diff --git a/PythonAI/JupyterLab/Cwiczenie.ipynb b/PythonAI/JupyterLab/Cwiczenie.ipynb new file mode 100644 index 0000000..6dcd2b2 --- /dev/null +++ b/PythonAI/JupyterLab/Cwiczenie.ipynb @@ -0,0 +1,1383 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "3b43758b-7204-4cd1-b0dc-9a2ce7926ac7", + "metadata": {}, + "source": [ + "# Ćwiczenie" + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "id": "178ae645-cad9-491c-9a26-c173eda34a00", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "id": "540f9f69-d932-4b5a-8d70-e18a08bdcf46", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
datestationPRCPSNOWSNWDTMAXTMINTOBSWESFinclement_weather
02018-01-01?0.00.0-inf5505.0-40.0NaNNaNNaN
12018-01-02GHCND:USC002809070.00.0-inf-8.3-16.1-12.2NaNFalse
22018-01-03GHCND:USC002809070.00.0-inf-4.4-13.9-13.3NaNFalse
32018-01-04?20.6229.0inf5505.0-40.0NaN19.3True
42018-01-05?0.3NaNNaN5505.0-40.0NaNNaNNaN
.................................
4762018-12-28GHCND:USC0028090711.70.0-inf6.1-1.75.0NaNFalse
4772018-12-29?21.3NaNNaN5505.0-40.0NaNNaNNaN
4782018-12-30?0.0NaNNaN5505.0-40.0NaNNaNNaN
4792018-12-31GHCND:USC002809070.00.0-inf3.3-3.3-2.8NaNFalse
4802018-12-31?0.00.0-inf5505.0-40.0NaNNaNNaN
\n", + "

481 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " date station PRCP SNOW SNWD TMAX TMIN TOBS \\\n", + "0 2018-01-01 ? 0.0 0.0 -inf 5505.0 -40.0 NaN \n", + "1 2018-01-02 GHCND:USC00280907 0.0 0.0 -inf -8.3 -16.1 -12.2 \n", + "2 2018-01-03 GHCND:USC00280907 0.0 0.0 -inf -4.4 -13.9 -13.3 \n", + "3 2018-01-04 ? 20.6 229.0 inf 5505.0 -40.0 NaN \n", + "4 2018-01-05 ? 0.3 NaN NaN 5505.0 -40.0 NaN \n", + ".. ... ... ... ... ... ... ... ... \n", + "476 2018-12-28 GHCND:USC00280907 11.7 0.0 -inf 6.1 -1.7 5.0 \n", + "477 2018-12-29 ? 21.3 NaN NaN 5505.0 -40.0 NaN \n", + "478 2018-12-30 ? 0.0 NaN NaN 5505.0 -40.0 NaN \n", + "479 2018-12-31 GHCND:USC00280907 0.0 0.0 -inf 3.3 -3.3 -2.8 \n", + "480 2018-12-31 ? 0.0 0.0 -inf 5505.0 -40.0 NaN \n", + "\n", + " WESF inclement_weather \n", + "0 NaN NaN \n", + "1 NaN False \n", + "2 NaN False \n", + "3 19.3 True \n", + "4 NaN NaN \n", + ".. ... ... \n", + "476 NaN False \n", + "477 NaN NaN \n", + "478 NaN NaN \n", + "479 NaN False \n", + "480 NaN NaN \n", + "\n", + "[481 rows x 10 columns]" + ] + }, + "execution_count": 203, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv(\"data/dirty_data.csv\")\n", + "df = df.drop_duplicates().reset_index()\n", + "del df['index']\n", + "df.date = pd.to_datetime(df.date)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "id": "62e61e9e-f06e-4124-9a89-1979dc071c47", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
datestationPRCPSNOWSNWDTMAXTMINTOBSWESFincl_weather_trueincl_weather_false
02018-01-01?0.00.0-inf5505.0-40.0NaNNaN00
12018-01-02GHCND:USC002809070.00.0-inf-8.3-16.1-12.2NaN01
22018-01-03GHCND:USC002809070.00.0-inf-4.4-13.9-13.3NaN01
32018-01-04?20.6229.0inf5505.0-40.0NaN19.310
42018-01-05?0.3NaNNaN5505.0-40.0NaNNaN00
....................................
4762018-12-28GHCND:USC0028090711.70.0-inf6.1-1.75.0NaN01
4772018-12-29?21.3NaNNaN5505.0-40.0NaNNaN00
4782018-12-30?0.0NaNNaN5505.0-40.0NaNNaN00
4792018-12-31GHCND:USC002809070.00.0-inf3.3-3.3-2.8NaN01
4802018-12-31?0.00.0-inf5505.0-40.0NaNNaN00
\n", + "

