├── Datasets ├── readme.md ├── linear_data.csv └── nonlinear_data.csv ├── Documents ├── Readme.md ├── Data Wrangling.pdf ├── Handle Imbalanced Dataset.pdf ├── 60 SQL Interview Questions and Answers.pdf └── Roadmap on Data Analysis with Python in 2024.pdf ├── Plots.pdf ├── Standard Deviation and Variance.ipynb ├── Min Max Scaling with Python.ipynb ├── Pearson correlation coefficient.ipynb ├── Measures of Central Tendency.ipynb ├── Standard Scaling using Python.ipynb ├── 4. Matplotlib Histogram or histplot.ipynb ├── 5. Boxplot using Python.ipynb └── 3. Matplotlib Bar Plot .ipynb /Datasets/readme.md: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /Documents/Readme.md: -------------------------------------------------------------------------------- 1 | www.aiquest.org 2 | -------------------------------------------------------------------------------- /Plots.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rashakil-ds/Data-Analysis-with-Python/main/Plots.pdf -------------------------------------------------------------------------------- /Documents/Data Wrangling.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rashakil-ds/Data-Analysis-with-Python/main/Documents/Data Wrangling.pdf -------------------------------------------------------------------------------- /Documents/Handle Imbalanced Dataset.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rashakil-ds/Data-Analysis-with-Python/main/Documents/Handle Imbalanced Dataset.pdf -------------------------------------------------------------------------------- /Documents/60 SQL Interview Questions and Answers.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rashakil-ds/Data-Analysis-with-Python/main/Documents/60 SQL Interview Questions and Answers.pdf -------------------------------------------------------------------------------- /Documents/Roadmap on Data Analysis with Python in 2024.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rashakil-ds/Data-Analysis-with-Python/main/Documents/Roadmap on Data Analysis with Python in 2024.pdf -------------------------------------------------------------------------------- /Standard Deviation and Variance.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "id": "7dc7f7b5", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import pandas as pd" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": 2, 16 | "id": "9d6bb6b8", 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "data = {'Scores': [88, 92, 100, 83, 97, 75]}\n", 21 | "df = pd.DataFrame(data)" 22 | ] 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": 3, 27 | "id": "831e5c77", 28 | "metadata": {}, 29 | "outputs": [ 30 | { 31 | "data": { 32 | "text/html": [ 33 | "
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" 82 | ], 83 | "text/plain": [ 84 | " Scores\n", 85 | "0 88\n", 86 | "1 92\n", 87 | "2 100\n", 88 | "3 83\n", 89 | "4 97\n", 90 | "5 75" 91 | ] 92 | }, 93 | "execution_count": 3, 94 | "metadata": {}, 95 | "output_type": "execute_result" 96 | } 97 | ], 98 | "source": [ 99 | "df" 100 | ] 101 | }, 102 | { 103 | "cell_type": "code", 104 | "execution_count": 4, 105 | "id": "1c6d9412", 106 | "metadata": {}, 107 | "outputs": [ 108 | { 109 | "name": "stdout", 110 | "output_type": "stream", 111 | "text": [ 112 | "Standard Deviation of Scores: 9.239408350466315\n" 113 | ] 114 | } 115 | ], 116 | "source": [ 117 | "standard_deviation = df['Scores'].std()\n", 118 | "print(\"Standard Deviation of Scores:\", standard_deviation)" 119 | ] 120 | }, 121 | { 122 | "cell_type": "code", 123 | "execution_count": 5, 124 | "id": "beaf3904", 125 | "metadata": {}, 126 | "outputs": [ 127 | { 128 | "name": "stdout", 129 | "output_type": "stream", 130 | "text": [ 131 | "Variance of Scores: 85.36666666666666\n" 132 | ] 133 | } 134 | ], 135 | "source": [ 136 | "variance = df['Scores'].var()\n", 137 | "print(\"Variance of Scores:\", variance)" 138 | ] 139 | }, 140 | { 141 | "cell_type": "code", 142 | "execution_count": null, 143 | "id": "20c68863", 144 | "metadata": {}, 145 | "outputs": [], 146 | "source": [] 147 | } 148 | ], 149 | "metadata": { 150 | "kernelspec": { 151 | "display_name": "Python 3 (ipykernel)", 152 | "language": "python", 153 | "name": "python3" 154 | }, 155 | "language_info": { 156 | "codemirror_mode": { 157 | "name": "ipython", 158 | "version": 3 159 | }, 160 | "file_extension": ".py", 161 | "mimetype": "text/x-python", 162 | "name": "python", 163 | "nbconvert_exporter": "python", 164 | "pygments_lexer": "ipython3", 165 | "version": "3.9.13" 166 | } 167 | }, 168 | "nbformat": 4, 169 | "nbformat_minor": 5 170 | } 171 | -------------------------------------------------------------------------------- /Min Max Scaling with Python.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "fc529592", 6 | "metadata": {}, 7 | "source": [ 8 | "# Raw Code" 9 | ] 10 | }, 11 | { 12 | "cell_type": "code", 13 | "execution_count": 1, 14 | "id": "1c33a4de", 15 | "metadata": {}, 16 | "outputs": [], 17 | "source": [ 18 | "def min_max_scaling(data):\n", 19 | " min_val = min(data)\n", 20 | " max_val = max(data)\n", 21 | " scaled_data = [(x - min_val) / (max_val - min_val) for x in data]\n", 22 | " return scaled_data" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": 2, 28 | "id": "53cba510", 29 | "metadata": {}, 30 | "outputs": [ 31 | { 32 | "name": "stdout", 33 | "output_type": "stream", 34 | "text": [ 35 | "Original Data: [1, 20, 30, 4, 5]\n", 36 | "Scaled data (raw): [0.0, 0.6551724137931034, 1.0, 0.10344827586206896, 0.13793103448275862]\n" 37 | ] 38 | } 39 | ], 40 | "source": [ 41 | "data = [1, 20, 30, 4, 5]\n", 42 | "scaled_data = min_max_scaling(data)\n", 43 | "print('Original Data: ', data)\n", 44 | "print(\"Scaled data (raw):\", scaled_data)" 45 | ] 46 | }, 47 | { 48 | "cell_type": "markdown", 49 | "id": "c9912be9", 50 | "metadata": {}, 51 | "source": [ 52 | "# using Sklearn" 53 | ] 54 | }, 55 | { 56 | "cell_type": "code", 57 | "execution_count": 3, 58 | "id": "e2326ee0", 59 | "metadata": {}, 60 | "outputs": [], 61 | "source": [ 62 | "import pandas as pd\n", 63 | "from sklearn.preprocessing import MinMaxScaler" 64 | ] 65 | }, 66 | { 67 | "cell_type": "code", 68 | "execution_count": 4, 69 | "id": "6c2eab99", 70 | "metadata": {}, 71 | "outputs": [ 72 | { 73 | "data": { 74 | "text/html": [ 75 | "
