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ls-da3m0ns/radiation_prediction.ipynb

Created January 28, 2021 18:50
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radiation_prediction.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "radiation_prediction.ipynb",
"provenance": [],
"authorship_tag": "ABX9TyO+S2J0e1B41rKWP3Dj3ckG",
"include_colab_link": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/ls-da3m0ns/eb3b23c5f9dc34962b0724ee6bd81808/radiation_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "O2iR3DzeS-fa",
"outputId": "25528d42-7c56-4169-ef6e-3cf03091fef7"
},
"source": [
"!wget -O \"solar_radiation_prediction_ai_challenge-dataset.zip\" \"https://dockship-job-models.s3.ap-south-1.amazonaws.com/a379f6594a948741d92dba42178aff0b?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIDOPTEUZ2LEOQEGQ%2F20201224%2Fap-south-1%2Fs3%2Faws4_request&X-Amz-Date=20201224T105711Z&X-Amz-Expires=1800&X-Amz-Signature=d2b8ed7f01bc0cf7dc83eece45936fbcd2cd98f54b46d95894cc64815ee2eabd&X-Amz-SignedHeaders=host&response-content-disposition=attachment%3B%20filename%3D%22solar_radiation_prediction_ai_challenge-dataset.zip%22\""
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"--2020-12-24 10:57:37-- https://dockship-job-models.s3.ap-south-1.amazonaws.com/a379f6594a948741d92dba42178aff0b?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIDOPTEUZ2LEOQEGQ%2F20201224%2Fap-south-1%2Fs3%2Faws4_request&X-Amz-Date=20201224T105711Z&X-Amz-Expires=1800&X-Amz-Signature=d2b8ed7f01bc0cf7dc83eece45936fbcd2cd98f54b46d95894cc64815ee2eabd&X-Amz-SignedHeaders=host&response-content-disposition=attachment%3B%20filename%3D%22solar_radiation_prediction_ai_challenge-dataset.zip%22\n",
"Resolving dockship-job-models.s3.ap-south-1.amazonaws.com (dockship-job-models.s3.ap-south-1.amazonaws.com)... 52.219.62.99\n",
"Connecting to dockship-job-models.s3.ap-south-1.amazonaws.com (dockship-job-models.s3.ap-south-1.amazonaws.com)|52.219.62.99|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 787305 (769K) [binary/octet-stream]\n",
"Saving to: ‘solar_radiation_prediction_ai_challenge-dataset.zip’\n",
"\n",
"solar_radiation_pre 100%[===================>] 768.85K 631KB/s in 1.2s \n",
"\n",
"2020-12-24 10:57:39 (631 KB/s) - ‘solar_radiation_prediction_ai_challenge-dataset.zip’ saved [787305/787305]\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "PkjILcJnTJvq",
"outputId": "8172c417-41d5-4e86-95db-da3717a7948b"
},
"source": [
"!unzip solar_radiation_prediction_ai_challenge-dataset.zip"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Archive: solar_radiation_prediction_ai_challenge-dataset.zip\n",
" inflating: sample_submission.csv \n",
" inflating: TEST.csv \n",
" inflating: TRAIN.csv \n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "7aj8iXYJTMCB"
},
"source": [
"import pandas as pd \n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"import time\n",
"from datetime import datetime"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "0MabYHuDTZdD"
},
"source": [
"train = pd.read_csv(\"TRAIN.csv\")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "7qp06a9tTgr9",
"outputId": "308b569f-95b1-4728-a75f-138258fa53fe"
},
"source": [
"train.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>idx</th>\n",
" <th>UNIXTime</th>\n",
