In addition to the core Python, we installed the MySQL Connector for Python. At Meteomatics, we see ourselves as the point of contact for the world's best weather data. Winds NE at 5 to 10 mph. New Study Supports Popular Theory, Astounding Levels of 'Forever Chemicals' Found In Fish, Heres Why Its So Quiet Outside After It Snows, Scientists Redirect Lightning In A Storm For The First Time, Satellite Images Show Trees Mowed Like Grass After Selma Tornado, The Case Of The Missing Snow In Some Northeast Big Cities, This Cozy Cashmere Shawl is Under $15 and Makes A Great Winter Gift. For each point on the globe, we provide historical, current and forecasted weather data via light-speed APIs. The README on Github comprises the Weather Analysis portion of the project. The URL Get quick and easy access to the world's most accurate weather, marine, environmental and climate data for every coordinate on Earth. We are reading to load some weather data! The data sources in SWDI will not provide complete severe weather coverage of a geographic region or time period due to a number of factors (e.g., reports for a location or time period not provided to NOAA). Weather Database relates to Development Tools. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n", "text": "[10 10 10 9 4 4]\n 0\n0 \n3 6804\n4 1966\n9 7225\n10 10142\n\n Accuracy Score\n0.9508742395837319\n\nClassification Report\n precision recall f1-score support\n\n 0 0.00 0.00 0.00 217\n 1 0.00 0.00 0.00 152\n 2 0.00 0.00 0.00 110\n 3 0.99 0.99 0.99 6766\n 4 0.74 0.79 0.77 1828\n 5 0.00 0.00 0.00 6\n 6 0.00 0.00 0.00 4\n 7 0.00 0.00 0.00 7\n 8 0.00 0.00 0.00 6\n 9 0.92 0.96 0.94 6965\n 10 0.99 0.99 0.99 10076\n\n accuracy 0.95 26137\n macro avg 0.33 0.34 0.34 26137\nweighted avg 0.93 0.95 0.94 26137\n\nConfusion Matrix\n[[ 0 0 0 0 209 0 0 0 0 8 0]\n [ 0 0 0 6 0 0 0 0 0 123 23]\n [ 0 0 0 13 15 0 0 0 0 55 27]\n [ 0 0 0 6720 0 0 0 0 0 0 46]\n [ 0 0 0 1 1453 0 0 0 0 339 35]\n [ 0 0 0 0 2 0 0 0 0 2 2]\n [ 0 0 0 0 2 0 0 0 0 2 0]\n [ 0 0 0 2 0 0 0 0 0 0 5]\n [ 0 0 0 6 0 0 0 0 0 0 0]\n [ 0 0 0 0 285 0 0 0 0 6678 2]\n [ 0 0 0 56 0 0 0 0 0 18 10002]]\n", "text": "[10 10 10 9 4 4]\n 0\n0 \n3 6771\n4 1956\n9 7247\n10 10163\n\n Accuracy Score\n0.9520602976623178\n\nClassification Report\n precision recall f1-score support\n\n 0 