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" 126 | ], 127 | "text/plain": [ 128 | " Feature1 Feature2\n", 129 | "0 1 6\n", 130 | "1 5 7\n", 131 | "2 10 8\n", 132 | "3 4 19\n", 133 | "4 5 10" 134 | ] 135 | }, 136 | "execution_count": 4, 137 | "metadata": {}, 138 | "output_type": "execute_result" 139 | } 140 | ], 141 | "source": [ 142 | "data = {'Feature1': [1, 5, 10, 4, 5],\n", 143 | " 'Feature2': [6, 7, 8, 19, 10]}\n", 144 | "\n", 145 | "df = pd.DataFrame(data)\n", 146 | "df.head()" 147 | ] 148 | }, 149 | { 150 | "cell_type": "code", 151 | "execution_count": 5, 152 | "id": "03a59524", 153 | "metadata": {}, 154 | "outputs": [], 155 | "source": [ 156 | "scaler = MinMaxScaler()\n", 157 | "scaled_data = scaler.fit_transform(df)\n", 158 | "scaled_df = pd.DataFrame(scaled_data, columns=df.columns)" 159 | ] 160 | }, 161 | { 162 | "cell_type": "code", 163 | "execution_count": 6, 164 | "id": "b32cc8d6", 165 | "metadata": {}, 166 | "outputs": [ 167 | { 168 | "name": "stdout", 169 | "output_type": "stream", 170 | "text": [ 171 | "Original DataFrame:\n", 172 | " Feature1 Feature2\n", 173 | "0 1 6\n", 174 | "1 5 7\n", 175 | "2 10 8\n", 176 | "3 4 19\n", 177 | "4 5 10\n", 178 | "\n", 179 | "Scaled DataFrame:\n", 180 | " Feature1 Feature2\n", 181 | "0 0.000000 0.000000\n", 182 | "1 0.444444 0.076923\n", 183 | "2 1.000000 0.153846\n", 184 | "3 0.333333 1.000000\n", 185 | "4 0.444444 0.307692\n" 186 | ] 187 | } 188 | ], 189 | "source": [ 190 | "print(\"Original DataFrame:\")\n", 191 | "print(df)\n", 192 | "print(\"\\nScaled DataFrame:\")\n", 193 | "print(scaled_df)" 194 | ] 195 | }, 196 | { 197 | "cell_type": "code", 198 | "execution_count": null, 199 | "id": "0e898642", 200 | "metadata": {}, 201 | "outputs": [], 202 | "source": [] 203 | } 204 | ], 205 | "metadata": { 206 | "kernelspec": { 207 | "display_name": "Python 3 (ipykernel)", 208 | "language": "python", 209 | "name": "python3" 210 | }, 211 | "language_info": { 212 | "codemirror_mode": { 213 | "name": "ipython", 214 | "version": 3 215 | }, 216 | "file_extension": ".py", 217 | "mimetype": "text/x-python", 218 | "name": "python", 219 | "nbconvert_exporter": "python", 220 | "pygments_lexer": "ipython3", 221 | "version": "3.9.13" 222 | } 223 | }, 224 | "nbformat": 4, 225 | "nbformat_minor": 5 226 | } 227 | -------------------------------------------------------------------------------- /Pearson correlation coefficient.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "id": "a7e33214", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import pandas as pd" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": 2, 16 | "id": "374e7d93", 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "data = {\n", 21 | " 'X': [1, 2, 3, 4, 5, 7, 10],\n", 22 | " 'Y': [2, 3, 5, 4, 6, 6, 8]\n", 23 | "}" 24 | ] 25 | }, 26 | { 27 | "cell_type": "code", 28 | "execution_count": 3, 29 | "id": "88da6420", 30 | "metadata": {}, 31 | "outputs": [ 32 | { 33 | "data": { 34 | "text/plain": [ 35 | "{'X': [1, 2, 3, 4, 5, 7, 10], 'Y': [2, 3, 5, 4, 6, 6, 8]}" 36 | ] 37 | }, 38 | "execution_count": 3, 39 | "metadata": {}, 40 | "output_type": "execute_result" 41 | } 42 | ], 43 | "source": [ 44 | "data" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 4, 50 | "id": "8606e6c8", 51 | "metadata": {}, 52 | "outputs": [ 53 | { 54 | "data": { 55 | "text/html": [ 56 | "
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" 107 | ], 108 | "text/plain": [ 109 | " X Y\n", 110 | "0 1 2\n", 111 | "1 2 3\n", 112 | "2 3 5\n", 113 | "3 4 4\n", 114 | "4 5 6" 115 | ] 116 | }, 117 | "execution_count": 4, 118 | "metadata": {}, 119 | "output_type": "execute_result" 120 | } 121 | ], 122 | "source": [ 123 | "df = pd.DataFrame(data)\n", 124 | "df.head()" 125 | ] 126 | }, 127 | { 128 | "cell_type": "markdown", 129 | "id": "5c3e6ee6", 130 | "metadata": {}, 131 | "source": [ 132 | "# Calculate Pearson correlation" 133 | ] 134 | }, 135 | { 136 | "cell_type": "code", 137 | "execution_count": 5, 138 | "id": "c73c9fc9", 139 | "metadata": {}, 140 | "outputs": [ 141 | { 142 | "name": "stdout", 143 | "output_type": "stream", 144 | "text": [ 145 | "Pearson correlation coefficient: 0.9391480052471199\n" 146 | ] 147 | } 148 | ], 149 | "source": [ 150 | "pearson_corr = df['X'].corr(df['Y'])\n", 151 | "print(\"Pearson correlation coefficient:\", pearson_corr)" 152 | ] 153 | }, 154 | { 155 | "cell_type": "code", 156 | "execution_count": 6, 157 | "id": "29b1b22d", 158 | "metadata": {}, 159 | "outputs": [ 160 | { 161 | "data": { 162 | "text/html": [ 163 | "
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XY
X1.0000000.939148
Y0.9391481.000000
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" 199 | ], 200 | "text/plain": [ 201 | " X Y\n", 202 | "X 1.000000 0.939148\n", 203 | "Y 0.939148 1.000000" 204 | ] 205 | }, 206 | "execution_count": 6, 207 | "metadata": {}, 208 | "output_type": "execute_result" 209 | } 210 | ], 211 | "source": [ 212 | "df.corr()" 213 | ] 214 | }, 215 | { 216 | "cell_type": "code", 217 | "execution_count": null, 218 | "id": "754b1a48", 219 | "metadata": {}, 220 | "outputs": [], 221 | "source": [] 222 | } 223 | ], 224 | "metadata": { 225 | "kernelspec": { 226 | "display_name": "Python 3 (ipykernel)", 227 | "language": "python", 228 | "name": "python3" 229 | }, 230 | "language_info": { 231 | "codemirror_mode": { 232 | "name": "ipython", 233 | "version": 3 234 | }, 235 | "file_extension": ".py", 236 | "mimetype": "text/x-python", 237 | "name": "python", 238 | "nbconvert_exporter": "python", 239 | "pygments_lexer": "ipython3", 240 | "version": "3.9.13" 241 | } 242 | }, 243 | "nbformat": 4, 244 | "nbformat_minor": 5 245 | } 246 | -------------------------------------------------------------------------------- /Measures of Central Tendency.