diff --git a/Introduction-to-Python b/Introduction-to-Python new file mode 160000 index 0000000..eda4e59 --- /dev/null +++ b/Introduction-to-Python @@ -0,0 +1 @@ +Subproject commit eda4e59af2fe84c073f3b35d84674b6cd9aa7a68 diff --git a/NumPy/3. Inspect an Array.ipynb b/NumPy/3. Inspect an Array.ipynb index 8ffc3e0..e8deec5 100644 --- a/NumPy/3. Inspect an Array.ipynb +++ b/NumPy/3. Inspect an Array.ipynb @@ -240,7 +240,7 @@ ], "metadata": { "kernelspec": { - "display_name": "p310env", + "display_name": "Python 3", "language": "python", "name": "python3" }, diff --git a/NumPy/4. Sampling Methods.ipynb b/NumPy/4. Sampling Methods.ipynb index 0d19af7..c4db4cc 100644 --- a/NumPy/4. Sampling Methods.ipynb +++ b/NumPy/4. Sampling Methods.ipynb @@ -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, @@ -427,7 +486,7 @@ ], "metadata": { "kernelspec": { - "display_name": "p310env", + "display_name": "Python 3", "language": "python", "name": "python3" }, diff --git a/NumPy/5. Math Functions.ipynb b/NumPy/5. Math Functions.ipynb index 55b1106..7b9accd 100644 --- a/NumPy/5. Math Functions.ipynb +++ b/NumPy/5. Math Functions.ipynb @@ -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", @@ -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", @@ -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", @@ -928,7 +1002,7 @@ ], "metadata": { "kernelspec": { - "display_name": "p310env", + "display_name": "Python 3", "language": "python", "name": "python3" }, diff --git a/NumPy/7. Slicing & Indexing.ipynb b/NumPy/7. Slicing & Indexing.ipynb index 87c4019..4cabead 100644 --- a/NumPy/7. Slicing & Indexing.ipynb +++ b/NumPy/7. Slicing & Indexing.ipynb @@ -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", @@ -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", @@ -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", @@ -619,7 +682,7 @@ ], "metadata": { "kernelspec": { - "display_name": "p310env", + "display_name": "Python 3", "language": "python", "name": "python3" }, diff --git a/Pandas/PandasExercise1.ipynb b/Pandas/PandasExercise1.ipynb index b13c275..dd637e8 100644 --- a/Pandas/PandasExercise1.ipynb +++ b/Pandas/PandasExercise1.ipynb @@ -1,5 +1,31 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "8be1e28c", + "metadata": {}, + "source": [ + "The \"Broken\" Weather Station\n", + "Background: A mountain weather station has been offline for months and recently sent a burst of messy data. Before we can use the data and connect it to streamflow, it must be repaired and the Pandas toolkit can support this.\n", + "\n", + "Task 1: The Raw Feed\n", + "Load the .csv of rainfall values and convert into a Pandas DataFrame named Rainfall_mm. The data can be found here:\n", + "\n", + "data/snotel_rainfall_data.csv\n", + "\n", + "Check your work: Use .head() and .describe(). Does the data look right?" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "992a43f5", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, { "cell_type": "markdown", "id": "de778329", @@ -19,11 +45,250 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "2e231547", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Date Precip_in\n", + "0 2024-01-01 0.14028\n", + "1 2024-01-02 0.659018\n", + "2 2024-01-03 0.0\n", + "3 2024-01-04 0.0\n", + "4 2024-01-05 0.375729\n", + "5 2024-01-06 0.0\n", + "6 2024-01-07 1.095458\n", + "7 2024-01-08 0.0\n", + "8 2024-01-09 0.0\n", + "9 2024-01-10 0.186576\n", + "13 2024-01-14 0.0\n", + "14 2024-01-15 0.385756\n", + "15 2024-01-16 0.0\n", + "16 2024-01-16 0.0\n", + "17 2024-01-17 3.380971\n", + "18 2024-01-18 0.0\n", + "19 2024-01-19 0.532611\n", + "20 2024-01-20 0.0\n", + "21 2024-01-21 0.0\n", + "22 2024-01-22 