" <th>Data</th>\n",
" <th>Time</th>\n",
" <th>Radiation</th>\n",
" <th>Temperature</th>\n",
" <th>Pressure</th>\n",
" <th>Humidity</th>\n",
" <th>WindDirection(Degrees)</th>\n",
" <th>Speed</th>\n",
" <th>TimeSunRise</th>\n",
" <th>TimeSunSet</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>1480107904</td>\n",
" <td>11/25/2016 12:00:00 AM</td>\n",
" <td>11:05:04</td>\n",
" <td>288.44</td>\n",
" <td>46</td>\n",
" <td>30.48</td>\n",
" <td>101</td>\n",
" <td>129.84</td>\n",
" <td>13.50</td>\n",
" <td>06:37:00</td>\n",
" <td>17:42:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1472818508</td>\n",
" <td>9/2/2016 12:00:00 AM</td>\n",
" <td>02:15:08</td>\n",
" <td>2.79</td>\n",
" <td>50</td>\n",
" <td>30.42</td>\n",
" <td>75</td>\n",
" <td>173.90</td>\n",
" <td>6.75</td>\n",
" <td>06:07:00</td>\n",
" <td>18:37:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>1475804719</td>\n",
" <td>10/6/2016 12:00:00 AM</td>\n",
" <td>15:45:19</td>\n",
" <td>118.05</td>\n",
" <td>54</td>\n",
" <td>30.42</td>\n",
" <td>100</td>\n",
" <td>7.35</td>\n",
" <td>1.12</td>\n",
" <td>06:15:00</td>\n",
" <td>18:07:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>1482533149</td>\n",
" <td>12/23/2016 12:00:00 AM</td>\n",
" <td>12:45:49</td>\n",
" <td>853.17</td>\n",
" <td>58</td>\n",
" <td>30.44</td>\n",
" <td>57</td>\n",
" <td>81.67</td>\n",
" <td>11.25</td>\n",
" <td>06:54:00</td>\n",
" <td>17:50:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>1481883019</td>\n",
" <td>12/16/2016 12:00:00 AM</td>\n",
" <td>00:10:19</td>\n",
" <td>1.24</td>\n",
" <td>42</td>\n",
" <td>30.24</td>\n",
" <td>103</td>\n",
" <td>171.13</td>\n",
" <td>2.25</td>\n",
" <td>06:50:00</td>\n",
" <td>17:46:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" idx UNIXTime Data ... Speed TimeSunRise TimeSunSet\n",
"0 0 1480107904 11/25/2016 12:00:00 AM ... 13.50 06:37:00 17:42:00\n",
"1 1 1472818508 9/2/2016 12:00:00 AM ... 6.75 06:07:00 18:37:00\n",
"2 2 1475804719 10/6/2016 12:00:00 AM ... 1.12 06:15:00 18:07:00\n",
"3 3 1482533149 12/23/2016 12:00:00 AM ... 11.25 06:54:00 17:50:00\n",
"4 4 1481883019 12/16/2016 12:00:00 AM ... 2.25 06:50:00 17:46:00\n",
"\n",
"[5 rows x 12 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "WPeqjaRtTkgB"
},
"source": [
"train.reindex(train['idx'])\n",
"train = train.drop(\"idx\",axis=1)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "pCxdeOoITq4n",
"outputId": "b1eefd53-1552-4183-d463-db6588ca05c5"
},
"source": [
"train.info()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 26149 entries, 0 to 26148\n",
"Data columns (total 11 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 UNIXTime 26149 non-null int64 \n",
" 1 Data 26149 non-null object \n",
" 2 Time 26149 non-null object \n",
" 3 Radiation 26149 non-null float64\n",
" 4 Temperature 26149 non-null int64 \n",
" 5 Pressure 26149 non-null float64\n",
" 6 Humidity 26149 non-null int64 \n",
" 7 WindDirection(Degrees) 26149 non-null float64\n",
" 8 Speed 26149 non-null float64\n",
" 9 TimeSunRise 26149 non-null object \n",
" 10 TimeSunSet 26149 non-null object \n",
"dtypes: float64(4), int64(3), object(4)\n",
"memory usage: 2.2+ MB\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4Exazzz3UDs9"
},
"source": [
"## NO NULL Values in any column\n"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 301
},
"id": "Nls7xtqRUBLF",
"outputId": "30db0f36-d268-4249-8fb7-8e49d986f2a4"
},
"source": [
"train.describe()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>UNIXTime</th>\n",
" <th>Radiation</th>\n",
" <th>Temperature</th>\n",
" <th>Pressure</th>\n",
" <th>Humidity</th>\n",
" <th>WindDirection(Degrees)</th>\n",
" <th>Speed</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>2.614900e+04</td>\n",
" <td>26149.000000</td>\n",
" <td>26149.000000</td>\n",
" <td>26149.000000</td>\n",
" <td>26149.000000</td>\n",
" <td>26149.000000</td>\n",
" <td>26149.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1.478046e+09</td>\n",
" <td>208.621000</td>\n",
" <td>51.135110</td>\n",
" <td>30.423005</td>\n",
" <td>74.970936</td>\n",
" <td>143.368247</td>\n",
" <td>6.262052</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>3.004309e+06</td>\n",
" <td>316.089736</td>\n",
" <td>6.212018</td>\n",
" <td>0.054679</td>\n",
" <td>26.008728</td>\n",
" <td>83.424186</td>\n",
" <td>3.503488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.472724e+09</td>\n",
" <td>1.110000</td>\n",
" <td>35.000000</td>\n",
" <td>30.190000</td>\n",
" <td>8.000000</td>\n",
" <td>0.090000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1.475544e+09</td>\n",
" <td>1.230000</td>\n",
" <td>46.000000</td>\n",
" <td>30.400000</td>\n",
" <td>56.000000</td>\n",
" <td>81.580000</td>\n",
" <td>3.370000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1.478029e+09</td>\n",
" <td>2.720000</td>\n",
" <td>50.000000</td>\n",
" <td>30.430000</td>\n",
" <td>85.000000</td>\n",
" <td>147.320000</td>\n",
" <td>5.620000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1.480473e+09</td>\n",
" <td>360.260000</td>\n",
" <td>55.000000</td>\n",
" <td>30.460000</td>\n",
" <td>97.000000</td>\n",
" <td>179.240000</td>\n",
" <td>7.870000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.483264e+09</td>\n",
" <td>1601.260000</td>\n",
" <td>71.000000</td>\n",
" <td>30.560000</td>\n",
" <td>103.000000</td>\n",
" <td>359.940000</td>\n",
" <td>40.500000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" UNIXTime Radiation ... WindDirection(Degrees) Speed\n",
"count 2.614900e+04 26149.000000 ... 26149.000000 26149.000000\n",
"mean 1.478046e+09 208.621000 ... 143.368247 6.262052\n",
"std 3.004309e+06 316.089736 ... 83.424186 3.503488\n",
"min 1.472724e+09 1.110000 ... 0.090000 0.000000\n",
"25% 1.475544e+09 1.230000 ... 81.580000 3.370000\n",
"50% 1.478029e+09 2.720000 ... 147.320000 5.620000\n",
"75% 1.480473e+09 360.260000 ... 179.240000 7.870000\n",
"max 1.483264e+09 1601.260000 ... 359.940000 40.500000\n",
"\n",
"[8 rows x 7 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hvs3jjC7UlR0"
},
"source": [
"## Radiation max value is nearly 8 time that of mean and greater than mean+2*sd\n",
"## the 75th percentile is very large than 50th and 25th percentile\n",
"## WindDirection max value also dose not depict a normal behaviour\n",
"## deviation of pressure values is very low\n",
"## Wind max value looks like an outlier"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "trggKoJeU9gx",
"outputId": "6eaf1ae8-bacd-4bf6-9a2e-f433628d6b60"
},
"source": [
"train.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>UNIXTime</th>\n",