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "7fbb45dc", 6 | "metadata": {}, 7 | "source": [ 8 | "# Raw Python Code" 9 | ] 10 | }, 11 | { 12 | "cell_type": "code", 13 | "execution_count": 1, 14 | "id": "08266211", 15 | "metadata": {}, 16 | "outputs": [], 17 | "source": [ 18 | "ages = [21, 25, 19, 19, 25, 22, 24, 24, 27]" 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": 2, 24 | "id": "d7d2baf6", 25 | "metadata": {}, 26 | "outputs": [ 27 | { 28 | "data": { 29 | "text/plain": [ 30 | "22.88888888888889" 31 | ] 32 | }, 33 | "execution_count": 2, 34 | "metadata": {}, 35 | "output_type": "execute_result" 36 | } 37 | ], 38 | "source": [ 39 | "mean_age = sum(ages) / len(ages)\n", 40 | "mean_age" 41 | ] 42 | }, 43 | { 44 | "cell_type": "code", 45 | "execution_count": 3, 46 | "id": "fa4256f4", 47 | "metadata": {}, 48 | "outputs": [ 49 | { 50 | "data": { 51 | "text/plain": [ 52 | "24" 53 | ] 54 | }, 55 | "execution_count": 3, 56 | "metadata": {}, 57 | "output_type": "execute_result" 58 | } 59 | ], 60 | "source": [ 61 | "sorted_ages = sorted(ages)\n", 62 | "n = len(sorted_ages)\n", 63 | "median_age = (sorted_ages[n//2] if n % 2 != 0 else (sorted_ages[n//2 - 1] + sorted_ages[n//2]) / 2)\n", 64 | "median_age" 65 | ] 66 | }, 67 | { 68 | "cell_type": "code", 69 | "execution_count": 4, 70 | "id": "5555062d", 71 | "metadata": {}, 72 | "outputs": [ 73 | { 74 | "data": { 75 | "text/plain": [ 76 | "[25, 19, 24]" 77 | ] 78 | }, 79 | "execution_count": 4, 80 | "metadata": {}, 81 | "output_type": "execute_result" 82 | } 83 | ], 84 | "source": [ 85 | "from collections import Counter\n", 86 | "\n", 87 | "mode_data = Counter(ages)\n", 88 | "max_frequency = max(mode_data.values())\n", 89 | "modes = [k for k, v in mode_data.items() if v == max_frequency]\n", 90 | "modes" 91 | ] 92 | }, 93 | { 94 | "cell_type": "markdown", 95 | "id": "ef18186c", 96 | "metadata": {}, 97 | "source": [ 98 | "# Pandas" 99 | ] 100 | }, 101 | { 102 | "cell_type": "code", 103 | "execution_count": 5, 104 | "id": "12941de5", 105 | "metadata": {}, 106 | "outputs": [], 107 | "source": [ 108 | "import pandas as pd" 109 | ] 110 | }, 111 | { 112 | "cell_type": "code", 113 | "execution_count": 6, 114 | "id": "c6cde718", 115 | "metadata": {}, 116 | "outputs": [ 117 | { 118 | "data": { 119 | "text/html": [ 120 | "
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age
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" 165 | ], 166 | "text/plain": [ 167 | " age\n", 168 | "0 21\n", 169 | "1 25\n", 170 | "2 19\n", 171 | "3 19\n", 172 | "4 25" 173 | ] 174 | }, 175 | "execution_count": 6, 176 | "metadata": {}, 177 | "output_type": "execute_result" 178 | } 179 | ], 180 | "source": [ 181 | "df = pd.DataFrame(ages, columns=['age'])\n", 182 | "df.head()" 183 | ] 184 | }, 185 | { 186 | "cell_type": "code", 187 | "execution_count": 7, 188 | "id": "59ee5f33", 189 | "metadata": {}, 190 | "outputs": [ 191 | { 192 | "data": { 193 | "text/plain": [ 194 | "22.88888888888889" 195 | ] 196 | }, 197 | "execution_count": 7, 198 | "metadata": {}, 199 | "output_type": "execute_result" 200 | } 201 | ], 202 | "source": [ 203 | "df.age.mean()" 204 | ] 205 | }, 206 | { 207 | "cell_type": "code", 208 | "execution_count": 8, 209 | "id": "a463870d", 210 | "metadata": {}, 211 | "outputs": [ 212 | { 213 | "data": { 214 | "text/plain": [ 215 | "24.0" 216 | ] 217 | }, 218 | "execution_count": 8, 219 | "metadata": {}, 220 | "output_type": "execute_result" 221 | } 222 | ], 223 | "source": [ 224 | "df.age.median()" 225 | ] 226 | }, 227 | { 228 | "cell_type": "code", 229 | "execution_count": 9, 230 | "id": "1a34ffa0", 231 | "metadata": {}, 232 | "outputs": [ 233 | { 234 | "data": { 235 | "text/plain": [ 236 | "0 19\n", 237 | "1 24\n", 238 | "2 25\n", 239 | "Name: age, dtype: int64" 240 | ] 241 | }, 242 | "execution_count": 9, 243 | "metadata": {}, 244 | "output_type": "execute_result" 245 | } 246 | ], 247 | "source": [ 248 | "df.age.mode()" 249 | ] 250 | }, 251 | { 252 | "cell_type": "code", 253 | "execution_count": null, 254 | "id": "f86a328a", 255 | "metadata": {}, 256 | "outputs": [], 257 | "source": [] 258 | } 259 | ], 260 | "metadata": { 261 | "kernelspec": { 262 | "display_name": "Python 3 (ipykernel)", 263 | "language": "python", 264 | "name": "python3" 265 | }, 266 | "language_info": { 267 | "codemirror_mode": { 268 | "name": "ipython", 269 | "version": 3 270 | }, 271 | "file_extension": ".py", 272 | "mimetype": "text/x-python", 273 | "name": "python", 274 | "nbconvert_exporter": "python", 275 | "pygments_lexer": "ipython3", 276 | "version": "3.9.13" 277 | } 278 | }, 279 | "nbformat": 4, 280 | "nbformat_minor": 5 281 | } 282 | -------------------------------------------------------------------------------- /Standard Scaling using Python.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "id": "20c48bc4", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import pandas as pd" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": 2, 16 | "id": "cf6184fa", 17 | "metadata": {}, 18 | "outputs": [ 19 | { 20 | "data": { 21 | "text/html": [ 22 | "
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Feature 1Feature 2
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" 73 | ], 74 | "text/plain": [ 75 | " Feature 1 Feature 2\n", 76 | "0 1 6\n", 77 | "1 20 7\n", 78 | "2 3 18\n", 79 | "3 40 19\n", 80 | "4 5 10" 81 | ] 82 | }, 83 | "execution_count": 2, 84 | "metadata": {}, 85 | "output_type": "execute_result" 86 | } 87 | ], 88 | "source": [ 89 | "data = {'Feature 1': [1, 20, 3, 40, 5],\n", 90 | " 'Feature 2': [6, 7, 18, 19, 10]}\n", 91 | "\n", 92 | "df = pd.DataFrame(data)\n", 93 | "df.head()" 94 | ] 95 | }, 96 | { 97 | "cell_type": "code", 98 | "execution_count": 3, 99 | "id": "8fa7404f", 100 | "metadata": {}, 101 | "outputs": [], 102 | "source": [ 103 | "def standardize_data(data):\n", 104 | " mean = data.mean()\n", 105 | " std_dev = data.std() # delta degree of freedom ddof=0\n", 106 | " standardized_data_raw = (data - mean) / std_dev\n", 107 | " return standardized_data_raw" 108 | ] 109 | }, 110 | { 111 | "cell_type": "code", 112 | "execution_count": 4, 113 | "id": "f2c02d59", 114 | "metadata": {}, 115 | "outputs": [], 116 | "source": [ 117 | "standardized_df_raw = df.apply(standardize_data)" 118 | ] 119 | }, 120 | { 121 | "cell_type": "code", 122 | "execution_count": 5, 123 | "id": "f22c9db0", 124 | "metadata": {}, 125 | "outputs": [ 126 | { 127 | "name": "stdout", 128 | "output_type": "stream", 129 | "text": [ 130 | "Original DataFrame:\n", 131 | " Feature 1 Feature 2\n", 132 | "0 1 6\n", 133 | "1 20 7\n", 134 | "2 3 18\n", 135 | "3 40 19\n", 136 | "4 5 10\n", 137 | "\n", 138 | "Standardized DataFrame:\n", 139 | " Feature 1 Feature 2\n", 140 | "0 -0.777975 -0.979796\n", 141 | "1 0.376832 -0.816497\n", 142 | "2 -0.656417 0.979796\n", 143 | "3 1.592418 1.143095\n", 144 | "4 -0.534858 -0.326599\n" 145 | ] 146 | } 147 | ], 148 | "source": [ 149 | "print(\"Original DataFrame:\")\n", 150 | "print(df)\n", 151 | "print(\"\\nStandardized DataFrame:\")\n", 152 | "print(standardized_df_raw)" 153 | ] 154 | }, 155 | { 156 | "cell_type": "code", 157 | "execution_count": 6, 158 | "id": "3fb91ce8", 159 | "metadata": {}, 160 | "outputs": [ 161 | { 162 | "data": { 163 | "text/html": [ 164 | "