0.0" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# for the rainfall replace values less than 0 and greater than 20 with NaN\n", + "Rainfall_mm_cleaned = Rainfall_mm.copy()\n", + "Rainfall_mm_cleaned['Precip_in'] = Rainfall_mm_cleaned['Precip_in'].apply(lambda x: x if 0 <= x <= 20 else pd.NA)\n", + "# remove NaN values from the rainfall data calling it Rainfall_mm_removed\n", + "Rainfall_mm_removed = Rainfall_mm_cleaned.dropna(subset=['Precip_in'])\n", + "Rainfall_mm_removed.head(20)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "23047cc7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DateStreamflow_cfs
02024-01-0110.25
12024-01-0210.7
22024-01-0313.3
32024-01-0410.1
42024-01-0510.0
52024-01-0611.9
62024-01-0710.0
72024-01-0815.5
82024-01-0910.15
92024-01-1010.45
102024-01-11<NA>
112024-01-12<NA>
122024-01-13<NA>
132024-01-14<NA>
142024-01-15<NA>
152024-01-1611.95
162024-01-1710.0
172024-01-1826.9
182024-01-1910.45
192024-01-2012.65
\n", + "
" + ], + "text/plain": [ + " Date Streamflow_cfs\n", + "0 2024-01-01 10.25\n", + "1 2024-01-02 10.7\n", + "2 2024-01-03 13.3\n", + "3 2024-01-04 10.1\n", + "4 2024-01-05 10.0\n", + "5 2024-01-06 11.9\n", + "6 2024-01-07 10.0\n", + "7 2024-01-08 15.5\n", + "8 2024-01-09 10.15\n", + "9 2024-01-10 10.45\n", + "10 2024-01-11 \n", + "11 2024-01-12 \n", + "12 2024-01-13 \n", + "13 2024-01-14 \n", + "14 2024-01-15 \n", + "15 2024-01-16 11.95\n", + "16 2024-01-17 10.0\n", + "17 2024-01-18 26.9\n", + "18 2024-01-19 10.45\n", + "19 2024-01-20 12.65" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# for streamflow data replace values less than 0 and greater than 1000 with NaN\n", + "streamflow_cfs_cleaned = streamflow.copy()\n", + "streamflow_cfs_cleaned['Streamflow_cfs'] = streamflow_cfs_cleaned['Streamflow_cfs'].apply(lambda x: x if 0 <= x <= 1000 else pd.NA)\n", + "\n", + "# remove NaN values from the streamflow data calling it streamflow_cfs_removed\n", + "streamflow_cfs_removed = streamflow_cfs_cleaned.dropna(subset=['Streamflow_cfs'])\n", + "streamflow_cfs_cleaned.head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "97ca8838", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/scratch/local/u1589214/860851/ipykernel_677804/3262729854.py:3: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", + " streamflow_cfs_filled['Streamflow_cfs'] = streamflow_cfs_filled['Streamflow_cfs'].fillna(streamflow_cfs_filled['Streamflow_cfs'].mean())\n" + ] + }, + { + "data": { + "text/html": [ + "
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DateStreamflow_cfs
02024-01-0110.250000
12024-01-0210.700000
22024-01-0313.300000
32024-01-0410.100000
42024-01-0510.000000
52024-01-0611.900000
62024-01-0710.000000
72024-01-0815.500000
82024-01-0910.150000
92024-01-1010.450000
102024-01-1112.541346
112024-01-1212.541346
122024-01-1312.541346
132024-01-1412.541346
142024-01-1512.541346
152024-01-1611.950000
162024-01-1710.000000
172024-01-1826.900000
182024-01-1910.450000
192024-01-2012.650000
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" + ], + "text/plain": [ + " Date Streamflow_cfs\n", + "0 2024-01-01 10.250000\n", + "1 2024-01-02 10.700000\n", + "2 2024-01-03 13.300000\n", + "3 2024-01-04 10.100000\n", + "4 2024-01-05 10.000000\n", + "5 2024-01-06 11.900000\n", + "6 2024-01-07 10.000000\n", + "7 2024-01-08 15.500000\n", + "8 2024-01-09 10.150000\n", + "9 2024-01-10 10.450000\n", + "10 2024-01-11 12.541346\n", + "11 2024-01-12 12.541346\n", + "12 2024-01-13 12.541346\n", + "13 2024-01-14 12.541346\n", + "14 2024-01-15 12.541346\n", + "15 2024-01-16 11.950000\n", + "16 2024-01-17 10.000000\n", + "17 2024-01-18 26.900000\n", + "18 2024-01-19 10.450000\n", + "19 2024-01-20 12.650000" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# replace NaN values with the mean of the column for streamflow data\n", + "streamflow_cfs_filled = streamflow_cfs_cleaned.copy()\n", + "streamflow_cfs_filled['Streamflow_cfs'] = streamflow_cfs_filled['Streamflow_cfs'].fillna(streamflow_cfs_filled['Streamflow_cfs'].mean())\n", + "streamflow_cfs_filled.head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b2d330e3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/scratch/local/u1589214/860851/ipykernel_677804/2901000763.py:3: FutureWarning: Series.