0.00 0.00 0.00 217\n 1 0.00 0.00 0.00 152\n 2 0.00 0.00 0.00 110\n 3 0.99 0.99 0.99 6766\n 4 0.74 0.79 0.77 1828\n 5 0.00 0.00 0.00 6\n 6 0.00 0.00 0.00 4\n 7 0.00 0.00 0.00 7\n 8 0.00 0.00 0.00 6\n 9 0.92 0.96 0.94 6965\n 10 0.99 1.00 0.99 10076\n\n accuracy 0.95 26137\n macro avg 0.33 0.34 0.34 26137\nweighted avg 0.94 0.95 0.94 26137\n\nConfusion Matrix\n[[ 0 0 0 0 209 0 0 0 0 8 0]\n [ 0 0 0 6 0 0 0 0 0 123 23]\n [ 0 0 0 12 7 0 0 0 0 63 28]\n [ 0 0 0 6724 0 0 0 0 0 0 42]\n [ 0 0 0 1 1451 0 0 0 0 342 34]\n [ 0 0 0 0 2 0 0 0 0 2 2]\n [ 0 0 0 0 2 0 0 0 0 2 0]\n [ 0 0 0 2 0 0 0 0 0 0 5]\n [ 0 0 0 6 0 0 0 0 0 0 0]\n [ 0 0 0 0 285 0 0 0 0 6680 0]\n [ 0 0 0 20 0 0 0 0 0 27 10029]]\n", "text": "[1 1 1 0 0 0]\n 0\n0 \n0 2977\n1 19729\n3 1052\n4 9\n5 239\n6 35\n7 1862\n8 19\n10 215\n\n Accuracy Score\n0.05543865018938669\n\nClassification Report\n precision recall f1-score support\n\n 0 0.07 1.00 0.14 217\n 1 0.01 1.00 0.02 152\n 2 0.00 0.00 0.00 110\n 3 1.00 0.16 0.27 6766\n 4 0.00 0.00 0.00 1828\n 5 0.01 0.33 0.02 6\n 6 0.06 0.50 0.10 4\n 7 0.00 1.00 0.01 7\n 8 0.26 0.83 0.40 6\n 9 0.00 0.00 0.00 6965\n 10 0.06 0.00 0.00 10076\n\n accuracy 0.06 26137\n macro avg 0.13 0.44 0.09 26137\nweighted avg 0.28 0.06 0.07 26137\n\nConfusion Matrix\n[[ 217 0 0 0 0 0 0 0 0 0 0]\n [ 0 152 0 0 0 0 0 0 0 0 0]\n [ 13 86 0 0 6 1 3 1 0 0 0]\n [ 0 5499 0 1051 0 0 0 0 14 0 202]\n [1640 129 0 0 0 15 20 24 0 0 0]\n [ 4 0 0 0 0 2 0 0 0 0 0]\n [ 2 0 0 0 0 0 2 0 0 0 0]\n [ 0 0 0 0 0 0 0 7 0 0 0]\n [ 0 0 0 1 0 0 0 0 5 0 0]\n [1101 5848 0 0 0 5 10 1 0 0 0]\n [ 0 8015 0 0 3 216 0 1829 0 0 13]]\n", "text": "[9 9 9 9 9 9]\n 0\n0 \n3 19\n7 661\n8 1235\n9 22770\n10 1452\n\n Accuracy Score\n0.3204269809082909\n\nClassification Report\n precision recall f1-score support\n\n 0 0.00 0.00 0.00 217\n 1 0.00 0.00 0.00 152\n 2 0.00 0.00 0.00 110\n 3 0.00 0.00 0.00 6766\n 4 0.00 0.00 0.00 1828\n 5 0.00 0.00 0.00 6\n 6 0.00 0.00 0.00 4\n 7 0.00 0.29 0.01 7\n 8 0.00 1.00 0.01 6\n 9 0.31 1.00 0.47 6965\n 10 0.97 0.14 0.24 10076\n\n accuracy 0.32 26137\n macro avg 0.12 0.22 0.07 26137\nweighted avg 0.45 0.32 0.22 26137\n\nConfusion Matrix\n[[ 0 0 0 0 0 0 0 0 0 217 0]\n [ 0 0 0 0 0 0 0 0 0 152 0]\n [ 0 0 0 0 0 0 0 2 0 103 5]\n [ 0 0 0 0 0 0 0 18 1228 5520 0]\n [ 0 0 0 0 0 0 0 1 0 1791 36]\n [ 0 0 0 0 0 0 0 0 0 4 2]\n [ 