" <th>Data</th>\n",
" <th>Time</th>\n",
" <th>Radiation</th>\n",
" <th>Temperature</th>\n",
" <th>Pressure</th>\n",
" <th>Humidity</th>\n",
" <th>WindDirection(Degrees)</th>\n",
" <th>Speed</th>\n",
" <th>TimeSunRise</th>\n",
" <th>TimeSunSet</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1480107904</td>\n",
" <td>11/25/2016 12:00:00 AM</td>\n",
" <td>11:05:04</td>\n",
" <td>288.44</td>\n",
" <td>46</td>\n",
" <td>30.48</td>\n",
" <td>101</td>\n",
" <td>129.84</td>\n",
" <td>13.50</td>\n",
" <td>06:37:00</td>\n",
" <td>17:42:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1472818508</td>\n",
" <td>9/2/2016 12:00:00 AM</td>\n",
" <td>02:15:08</td>\n",
" <td>2.79</td>\n",
" <td>50</td>\n",
" <td>30.42</td>\n",
" <td>75</td>\n",
" <td>173.90</td>\n",
" <td>6.75</td>\n",
" <td>06:07:00</td>\n",
" <td>18:37:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1475804719</td>\n",
" <td>10/6/2016 12:00:00 AM</td>\n",
" <td>15:45:19</td>\n",
" <td>118.05</td>\n",
" <td>54</td>\n",
" <td>30.42</td>\n",
" <td>100</td>\n",
" <td>7.35</td>\n",
" <td>1.12</td>\n",
" <td>06:15:00</td>\n",
" <td>18:07:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1482533149</td>\n",
" <td>12/23/2016 12:00:00 AM</td>\n",
" <td>12:45:49</td>\n",
" <td>853.17</td>\n",
" <td>58</td>\n",
" <td>30.44</td>\n",
" <td>57</td>\n",
" <td>81.67</td>\n",
" <td>11.25</td>\n",
" <td>06:54:00</td>\n",
" <td>17:50:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1481883019</td>\n",
" <td>12/16/2016 12:00:00 AM</td>\n",
" <td>00:10:19</td>\n",
" <td>1.24</td>\n",
" <td>42</td>\n",
" <td>30.24</td>\n",
" <td>103</td>\n",
" <td>171.13</td>\n",
" <td>2.25</td>\n",
" <td>06:50:00</td>\n",
" <td>17:46:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" UNIXTime Data Time ... Speed TimeSunRise TimeSunSet\n",
"0 1480107904 11/25/2016 12:00:00 AM 11:05:04 ... 13.50 06:37:00 17:42:00\n",
"1 1472818508 9/2/2016 12:00:00 AM 02:15:08 ... 6.75 06:07:00 18:37:00\n",
"2 1475804719 10/6/2016 12:00:00 AM 15:45:19 ... 1.12 06:15:00 18:07:00\n",
"3 1482533149 12/23/2016 12:00:00 AM 12:45:49 ... 11.25 06:54:00 17:50:00\n",
"4 1481883019 12/16/2016 12:00:00 AM 00:10:19 ... 2.25 06:50:00 17:46:00\n",
"\n",
"[5 rows x 11 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HqqinkxIVq-t"
},
"source": [
"## Data is date here time dose not make sense with other time variable"
]
},
{
"cell_type": "code",
"metadata": {
"id": "iNbla67rWCgN"
},
"source": [
"train.rename(columns = {\"Data\":\"Date\"}, inplace=True)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "TplUboAUWWra",
"outputId": "450cfd0d-1933-41ca-aad7-f83c78bc50ce"
},
"source": [
"train.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" }\n",
"\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>UNIXTime</th>\n",
" <th>Date</th>\n",
" <th>Time</th>\n",
" <th>Radiation</th>\n",
" <th>Temperature</th>\n",
" <th>Pressure</th>\n",
" <th>Humidity</th>\n",
" <th>WindDirection(Degrees)</th>\n",
" <th>Speed</th>\n",
" <th>TimeSunRise</th>\n",
" <th>TimeSunSet</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1480107904</td>\n",
" <td>11/25/2016 12:00:00 AM</td>\n",
" <td>11:05:04</td>\n",
" <td>288.44</td>\n",
" <td>46</td>\n",
" <td>30.48</td>\n",
" <td>101</td>\n",
" <td>129.84</td>\n",