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.\n", + " streamflow_cfs_interpolated['Streamflow_cfs'] = streamflow_cfs_interpolated['Streamflow_cfs'].interpolate(method='linear')\n" + ] + }, + { + "data": { + "text/html": [ + "
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DateStreamflow_cfs
02024-01-0110.25
12024-01-0210.7
22024-01-0313.3
32024-01-0410.1
42024-01-0510.0
52024-01-0611.9
62024-01-0710.0
72024-01-0815.5
82024-01-0910.15
92024-01-1010.45
102024-01-11<NA>
112024-01-12<NA>
122024-01-13<NA>
132024-01-14<NA>
142024-01-15<NA>
152024-01-1611.95
162024-01-1710.0
172024-01-1826.9
182024-01-1910.45
192024-01-2012.65
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" + ], + "text/plain": [ + " Date Streamflow_cfs\n", + "0 2024-01-01 10.25\n", + "1 2024-01-02 10.7\n", + "2 2024-01-03 13.3\n", + "3 2024-01-04 10.1\n", + "4 2024-01-05 10.0\n", + "5 2024-01-06 11.9\n", + "6 2024-01-07 10.0\n", + "7 2024-01-08 15.5\n", + "8 2024-01-09 10.15\n", + "9 2024-01-10 10.45\n", + "10 2024-01-11 \n", + "11 2024-01-12 \n", + "12 2024-01-13 \n", + "13 2024-01-14 \n", + "14 2024-01-15 \n", + "15 2024-01-16 11.95\n", + "16 2024-01-17 10.0\n", + "17 2024-01-18 26.9\n", + "18 2024-01-19 10.45\n", + "19 2024-01-20 12.65" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# replace NaN values with interpolated values for streamflow data using .interpolate()\n", + "streamflow_cfs_interpolated = streamflow_cfs_cleaned.copy()\n", + "streamflow_cfs_interpolated['Streamflow_cfs'] = streamflow_cfs_interpolated['Streamflow_cfs'].interpolate(method='linear')\n", + "streamflow_cfs_interpolated.head(20) " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "507aa64c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Date Precip_in\n", + "15 2024-01-16 0.0\n", + "16 2024-01-16 0.0\n" + ] + }, + { + "data": { + "text/html": [ + "
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Precip_in
count53.000000
mean0.468736
std0.795391
min0.000000
25%0.000000
50%0.101126
75%0.659018
max3.609354
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" + ], + "text/plain": [ + " Precip_in\n", + "count 53.000000\n", + "mean 0.468736\n", + "std 0.795391\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.101126\n", + "75% 0.659018\n", + "max 3.609354" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#remove NaN and -999 and greater than 20 values from the precipitation data\n", + "Rainfall_mm_cleaned = Rainfall_mm[(Rainfall_mm['Precip_in'] >= 0) & (Rainfall_mm['Precip_in'] <= 20) & (Rainfall_mm['Precip_in'] != -999) & (Rainfall_mm['Precip_in'].notna())]\n", + "Rainfall_mm_cleaned.describe() \n", + "# create a data set replacing the removed values with the mean of the column\n", + "Rainfall_mm_filled = Rainfall_mm_cleaned.copy()\n", + "Rainfall_mm_filled['Precip_in'] = Rainfall_mm_filled['Precip_in'].replace(-999, Rainfall_mm_filled['Precip_in'].mean())\n", + "Rainfall_mm_filled['Precip_in'] = Rainfall_mm_filled['Precip_in'].fillna(Rainfall_mm_filled['Precip_in'].mean())\n", + "Rainfall_mm_filled.describe() \n", + "# create a data set replacing the removed values with interpolated values\n", + "Rainfall_mm_interpolated = Rainfall_mm_cleaned.copy()\n", + "Rainfall_mm_interpolated['Precip_in'] = Rainfall_mm_interpolated['Precip_in'].fillna(Rainfall_mm_interpolated['Precip_in'].interpolate(method='linear')) \n", + "Rainfall_mm_interpolated.describe()\n", + "\n", + "# find and remove one duplicate date in the rainfall data\n", + "duplicate_dates = Rainfall_mm_interpolated[Rainfall_mm_interpolated.duplicated(subset='Date', keep=False)]\n", + "print(duplicate_dates)\n", + "Rainfall_mm_interpolated = Rainfall_mm_interpolated.drop_duplicates(subset='Date', keep='first')\n", + "Rainfall_mm_interpolated.describe() " + ] }, { "cell_type": "markdown", @@ -108,11 +1356,760 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "bd2fa519", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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Streamflow_cfs_cleanedStreamflow_cfs_filledStreamflow_cfs_interpolated
Date
2024-01-0110.2510.25000010.25
2024-01-0210.710.70000010.7
2024-01-0313.313.30000013.3
2024-01-0410.110.10000010.1
2024-01-0510.010.00000010.0
2024-01-0611.911.90000011.9
2024-01-0710.010.00000010.0
2024-01-0815.515.50000015.5
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" + ], + "text/plain": [ + " Streamflow_cfs_cleaned Streamflow_cfs_filled \\\n", + "Date \n", + "2024-01-01 10.25 10.250000 \n", + "2024-01-02 10.7 10.700000 \n", + "2024-01-03 13.3 13.300000 \n", + "2024-01-04 10.1 10.100000 \n", + "2024-01-05 10.0 10.000000 \n", + "2024-01-06 11.9 11.900000 \n", + "2024-01-07 10.0 10.000000 \n", + "2024-01-08 15.5 15.500000 \n", + "2024-01-09 10.15 10.150000 \n", + "2024-01-10 10.45 10.450000 \n", + "2024-01-11 12.541346 \n", + "2024-01-12 12.541346 \n", + "2024-01-13 12.541346 \n", + "2024-01-14 12.541346 \n", + "2024-01-15 12.541346 \n", + "2024-01-16 11.95 11.950000 \n", + "2024-01-17 10.0 10.000000 \n", + "2024-01-18 26.9 26.900000 \n", + "2024-01-19 10.45 10.450000 \n", + "2024-01-20 12.65 12.650000 \n", + "2024-01-21 10.05 10.050000 \n", + "2024-01-22 10.0 10.000000 \n", + "2024-01-23 10.35 10.350000 \n", + "2024-01-24 10.5 10.500000 \n", + "2024-01-25 11.5 11.500000 \n", + "2024-01-26 12.541346 \n", + "2024-01-27 22.1 22.100000 \n", + "2024-01-28 11.85 11.850000 \n", + "2024-01-29 10.05 10.050000 \n", + "2024-01-30 10.0 10.000000 \n", + "2024-01-31 12.541346 \n", + "2024-02-01 15.35 15.350000 \n", + "2024-02-02 10.4 10.400000 \n", + "2024-02-03 10.1 10.100000 \n", + "2024-02-04 10.05 10.050000 \n", + "2024-02-05 14.0 14.000000 \n", + "2024-02-06 10.0 10.000000 \n", + "2024-02-07 12.95 12.950000 \n", + "2024-02-08 13.95 13.950000 \n", + "2024-02-09 16.6 16.600000 \n", + "2024-02-10 10.5 10.500000 \n", + "2024-02-11 13.25 13.250000 \n", + "2024-02-12 17.75 17.750000 \n", + "2024-02-13 10.05 10.050000 \n", + "2024-02-14 13.75 13.750000 \n", + "2024-02-15 13.95 13.950000 \n", + "2024-02-16 11.35 11.350000 \n", + "2024-02-17 10.0 10.000000 \n", + "2024-02-18 10.0 10.000000 \n", + "2024-02-19 10.5 10.500000 \n", + "2024-02-20 19.05 19.050000 \n", + "2024-02-21 10.85 10.850000 \n", + "2024-02-22 10.25 10.250000 \n", + "2024-02-23 10.4 10.400000 \n", + "2024-02-24 20.95 20.950000 \n", + "2024-02-25 12.15 12.150000 \n", + "2024-02-26 12.541346 \n", + "2024-02-27 18.05 18.050000 \n", + "2024-02-28 14.75 14.750000 \n", + "2024-02-29 10.5 10.500000 \n", + "\n", + " Streamflow_cfs_interpolated \n", + "Date \n", + "2024-01-01 10.25 \n", + "2024-01-02 10.7 \n", + "2024-01-03 13.3 \n", + "2024-01-04 10.1 \n", + "2024-01-05 10.0 \n", + "2024-01-06 11.9 \n", + "2024-01-07 10.0 \n", + "2024-01-08 15.5 \n", + "2024-01-09 10.15 \n", + "2024-01-10 10.45 \n", + "2024-01-11 \n", + "2024-01-12 \n", + "2024-01-13 \n", + "2024-01-14 \n", + "2024-01-15 \n", + "2024-01-16 11.95 \n", + "2024-01-17 10.0 \n", + "2024-01-18 26.9 \n", + "2024-01-19 10.45 \n", + "2024-01-20 12.65 \n", + "2024-01-21 10.05 \n", + "2024-01-22 10.0 \n", + "2024-01-23 10.35 \n", + "2024-01-24 10.5 \n", + "2024-01-25 11.5 \n", + "2024-01-26 \n", + "2024-01-27 22.1 \n", + "2024-01-28 11.85 \n", + "2024-01-29 10.05 \n", + "2024-01-30 10.0 \n", + "2024-01-31 \n", + "2024-02-01 15.35 \n", + "2024-02-02 10.4 \n", + "2024-02-03 10.1 \n", + "2024-02-04 10.05 \n", + "2024-02-05 14.0 \n", + "2024-02-06 10.0 \n", + "2024-02-07 12.95 \n", + "2024-02-08 13.95 \n", + "2024-02-09 16.6 \n", + "2024-02-10 10.5 \n", + "2024-02-11 13.25 \n", + "2024-02-12 17.75 \n", + "2024-02-13 10.05 \n", + "2024-02-14 13.75 \n", + "2024-02-15 13.95 \n", + "2024-02-16 11.35 \n", + "2024-02-17 10.0 \n", + "2024-02-18 10.0 \n", + "2024-02-19 10.5 \n", + "2024-02-20 19.05 \n", + "2024-02-21 10.85 \n", + "2024-02-22 10.25 \n", + "2024-02-23 10.4 \n", + "2024-02-24 20.95 \n", + "2024-02-25 12.15 \n", + "2024-02-26 \n", + "2024-02-27 18.05 \n", + "2024-02-28 14.75 \n", + "2024-02-29 10.5 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#create merged data set of streamflow_cfs cleaned, streamfall_cfs filled, and streamflow_cfs interpolated with date as the index\n", + "streamflow_methods = pd.DataFrame({\n", + " 'Streamflow_cfs_cleaned': streamflow_cfs_cleaned['Streamflow_cfs'],\n", + " 'Streamflow_cfs_filled': streamflow_cfs_filled['Streamflow_cfs'],\n", + " 'Streamflow_cfs_interpolated': streamflow_cfs_interpolated['Streamflow_cfs']\n", + "})\n", + "streamflow_methods.head() \n", + "# set date as the index\n", + "streamflow_methods.index = streamflow_cfs_cleaned['Date']\n", + "streamflow_methods.head(200)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "2898fd72", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Precip_in_cleanedPrecip_in_filledPrecip_in_interpolated
Date
2024-01-010.1402800.1402800.140280
2024-01-020.6590180.6590180.659018
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2024-01-040.0000000.0000000.000000
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" + ], + "text/plain": [ + " Precip_in_cleaned Precip_in_filled Precip_in_interpolated\n", + "Date \n", + "2024-01-01 0.140280 0.140280 0.140280\n", + "2024-01-02 0.659018 0.659018 0.659018\n", + "2024-01-03 0.000000 0.000000 0.000000\n", + "2024-01-04 0.000000 0.000000 0.000000\n", + "2024-01-05 0.375729 0.375729 0.375729\n", + "2024-01-06 0.000000 0.000000 0.000000\n", + "2024-01-07 1.095458 1.095458 1.095458\n", + "2024-01-08 0.000000 0.000000 0.000000\n", + "2024-01-09 0.000000 0.000000 0.000000\n", + "2024-01-10 0.186576 0.186576 0.186576\n", + "2024-01-14 0.000000 0.000000 0.000000\n", + "2024-01-15 0.385756 0.385756 0.385756\n", + "2024-01-16 0.000000 0.000000 0.000000\n", + "2024-01-16 0.000000 0.000000 NaN\n", + "2024-01-17 3.380971 3.380971 3.380971\n", + "2024-01-18 0.000000 0.000000 0.000000\n", + "2024-01-19 0.532611 0.532611 0.532611\n", + "2024-01-20 0.000000 0.000000 0.000000\n", + "2024-01-21 0.000000 0.000000 0.000000\n", + "2024-01-22 0.000000 0.000000 0.000000" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create a data set with the cleaned, filled, and interpolated rainfall data\n", + "rainfall_methods = pd.DataFrame({\n", + " 'Precip_in_cleaned': Rainfall_mm_cleaned['Precip_in'],\n", + " 'Precip_in_filled': Rainfall_mm_filled['Precip_in'],\n", + " 'Precip_in_interpolated': Rainfall_mm_interpolated['Precip_in']\n", + "})\n", + "rainfall_methods.head()\n", + "\n", + "# set date as the index\n", + "rainfall_methods.index = Rainfall_mm_cleaned['Date']\n", + "rainfall_methods.head(20)\n" + ] }, { "cell_type": "markdown", @@ -126,11 +2123,67 