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Feature 1Feature 2
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" 215 | ], 216 | "text/plain": [ 217 | " Feature 1 Feature 2\n", 218 | "0 1 6\n", 219 | "1 20 7\n", 220 | "2 3 18\n", 221 | "3 40 19\n", 222 | "4 5 10" 223 | ] 224 | }, 225 | "execution_count": 6, 226 | "metadata": {}, 227 | "output_type": "execute_result" 228 | } 229 | ], 230 | "source": [ 231 | "df.head()" 232 | ] 233 | }, 234 | { 235 | "cell_type": "code", 236 | "execution_count": 7, 237 | "id": "dcfedb7b", 238 | "metadata": {}, 239 | "outputs": [ 240 | { 241 | "data": { 242 | "text/html": [ 243 | "
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Feature 1Feature 2
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2-0.6564170.979796
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\n", 293 | "
" 294 | ], 295 | "text/plain": [ 296 | " Feature 1 Feature 2\n", 297 | "0 -0.777975 -0.979796\n", 298 | "1 0.376832 -0.816497\n", 299 | "2 -0.656417 0.979796\n", 300 | "3 1.592418 1.143095\n", 301 | "4 -0.534858 -0.326599" 302 | ] 303 | }, 304 | "execution_count": 7, 305 | "metadata": {}, 306 | "output_type": "execute_result" 307 | } 308 | ], 309 | "source": [ 310 | "standardized_df_raw.head()" 311 | ] 312 | }, 313 | { 314 | "cell_type": "code", 315 | "execution_count": 8, 316 | "id": "daaa1204", 317 | "metadata": {}, 318 | "outputs": [ 319 | { 320 | "data": { 321 | "text/plain": [ 322 | "1.0" 323 | ] 324 | }, 325 | "execution_count": 8, 326 | "metadata": {}, 327 | "output_type": "execute_result" 328 | } 329 | ], 330 | "source": [ 331 | "standardized_df_raw['Feature 1'].mean() + 1" 332 | ] 333 | }, 334 | { 335 | "cell_type": "code", 336 | "execution_count": 9, 337 | "id": "56277f06", 338 | "metadata": {}, 339 | "outputs": [ 340 | { 341 | "data": { 342 | "text/plain": [ 343 | "0.9999999999999999" 344 | ] 345 | }, 346 | "execution_count": 9, 347 | "metadata": {}, 348 | "output_type": "execute_result" 349 | } 350 | ], 351 | "source": [ 352 | "standardized_df_raw['Feature 1'].std()" 353 | ] 354 | }, 355 | { 356 | "cell_type": "markdown", 357 | "id": "690b8116", 358 | "metadata": {}, 359 | "source": [ 360 | "# Sklearn" 361 | ] 362 | }, 363 | { 364 | "cell_type": "code", 365 | "execution_count": 10, 366 | "id": "11c15507", 367 | "metadata": {}, 368 | "outputs": [], 369 | "source": [ 370 | "from sklearn.preprocessing import StandardScaler" 371 | ] 372 | }, 373 | { 374 | "cell_type": "code", 375 | "execution_count": 11, 376 | "id": "6f37ac57", 377 | "metadata": {}, 378 | "outputs": [ 379 | { 380 | "data": { 381 | "text/html": [ 382 | "
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Feature 1Feature 2
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" 433 | ], 434 | "text/plain": [ 435 | " Feature 1 Feature 2\n", 436 | "0 1 6\n", 437 | "1 20 7\n", 438 | "2 3 18\n", 439 | "3 40 19\n", 440 | "4 5 10" 441 | ] 442 | }, 443 | "execution_count": 11, 444 | "metadata": {}, 445 | "output_type": "execute_result" 446 | } 447 | ], 448 | "source": [ 449 | "df" 450 | ] 451 | }, 452 | { 453 | "cell_type": "code", 454 | "execution_count": 12, 455 | "id": "ea267703", 456 | "metadata": {}, 457 | "outputs": [], 458 | "source": [ 459 | "scaler = StandardScaler()\n", 460 | "standardized_data = scaler.fit_transform(df)\n", 461 | "standardized_df = pd.DataFrame(standardized_data, columns=df.columns)" 462 | ] 463 | }, 464 | { 465 | "cell_type": "code", 466 | "execution_count": 13, 467 | "id": "c68cbfde", 468 | "metadata": {}, 469 | "outputs": [ 470 | { 471 | "name": "stdout", 472 | "output_type": "stream", 473 | "text": [ 474 | "Original DataFrame:\n", 475 | " Feature 1 Feature 2\n", 476 | "0 1 6\n", 477 | "1 20 7\n", 478 | "2 3 18\n", 479 | "3 40 19\n", 480 | "4 5 10\n", 481 | "\n", 482 | "Standardized DataFrame: raw code\n", 483 | " Feature 1 Feature 2\n", 484 | "0 -0.777975 -0.979796\n", 485 | "1 0.376832 -0.816497\n", 486 | "2 -0.656417 0.979796\n", 487 | "3 1.592418 1.143095\n", 488 | "4 -0.534858 -0.326599\n", 489 | "\n", 490 | "Standardized DataFrame: sklearn\n", 491 | " Feature 1 Feature 2\n", 492 | "0 -0.869803 -1.095445\n", 493 | "1 0.421311 -0.912871\n", 494 | "2 -0.733896 1.095445\n", 495 | "3 1.780378 1.278019\n", 496 | "4 -0.597989 -0.365148\n" 497 | ] 498 | } 499 | ], 500 | "source": [ 501 | "print(\"Original DataFrame:\")\n", 502 | "print(df)\n", 503 | "\n", 504 | "print(\"\\nStandardized DataFrame: raw code\")\n", 505 | "print(standardized_df_raw)\n", 506 | "\n", 507 | "print(\"\\nStandardized DataFrame: sklearn\")\n", 508 | "print(standardized_df)" 509 | ] 510 | }, 511 | { 512 | "cell_type": "code", 513 | "execution_count": null, 514 | "id": "f764d2ec", 515 | "metadata": {}, 516 | "outputs": [], 517 | "source": [] 518 | }, 519 | { 520 | "cell_type": "code", 521 | "execution_count": null, 522 | "id": "b1af06be", 523 | "metadata": {}, 524 | "outputs": [], 525 | "source": [] 526 | } 527 | ], 528 | "metadata": { 529 | "kernelspec": { 530 | "display_name": "Python 3 (ipykernel)", 531 | "language": "python", 532 | "name": "python3" 533 | }, 534 | "language_info": { 535 | "codemirror_mode": { 536 | "name": "ipython", 537 | "version": 3 538 | }, 539 | "file_extension": ".py", 540 | "mimetype": "text/x-python", 541 | "name": "python", 542 | "nbconvert_exporter": "python", 543 | "pygments_lexer": "ipython3", 544 | "version": "3.9.13" 545 | } 546 | }, 547 | "nbformat": 4, 548 | "nbformat_minor": 5 549 | } 550 | -------------------------------------------------------------------------------- /Datasets/linear_data.csv: -------------------------------------------------------------------------------- 1 | x,y 2 | 0.0,4.4476917012367645 3 | 0.02004008016032064,5.024612031683661 4 | 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Matplotlib Histogram or histplot.