{ "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": [ "
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datestationPRCPSNOWSNWDTMAXTMINTOBSWESFinclement_weather
02018-01-01?0.00.0-inf5505.0-40.0NaNNaNNaN
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42018-01-05?0.3NaNNaN5505.0-40.0NaNNaNNaN
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4762018-12-28GHCND:USC0028090711.70.0-inf6.1-1.75.0NaNFalse
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4792018-12-31GHCND:USC002809070.00.0-inf3.3-3.3-2.8NaNFalse
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481 rows × 10 columns

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" ], "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": [ "
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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
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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
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481 rows × 11 columns

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" ], "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": [ "
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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
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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
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481 rows × 11 columns

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" ], "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": [ "
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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
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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
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481 rows × 11 columns

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" ], "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": [ "
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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
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max2018-12-31 00:00:0061.700000178.00000035.00000023.90000026.1000001.0000001.0000001.000000
stdNaN10.66519719.83204411.00061510.1578099.9364680.1766970.1766970.176697
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