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "9c76bf67", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# create a plot with df.plot() with the cleaned, filled, and interpolated precipitation data \n", + "rainfall_methods.plot(figsize=(10,6), title='Precipitation Data with Different Methods', ylabel='Precipitation (inches)', xlabel='Date')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "af9d20cf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# create a plot with df.plot() with the cleaned, filled, and interpolated streamflow data\n", + "streamflow_methods.plot(figsize=(10,6), title='Streamflow Data with Different Methods', ylabel='Streamflow (cfs)', xlabel='Date') \n" + ] }, { "cell_type": "markdown", @@ -147,8 +2200,30 @@ "execution_count": null, "id": "1e688b18", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'ax1' 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[20], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# create a plot with the filled streamflow data and the filled precipitation data on the same plot with two y-axes\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43max1\u001b[49m\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDate\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 3\u001b[0m ax1\u001b[38;5;241m.\u001b[39mset_ylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mStreamflow (cfs)\u001b[39m\u001b[38;5;124m'\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtab:blue\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 4\u001b[0m ax1\u001b[38;5;241m.\u001b[39mplot(streamflow_methods\u001b[38;5;241m.\u001b[39mindex, streamflow_methods[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mStreamflow_cfs_filled\u001b[39m\u001b[38;5;124m'\u001b[39m], color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtab:blue\u001b[39m\u001b[38;5;124m'\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mStreamflow (cfs)\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "\u001b[0;31mNameError\u001b[0m: name 'ax1' is not defined" + ] + } + ], + "source": [ + "# plot filled streamflow and rainfall data on the same plot\n", + "fig, ax1 = plt.subplots(figsize=(10,6))\n", + "ax1.set_xlabel('Date')\n", + "ax1.set_ylabel('Streamflow (cfs)', color='tab:blue')\n", + "ax1.plot(streamflow_methods.index, streamflow_methods['Streamflow_cfs_filled'], color='tab:blue', label='Streamflow (cfs)')\n", + "ax1.tick_params(axis='y', labelcolor='tab:blue')\n", + "ax2 = ax1.twinx()\n", + "ax2.set_ylabel('Precipitation (inches)', color='tab:orange')\n", + "ax2.plot(rainfall_methods.index, rainfall_methods['Precip_in_filled'], color" + ] }, { "cell_type": "markdown", @@ -162,11 +2237,121 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "d481b613", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Monthly Total Streamflow:\n", + " Streamflow_cfs\n", + "Date \n", + "2024-01-31 290.7\n", + "2024-02-29 361.45\n", + "\n", + "Monthly Mean Streamflow:\n", + " Streamflow_cfs\n", + "Date \n", + "2024-01-31 12.1125\n", + "2024-02-29 12.908929\n", + "\n", + "Monthly Max Streamflow:\n", + " Streamflow_cfs\n", + "Date \n", + "2024-01-31 26.9\n", + "2024-02-29 20.95\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/scratch/local/u1589214/860851/ipykernel_677804/1044543308.py:5: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", + " streamflow_monthly_total = streamflow_monthly.resample('M').sum()\n", + "/scratch/local/u1589214/860851/ipykernel_677804/1044543308.py:6: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", + " streamflow_monthly_mean = streamflow_monthly.resample('M').mean()\n", + "/scratch/local/u1589214/860851/ipykernel_677804/1044543308.py:7: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", + " streamflow_monthly_max = streamflow_monthly.resample('M').max()\n" + ] + } + ], + "source": [ + "# calculate the monthly total, mean daily for each month, and maximum for each month for streamflow data\n", + "streamflow_monthly = streamflow_cfs_cleaned.copy()\n", + "streamflow_monthly['Date'] = pd.to_datetime(streamflow_monthly['Date'])\n", + "streamflow_monthly.set_index('Date', inplace=True)\n", + "streamflow_monthly_total = streamflow_monthly.resample('M').sum()\n", + "streamflow_monthly_mean = streamflow_monthly.resample('M').mean()\n", + "streamflow_monthly_max = streamflow_monthly.resample('M').max() \n", + "# show the first 5 rows of each of the monthly data sets\n", + "print(\"Monthly Total Streamflow:\")\n", + "print(streamflow_monthly_total.head())\n", + "print(\"\\nMonthly Mean Streamflow:\")\n", + "print(streamflow_monthly_mean.head())\n", + "print(\"\\nMonthly Max Streamflow:\")\n", + "print(streamflow_monthly_max.head()) " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "22d8a174", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Monthly Total Precipitation:\n", + " Precip_in\n", + "Date \n", + "2024-01-31 12.261777\n", + "2024-02-29 12.581229\n", + "\n", + "Monthly Mean Precipitation:\n", + " Precip_in\n", + "Date \n", + "2024-01-31 0.437921\n", + "2024-02-29 0.483893\n", + "\n", + "Monthly Max Precipitation:\n", + " Precip_in\n", + "Date \n", + "2024-01-31 3.609354\n", + "2024-02-29 2.193069\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/scratch/local/u1589214/860851/ipykernel_677804/2919713905.py:5: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", + " rainfall_monthly_total = rainfall_monthly.resample('M').sum()\n", + "/scratch/local/u1589214/860851/ipykernel_677804/2919713905.py:6: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", + " rainfall_monthly_mean = rainfall_monthly.resample('M').mean()\n", + "/scratch/local/u1589214/860851/ipykernel_677804/2919713905.py:7: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", + " rainfall_monthly_max = rainfall_monthly.resample('M').max()\n" + ] + } + ], + "source": [ + "# calculate the monthly total, mean daily for each month, and maximum for each month for precipitation data\n", + "rainfall_monthly = Rainfall_mm_cleaned.copy()\n", + "rainfall_monthly['Date'] = pd.to_datetime(rainfall_monthly['Date'])\n", + "rainfall_monthly.set_index('Date', inplace=True)\n", + "rainfall_monthly_total = rainfall_monthly.resample('M').sum()\n", + "rainfall_monthly_mean = rainfall_monthly.resample('M').mean()\n", + "rainfall_monthly_max = rainfall_monthly.resample('M').max()\n", + "# show the first 5 rows of each of the monthly data sets\n", + "print(\"Monthly Total Precipitation:\")\n", + "print(rainfall_monthly_total.head())\n", + "print(\"\\nMonthly Mean Precipitation:\")\n", + "print(rainfall_monthly_mean.head())\n", + "print(\"\\nMonthly Max Precipitation:\")\n", + "print(rainfall_monthly_max.head()) \n" + ] }, { "cell_type": "markdown", @@ -180,11 +2365,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "6d73bc7f", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Converted Rainfall Data (mm):\n", + " Date Precip_in Precip_mm\n", + "0 2024-01-01 0.140280 3.563120\n", + "1 2024-01-02 0.659018 16.739058\n", + "2 2024-01-03 0.000000 0.000000\n", + "3 2024-01-04 0.000000 0.000000\n", + "4 2024-01-05 0.375729 9.543520\n", + "\n", + "Converted Streamflow Data (cms):\n", + " Date Streamflow_cfs Streamflow_cms\n", + "0 2024-01-01 10.25 0.290247\n", + "1 2024-01-02 10.7 0.30299\n", + "2 2024-01-03 13.3 0.376613\n", + "3 2024-01-04 10.1 0.286\n", + "4 2024-01-05 10.0 0.283168\n" + ] + } + ], + "source": [ + "# convert precipitation data from inches to millimeters and