0 0 0 0 0 0 0 0 0 4 0]\n [ 0 0 0 0 0 0 0 2 0 0 5]\n [ 0 0 0 0 0 0 0 0 6 0 0]\n [ 0 0 0 0 0 0 0 0 0 6964 1]\n [ 0 0 0 19 0 0 0 638 1 8015 1403]]\n", "text": " precision recall f1-score support\n\n 0 0.00 0.00 0.00 217\n 1 0.00 0.00 0.00 152\n 2 0.00 0.00 0.00 110\n 3 1.00 0.12 0.21 6766\n 4 0.00 0.00 0.00 1828\n 5 0.00 0.00 0.00 6\n 6 0.00 0.00 0.00 4\n 7 0.00 0.00 0.00 7\n 8 0.00 0.00 0.00 6\n 9 0.00 0.00 0.00 6965\n 10 0.40 1.00 0.57 10076\n\n accuracy 0.42 26137\n macro avg 0.13 0.10 0.07 26137\nweighted avg 0.41 0.42 0.27 26137\n\nConfusion Matrix\n[[ 0 0 0 0 0 0 0 0 0 0 217]\n [ 0 0 0 0 0 0 0 0 0 0 152]\n [ 0 0 0 0 0 0 0 0 0 0 110]\n [ 0 0 0 801 0 0 0 0 0 0 5965]\n [ 0 0 0 0 0 0 0 0 0 0 1828]\n [ 0 0 0 0 0 0 0 0 0 0 6]\n [ 0 0 0 0 0 0 0 0 0 0 4]\n [ 0 0 0 0 0 0 0 0 0 0 7]\n [ 0 0 0 0 0 0 0 0 0 0 6]\n [ 0 0 0 0 0 0 0 0 0 0 6965]\n [ 0 0 0 0 0 0 0 0 0 0 10076]]\n", "text": "[10 10 10 3 3 3]\n 0\n0 \n3 4350\n4 2037\n9 6946\n10 12804\n\n Accuracy Score\n0.2898190304931706\n\nClassification Report\n precision recall f1-score support\n\n 0 0.00 0.00 0.00 217\n 1 0.00 0.00 0.00 152\n 2 0.00 0.00 0.00 110\n 3 0.00 0.00 0.00 6766\n 4 0.00 0.00 0.00 1828\n 5 0.00 0.00 0.00 6\n 6 0.00 0.00 0.00 4\n 7 0.00 0.00 0.00 7\n 8 0.00 0.00 0.00 6\n 9 0.31 0.31 0.31 6965\n 10 0.42 0.54 0.47 10076\n\n accuracy 0.29 26137\n macro avg 0.07 0.08 0.07 26137\nweighted avg 0.25 0.29 0.27 26137\n\nConfusion Matrix\n[[ 0 0 0 217 0 0 0 0 0 0 0]\n [ 0 0 0 0 0 0 0 0 0 2 150]\n [ 0 0 0 21 3 0 0 0 0 38 48]\n [ 0 0 0 0 1246 0 0 0 0 1989 3531]\n [ 0 0 0 1696 3 0 0 0 0 112 17]\n [ 0 0 0 6 0 0 0 0 0 0 0]\n [ 0 0 0 4 0 0 0 0 0 0 0]\n [ 0 0 0 3 4 0 0 0 0 0 0]\n [ 0 0 0 0 6 0 0 0 0 0 0]\n [ 0 0 0 1117 0 0 0 0 0 2181 3667]\n [ 0 0 0 1286 775 0 0 0 0 2624 5391]]\n", "text": "[10 10 10 9 4 4]\n 0\n0 \n3 6783\n4 2141\n8 7\n9 7033\n10 10173\n\n Accuracy Score\n0.9541645942533573\n\nClassification Report\n precision recall f1-score support\n\n 0 0.00 0.00 0.00 217\n 1 0.00 0.00 0.00 152\n 2 0.00 0.00 0.00 110\n 3 1.00 1.00 1.00 6766\n 4 0.71 0.83 0.77 1828\n 5 0.00 0.00 0.00 6\n 6 0.00 0.00 0.00 4\n 7 0.00 0.00 0.00 7\n 8 0.71 0.83 0.77 6\n 9 0.93 0.94 0.94 6965\n 10 0.99 1.00 1.00 10076\n\n accuracy 0.95 26137\n macro avg 0.40 0.42 0.41 26137\nweighted avg 0.94 0.95 0.95 26137\n\nConfusion Matrix\n[[ 0 0 0 0 