" <td>13.50</td>\n",
" <td>06:37:00</td>\n",
" <td>17:42:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1472818508</td>\n",
" <td>9/2/2016 12:00:00 AM</td>\n",
" <td>02:15:08</td>\n",
" <td>2.79</td>\n",
" <td>50</td>\n",
" <td>30.42</td>\n",
" <td>75</td>\n",
" <td>173.90</td>\n",
" <td>6.75</td>\n",
" <td>06:07:00</td>\n",
" <td>18:37:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1475804719</td>\n",
" <td>10/6/2016 12:00:00 AM</td>\n",
" <td>15:45:19</td>\n",
" <td>118.05</td>\n",
" <td>54</td>\n",
" <td>30.42</td>\n",
" <td>100</td>\n",
" <td>7.35</td>\n",
" <td>1.12</td>\n",
" <td>06:15:00</td>\n",
" <td>18:07:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1482533149</td>\n",
" <td>12/23/2016 12:00:00 AM</td>\n",
" <td>12:45:49</td>\n",
" <td>853.17</td>\n",
" <td>58</td>\n",
" <td>30.44</td>\n",
" <td>57</td>\n",
" <td>81.67</td>\n",
" <td>11.25</td>\n",
" <td>06:54:00</td>\n",
" <td>17:50:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1481883019</td>\n",
" <td>12/16/2016 12:00:00 AM</td>\n",
" <td>00:10:19</td>\n",
" <td>1.24</td>\n",
" <td>42</td>\n",
" <td>30.24</td>\n",
" <td>103</td>\n",
" <td>171.13</td>\n",
" <td>2.25</td>\n",
" <td>06:50:00</td>\n",
" <td>17:46:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text/plain": [
" UNIXTime Date Time ... Speed TimeSunRise TimeSunSet\n",
"0 1480107904 11/25/2016 12:00:00 AM 11:05:04 ... 13.50 06:37:00 17:42:00\n",
"1 1472818508 9/2/2016 12:00:00 AM 02:15:08 ... 6.75 06:07:00 18:37:00\n",
"2 1475804719 10/6/2016 12:00:00 AM 15:45:19 ... 1.12 06:15:00 18:07:00\n",
"3 1482533149 12/23/2016 12:00:00 AM 12:45:49 ... 11.25 06:54:00 17:50:00\n",
"4 1481883019 12/16/2016 12:00:00 AM 00:10:19 ... 2.25 06:50:00 17:46:00\n",
"\n",
"[5 rows x 11 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 9
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YMRUK0MFVYoO"
},
"source": [
"# Exploring Each Variable"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BbpUw0L3XCFa"
},
"source": [
"## Date"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "iz6HKxyeXKP6"
},
"source": [
"### removing time and converting it to only date"
]
},
{
"cell_type": "code",
"metadata": {
"id": "oRLdNiDIU9eX"
},
"source": [
"train[\"Date\"] = [datetime.strptime(dt, '%m/%d/%Y %H:%M:%S AM').date() for dt in train[\"Date\"]]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "c9wBCKq-bmmF",
"outputId": "cc48edf6-e017-4a27-85e7-7659cab6f425"
},
"source": [
"train"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th></th>\n",
" <th>UNIXTime</th>\n",
" <th>Date</th>\n",
" <th>Time</th>\n",
" <th>Radiation</th>\n",
" <th>Temperature</th>\n",
" <th>Pressure</th>\n",
" <th>Humidity</th>\n",
" <th>WindDirection(Degrees)</th>\n",
" <th>Speed</th>\n",
" <th>TimeSunRise</th>\n",
" <th>TimeSunSet</th>\n",
" </tr>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>1472818508</td>\n",
" <td>2016-09-02</td>\n",
" <td>02:15:08</td>\n",
" <td>2.79</td>\n",
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" <th>2</th>\n",
" <td>1475804719</td>\n",
" <td>2016-10-06</td>\n",
" <td>15:45:19</td>\n",
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" <td>30.42</td>\n",
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" <td>06:15:00</td>\n",
