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Num py exercise #65

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e4899d9
Completed NumPy Module 4 Exercises
Feb 9, 2026
72d548c
Merge pull request #1 from annasosnovske/NumPyModule4
annasosnovske Feb 9, 2026
f934a79
completed exercises
Feb 9, 2026
dd4b965
Merge pull request #2 from annasosnovske/NumPyExercise5
annasosnovske Feb 9, 2026
d0a673c
Completed module 7 exercises
Feb 10, 2026
c277c10
Merge pull request #3 from annasosnovske/NumPyModule7
annasosnovske Feb 10, 2026
543af94
Merge branch 'UUHydroInformatics:main' into main
annasosnovske Feb 10, 2026
7576afc
Worked through Pandas exercise 1
Feb 11, 2026
d7c1e8b
more work on exercise
Feb 13, 2026
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1 change: 1 addition & 0 deletions Introduction-to-Python
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Submodule Introduction-to-Python added at eda4e5
2 changes: 1 addition & 1 deletion NumPy/3. Inspect an Array.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -240,7 +240,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "p310env",
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
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61 changes: 60 additions & 1 deletion NumPy/4. Sampling Methods.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -417,6 +417,65 @@
"Next [Module](./5.%20Math%20Functions.ipynb)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0.77395605 0.43887844 0.85859792 0.69736803 0.09417735 0.97562235\n",
" 0.7611397 0.78606431 0.12811363 0.45038594 0.37079802 0.92676499\n",
" 0.64386512 0.82276161 0.4434142 0.22723872 0.55458479 0.06381726\n",
" 0.82763117 0.6316644 ]\n",
" [0.75808774 0.35452597 0.97069802 0.89312112 0.7783835 0.19463871\n",
" 0.466721 0.04380377 0.15428949 0.68304895 0.74476216 0.96750973\n",
" 0.32582536 0.37045971 0.46955581 0.18947136 0.12992151 0.47570493\n",
" 0.22690935 0.66981399]\n",
" [0.43715192 0.8326782 0.7002651 0.31236664 0.8322598 0.80476436\n",
" 0.38747838 0.2883281 0.6824955 0.13975248 0.1999082 0.00736227\n",
" 0.78692438 0.66485086 0.70516538 0.78072903 0.45891578 0.5687412\n",
" 0.139797 0.11453007]\n",
" [0.66840296 0.47109621 0.56523611 0.76499886 0.63471832 0.5535794\n",
" 0.55920716 0.3039501 0.03081783 0.43671739 0.21458467 0.40852864\n",
" 0.85340307 0.23393949 0.05830274 0.28138389 0.29359376 0.66191651\n",
" 0.55703215 0.78389821]\n",
" [0.66431354 0.40638686 0.81402038 0.16697292 0.02271207 0.09004786\n",
" 0.72235935 0.46187723 0.16127178 0.50104478 0.1523121 0.69632038\n",
" 0.44615628 0.38102123 0.30151209 0.63028259 0.36181261 0.08764992\n",
" 0.1180059 0.96189766]\n",
" [0.90858069 0.69970713 0.26586996 0.96917638 0.7787509 0.71689019\n",
" 0.4493615 0.27224156 0.09639096 0.9026024 0.45577629 0.20236336\n",
" 0.30595662 0.57921957 0.17677278 0.85661428 0.75851953 0.71946296\n",
" 0.43209304 0.62730884]\n",
" [0.58409797 0.6498466 0.08444432 0.4158074 0.04161417 0.49399082\n",
" 0.32986121 0.14452419 0.10340297 0.58764457 0.17059297 0.92512012\n",
" 0.58106114 0.3468698 0.59091549 0.02280387 0.95855921 0.48230344\n",
" 0.78273523 0.08273 ]\n",
" [0.48665833 0.49070699 0.93782645 0.57172805 0.4734894 0.26697566\n",
" 0.331569 0.5206724 0.43891146 0.02161208 0.82629192 0.89616077\n",
" 0.14024909 0.55403614 0.10857574 0.67224009 0.28123378 0.65942263\n",
" 0.72699461 0.76864749]\n",
" [0.10774095 0.91601185 0.23021399 0.03741256 0.55485247 0.37092228\n",
" 0.82978974 0.80825147 0.31713889 0.9528994 0.29091784 0.51505713\n",
" 0.25596509 0.93604357 0.16460782 0.04491062 0.43509706 0.99237556\n",
" 0.89167727 0.74860802]\n",
" [0.89079249 0.89344664 0.51885836 0.31592905 0.77201243 0.66166126\n",
" 0.37365773 0.09446667 0.74678961 0.26246052 0.93681315 0.24097058\n",
" 0.12275793 0.83111267 0.15328432 0.17926831 0.59938279 0.87456204\n",
" 0.19643467 0.31032367]]\n"
]
}
],
"source": [
"import numpy as np\n",
"rng = np.random.default_rng(42) # choose any integer seed\n",
"arr = rng.random((10, 20)) \n",
