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@deepanshululla
Created November 18, 2018 17:23
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Data in populations.txt describes the populations of hares and lynxes (and carrots) in northern Canada during 20 years."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"#load data into numpy array object\n",
"data = np.loadtxt('populations.txt')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1900., 30000., 4000., 48300.],\n",
" [ 1901., 47200., 6100., 48200.],\n",
" [ 1902., 70200., 9800., 41500.],\n",
" [ 1903., 77400., 35200., 38200.],\n",
" [ 1904., 36300., 59400., 40600.],\n",
" [ 1905., 20600., 41700., 39800.],\n",
" [ 1906., 18100., 19000., 38600.],\n",
" [ 1907., 21400., 13000., 42300.],\n",
" [ 1908., 22000., 8300., 44500.],\n",
" [ 1909., 25400., 9100., 42100.],\n",
" [ 1910., 27100., 7400., 46000.],\n",
" [ 1911., 40300., 8000., 46800.],\n",
" [ 1912., 57000., 12300., 43800.],\n",
" [ 1913., 76600., 19500., 40900.],\n",
" [ 1914., 52300., 45700., 39400.],\n",
" [ 1915., 19500., 51100., 39000.],\n",
" [ 1916., 11200., 29700., 36700.],\n",
" [ 1917., 7600., 15800., 41800.],\n",
" [ 1918., 14600., 9700., 43300.],\n",
" [ 1919., 16200., 10100., 41300.],\n",
" [ 1920., 24700., 8600., 47300.]])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1900. 1901. 1902. 1903. 1904. 1905. 1906. 1907. 1908. 1909. 1910. 1911.\n",
" 1912. 1913. 1914. 1915. 1916. 1917. 1918. 1919. 1920.]\n"
]
}
],
"source": [
"year, hares, lynxes, carrots = data.T #columns to variables\n",
"print(year)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Average population of Hares,lynxes and carrots over all years**"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hares mean: 34080.95238095238\n",
"lynxes mean: 20166.666666666668\n",
"carrots mean: 42400.0\n"
]
}
],
"source": [
"print(\"hares mean:\",hares.mean())\n",
"print(\"lynxes mean:\",lynxes.mean())\n",
"print(\"carrots mean:\",carrots.mean())"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hares median: 25400.0\n",
"lynxes median: 12300.0\n",
"carrots median: 41800.0\n"
]
}
],
"source": [
"print(\"hares median:\",np.median(hares))\n",
"print(\"lynxes median:\",np.median(lynxes))\n",
"print(\"carrots median:\",np.median(carrots))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([2, 2, 0, 0, 1, 1, 2, 2, 2, 2, 2, 2, 0, 0, 0, 1, 2, 2, 2, 2, 2])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#which species has the highest population each year?\n",
"populations = data[:, 1:]\n",
"np.argmax(populations, axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(year,hares,'r-',label='hares')\n",
"plt.plot(year,lynxes,'b-',label='lynxes')\n",
"plt.plot(year,carrots,'g-',label='carrots')\n",
"plt.ylabel('Population of Hares')\n",
"plt.xlabel('Time')\n",
"plt.legend()\n",
"plt.grid()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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