" <td>18:07:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1482533149</td>\n",
" <td>2016-12-23</td>\n",
" <td>12:45:49</td>\n",
" <td>853.17</td>\n",
" <td>58</td>\n",
" <td>30.44</td>\n",
" <td>57</td>\n",
" <td>81.67</td>\n",
" <td>11.25</td>\n",
" <td>06:54:00</td>\n",
" <td>17:50:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1481883019</td>\n",
" <td>2016-12-16</td>\n",
" <td>00:10:19</td>\n",
" <td>1.24</td>\n",
" <td>42</td>\n",
" <td>30.24</td>\n",
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" <td>1479968101</td>\n",
" <td>2016-11-23</td>\n",
" <td>20:15:01</td>\n",
" <td>1.20</td>\n",
" <td>45</td>\n",
" <td>30.46</td>\n",
" <td>98</td>\n",
" <td>129.17</td>\n",
" <td>5.62</td>\n",
" <td>06:36:00</td>\n",
" <td>17:42:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26145</th>\n",
" <td>1475761224</td>\n",
" <td>2016-10-06</td>\n",
" <td>03:40:24</td>\n",
" <td>1.27</td>\n",
" <td>47</td>\n",
" <td>30.43</td>\n",
" <td>95</td>\n",
" <td>194.87</td>\n",
" <td>6.75</td>\n",
" <td>06:15:00</td>\n",
" <td>18:07:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26146</th>\n",
" <td>1476618020</td>\n",
" <td>2016-10-16</td>\n",
" <td>01:40:20</td>\n",
" <td>1.22</td>\n",
" <td>49</td>\n",
" <td>30.50</td>\n",
" <td>24</td>\n",
" <td>185.06</td>\n",
" <td>6.75</td>\n",
" <td>06:18:00</td>\n",
" <td>17:59:00</td>\n",
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" <tr>\n",
" <th>26147</th>\n",
" <td>1473947404</td>\n",
" <td>2016-09-15</td>\n",
" <td>03:50:04</td>\n",
" <td>1.25</td>\n",
" <td>49</td>\n",
" <td>30.41</td>\n",
" <td>92</td>\n",
" <td>122.89</td>\n",
" <td>1.12</td>\n",
" <td>06:10:00</td>\n",
" <td>18:26:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26148</th>\n",
" <td>1474389307</td>\n",
" <td>2016-09-20</td>\n",
" <td>06:35:07</td>\n",
" <td>15.62</td>\n",
" <td>46</td>\n",
" <td>30.40</td>\n",
" <td>41</td>\n",
" <td>160.47</td>\n",
" <td>4.50</td>\n",
" <td>06:11:00</td>\n",
" <td>18:21:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>26149 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" UNIXTime Date Time ... Speed TimeSunRise TimeSunSet\n",
"0 1480107904 2016-11-25 11:05:04 ... 13.50 06:37:00 17:42:00\n",
"1 1472818508 2016-09-02 02:15:08 ... 6.75 06:07:00 18:37:00\n",
"2 1475804719 2016-10-06 15:45:19 ... 1.12 06:15:00 18:07:00\n",
"3 1482533149 2016-12-23 12:45:49 ... 11.25 06:54:00 17:50:00\n",
"4 1481883019 2016-12-16 00:10:19 ... 2.25 06:50:00 17:46:00\n",
"... ... ... ... ... ... ... ...\n",
"26144 1479968101 2016-11-23 20:15:01 ... 5.62 06:36:00 17:42:00\n",
"26145 1475761224 2016-10-06 03:40:24 ... 6.75 06:15:00 18:07:00\n",
"26146 1476618020 2016-10-16 01:40:20 ... 6.75 06:18:00 17:59:00\n",
"26147 1473947404 2016-09-15 03:50:04 ... 1.12 06:10:00 18:26:00\n",
"26148 1474389307 2016-09-20 06:35:07 ... 4.50 06:11:00 18:21:00\n",
"\n",
"[26149 rows x 11 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8xjRWAiLU9bf",
"outputId": "aba9a84c-5fef-4c8e-fcd7-1dc2a6a9872e"
},
"source": [
"dict(train[\"Date\"].value_counts())"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{datetime.date(2016, 9, 1): 200,\n",
" datetime.date(2016, 9, 2): 220,\n",
" datetime.date(2016, 9, 3): 222,\n",
" datetime.date(2016, 9, 4): 229,\n",
" datetime.date(2016, 9, 5): 215,\n",