481 rows × 11 columns

\n", + "
" + ], + "text/plain": [ + " date station PRCP SNOW SNWD TMAX TMIN TOBS \\\n", + "0 2018-01-01 ? 0.0 0.0 -inf 5505.0 -40.0 NaN \n", + "1 2018-01-02 GHCND:USC00280907 0.0 0.0 -inf -8.3 -16.1 -12.2 \n", + "2 2018-01-03 GHCND:USC00280907 0.0 0.0 -inf -4.4 -13.9 -13.3 \n", + "3 2018-01-04 ? 20.6 229.0 inf 5505.0 -40.0 NaN \n", + "4 2018-01-05 ? 0.3 NaN NaN 5505.0 -40.0 NaN \n", + ".. ... ... ... ... ... ... ... ... \n", + "476 2018-12-28 GHCND:USC00280907 11.7 0.0 -inf 6.1 -1.7 5.0 \n", + "477 2018-12-29 ? 21.3 NaN NaN 5505.0 -40.0 NaN \n", + "478 2018-12-30 ? 0.0 NaN NaN 5505.0 -40.0 NaN \n", + "479 2018-12-31 GHCND:USC00280907 0.0 0.0 -inf 3.3 -3.3 -2.8 \n", + "480 2018-12-31 ? 0.0 0.0 -inf 5505.0 -40.0 NaN \n", + "\n", + " WESF incl_weather_true incl_weather_false \n", + "0 NaN 0 0 \n", + "1 NaN 0 1 \n", + "2 NaN 0 1 \n", + "3 19.3 1 0 \n", + "4 NaN 0 0 \n", + ".. ... ... ... \n", + "476 NaN 0 1 \n", + "477 NaN 0 0 \n", + "478 NaN 0 0 \n", + "479 NaN 0 1 \n", + "480 NaN 0 0 \n", + "\n", + "[481 rows x 11 columns]" + ] + }, + "execution_count": 204, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Clean inclement_weather\n", + "df = df.assign(\n", + " incl_weather_true = lambda x: np.where(x.inclement_weather == True, 1, 0),\n", + " incl_weather_false = lambda x: np.where(x.inclement_weather == False, 1, 0)\n", + ")\n", + "del df['inclement_weather']\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 205, + "id": "e06e7a55-1837-456b-97ef-4c58276bd7b6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
datestationPRCPSNOWTMAXTMINTOBSWESFincl_weather_trueincl_weather_falsesnow
02018-01-01?0.00.05505.0-40.0NaNNaN000
12018-01-02GHCND:USC002809070.00.0-8.3-16.1-12.2NaN010
22018-01-03GHCND:USC002809070.00.0-4.4-13.9-13.3NaN010
32018-01-04?20.6229.05505.0-40.0NaN19.3101
42018-01-05?0.30.05505.0-40.0NaNNaN000
....................................
4762018-12-28GHCND:USC0028090711.70.06.1-1.75.0NaN010
4772018-12-29?21.30.05505.0-40.0NaNNaN000
4782018-12-30?0.00.05505.0-40.0NaNNaN000
4792018-12-31GHCND:USC002809070.00.03.3-3.3-2.8NaN010
4802018-12-31?0.00.05505.0-40.0NaNNaN000
\n", + "

