbf21", "metadata": {}, "outputs": [], @@ -2007,10 +2984,18 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 52, "id": "b1e06e4f-6b9e-42af-a27c-dbb525a259ce", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Matplotlib is building the font cache; this may take a moment.\n" + ] + } + ], "source": [ "import seaborn as sns\n", "sns.set_theme()" @@ -2030,7 +3015,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 53, "id": "5ce066fe-f81d-4b78-a394-c5c2f4dc9f46", "metadata": {}, "outputs": [ @@ -2040,7 +3025,7 @@ "" ] }, - "execution_count": 27, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, @@ -2061,7 +3046,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 54, "id": "bceb253b-ef4f-4aa2-aef4-cab2b3ca6d59", "metadata": {}, "outputs": [ @@ -2071,7 +3056,7 @@ "" ] }, - "execution_count": 28, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" }, @@ -2102,7 +3087,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 55, "id": "92be1ad0-12bb-49f0-a3f6-85fcfd98e943", "metadata": {}, "outputs": [ @@ -2112,7 +3097,7 @@ "" ] }, - "execution_count": 29, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" }, @@ -2141,7 +3126,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 56, "id": "17f1c289-5990-4420-bfcb-e50eee0b8af6", "metadata": {}, "outputs": [ @@ -2151,7 +3136,7 @@ "" ] }, - "execution_count": 30, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" }, @@ -2182,7 +3167,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 57, "id": "3c8e9f47-9aea-4bf0-a628-7aa1a66a8eee", "metadata": {}, "outputs": [], @@ -2194,7 +3179,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 58, "id": "22f78bec-3d18-4133-ba9f-6595d7181ded", "metadata": {}, "outputs": [ @@ -2204,7 +3189,7 @@ "" ] }, - "execution_count": 32, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" }, @@ -2235,7 +3220,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 59, "id": "444d9832-bd57-4238-9ea4-5ee898847170", "metadata": {}, "outputs": [ @@ -2245,7 +3230,7 @@ "" ] }, - "execution_count": 33, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" }, @@ -2274,7 +3259,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 60, "id": "86f9747b-00a3-407f-9b73-0bce40bac50d", "metadata": {}, "outputs": [ @@ -2284,7 +3269,7 @@ "" ] }, - "execution_count": 34, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" }, @@ -2313,17 +3298,17 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 61, "id": "7385237c-6a5c-4041-af46-559d6d84d1fa", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 35, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" }, @@ -2355,12 +3340,33 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 75, "id": "3b268a30-42ff-4ab8-b2cd-c58a76121f9c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Your code here" + "sns.countplot(data=people, x=\"height\")" ] }, { @@ -2373,12 +3379,33 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 79, "id": "ee30c851-14b1-4901-9182-4304d54d53a6", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Your code here" + "sns.countplot(data=people, x=\"age\", hue=\"sleep\")" ] }, { @@ -2391,7 +3418,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 64, "id": "13eeecd8-2518-4ed9-aac5-727a96b5bf80", "metadata": {}, "outputs": [], @@ -2409,7 +3436,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 65, "id": "4ee2eb69-2f9a-42e7-b5d3-9499631bfd06", "metadata": {}, "outputs": [], @@ -2427,7 +3454,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 66, "id": "d7e02da8-beab-40e7-95d0-74a5c2bc838e", "metadata": {}, "outputs": [], @@ -2445,7 +3472,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 67, "id": "b00dd7d6-226b-469c-86d8-b71b328aa576", "metadata": {}, "outputs": [], @@ -2473,12 +3500,20 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 80, "id": "6f934273-b829-4bc2-a7f4-a27a3fc44a99", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Object `men` not found.\n" + ] + } + ], "source": [ - "# Your code here. Feel free to add new text cells and code cells as necessary." + "Do women sleep more than men?" ] }, { @@ -2488,6 +3523,54 @@ "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c9e2397f-9228-450d-96a7-14ed2b6540b0", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ffb37d7c-5ef0-4643-a4bb-b7a0b798cbd6", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc529381-49b3-4e1e-9a2e-f09e4a89c440", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "454213a7-8f17-4de4-8e1e-cfcc48d1aa06", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "013d16b0-9ffe-494d-a9ae-7d2ab3d218eb", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5f72022d-cbfc-4046-8752-a2d89f85e53c", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -2506,7 +3589,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.5" + "version": "3.13.7" } }, "nbformat": 4,