" datetime.date(2016, 9, 6): 231,\n",
" datetime.date(2016, 9, 7): 165,\n",
" datetime.date(2016, 9, 8): 115,\n",
" datetime.date(2016, 9, 9): 102,\n",
" datetime.date(2016, 9, 10): 154,\n",
" datetime.date(2016, 9, 11): 217,\n",
" datetime.date(2016, 9, 12): 226,\n",
" datetime.date(2016, 9, 13): 233,\n",
" datetime.date(2016, 9, 14): 225,\n",
" datetime.date(2016, 9, 15): 111,\n",
" datetime.date(2016, 9, 16): 154,\n",
" datetime.date(2016, 9, 17): 231,\n",
" datetime.date(2016, 9, 18): 225,\n",
" datetime.date(2016, 9, 19): 217,\n",
" datetime.date(2016, 9, 20): 232,\n",
" datetime.date(2016, 9, 21): 222,\n",
" datetime.date(2016, 9, 22): 238,\n",
" datetime.date(2016, 9, 23): 241,\n",
" datetime.date(2016, 9, 24): 225,\n",
" datetime.date(2016, 9, 25): 225,\n",
" datetime.date(2016, 9, 26): 208,\n",
" datetime.date(2016, 9, 27): 225,\n",
" datetime.date(2016, 9, 28): 220,\n",
" datetime.date(2016, 9, 29): 226,\n",
" datetime.date(2016, 10, 1): 225,\n",
" datetime.date(2016, 10, 2): 223,\n",
" datetime.date(2016, 10, 3): 220,\n",
" datetime.date(2016, 10, 4): 214,\n",
" datetime.date(2016, 10, 5): 226,\n",
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" datetime.date(2016, 10, 12): 222,\n",
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" datetime.date(2016, 11, 16): 228,\n",
" datetime.date(2016, 11, 17): 231,\n",
" datetime.date(2016, 11, 18): 234,\n",
" datetime.date(2016, 11, 19): 231,\n",
" datetime.date(2016, 11, 20): 227,\n",
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" datetime.date(2016, 11, 24): 239,\n",
" datetime.date(2016, 11, 25): 232,\n",
" datetime.date(2016, 11, 26): 241,\n",
" datetime.date(2016, 11, 27): 236,\n",
" datetime.date(2016, 11, 28): 253,\n",
" datetime.date(2016, 11, 29): 173,\n",
" datetime.date(2016, 12, 1): 240,\n",
" datetime.date(2016, 12, 2): 225,\n",
" datetime.date(2016, 12, 3): 235,\n",
" datetime.date(2016, 12, 4): 212,\n",
" datetime.date(2016, 12, 5): 209,\n",
" datetime.date(2016, 12, 8): 135,\n",
" datetime.date(2016, 12, 9): 218,\n",
" datetime.date(2016, 12, 10): 223,\n",
" datetime.date(2016, 12, 11): 237,\n",
" datetime.date(2016, 12, 12): 238,\n",
" datetime.date(2016, 12, 13): 227,\n",
" datetime.date(2016, 12, 14): 228,\n",
" datetime.date(2016, 12, 15): 221,\n",
" datetime.date(2016, 12, 16): 236,\n",
" datetime.date(2016, 12, 17): 220,\n",
" datetime.date(2016, 12, 18): 229,\n",
" datetime.date(2016, 12, 19): 236,\n",
" datetime.date(2016, 12, 20): 233,\n",
" datetime.date(2016, 12, 21): 223,\n",
" datetime.date(2016, 12, 22): 229,\n",
" datetime.date(2016, 12, 23): 233,\n",
" datetime.date(2016, 12, 24): 225,\n",
" datetime.date(2016, 12, 25): 225,\n",
" datetime.date(2016, 12, 26): 228,\n",
" datetime.date(2016, 12, 27): 233,\n",
" datetime.date(2016, 12, 28): 229,\n",
" datetime.date(2016, 12, 29): 232,\n",
" datetime.date(2016, 12, 30): 222,\n",
" datetime.date(2016, 12, 31): 230}"
]
},
"metadata": {
"tags": []
},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 514
},
"id": "kKFhazsRZV_f",
"outputId": "8a1677c0-d0d5-4602-e402-64020b36c424"
},
"source": [
"#variation of other numerical variables with date\n",
"date_variation = train[[\"Date\",\"Radiation\",\"Temperature\",\"Pressure\", \"Humidity\", \"Speed\",]].groupby(\"Date\").mean()\n",