481 rows × 11 columns

\n", + "
" + ], + "text/plain": [ + " date station PRCP SNOW TMAX TMIN TOBS WESF \\\n", + "0 2018-01-01 ? 0.0 0.0 5505.0 -40.0 NaN NaN \n", + "1 2018-01-02 GHCND:USC00280907 0.0 0.0 -8.3 -16.1 -12.2 NaN \n", + "2 2018-01-03 GHCND:USC00280907 0.0 0.0 -4.4 -13.9 -13.3 NaN \n", + "3 2018-01-04 ? 20.6 229.0 5505.0 -40.0 NaN 19.3 \n", + "4 2018-01-05 ? 0.3 0.0 5505.0 -40.0 NaN NaN \n", + ".. ... ... ... ... ... ... ... ... \n", + "476 2018-12-28 GHCND:USC00280907 11.7 0.0 6.1 -1.7 5.0 NaN \n", + "477 2018-12-29 ? 21.3 0.0 5505.0 -40.0 NaN NaN \n", + "478 2018-12-30 ? 0.0 0.0 5505.0 -40.0 NaN NaN \n", + "479 2018-12-31 GHCND:USC00280907 0.0 0.0 3.3 -3.3 -2.8 NaN \n", + "480 2018-12-31 ? 0.0 0.0 5505.0 -40.0 NaN NaN \n", + "\n", + " incl_weather_true incl_weather_false snow \n", + "0 0 0 0 \n", + "1 0 1 0 \n", + "2 0 1 0 \n", + "3 1 0 1 \n", + "4 0 0 0 \n", + ".. ... ... ... \n", + "476 0 1 0 \n", + "477 0 0 0 \n", + "478 0 0 0 \n", + "479 0 1 0 \n", + "480 0 0 0 \n", + "\n", + "[481 rows x 11 columns]" + ] + }, + "execution_count": 205, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# remove SNWD\n", + "df = df.assign(\n", + " snow = lambda x: np.where(x.SNWD == np.inf, 1, 0)\n", + ")\n", + "del df['SNWD']\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "id": "389f0c22-b210-45f3-9627-02c06d255a69", + "metadata": {}, + "outputs": [], + "source": [ + "# Replace NaN with zero\n", + "df['SNOW'] = df['SNOW'].fillna(0)\n", + "df['WESF'] = df['WESF'].fillna(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "id": "60362a3d-91ab-4666-bf26-afdeb3cd5c9c", + "metadata": {}, + "outputs": [], + "source": [ + "# Split data into temperature and snow\n", + "df_snow = df[df['station']=='?']\n", + "df_temp = df[df['station']!='?']" + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "id": "a68dd0eb-6085-483b-99f3-a540a35515c2", + "metadata": {}, + "outputs": [], + "source": [ + "del df_snow['station']\n", + "del df_snow['TMIN']\n", + "del df_snow['TMAX']\n", + "del df_snow['TOBS']\n", + "# del df_snow['']\n", + "del df_temp['station']\n", + "del df_temp['WESF']" + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "id": "bb4ecdab-2136-4970-a38d-6eaec8ec92ae", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
datestationPRCPSNOWTMAXTMINTOBSWESFincl_weather_trueincl_weather_falsesnow
02018-01-01?0.00.05505.0-40.0NaN0.0000
12018-01-02GHCND:USC002809070.00.0-8.3-16.1-12.20.0010
22018-01-03GHCND:USC002809070.00.0-4.4-13.9-13.30.0010
32018-01-04?20.6229.05505.0-40.0NaN19.3101
42018-01-05?0.30.05505.0-40.0NaN0.0000
....................................
4762018-12-28GHCND:USC0028090711.70.06.1-1.75.00.0010
4772018-12-29?21.30.05505.0-40.0NaN0.0000
4782018-12-30?0.00.05505.0-40.0NaN0.0000
4792018-12-31GHCND:USC002809070.00.03.3-3.3-2.80.0010
4802018-12-31?0.00.05505.0-40.0NaN0.0000
\n", + "