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "135b8d23", 6 | "metadata": {}, 7 | "source": [ 8 | "**Histograms and bar plots are similar in that they both display data distribution, but they are used for different types of data.**\n", 9 | "\n", 10 | "**1. Histogram:**\n", 11 | "- Histograms are used to visualize the distribution of continuous data.\n", 12 | "- The x-axis of a histogram represents the range of values (bins) of the continuous variable, and the y-axis represents the frequency or density of occurrences within each bin.\n", 13 | "- Histograms are typically used to show the underlying probability distribution of a continuous variable.\n", 14 | "\n", 15 | "**2. Bar Plot:**\n", 16 | "- Bar plots, on the other hand, are used for visualizing the distribution of categorical data or the relationship between two categorical variables.\n", 17 | "- The x-axis of a bar plot represents the categories, and the y-axis represents the value associated with each category. Each bar represents a different category, and the height of the bar corresponds to the value.\n", 18 | "- Bar plots are used when you have distinct categories and want to compare their values.\n" 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": 10, 24 | "id": "ea6aaf97", 25 | "metadata": {}, 26 | "outputs": [ 27 | { 28 | "data": { 29 | "text/plain": [ 30 | "Text(0.5, 1.0, 'Histogram Example')" 31 | ] 32 | }, 33 | "execution_count": 10, 34 | "metadata": {}, 35 | "output_type": "execute_result" 36 | }, 37 | { 38 | "data": { 39 | "image/png": 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\n", 40 | "text/plain": [ 41 | "
" 42 | ] 43 | }, 44 | "metadata": {}, 45 | "output_type": "display_data" 46 | } 47 | ], 48 | "source": [ 49 | "import matplotlib.pyplot as plt\n", 50 | "import numpy as np\n", 51 | "\n", 52 | "data = np.random.randn(2000) #normal distribution\n", 53 | "\n", 54 | "plt.hist(data, bins=10, color='black', edgecolor='white')\n", 55 | "\n", 56 | "plt.xlabel('Values')\n", 57 | "plt.ylabel('Frequency')\n", 58 | "plt.title('Histogram Example')\n" 59 | ] 60 | }, 61 | { 62 | "cell_type": "code", 63 | "execution_count": null, 64 | "id": "10231428", 65 | "metadata": {}, 66 | "outputs": [], 67 | "source": [] 68 | } 69 | ], 70 | "metadata": { 71 | "kernelspec": { 72 | "display_name": "Python 3 (ipykernel)", 73 | "language": "python", 74 | "name": "python3" 75 | }, 76 | "language_info": { 77 | "codemirror_mode": { 78 | "name": "ipython", 79 | "version": 3 80 | }, 81 | "file_extension": ".py", 82 | "mimetype": "text/x-python", 83 | "name": "python", 84 | "nbconvert_exporter": "python", 85 | "pygments_lexer": "ipython3", 86 | "version": "3.9.13" 87 | } 88 | }, 89 | "nbformat": 4, 90 | "nbformat_minor": 5 91 | } 92 | -------------------------------------------------------------------------------- /5. Boxplot using Python.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "9a680aea", 6 | "metadata": {}, 7 | "source": [ 8 | "# Boxplot with Matplotlib" 9 | ] 10 | }, 11 | { 12 | "cell_type": "code", 13 | "execution_count": 1, 14 | "id": "838a90b7", 15 | "metadata": {}, 16 | "outputs": [], 17 | "source": [ 18 | "from matplotlib import pyplot as plt\n", 19 | "import numpy as np" 20 | ] 21 | }, 22 | { 23 | "cell_type": "code", 24 | "execution_count": 2, 25 | "id": "3d4ced95", 26 | "metadata": {}, 27 | "outputs": [ 28 | { 29 | "data": { 30 | "image/png": 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", 31 | "text/plain": [ 32 | "
" 33 | ] 34 | }, 35 | "metadata": {}, 36 | "output_type": "display_data" 37 | } 38 | ], 39 | "source": [ 40 | "data = np.array([1,2,3,3,5,7,7,8,9,10,20])\n", 41 | "q1 = np.percentile(data, 25) # 25% percentile/ Q1\n", 42 | "q3 = np.percentile(data, 75) # 75% percentile/ Q3\n", 43 | "iqr = q3 - q1 #inter quartile range\n", 44 | "\n", 45 | "upper_whisker = q3 + (1.5 * iqr) # Q3 + (1.5*IQR)\n", 46 | "lower_whisker = q1 - (1.5 * iqr) #Q1 – (1.5*IQR)\n", 47 | "\n", 48 | "\n", 49 | "outliers = (data < lower_whisker) | (data > upper_whisker)\n", 50 | "\n", 51 | "# Create the boxplot\n", 52 | "fig, ax = plt.subplots()\n", 53 | "ax.boxplot(data)\n", 54 | "\n", 55 | "# Show the plot\n", 56 | "ax.set_title('Box Plot Example')\n", 57 | "ax.set_xlabel('Data')\n", 58 | "ax.set_ylabel('Values')\n", 59 | "plt.show()\n" 60 | ] 61 | }, 62 | { 63 | "cell_type": "code", 64 | "execution_count": 3, 65 | "id": "8d52a6ee", 66 | "metadata": {}, 67 | "outputs": [ 68 | { 69 | "data": { 70 | "text/plain": [ 71 | "array([False, False, False, False, False, False, False, False, False,\n", 72 | " False, True])" 73 | ] 74 | }, 75 | "execution_count": 3, 76 | "metadata": {}, 77 | "output_type": "execute_result" 78 | } 79 | ], 80 | "source": [ 81 | "outliers" 82 | ] 83 | }, 84 | { 85 | "cell_type": "code", 86 | "execution_count": 4, 87 | "id": "f459f866", 88 | "metadata": {}, 89 | "outputs": [ 90 | { 91 | "data": { 92 | "text/plain": [ 93 | "Text(0, 0.5, 'Values')" 94 | ] 95 | }, 96 | "execution_count": 4, 97 | "metadata": {}, 98 | "output_type": "execute_result" 99 | }, 100 | { 101 | "data": { 102 | "image/png": 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", 103 | "text/plain": [ 104 | "
" 105 | ] 106 | }, 107 | "metadata": {}, 108 | "output_type": "display_data" 109 | } 110 | ], 111 | "source": [ 112 | "plt.boxplot(data)\n", 113 | "plt.title('Box Plot Example')\n", 114 | "plt.xlabel('Data')\n", 115 | "plt.ylabel('Values')" 116 | ] 117 | }, 118 | { 119 | "cell_type": "markdown", 120 | "id": "9aa02820", 121 | "metadata": {}, 122 | "source": [ 123 | "# Boxplot with Seaborn" 124 | ] 125 | }, 126 | { 127 | "cell_type": "code", 128 | "execution_count": 5, 129 | "id": "8dec849a", 130 | "metadata": {}, 131 | "outputs": [ 132 | { 133 | "data": { 134 | "image/png": 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", 135 | "text/plain": [ 136 | "
