Hi, I need to develop a query and plot a graph for two questions based on a database. I have attached files for questions, a reference sheet for tables and graphs, and the CSV file.

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"source": [
"import pandas as pd \n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"shark = pd.read_csv('SharkData.csv')"
]
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"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
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"output_type": "execute_result",
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" SharkSpeciesName NumberSpecimens Specimenid SharkLength \\\n",
"0 Smoothhound 3 1 104.81 \n",
"1 Smoothhound 3 1 104.81 \n",
"2 Smoothhound 3 2 100.00 \n",
"3 Smoothhound 3 2 100.00 \n",
"4 Smoothhound 3 3 62.00 \n",
"5 Smoothhound 3 3 62.00 \n",
"6 Spiny 3 1 92.00 \n",
"7 Spiny 3 1 92.00 \n",
"8 Spiny 3 2 85.00 \n",
"9 Spiny 3 2 85.00 \n",
"10 Spiny 3 4 97.00 \n",
"11 Spiny 3 4 97.00 \n",
"12 Bamboo 3 1 88.40 \n",
"13 Bamboo 3 1 88.40 \n",
"14 Bamboo 3 2 77.80 \n",
"15 Bamboo 3 2 77.80 \n",
"16 Bamboo 3 3 84.50 \n",
"17 Bamboo 3 3 84.50 \n",
"18 Sandbar 3 1 68.40 \n",
"19 Sandbar 3 1 68.40 \n",
"20 Sandbar 3 2 54.10 \n",
"21 Sandbar 3 2 54.10 \n",
"22 Sandbar 3 3 56.00 \n",
"23 Sandbar 3 3 56.00 \n",
"24 Mako 3 1 175.00 \n",
"25 Mako 3 1 175.00 \n",
"26 Mako 3 2 194.00 \n",
"27 Mako 3 2 194.00 \n",
"28 Mako 3 3 185.00 \n",
"29 Mako 3 3 185.00 \n",
"30 Skate 3 1 46.90 \n",
"31 Skate 3 1 46.90 \n",
"32 Skate 3 2 44.37 \n",
"33 Skate 3 2 44.37 \n",
"34 Skate 3 3 45.33 \n",
"35 Skate 3 3 45.33 \n",
"36 Thresher 1 2 225.00 \n",
"37 Thresher 1 2 225.00 \n",
"\n",
" MuscleName MuscleMass MuscleForce MuscleCrossSectionalArea \\\n",
"0 Smoothhound 1 PO 1.286 1.925 0.325 \n",
"1 Smoothhound 1 QM 4.753 36.127 2.199 \n",
"2 Smoothhound 2 PO 1.469 2.685 0.444 \n",
"3 Smoothhound 2 QM 5.978 34.755 2.682 \n",
"4 Smoothhound 3 PO 0.460 1.348 0.241 \n",
"5 Smoothhound 3 QM 1.706 17.295 1.345 \n",
"6 Spiny 1 PO 1.150 5.520 0.984 \n",
"7 Spiny 1 QM 6.681 31.670 3.891 \n",
"8 Spiny 2 PO 0.867 4.108 0.493 \n",
"9 Spiny 2 QM 5.001 23.188 2.587 \n",
"10 Spiny 4 PO 1.119 2.022 0.302 \n",
"11 Spiny 4 QM 7.788 38.920 3.746 \n",
"12 Bamboo 1 PO 3.576 29.477 1.223 \n",
"13 Bamboo 1 QM 6.513 34.499 2.844 \n",
"14 Bamboo 2 PO 3.551 45.326 1.878 \n",
"15 Bamboo 2 QM 7.022 36.430 2.938 \n",
"16 Bamboo 3 PO 0.460 NaN 1.693 \n",
"17 Bamboo 3 QM 7.128 NaN 3.945 \n",
"18 Sandbar 1 PO 1.886 4.232 0.438 \n",
"19 Sandbar 1 QM 3.986 17.845 1.988 \n",
"20 Sandbar 2 PO 0.859 4.270 0.328 \n",
"21 Sandbar 2 QM 1.567 9.029 0.968 \n",
"22 Sandbar 3 PO 0.640 2.618 0.213 \n",
"23 Sandbar 3 QM 1.232 6.197 0.756 \n",
"24 Mako 1 PO 0.710 6.659 0.683 \n",
"25 Mako 1 QM 108.750 165.931 13.450 \n",
"26 Mako 2 PO 5.201 5.385 0.543 \n",
"27 Mako 2 QM 86.197 148.462 14.242 \n",
"28 Mako 3 PO 13.772 12.956 1.468 \n",
"29 Mako 3 QM 188.000 216.035 23.442 \n",
"30 Skate 1 PO 0.346 3.062 0.260 \n",
"31 Skate 1 QM 5.753 57.628 5.420 \n",
"32 Skate 2 PO 0.085 0.978 0.083 \n",
"33 Skate 2 QM 1.660 14.173 1.555 \n",
"34 Skate 3 PO 0.137 1.740 0.153 \n",
"35 Skate 3 QM 3.768 26.207 27.540 \n",
"36 Thresher 2 PO 6.006 18.475 1.500 \n",
"37 Thresher 2 QM 43.494 144.641 10.753 \n",
"\n",
" SharkBiteForce \n",
"0 38.052 \n",
"1 38.052 \n",
"2 37.440 \n",
"3 37.440 \n",
"4 18.643 \n",
"5 18.643 \n",
"6 37.222 \n",
"7 37.222 \n",
"8 27.296 \n",
"9 27.296 \n",
"10 40.942 \n",
"11 40.942 \n",
"12 39.236 \n",
"13 39.236 \n",
"14 39.368 \n",
"15 39.368 \n",
"16 NaN \n",
"17 NaN \n",
"18 22.077 \n",
"19 22.077 \n",
"20 13.300 \n",
"21 13.300 \n",
"22 8.814 \n",
"23 8.814 \n",
"24 172.590 \n",
"25 172.590 \n",
"26 153.847 \n",
"27 153.847 \n",
"28 228.991 \n",
"29 228.991 \n",
"30 60.690 \n",
"31 60.690 \n",
"32 15.151 \n",
"33 15.151 \n",
"34 27.947 \n",
"35 27.947 \n",
"36 163.116 \n",
"37 163.116 "
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"metadata": {},
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"source": [
"shark"
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{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"i = shark[\"SharkBiteForce\"].value_counts()\n",
"count = 0"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"subset_df = shark[shark[\"SharkBiteForce\"] > 100]\n",
"column_count = subset_df.count()"
]
},
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"cell_type": "code",
"execution_count": 7,
"metadata": {},
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"output_type": "execute_result",
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"text/plain": [
"8"
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"metadata": {},
"execution_count": 7
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"source": [
"column_count[\"SharkBiteForce\"]"
]
},
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"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
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"[]"
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"metadata": {},
"execution_count": 8
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"output_type": "display_data",
"data": {
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