209 0 0 0 0 8 0]\n [ 0 0 0 6 1 0 0 0 0 122 23]\n [ 0 0 0 11 14 0 0 0 0 57 28]\n [ 0 0 0 6763 0 0 0 0 2 0 1]\n [ 0 0 0 0 1523 0 0 0 0 268 37]\n [ 0 0 0 0 2 0 0 0 0 2 2]\n [ 0 0 0 0 2 0 0 0 0 2 0]\n [ 0 0 0 0 0 0 0 0 0 0 7]\n [ 0 0 0 1 0 0 0 0 5 0 0]\n [ 0 0 0 0 390 0 0 0 0 6574 1]\n [ 0 0 0 2 0 0 0 0 0 0 10074]]\n", "text": "[10 10 10 4 4 4]\n 0\n0 \n3 5219\n4 3259\n10 17659\n\n Accuracy Score\n0.6367984083865784\n\nClassification Report\n precision recall f1-score support\n\n 0 0.00 0.00 0.00 217\n 1 0.00 0.00 0.00 152\n 2 0.00 0.00 0.00 110\n 3 0.99 0.76 0.86 6766\n 4 0.51 0.92 0.66 1828\n 5 0.00 0.00 0.00 6\n 6 0.00 0.00 0.00 4\n 7 0.00 0.00 0.00 7\n 8 0.00 0.00 0.00 6\n 9 0.00 0.00 0.00 6965\n 10 0.56 0.97 0.71 10076\n\n accuracy 0.64 26137\n macro avg 0.19 0.24 0.20 26137\nweighted avg 0.51 0.64 0.54 26137\n\nConfusion Matrix\n[[ 0 0 0 0 217 0 0 0 0 0 0]\n [ 0 0 0 3 0 0 0 0 0 0 149]\n [ 0 0 0 13 17 0 0 0 0 0 80]\n [ 0 0 0 5153 0 0 0 0 0 0 1613]\n [ 0 0 0 1 1678 0 0 0 0 0 149]\n [ 0 0 0 0 4 0 0 0 0 0 2]\n [ 0 0 0 0 4 0 0 0 0 0 0]\n [ 0 0 0 2 0 0 0 0 0 0 5]\n [ 0 0 0 6 0 0 0 0 0 0 0]\n [ 0 0 0 0 1117 0 0 0 0 0 5848]\n [ 0 0 0 41 222 0 0 0 0 0 9813]]\n", "text": " precision recall f1-score support\n\n 0 0.50 0.01 0.03 217\n 1 0.00 0.00 0.00 152\n 2 0.91 0.19 0.32 110\n 3 1.00 1.00 1.00 6766\n 4 0.74 0.84 0.79 1828\n 5 1.00 0.33 0.50 6\n 6 0.00 0.00 0.00 4\n 7 0.78 1.00 0.88 7\n 8 0.83 0.83 0.83 6\n 9 0.94 0.96 0.95 6965\n 10 0.99 1.00 1.00 10076\n\n accuracy 0.96 26137\n macro avg 0.70 0.56 0.57 26137\nweighted avg 0.95 0.96 0.95 26137\n\nConfusion Matrix\n[[ 3 0 0 0 205 0 0 0 0 9 0]\n [ 0 0 0 6 0 0 0 0 0 123 23]\n [ 0 0 21 11 7 0 0 0 0 48 23]\n [ 0 0 0 6765 0 0 0 0 1 0 0]\n [ 3 0 0 0 1534 0 0 0 0 279 12]\n [ 0 0 0 0 2 2 0 0 0 2 0]\n [ 0 0 0 0 2 0 0 0 0 2 0]\n [ 0 0 0 0 0 0 0 7 0 0 0]\n [ 0 0 0 1 0 0 0 0 5 0 0]\n [ 0 0 1 0 301 0 0 0 0 6662 1]\n [ 0 0 1 1 11 0 0 2 0 0 10061]]\n", "text": " 0\n0 \n0 6\n2 26\n3 6784\n4 2062\n5 2\n7 11\n8 6\n9 7125\n10 10115\n\n Accuracy Score\n0.9586027470635498\n\nClassification Report\n precision recall f1-score support\n\n 0 0.50 0.01 0.03 217\n 1 0.00 0.00 0.00 152\n 2 0.81 0.19 0.31 110\n 3 1.00 1.00 1.00 6766\n 4 0.74 0.84 0.79 1828\n 5 1.00 0.33 0.50 6\n 6 0.00 0.00 0.00 4\n 7 0.64 1.00 0.78 7\n 8 0.83 0.83 0.83 6\n 9 0.94 0.96 