" 137 | ] 138 | }, 139 | "metadata": {}, 140 | "output_type": "display_data" 141 | } 142 | ], 143 | "source": [ 144 | "import seaborn as sns\n", 145 | "# Sample dataset\n", 146 | "data_points = [1,2,3,3,5,7,7,8,9,10,20]\n", 147 | "\n", 148 | "# Create a box plot\n", 149 | "sns.boxplot(data = data_points, width=0.1)\n", 150 | "\n", 151 | "# Add labels and title\n", 152 | "plt.xlabel('Data')\n", 153 | "plt.ylabel('Values')\n", 154 | "plt.title('Box Plot Example')\n", 155 | "\n", 156 | "# Display the plot\n", 157 | "plt.show()" 158 | ] 159 | }, 160 | { 161 | "cell_type": "code", 162 | "execution_count": null, 163 | "id": "fb100325", 164 | "metadata": {}, 165 | "outputs": [], 166 | "source": [] 167 | } 168 | ], 169 | "metadata": { 170 | "kernelspec": { 171 | "display_name": "Python 3 (ipykernel)", 172 | "language": "python", 173 | "name": "python3" 174 | }, 175 | "language_info": { 176 | "codemirror_mode": { 177 | "name": "ipython", 178 | "version": 3 179 | }, 180 | "file_extension": ".py", 181 | "mimetype": "text/x-python", 182 | "name": "python", 183 | "nbconvert_exporter": "python", 184 | "pygments_lexer": "ipython3", 185 | "version": "3.9.13" 186 | } 187 | }, 188 | "nbformat": 4, 189 | "nbformat_minor": 5 190 | } 191 | -------------------------------------------------------------------------------- /3. Matplotlib Bar Plot .ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "ce57ee69", 6 | "metadata": {}, 7 | "source": [ 8 | "**Bar Plot:** Bar plots are used for visualizing the distribution of *categorical data* or the relationship between two categorical variables. The x-axis of a bar plot represents the categories, and the y-axis represents the value associated with each category. Each bar represents a different category, and the height of the bar corresponds to the value.\n", 9 | "Bar plots are used when you have distinct categories and want to compare their values." 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": 1, 15 | "id": "5443a219", 16 | "metadata": {}, 17 | "outputs": [ 18 | { 19 | "data": { 20 | "text/plain": [ 21 | "" 22 | ] 23 | }, 24 | "execution_count": 1, 25 | "metadata": {}, 26 | "output_type": "execute_result" 27 | }, 28 | { 29 | "data": { 30 | "image/png": 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\n", 31 | "text/plain": [ 32 | "
" 33 | ] 34 | }, 35 | "metadata": {}, 36 | "output_type": "display_data" 37 | } 38 | ], 39 | "source": [ 40 | "import matplotlib.pyplot as plt\n", 41 | "\n", 42 | "categories = ['AI', 'Data Science', 'ML', 'DeepML']\n", 43 | "values = [20, 35, 30, 55]\n", 44 | "\n", 45 | "plt.bar(categories, values, color='blue')\n" 46 | ] 47 | }, 48 | { 49 | "cell_type": "code", 50 | "execution_count": 2, 51 | "id": "ad37bbcb", 52 | "metadata": {}, 53 | "outputs": [ 54 | { 55 | "data": { 56 | "text/plain": [ 57 | "" 58 | ] 59 | }, 60 | "execution_count": 2, 61 | "metadata": {}, 62 | "output_type": "execute_result" 63 | }, 64 | { 65 | "data": { 66 | "image/png": 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\n", 67 | "text/plain": [ 68 | "
" 69 | ] 70 | }, 71 | "metadata": {}, 72 | "output_type": "display_data" 73 | } 74 | ], 75 | "source": [ 76 | "plt.barh(categories, values, color='blue')" 77 | ] 78 | }, 79 | { 80 | "cell_type": "code", 81 | "execution_count": 3, 82 | "id": "9a5ffb52", 83 | "metadata": {}, 84 | "outputs": [ 85 | { 86 | "data": { 87 | "text/plain": [ 88 | "Text(0.5, 1.0, 'Bar Plot Example')" 89 | ] 90 | }, 91 | "execution_count": 3, 92 | "metadata": {}, 93 | "output_type": "execute_result" 94 | }, 95 | { 96 | "data": { 97 | "image/png": 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KK0WU5P27TM2ZAQAAKCnCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsLQyE2bi4uJks9kUExPjaDPGaOrUqQoNDZW3t7ciIyO1Z88e1xUJAADKnDIRZrZt26aFCxeqSZMmTu2zZs1SfHy85syZo23btikkJERRUVHKyspyUaUAAKCscXmYOXv2rAYOHKi33npLlSpVcrQbYzR79mxNmjRJffr0UaNGjbRkyRKdP39ey5cvd2HFAACgLHF5mBk1apS6deum+++/36k9NTVV6enp6tixo6PNbrerbdu22rx58x9dJgAAKKM8XHnnK1eu1I4dO7R9+/ZC69LT0yVJwcHBTu3BwcE6ePDgFfeZnZ2t7Oxsx3JmZuZNqhYAAJRFLhuZOXz4sJ566iktW7ZMXl5eV+xns9mclo0xhdp+Ly4uTgEBAY5bWFjYTasZAACUPS4LMzt27NCJEyd01113ycPDQx4eHkpOTtbrr78uDw8Px4hMwQhNgRMnThQarfm9CRMmKCMjw3E7fPhwqT4OAADgWi47zdShQwd99913Tm3Dhg1T/fr1NX78eNWuXVshISFat26dmjVrJkm6dOmSkpOTNXPmzCvu1263y263l2rtAACg7HBZmPHz81OjRo2c2nx9fRUUFORoj4mJUWxsrCIiIhQREaHY2Fj5+PhowIABrigZAACUQS6dAHwt48aN04ULFxQdHa0zZ86oZcuWWrt2rfz8/FxdGgAAKCNsxhjj6iJKU2ZmpgICApSRkSF/f39XlwMAKCVX+WwISlFppYiSvH+7/DozAAAAN4IwAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALM3D1QUAZZHN5uoKbl3GuLoCAFbDyAwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALC0EoeZw4cP68iRI47lrVu3KiYmRgsXLryphQEAABRHicPMgAEDlJSUJElKT09XVFSUtm7dqokTJ+r555+/6QUCAABcTYnDzPfff6977rlHkrRq1So1atRImzdv1vLly5WQkHCz6wMAALiqEoeZnJwc2e12SdL//d//6YEHHpAk1a9fX8eOHbu51QEAAFxDicPMnXfeqTfffFNffPGF1q1bp86dO0uSjh49qqCgoJteIAAAwNWUOMzMnDlTCxYsUGRkpPr376+mTZtKktasWeM4/QQAAPBHsRljTEk3ysvLU2ZmpipVquRoS0tLk4+Pj6pWrXpTC7xRmZmZCggIUEZGhvz9/V1dDizCZnN1Bbeukv9GAn7D69Y1Sus1W5L37+u6zowxRjt27NCCBQuUlZUlSfL09JSPj8/17A4AAOC6lTjMHDx4UI0bN1bPnj01atQonTx5UpI0a9YsjR07tkT7mj9/vpo0aSJ/f3/5+/urVatW+uSTTxzrjTGaOnWqQkND5e3trcjISO3Zs6ekJQMAgHKsxGHmqaeeUosWLXTmzBl5e3s72nv37q3169eXaF+33367XnzxRW3fvl3bt29X+/bt1bNnT0dgmTVrluLj4zVnzhxt27ZNISEhioqKcowGAQAAlHjOTOXKlfXll1+qXr168vPz0zfffKPatWsrLS1NDRs21Pnz52+ooMDAQL300ksaPny4QkNDFRMTo/Hjx0uSsrOzFRwcrJkzZ+qxxx4r1v6YM4Prwbl312HODK4Xr1vXsOScmfz8fOXl5RVqP3LkiPz8/Eq6O4e8vDytXLlS586dU6tWrZSamqr09HR17NjR0cdut6tt27bavHnzdd8PAAAoX0ocZqKiojR79mzHss1m09mzZzVlyhR17dq1xAV89913qlixoux2u0aOHKnVq1erYcOGSk9PlyQFBwc79Q8ODnasK0p2drYyMzOdbgAAoPzyKOkGr776qtq1a6eGDRvq4sWLGjBggPbt26fKlStrxYoVJS6gXr162r17t3799Vd98MEHGjJkiJKTkx3rbZeNGxpjCrX9XlxcnKZNm1biOgDcGjgV4TqcQkRpua7rzFy4cEErVqzQzp07lZ+fr+bNm2vgwIFOE4Kv1/333686depo/PjxqlOnjnbu3KlmzZo51vfs2VO33XablixZUuT22dnZys7OdixnZmYqLCyMOTMoEd7wXKe03/A4tq7DsS2fysKcmRKPzEiSt7e3hg8fruHDh19XgVdjjFF2drbCw8MVEhKidevWOcLMpUuXlJycrJkzZ15xe7vd7vjuKAAAUP6VOMwsXbr0qusHDx5c7H1NnDhRXbp0UVhYmLKysrRy5Upt3LhRn376qWw2m2JiYhQbG6uIiAhFREQoNjZWPj4+GjBgQEnLBgAA5VSJw8xTTz3ltJyTk6Pz5887rgBckjBz/PhxPfzwwzp27JgCAgLUpEkTffrpp4qKipIkjRs3ThcuXFB0dLTOnDmjli1bau3atTf0qSkAAFC+XNecmcvt27dPjz/+uJ555hl16tTpZtR103CdGVwPzr27DvMqyi+ObflUFubMXNd3M10uIiJCL774YqFRGwAAgNJ2U8KMJLm7u+vo0aM