"print(arr)"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand All @@ -427,7 +486,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "p310env",
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
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94 changes: 84 additions & 10 deletions NumPy/5. Math Functions.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -854,10 +854,34 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-0.5 0.91421356 1.5 0.91421356 -0.5 ]\n",
"[-0.34657359 -0.04484554 0.20273255 -0.04484554 -0.34657359]\n",
"[False False True False False]\n"
]
}
],
"source": [
"import numpy as np\n",
"arr = np.array([0, np.pi/4, np.pi/2, 3*np.pi/4, np.pi]) \n",
"math = np.sin(arr) * 2 \n",
"math2 = math - 0.5\n",
"print(math2)\n",
"math3= np.abs(math2)\n",
"math4 = np.sqrt(math3)\n",
"math5= np.log(math4)\n",
"print(math5)\n",
"mask= math5 > 0\n",
"print(mask)\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
Expand All @@ -884,10 +908,36 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[22.66 19.98 25.62 18.58]\n",
"[25.4 26.1 24.9 25.2 26.5]\n",
"[1 2 4 2]\n",
"8.3\n"
]
}
],
"source": [
"import numpy as np\n",
"temps = np.array([\n",
" [22.1, 23.5, 22.8, 21.9, 23.0],\n",
" [19.5, 20.1, 20.5, 19.8, 20.0],\n",
" [25.4, 26.1, 24.9, 25.2, 26.5],\n",
" [18.2, 18.5, 19.0, 18.8, 18.4]])\n",
"mean_axis1 = temps.mean(axis=1) \n",
"max_axis0 = temps.max(axis=0) \n",
"print(mean_axis1)\n",
"print(max_axis0)\n",
"outliers = np.argmax(temps, axis=1)\n",
"print(outliers)\n",
"range = temps.max() - temps.min()\n",
"print(range)"
]
},
{
"cell_type": "markdown",
Expand All @@ -913,10 +963,34 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[11.2 14.7 13.9 19.1 23.2 21.7 22.2]\n",
"14.5\n",
"[False False False True True True True]\n",
"4\n"
]
}
],
"source": [
"import numpy as np\n",
"inflow = np.array([1.2, 3.5, -.8, 5.2, 4.1, -1.5, 0.5])\n",
"initial = 10\n",
"result = initial + np.cumsum(inflow)\n",
"print(result)\n",
"volume = inflow[inflow > 0].sum()\n",
"print(volume)\n",
"flood = 18\n",
"mask = (initial + np.cumsum(inflow)) > flood\n",
"print(mask) \n",
"peak = np.argmax(result)\n",
"print(peak)"
]
},
{
"cell_type": "markdown",
Expand All @@ -928,7 +1002,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "p310env",
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
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83 changes: 73 additions & 10 deletions NumPy/7. Slicing & Indexing.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -540,10 +540,26 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"data": {
"text/plain": [
"(24, 3)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr1 = np.linspace(1.5,5.0,72)\n",
"arr = arr1.reshape(3,24)\n",
"arr2 = arr.transpose(1, 0)\n",
"arr2.shape"
]
},
{
"cell_type": "markdown",
Expand Down Expand Up @@ -571,10 +587,36 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"52\n",
"[[32 35 30]\n",
" [40 42 38]\n",
" [55 58 52]]\n",
"[28 35 48 58 68]\n"
]
}
],
"source": [
"moisture_grid = np.array([\n",
" [32, 35, 30, 28, 25],\n",
" [40, 42, 38, 35, 30],\n",
" [55, 58, 52, 48, 42],\n",
" [60, 65, 62, 58, 55],\n",
" [70, 72, 70, 68, 65]\n",
"])\n",
"value = moisture_grid[2,2]\n",
"print(value)\n",
"subbasin = moisture_grid[0:3, 0:3]\n",
"print(subbasin)\n",
"column4 = moisture_grid[:, 3]\n",
"print(column4)"
]
},
{
"cell_type": "markdown",
Expand All @@ -597,10 +639,31 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0. 0. 0.4875 1.755 0.2028 0. 0.7371 1.2168 0. 0.0819]\n",
"[45. 31.2]\n",
"(array([0, 1, 5, 8]),)\n",
"[12.5 45. 18.9]\n"
]
}
],
"source": [
"rain = np.array([0, 0, 12.5, 45.0, 5.2, 0, 18.9, 31.2, 0, 2.1])\n",
"inrain = rain * 0.039 \n",
"print(inrain)\n",
"heavy = rain[rain>20]\n",
"print(heavy)\n",
"norain= np.where(rain==0)\n",
"print(norain)\n",
"accurate=[2,3,6]\n",
"print(rain[accurate])"
]
},
{
"cell_type": "markdown",
Expand All @@ -619,7 +682,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "p310env",
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
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