0.95 6965\n 10 0.99 1.00 1.00 10076\n\n accuracy 0.96 26137\n macro avg 0.68 0.56 0.56 26137\nweighted avg 0.95 0.96 0.95 26137\n\nConfusion Matrix\n[[ 3 0 0 0 205 0 0 0 0 9 0]\n [ 0 0 0 6 0 0 0 0 0 123 23]\n [ 0 0 21 11 7 0 0 0 0 48 23]\n [ 0 0 0 6765 0 0 0 0 1 0 0]\n [ 3 0 0 0 1534 0 0 0 0 279 12]\n [ 0 0 0 0 2 2 0 0 0 2 0]\n [ 0 0 0 0 2 0 0 0 0 2 0]\n [ 0 0 0 0 0 0 0 7 0 0 0]\n [ 0 0 0 1 0 0 0 0 5 0 0]\n [ 0 0 1 0 301 0 0 0 0 6662 1]\n [ 0 0 4 1 11 0 0 4 0 0 10056]]\n", "text": "[10 10 10 9 4 4]\n 0\n0 \n0 4\n2 21\n3 6785\n4 2043\n5 2\n7 8\n8 5\n9 7139\n10 10130\n\n Accuracy Score\n0.9591383861958144\n\nClassification Report\n precision recall f1-score support\n\n 0 0.50 0.01 0.02 217\n 1 0.00 0.00 0.00 152\n 2 0.95 0.18 0.31 110\n 3 1.00 1.00 1.00 6766\n 4 0.75 0.84 0.79 1828\n 5 1.00 0.33 0.50 6\n 6 0.00 0.00 0.00 4\n 7 0.88 1.00 0.93 7\n 8 1.00 0.83 0.91 6\n 9 0.93 0.96 0.95 6965\n 10 0.99 1.00 1.00 10076\n\n accuracy 0.96 26137\n macro avg 0.73 0.56 0.58 26137\nweighted avg 0.95 0.96 0.95 26137\n\nConfusion Matrix\n[[ 2 0 0 0 206 0 0 0 0 9 0]\n [ 0 0 0 6 0 0 0 0 0 123 23]\n [ 0 0 20 11 7 0 0 0 0 49 23]\n [ 0 0 0 6766 0 0 0 0 0 0 0]\n [ 2 0 0 0 1528 0 0 0 0 285 13]\n [ 0 0 0 0 2 2 0 0 0 2 0]\n [ 0 0 0 0 2 0 0 0 0 2 0]\n [ 0 0 0 0 0 0 0 7 0 0 0]\n [ 0 0 0 1 0 0 0 0 5 0 0]\n [ 0 0 0 0 295 0 0 0 0 6669 1]\n [ 0 0 1 1 3 0 0 1 0 0 10070]]\n", "text": "[10 10 10 9 4 4]\n 0\n0 \n0 6\n2 21\n3 6785\n4 2042\n5 2\n7 8\n8 5\n9 7139\n10 10129\n\n Accuracy Score\n0.9591001262577955\n\nClassification Report\n precision recall f1-score support\n\n 0 0.50 0.01 0.03 217\n 1 0.00 0.00 0.00 152\n 2 0.95 0.18 0.31 110\n 3 1.00 1.00 1.00 6766\n 4 0.75 0.84 0.79 1828\n 5 1.00 0.33 0.50 6\n 6 0.00 0.00 0.00 4\n 7 0.88 1.00 0.93 7\n 8 1.00 0.83 0.91 6\n 9 0.93 0.96 0.95 6965\n 10 0.99 1.00 1.00 10076\n\n accuracy 0.96 26137\n macro avg 0.73 0.56 0.58 26137\nweighted avg 0.95 0.96 0.95 26137\n\nConfusion Matrix\n[[ 3 0 0 0 205 0 0 0 0 9 0]\n [ 0 0 0 6 0 0 0 0 0 123 23]\n [ 0 0 20 11 7 0 0 0 0 49 23]\n [ 0 0 0 6766 0 0 0 0 0 0 0]\n [ 3 0 0 0 1527 0 0 0 0 285 13]\n [ 0 0 0 0 2 2 0 0 0 2 0]\n [ 0 0 0 0 2 0 0 0 0 2 0]\n [ 0 0 0 0 0 0 0 7 0 0 0]\n [ 0 0 0 1 0 0 0 0 5 0 0]\n [ 0 0 0 0 295 0 0 0 0 6669 1]\n [ 0 0 1 1 4 0 0 1 0 0 10069]]\n".

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