streamflow data from cfs to cms\n", + "rainfall_mm_converted = Rainfall_mm_cleaned.copy()\n", + "rainfall_mm_converted['Precip_mm'] = rainfall_mm_converted['Precip_in'] * 25.4\n", + "streamflow_cfs_converted = streamflow_cfs_cleaned.copy()\n", + "streamflow_cfs_converted['Streamflow_cms'] = streamflow_cfs_converted['Streamflow_cfs'] * 0.0283168\n", + "# show the first 5 rows of the converted data sets\n", + "print(\"Converted Rainfall Data (mm):\")\n", + "print(rainfall_mm_converted.head())\n", + "print(\"\\nConverted Streamflow Data (cms):\")\n", + "print(streamflow_cfs_converted.head())\n" + ] }, { "cell_type": "markdown", @@ -200,11 +2418,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "2c9b3495", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rainfall Data with Precipitation Categories:\n", + " Date Precip_in Precip_mm Precip_Category\n", + "0 2024-01-01 0.140280 3.563120 Low\n", + "1 2024-01-02 0.659018 16.739058 High\n", + "2 2024-01-03 0.000000 0.000000 Low\n", + "3 2024-01-04 0.000000 0.000000 Low\n", + "4 2024-01-05 0.375729 9.543520 Medium\n" + ] + }, + { + "ename": "TypeError", + "evalue": "unsupported operand type(s) for -: 'int' and 'Timedelta'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[28], line 8\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# create a data set called storm_df that takes the data from 5 days before and 5 days after the date of the maximum streamflow value in the streamflow data set\u001b[39;00m\n\u001b[1;32m 7\u001b[0m max_streamflow_date \u001b[38;5;241m=\u001b[39m streamflow_cfs_cleaned[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mStreamflow_cfs\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39midxmax()\n\u001b[0;32m----> 8\u001b[0m storm_df \u001b[38;5;241m=\u001b[39m streamflow_cfs_cleaned\u001b[38;5;241m.\u001b[39mloc[\u001b[43mmax_streamflow_date\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTimedelta\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdays\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m)\u001b[49m: max_streamflow_date \u001b[38;5;241m+\u001b[39m pd\u001b[38;5;241m.\u001b[39mTimedelta(days\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)]\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStorm Data (5 days before and after max streamflow):\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28mprint\u001b[39m(storm_df) \n", + "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'int' and 'Timedelta'" + ] + } + ], + "source": [ + "# for the rainfall data, create a new column that categorizes the precipitation into 'Low', 'Medium', and 'High' based on the following thresholds: Low (< 5 mm), Medium (5-15 mm), High (> 15 mm)\n", + "rainfall_mm_converted['Precip_Category'] = pd.cut(rainfall_mm_converted['Precip_mm'], bins=[-float('inf'), 5, 15, float('inf')], labels=['Low', 'Medium', 'High'])\n", + "# show the first 5 rows of the data set with the new category column\n", + "print(\"Rainfall Data with Precipitation Categories:\")\n", + "print(rainfall_mm_converted.head())\n", + "# create a data set called storm_df that takes the data from 5 days before and 5 days after the date of the maximum streamflow value in the streamflow data set\n", + "max_streamflow_date = streamflow_cfs_cleaned['Streamflow_cfs'].idxmax()\n", + "storm_df = streamflow_cfs_cleaned.loc[max_streamflow_date - pd.Timedelta(days=5): max_streamflow_date + pd.Timedelta(days=5)]\n", + "print(\"Storm Data (5 days before and after max streamflow):\")\n", + "print(storm_df) " + ] }, { "cell_type": "markdown", @@ -239,7 +2493,7 @@ ], "metadata": { "kernelspec": { - "display_name": "p310env", + "display_name": "Python 3", "language": "python", "name": "python3" },