481 rows × 11 columns

\n", + "
" + ], + "text/plain": [ + " date station PRCP SNOW TMAX TMIN TOBS WESF \\\n", + "0 2018-01-01 ? 0.0 0.0 5505.0 -40.0 NaN 0.0 \n", + "1 2018-01-02 GHCND:USC00280907 0.0 0.0 -8.3 -16.1 -12.2 0.0 \n", + "2 2018-01-03 GHCND:USC00280907 0.0 0.0 -4.4 -13.9 -13.3 0.0 \n", + "3 2018-01-04 ? 20.6 229.0 5505.0 -40.0 NaN 19.3 \n", + "4 2018-01-05 ? 0.3 0.0 5505.0 -40.0 NaN 0.0 \n", + ".. ... ... ... ... ... ... ... ... \n", + "476 2018-12-28 GHCND:USC00280907 11.7 0.0 6.1 -1.7 5.0 0.0 \n", + "477 2018-12-29 ? 21.3 0.0 5505.0 -40.0 NaN 0.0 \n", + "478 2018-12-30 ? 0.0 0.0 5505.0 -40.0 NaN 0.0 \n", + "479 2018-12-31 GHCND:USC00280907 0.0 0.0 3.3 -3.3 -2.8 0.0 \n", + "480 2018-12-31 ? 0.0 0.0 5505.0 -40.0 NaN 0.0 \n", + "\n", + " incl_weather_true incl_weather_false snow \n", + "0 0 0 0 \n", + "1 0 1 0 \n", + "2 0 1 0 \n", + "3 1 0 1 \n", + "4 0 0 0 \n", + ".. ... ... ... \n", + "476 0 1 0 \n", + "477 0 0 0 \n", + "478 0 0 0 \n", + "479 0 1 0 \n", + "480 0 0 0 \n", + "\n", + "[481 rows x 11 columns]" + ] + }, + "execution_count": 216, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "id": "7c8199c4-9d1b-405a-9cda-b2aef1616999", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
datePRCPSNOWWESFincl_weather_trueincl_weather_falsesnow
count232232.000000232.000000232.000000232.000000232.0232.000000
mean2018-06-23 12:49:39.3103447044.1754312.9008620.4375000.0258620.00.025862
min2018-01-01 00:00:000.0000000.0000000.0000000.0000000.00.000000
25%2018-03-30 18:00:000.0000000.0000000.0000000.0000000.00.000000
50%2018-06-15 12:00:000.0000000.0000000.0000000.0000000.00.000000
75%2018-09-11 06:00:003.0750000.0000000.0000000.0000000.00.000000
max2018-12-31 00:00:0047.000000229.00000028.7000001.0000000.01.000000
stdNaN8.33315221.8248363.0072230.1590670.00.159067
\n", + "
" + ], + "text/plain": [ + " date PRCP SNOW WESF \\\n", + "count 232 232.000000 232.000000 232.000000 \n", + "mean 2018-06-23 12:49:39.310344704 4.175431 2.900862 0.437500 \n", + "min 2018-01-01 00:00:00 0.000000 0.000000 0.000000 \n", + "25% 2018-03-30 18:00:00 0.000000 0.000000 0.000000 \n", + "50% 2018-06-15 12:00:00 0.000000 0.000000 0.000000 \n", + "75% 2018-09-11 06:00:00 3.075000 0.000000 0.000000 \n", + "max 2018-12-31 00:00:00 47.000000 229.000000 28.700000 \n", + "std NaN 8.333152 21.824836 3.007223 \n", + "\n", + " incl_weather_true incl_weather_false snow \n", + "count 232.000000 232.0 232.000000 \n", + "mean 0.025862 0.0 0.025862 \n", + "min 0.000000 0.0 0.000000 \n", + "25% 0.000000 0.0 0.000000 \n", + "50% 0.000000 0.0 0.000000 \n", + "75% 0.000000 0.0 0.000000 \n", + "max 1.000000 0.0 1.000000 \n", + "std 0.159067 0.0 0.159067 " + ] + }, + "execution_count": 217, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_snow.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 218, + "id": "7468e63c-c72b-4150-bd40-6f6e61f17af8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
datePRCPSNOWTMAXTMINTOBSincl_weather_trueincl_weather_falsesnow
count249249.000000249.000000249.000000249.000000249.000000249.000000249.000000249.000000
mean2018-06-30 23:36:52.0481927685.5273092.90763116.0048196.3714868.7120480.0321290.9678710.032129
min2018-01-02 00:00:000.0000000.000000-11.700000-17.200000-16.1000000.0000000.0000000.000000
25%2018-03-27 00:00:000.0000000.0000006.700000-1.7000000.0000000.0000001.0000000.000000
50%2018-07-07 00:00:000.0000000.00000014.4000005.6000008.3000000.0000001.0000000.000000
75%2018-09-30 00:00:005.6000000.00000026.10000015.60000017.8000000.0000001.0000000.000000
max2018-12-31 00:00:0061.700000178.00000035.00000023.90000026.1000001.0000001.0000001.000000
stdNaN10.66519719.83204411.00061510.1578099.9364680.1766970.1766970.176697
\n", + "
" + ], + "text/plain": [ + " date PRCP SNOW TMAX \\\n", + "count 249 249.000000 249.000000 249.000000 \n", + "mean 2018-06-30 23:36:52.048192768 5.527309 2.907631 16.004819 \n", + "min 2018-01-02 00:00:00 0.000000 0.000000 -11.700000 \n", + "25% 2018-03-27 00:00:00 0.000000 0.000000 6.700000 \n", + "50% 2018-07-07 00:00:00 0.000000 0.000000 14.400000 \n", + "75% 2018-09-30 00:00:00 5.600000 0.000000 26.100000 \n", + "max 2018-12-31 00:00:00 61.700000 178.000000 35.000000 \n", + "std NaN 10.665197 19.832044 11.000615 \n", + "\n", + " TMIN TOBS incl_weather_true incl_weather_false \\\n", + "count 249.000000 249.000000 249.000000 249.000000 \n", + "mean 6.371486 8.712048 0.032129 0.967871 \n", + "min -17.200000 -16.100000 0.000000 0.000000 \n", + "25% -1.700000 0.000000 0.000000 1.000000 \n", + "50% 5.600000 8.300000 0.000000 1.000000 \n", + "75% 15.600000 17.800000 0.000000 1.000000 \n", + "max 23.900000 26.100000 1.000000 1.000000 \n", + "std 10.157809 9.936468 0.176697 0.176697 \n", + "\n", + " snow \n", + "count 249.000000 \n", + "mean 0.032129 \n", + "min 0.000000 \n", + "25% 0.000000 \n", + "50% 0.000000 \n", + "75% 0.000000 \n", + "max 1.000000 \n", + "std 0.176697 " + ] + }, + "execution_count": 218, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_temp.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "efa6a165-9988-420f-a2f0-10cbbcd5dc39", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}