3a3cAAADFUuI5M2vWrHFaNsbo2LFjmjNnjtq0aXPTCgMAACiOEoeZXr16OS3bbDZVqVJF7du31yuvvHKz6gIAACiWEoeZ/Pz80qgDAADguty0OTMAAACuUKyRmTFjxhR7h/Hx8dddDAAAQEkVK8zs2rWrWDu72hdAAgAAlIZihZmkpKTSrgMAAOC6MGcGAABY2nV9a/a2bdv0/vvv69ChQ7p06ZLTusTExJtSGAAAQHGUeGRm5cqVatOmjX744QetXr1aOTk5+uGHH7RhwwYFBASURo0AAABXVOIwExsbq1dffVUfffSRPD099dprryklJUV9+/ZVjRo1SqNGAACAKypxmPn555/VrVs3SZLdbte5c+dks9n09NNPa+HChTe9QAAAgKspcZgJDAxUVlaWJKl69er6/vvvJUm//vqrzp8/f3OrAwAAuIZih5ndu3dLkv7yl79o3bp1kqS+ffvqqaee0iOPPKL+/furQ4cOpVIkAADAlRT700zNmzdXs2bN1KtXL/Xv31+SNGHCBFWoUEGbNm1Snz59NHny5FIrFAAAoCg2Y4wpTsevvvpKixYt0qpVq5STk6M+ffpoxIgRateuXWnXeEMyMzMVEBCgjIwM+fv7u7ocWAQXs3ad4v1Gun4cW9fh2JZPpXVcS/L+XezTTK1atdJbb72l9PR0zZ8/X0eOHNH999+vOnXqaMaMGTpy5MgNFw4AAFBSJZ4A7O3trSFDhmjjxo366aef1L9/fy1YsEDh4eHq2rVradQIAABwRTf0dQZ16tTR3//+d02aNEn+/v767LPPblZdAAAAxXJdX2cgScnJyVq0aJE++OADubu7q2/fvhoxYsTNrA0AAOCaShRmDh8+rISEBCUkJCg1NVWtW7fWG2+8ob59+8rX17e0agQAALiiYoeZqKgoJSUlqUqVKho8eLCGDx+uevXqlWZtAAAA11TsMOPt7a0PPvhA3bt3l7u7e2nWBAAAUGzFDjNr1qwpzToAAACuyw19mgkAAMDVCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSXBpm4uLidPfdd8vPz09Vq1ZVr169tHfvXqc+xhhNnTpVoaGh8vb2VmRkpPbs2eOiigEAQFnj0jCTnJysUaNGacuWLVq3bp1yc3PVsWNHnTt3ztFn1qxZio+P15w5c7Rt2zaFhIQoKipKWVlZLqwcAACUFTZjjHF1EQVOnjypqlWrKjk5Wffdd5+MMQoNDVVMTIzGjx8vScrOzlZwcLBmzpypxx577Jr7zMzMVEBAgDIyMuTv71/aDwHlhM3m6gpuXaX9G4lj6zoc2/KptI5rSd6/y9ScmYyMDElSYGCgJCk1NVXp6enq2LGjo4/dblfbtm21efNml9QIAADKFg9XF1DAGKMxY8boz3/+sxo1aiRJSk9PlyQFBwc79Q0ODtbBgweL3E92drays7Mdy5mZmaVUMQAAKAvKTJh54okn9O2332rTpk2F1tkuGzs0xhRqKxAXF6dp06aVSo1FYVjTdcrOCVIAgCuVidNMo0eP1po1a5SUlKTbb7/d0R4SEiLpvyM0BU6cOFFotKbAhAkTlJGR4bgdPny49AoHAAAu59IwY4zRE088ocTERG3YsEHh4eFO68PDwxUSEqJ169Y52i5duqTk5GS1bt26yH3a7Xb5+/s73QAAQPnl0tNMo0aN0vLly/Wvf/1Lfn5+jhGYgIAAeXt7y2azKSYmRrGxsYqIiFBERIRiY2Pl4+OjAQMGuLJ0AABQRrg0zMyfP1+SFBkZ6dS+ePFiDR06VJI0btw4XbhwQdHR0Tpz5oxatmyptWvXys/P7w+uFgAAlEVl6jozpaG0rzPDBGDXKc2fXI6r63AtkvKLY1s+cZ0ZAACAG0SYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlubSMPP555+rR48eCg0Nlc1m04cffui03hijqVOnKjQ0VN7e3oqMjNSePXtcUywAACiTXBpmzp07p6ZNm2rOnDlFrp81a5bi4+M1Z84cbdu2TSEhIYqKilJWVtYfXCkAACirPFx55126dFGXLl2KXGeM0ezZszVp0iT16dNHkrRkyRIFBwdr+fLleuyxx/7IUgEAQBlVZufMpKamKj09XR07dnS02e12tW3bVps3b3ZhZQAAoCxx6cjM1aSnp0uSgoODndqDg4N18ODBK26XnZ2t7Oxsx3JmZmbpFAgAAMqEMjsyU8BmszktG2MKtf1eXFycAgICHLewsLDSLhEAALhQmQ0zISEhkv47QlPgxIkThUZrfm/ChAnKyMhw3A4fPlyqdQIAANcqs2EmPDxcISEhWrdunaPt0qVLSk5OVuvWra+4nd1ul7+/v9MNAACUXy6dM3P27Fnt37/fsZyamqrdu3crMDBQNWrUUExMjGJjYxUREaGIiAjFxsbKx8dHAwYMcGHVAACgLHFpmNm+fbvatWvnWB4zZowkaciQIUpISNC4ceN04cIFRUdH68yZM2rZsqXWrl0rPz8/V5UMAADKGJsxxri6iNKUmZmpgIAAZWRklMopp6vMRUYpK82fXI6r65T2bySOretwbMun0jquJXn/LrNzZgAAAIqDMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACzNEmFm3rx5Cg8Pl5eXl+666y598cUXri4JAACUEWU+zLz33nuKiYnRpEmTtGvXLv3lL39Rly5ddOjQIVeXBgAAygCbMca4uoiradmypZo3b6758+c72ho0aKBevXopLi7umttnZmYqICBAGRkZ8vf3v+n12Ww3fZcoptL8yeW4uk5p/0bi2LoOx7Z8Kq3jWpL37zI9MnPp0iXt2LFDHTt2dGrv2LGjNm/e7KKqAABAWeLh6gKu5tSpU8rLy1NwcLBTe3BwsNLT04vcJjs7W9nZ2Y7ljIwMSb8lPJQvHNLyieNafnFsy6fSOq4F79vFOYFUpsNMAdtlY4fGmEJtBeLi4jRt2rRC7WFhYaVSG1wnIMDVFaA0cFzLL45t+VTaxzUrK0sB17iTMh1mKleuLHd390KjMCdOnCg0WlNgwoQJGjNmjGM5Pz9fv/zyi4KCgq4YgG5FmZmZCgsL0+HDh0tlLhFch2NbPnFcyy+ObdGMMcrKylJoaOg1+5bpMOPp6am77rpL69atU+/evR3t69atU8+ePYvcxm63y263O7XddtttpVmmpfn7+/PiKac4tuUTx7X84tgWdq0RmQJlOsxI0pgxY/Twww+rRYsWatWqlRYuXKhDhw5p5MiRri4NAACUAWU+zPTr10+nT5/W888/r2PHjqlRo0b6+OOPVbNmTVeXBgAAyoAyH2YkKTo6WtHR0a4uo1yx2+2aMmVKoVNysD6ObfnEcS2/OLY3rsxfNA8AAOBqyvRF8wAAAK6FMAMAACyNMAMAACyNMAPcoqZOnao//elPri4DAG4YYeYWsnnzZrm7u6tz585O7WlpabLZbNq9e7drCivjhg4dKpvNJpvNpgoVKig4OFhRUVFatGiR8vPzS7SvhISEm3YRxwMHDqh///4KDQ2Vl5eXbr/9dvXs2VM//fRTsbYfO3as1q9ff1Nqwc1R8LNW1HW0oqOjZbPZNHToUEffXr16/bEF3mJu5mv/ZiioZcuWLU7t2dnZjqvcb9y40an/hx9++McW6SKEmVvIokWLNHr0aG3atEmHDh1ydTmW0rlzZx07dkxpaWn65JNP1K5dOz311FPq3r27cnNz//B6Ll26pKioKGVmZioxMVF79+7Ve++9p0aNGjm+XPVaKlasqKCgoFKuFCUVFhamlStX6sKFC462ixcvasWKFapRo4YLK7s1lbXXflhYmBYvXuzUtnr1alWsWPEPr6UsIczcIs6dO6dVq1bp8ccfV/fu3ZWQkODqkizFbrcrJCRE1atXV/PmzTVx4kT961//0ieffOL0XMbHx6tx48by9fVVWFiYoqOjdfbsWUnSxo0bNWzYMGVkZDj+wpo6daok6d1331WLFi3k5+enkJAQDRgwQCdOnLhiPT/88IMOHDigefPm6d5771XNmjXVpk0bzZgxQ3fffbej35EjR/S3v/1NgYGB8vX1VYsWLfT1119LKvo00+LFi9WgQQN5eXmpfv36mjdvnmNdwQheYmKi2rVrJx8fHzVt2lRfffWV0z6+/PJLtW3bVj4+PqpUqZI6deqkM2fOSPrtu1ZmzZql2rVry9vbW02bNtU///nPEh+P8qx58+aqUaOGEhMTHW2JiYkKCwtTs2bNXFjZrak4r/2MjAw9+uijqlq1qvz9/dW+fXt98803Tvv53//9X911113y8vJS7dq1NW3aNKcwZLPZNH/+fHXp0kXe3t4KDw/X+++/X6ieIUOGFAq7ixYt0pAhQ0rnCbAIwswt4r333lO9evVUr149DRo0SIsXLy7W16rjytq3b6+mTZs6vem4ubnp9ddf1/fff68lS5Zow4YNGjdunCSpdevWmj17tvz9/XXs2DEdO3ZMY8eOlfTbSMsLL7ygb775Rh9++KFSU1MdpxOKUqVKFbm5uemf//yn8vLyiuxz9uxZtW3bVkePHtWaNWv0zTffaNy4cVccHn/rrbc0adIkzZgxQykpKYqNjdXkyZO1ZMkSp36TJk3S2LFjtXv3btWtW1f9+/d3/FLevXu3OnTooDvvvFNfffWVNm3apB49ejhqfPbZZ7V48WLNnz9fe/bs0dNPP61BgwYpOTm5eE/6LWLYsGFOf30vWrRIw4cPd2FF+L3fv/aNMerWrZvS09P18ccfa8eOHWrevLk6dOigX375RZL02WefadCgQXryySf1ww8/aMGCBUpISNCMGTOc9jt58mT99a9/1TfffKNBgwapf//+SklJcepz1113KTw8XB988IEk6fDhw/r888/18MMP/zEPvqwyuCW0bt3azJ492xhjTE5OjqlcubJZt26dMcaY1NRUI8ns2rXLhRWWXUOGDDE9e/Yscl2/fv1MgwYNrrjtqlWrTFBQkGN58eLFJiAg4Jr3uXXrViPJZGVlXbHPnDlzjI+Pj/Hz8zPt2rUzzz//vPn5558d6xcsWGD8/PzM6dOni9x+ypQppmnTpo7lsLAws3z5cqc+L7zwgmnVqpUx5r8/J//4xz8c6/fs2WMkmZSUFGOMMf379zdt2rQp8v7Onj1rvLy8zObNm53aR4wYYfr373/Fx3krKfhZO3nypLHb7SY1NdWkpaUZLy8vc/LkSdOzZ08zZMgQp74oPcV57a9fv974+/ubixcvOq2vU6eOWbBggTHGmL/85S8mNjbWaf0777xjqlWr5liWZEaOHOnUp2XLlubxxx936rN69Woze/Zs065dO2OMMdOmTTO9e/c2Z86cMZJMUlJSof63Akt8nQFuzN69e7V161bHCIKHh4f69eunRYsW6f7773dxddZmjJHNZnMsJyUlKTY2Vj/88IMyMzOVm5urixcv6ty5c/L19b3ifnbt2qWpU6dq9+7d+uWXXxyjJ4cOHVLDhg2L3GbUqFEaPHiwkpKS9PXXX+v9999XbGys1qxZo6ioKO3evVvNmjVTYGDgNR/HyZMndfjwYY0YMUKPPPKIoz03N7fQt9Y2adLE8f9q1apJkk6cOKH69etr9+7deuihh4q8jx9++EEXL15UVFSUU/ulS5c4fXKZypUrq1u3blqyZInjL//KlSu7uiz8TsFrf8eOHTp79myh+WcXLlzQzz//LEnasWOHtm3b5jQSk5eXp4sXL+r8+fPy8fGRJLVq1cppH61atSrygxmDBg3S3//+dx04cEAJCQl6/fXXb/Kjsx7CzC3g7bffVm5urqpXr+5oM8aoQoUKjrkMuD4pKSkKDw+XJB08eFBdu3bVyJEj9cILLygwMFCbNm3SiBEjlJOTc8V9nDt3Th07dlTHjh317rvvqkqVKjp06JA6deqkS5cuXfX+/fz89MADD+iBBx7Q9OnT1alTJ02fPl1RUVHy9vYu9uMoCE9vvfWWWrZs6bTO3d3dablChQqO/xcEuYLtr3afBX3+/e9/O/0sSuI7aYowfPhwPfHEE5KkuXPnurgaXK7gtZ+fn69q1ao5fYqoQMEnF/Pz8zVt2jT16dOnUB8vL6+r3s/v/1gqEBQUpO7du2vEiBG6ePGiunTpoqysrOt6HOUFYaacy83N1dKlS/XKK6+oY8eOTuv++te/atmyZerevbuLqrO2DRs26LvvvtPTTz8tSdq+fbtyc3P1yiuvyM3tt+loq1atctrG09Oz0ByXH3/8UadOndKLL76osLAwx75KymazqX79+tq8ebOk30ZQ/vGPf+iXX3655uhMcHCwqlevrgMHDmjgwIElvu8CTZo00fr16zVt2rRC6xo2bCi73a5Dhw6pbdu2130ft4rOnTs7wmynTp1cXA1+7/ev/dtvv13p6eny8PBQrVq1iuzfvHlz7d27V3fcccdV97tlyxYNHjzYaflKo5bDhw9X165dNX78+EJ/cNyKCDPl3EcffaQzZ85oxIgRhU4XPPjgg3r77bcJM8WQnZ2t9PR05eXl6fjx4/r0008VFxen7t27O3751KlTR7m5uXrjjTfUo0cPffnll3rzzTed9lOrVi2dPXtW69evV9OmTeXj46MaNWrI09NTb7zxhkaOHKnvv/9eL7zwwlXr2b17t6ZMmaKHH35YDRs2lKenp5KTk7Vo0SKNHz9ektS/f3/FxsaqV69eiouLU7Vq1bRr1y6FhoYWGs6Wfvt005NPPil/f3916dJF2dnZ2r59u86cOaMxY8YU63maMGGCGjdurOjoaI0cOVKenp5KSkrSQw89pMqVK2vs2LF6+umnlZ+frz//+c/KzMzU5s2bVbFixVv+0xiXc3d3d0z+vNKbVUZGRqHTEIGBgXyE+ya61mvfzc1NrVq1Uq9evTRz5kzVq1dPR48e1ccff6xevXqpRYsWeu6559S9e3eFhYXpoYcekpubm7799lt99913mj59uuO+3n//fbVo0UJ//vOftWzZMm3dulVvv/12kXV17txZJ0+elL+//1XrT01NLfQzcscdd5S/j3K7dsoOSlv37t1N165di1y3Y8cOI8nxLxOAizZkyBAjyUgyHh4epkqVKub+++83ixYtMnl5eU594+PjTbVq1Yy3t7fp1KmTWbp0qZFkzpw54+gzcuRIExQUZCSZKVOmGGOMWb58ualVq5ax2+2mVatWZs2aNVc9JidPnjRPPvmkadSokalYsaLx8/MzjRs3Ni+//LJTTWlpaeavf/2r8ff3Nz4+PqZFixbm66+/NsYUngBsjDHLli0zf/rTn4ynp6epVKmSue+++0xiYqIxpuiJ4kVNOty4caNp3bq1sdvt5rbbbjOdOnVyPP78/Hzz2muvmXr16pkKFSqYKlWqmE6dOpnk5OTiH5By7FqTei+fAFzwc/n7W8F63LjivvYzMzPN6NGjTWhoqKlQoYIJCwszAwcONIcOHXL0+fTTT03r1q2Nt7e38ff3N/fcc49ZuHChY70kM3fuXBMVFWXsdrupWbOmWbFihVM9usqE3itNAC7q9vs+5YXNGD6fCwCAK9lsNq1evZqrOl8nrjMDAAAsjTADAAAsjQnAAAC4GDM+bgwjMwAAwNIIMwAAwNIIMwAAwNIIMwAAwNIIMwAgaePGjbLZbPr1119dXQqAEiLMACix9PR0jR49WrVr15bdbldYWJh69Oih9evXF2v7hIQEx5fwlRWtW7fWsWPHCn3tB4Cyj49mAyiRtLQ0tWnTRrfddptmzZqlJk2aKCcnR5999plGjRqlH3/80dUlllhOTo48PT0VEhLi6lIAXAdGZgCUSHR0tGw2m7Zu3aoHH3xQdevW1Z133qkxY8Zoy5YtkqT4+Hg1btxYvr6+CgsLU3R0tM6ePSvpt9M5w4YNU0ZGhmw2m2w2m6ZOnSpJunTpksaNG6fq1avL19dXLVu21MaNG53u/6233lJYWJh8fHzUu3dvxcfHFxrlmT9/vurUqSNPT0/Vq1dP77zzjtN6m82mN998Uz179pSvr6+mT59e5GmmzZs367777pO3t7fCwsL05JNP6ty5c4718+bNU0REhLy8vBQcHKwHH3zw5jzJAErGxd8NBcBCTp8+bWw2m4mNjb1qv1dffdVs2LDBHDhwwKxfv97Uq1fPPP7448YYY7Kzs83s2bONv7+/OXbsmDl27JjJysoyxhgzYMAA07p1a/P555+b/fv3m5deesnY7Xbz008/GWOM2bRpk3FzczMvvfSS2bt3r5k7d64JDAw0AQEBjvtOTEw0FSpUMHPnzjV79+41r7zyinF3dzcbNmxw9JFkqlatat5++23z888/m7S0NJOUlOT0paDffvutqVixonn11VfNTz/9ZL788kvTrFkzM3ToUGOMMdu2bTPu7u5m+fLlJi0tzezcudO89tprN+upBlAChBkAxfb1118bSY5v0i6uVatWmaCgIMfy4sWLnQKIMcbs37/f2Gw285///MepvUOHDmbChAnGGGP69etnunXr5rR+4MCBTvtq3bq1eeSRR5z6PPTQQ07fHi/JxMTEOPW5PMw8/PDD5tFHH3Xq88UXXxg3Nzdz4cIF88EHHxh/f3+TmZl57ScAQKniNBOAYjP//5LrNpvtqv2SkpIUFRWl6tWry8/PT4MHD9bp06edTtFcbufOnTLGqG7duqpYsaLjlpycrJ9//lmStHfvXt1zzz1O212+nJKSojZt2ji1tWnTRikpKU5tLVq0uOpj2LFjhxISEpxq6dSpk/Lz85WamqqoqCjVrFlTtWvX1sMPP6xly5bp/PnzV90ngNLBBGAAxRYRESGbzaaUlBT16tWryD4HDx5U165dNXLkSL3wwgsKDAzUpk2bNGLECOXk5Fxx3/n5+XJ3d9eOHTvk7u7utK5ixYqSfgtTlwcpU8R32hTV5/I2X1/fK9ZSUM9jjz2mJ598stC6GjVqyNPTUzt37tTGjRu1du1aPffcc5o6daq2bdtW5j6pBZR3jMwAKLbAwEB16tRJc+fOLXKU5ddff9X27duVm5urV155Rffee6/q1q2ro0ePOvXz9PRUXl6eU1uzZs2Ul5enEydO6I477nC6FXzKqH79+tq6davTdtu3b3dabtCggTZt2uTUtnnzZjVo0KBEj7V58+bas2dPoVruuOMOeXp6SpI8PDx0//33a9asWfr222+VlpamDRs2lOh+ANw4wgyAEpk3b57y8vJ0zz336IMPPtC+ffuUkpKi119/Xa1atVKdOnWUm5urN954QwcOHNA777yjN99802kftWrV0tmzZ7V+/XqdOnVK58+fV926dTVw4EANHjxYiYmJSk1N1bZt2zRz5kx9/PHHkqTRo0fr448/Vnx8vPbt26cFCxbok08+cRp1eeaZZ5SQkKA333xT+/btU3x8vBITEzV27NgSPc7x48frq6++0qhRo7R7927t27dPa9as0ejRoyVJH330kV5//XXt3r1bBw8e1NKlS5Wfn6969erd4DMMoMRcOmMHgCUdPXrUjBo1ytSsWdN4enqa6tWrmwceeMAkJSUZY4yJj4831apVM97e3qZTp05m6dKlTpNrjTFm5MiRJigoyEgyU6ZMMcYYc+nSJfPcc8+ZWrVqmQoVKpiQkBDTu3dv8+233zq2W7hwoalevbrx9vY2vXr1MtOnTzchISFO9c2bN8/Url3bVKhQwdStW9csXbrUab0ks3r1aqe2yycAG2PM1q1bTVRUlKlYsaLx9fU1TZo0MTNmzDDG/DYZuG3btqZSpUrG29vbNGnSxLz33ns39sQCuC42Y4o44QwAFvHII4/oxx9/1BdffOHqUgC4CBOAAVjKyy+/rKioKPn6+uqTTz7RkiVLNG/ePFeXBcCFGJkBYCl9+/bVxo0blZWVpdq1a2v06NEaOXKkq8sC4EKEGQAAYGl8mgkAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFja/wMFOdSp6bb0+AAAAABJRU5ErkJggg==\n", 98 | "text/plain": [ 99 | "
" 100 | ] 101 | }, 102 | "metadata": {}, 103 | "output_type": "display_data" 104 | } 105 | ], 106 | "source": [ 107 | "import matplotlib.pyplot as plt\n", 108 | "\n", 109 | "categories = ['AI', 'Data Science', 'ML', 'DeepML']\n", 110 | "values = [20, 35, 30, 55]\n", 111 | "\n", 112 | "plt.bar(categories, values, color='blue')\n", 113 | "\n", 114 | "plt.xlabel('Categories')\n", 115 | "plt.ylabel('Values')\n", 116 | "plt.title('Bar Plot Example')\n" 117 | ] 118 | }, 119 | { 120 | "cell_type": "code", 121 | "execution_count": 4, 122 | "id": "76d32837", 123 | "metadata": {}, 124 | "outputs": [ 125 | { 126 | "data": { 127 | "image/png": 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\n", 128 | "text/plain": [ 129 | "
" 130 | ] 131 | }, 132 | "metadata": {}, 133 | "output_type": "display_data" 134 | } 135 | ], 136 | "source": [ 137 | "categories = ['AI', 'Data Science', 'ML', 'DeepML']\n", 138 | "values = [20, 35, 30, 55]\n", 139 | "\n", 140 | "#plt.bar(categories, values, color='blue')\n", 141 | "\n", 142 | "fig, ax = plt.subplots()\n", 143 | "bars = ax.bar(categories, values, color='orange')\n", 144 | "\n", 145 | "for bar in bars:\n", 146 | " yval = bar.get_height()\n", 147 | " plt.text(bar.get_x() + bar.get_width()/2, yval, round(yval, 1), ha='center', va='bottom')\n", 148 | "\n", 149 | "plt.xlabel('Categories')\n", 150 | "plt.ylabel('Quantity')\n", 151 | "plt.title('Bar Plot with Quantity and Values')\n", 152 | "\n", 153 | "plt.show()" 154 | ] 155 | }, 156 | { 157 | "cell_type": "code", 158 | "execution_count": null, 159 | "id": "da084e87", 160 | "metadata": {}, 161 | "outputs": [], 162 | "source": [] 163 | } 164 | ], 165 | "metadata": { 166 | "kernelspec": { 167 | "display_name": "Python 3 (ipykernel)", 168 | "language": "python", 169 | "name": "python3" 170 | }, 171 | "language_info": { 172 | "codemirror_mode": { 173 | "name": "ipython", 174 | "version": 3 175 | }, 176 | "file_extension": ".py", 177 | "mimetype": "text/x-python", 178 | "name": "python", 179 | "nbconvert_exporter": "python", 180 | "pygments_lexer": "ipython3", 181 | "version": "3.9.13" 182 | } 183 | }, 184 | "nbformat": 4, 185 | "nbformat_minor": 5 186 | } 187 | --------------------------------------------------------------------------------