"plt.figure(figsize=(20,8))\n",
"sns.lineplot(data=date_variation)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f02c62bc550>"
]
},
"metadata": {
"tags": []
},
"execution_count": 13
},
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"<Figure size 1440x576 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Q6ZLwNADcAXH"
},
"source": [
"## general trend is increase in humidity lowers Radiation "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ven9zI5pZOc_"
},
"source": [
"## Creating New variables from Date\n",
"### Month\n",
"### Year\n",
"### Date\n",
"### WeekDay\n",
"### WeekNumber"
]
},
{
"cell_type": "code",
"metadata": {
"id": "O76wPBJJU9Yp"
},
"source": [
"train[\"Month\"] = [dt.month for dt in train[\"Date\"]]\n",
"train[\"Year\"] = [dt.year for dt in train[\"Date\"]]\n",
"train[\"Day\"] = [dt.day for dt in train[\"Date\"]]\n",
"train[\"WeekDay\"] = [dt.weekday() for dt in train[\"Date\"]]\n",
"train[\"WeekNumber\"] = [dt.isocalendar()[1] for dt in train[\"Date\"]]\n",
"train[\"Month_plus_day\"] = train[\"Month\"]*10 + train[\"Day\"]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 483
},
"id": "WehMC7JHU9Ty",
"outputId": "47fa5a68-863c-4fe8-8bf7-d1b69d2453f2"
},
"source": [
"weekday_freq = dict(train[\"WeekDay\"].value_counts())\n",
"plt.figure(figsize=(8,8))\n",
"temp = sns.barplot(x =list(weekday_freq.keys()),y = list(weekday_freq.values()))"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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oA8AkRBsAJiHaADAJ0QaASYg2AExCtAFgEqINAJNYM9pVta+qDlfVx1eN/WJVHaqqe8bPq1Ztu6aqlqvqk1V1yarxPWNsuaresPGnAgBb23o+af9ukj1HGb++u88fP7clSVWdl+TyJC8cx7y5qk6qqpOS/FaSVyY5L8mrx74AwDqdvNYO3f2Bqtq5zte7NMkt3f1Ykk9X1XKSC8a25e5+MEmq6pax7/3HPWMA2KZO5Dvtq6vq3nH5/LQxdlaSh1btc3CMHWv861TV3qo6UFUHjhw5cgLTA4Ct5elG+4Yk35bk/CQPJ/n1jZpQd9/Y3bu7e/fS0tJGvSwATG/Ny+NH092ff3K5qn47yXvG6qEkZ6/adccYy1OMAwDr8LQ+aVfVmatWfzjJk3eW709yeVU9t6rOTbIryZ1J7kqyq6rOrarnZOVmtf1Pf9oAsP2s+Um7qv4wycuSnF5VB5Ncm+RlVXV+kk7ymSQ/kSTdfV9V3ZqVG8weT3JVdz8xXufqJO9NclKSfd1934afDQBsYeu5e/zVRxm+6Sn2vy7JdUcZvy3Jbcc1OwDgKzwRDQAmIdoAMAnRBoBJiDYATEK0AWASog0AkxBtAJiEaAPAJEQbACYh2gAwCdEGgEmINgBMQrQBYBKiDQCTEG0AmIRoA8AkRBsAJiHaADAJ0QaASYg2AExCtAFgEqINAJMQbQCYhGgDwCREGwAmIdoAMAnRBoBJiDYATEK0AWASog0AkxBtAJiEaAPAJEQbACYh2gAwCdEGgEmINgBMQrQBYBKiDQCTEG0AmIRoA8AkRBsAJiHaADAJ0QaASawZ7araV1WHq+rjq8ZeUFW3V9Wnxu/TxnhV1Zuqarmq7q2qF6865oqx/6eq6opn5nQAYOtazyft302y52vG3pDkfd29K8n7xnqSvDLJrvGzN8kNyUrkk1yb5CVJLkhy7ZOhBwDWZ81od/cHkjzyNcOXJrl5LN+c5LJV42/tFR9KcmpVnZnkkiS3d/cj3f1oktvz9f8QAACewtP9TvuM7n54LH8uyRlj+awkD63a7+AYO9b416mqvVV1oKoOHDly5GlODwC2nhO+Ea27O0lvwFyefL0bu3t3d+9eWlraqJcFgOk93Wh/flz2zvh9eIwfSnL2qv12jLFjjQMA6/R0o70/yZN3gF+R5N2rxl877iJ/aZIvjsvo701ycVWdNm5Au3iMAQDrdPJaO1TVHyZ5WZLTq+pgVu4C/9Ukt1bVlUk+m+THxu63JXlVkuUkX07yuiTp7keq6peT3DX2e2N3f+3NbQDAU1gz2t396mNsevlR9u0kVx3jdfYl2XdcswMAvsIT0QBgEqINAJMQbQCYhGgDwCREGwAmIdoAMAnRBoBJiDYATEK0AWASog0AkxBtAJiEaAPAJEQbACYh2gAwCdEGgEmINgBMQrQBYBKiDQCTEG0AmIRoA8AkRBsAJiHaADAJ0QaASYg2AExCtAFgEqINAJMQbQCYhGgDwCREGwAmIdoAMAnRBoBJiDYATEK0AWASog0AkxBtAJiEaAPAJEQbACYh2gAwCdEGgEmINgBMQrQBYBKiDQCTOKFoV9VnqupjVXVPVR0YYy+oqtur6lPj92ljvKrqTVW1XFX3VtWLN+IEAGC72IhP2v+qu8/v7t1j/Q1J3tfdu5K8b6wnySuT7Bo/e5PcsAHvDQDbxjNxefzSJDeP5ZuTXLZq/K294kNJTq2qM5+B9weALelEo91J/qSq7q6qvWPsjO5+eCx/LskZY/msJA+tOvbgGAMA1uHkEzz+e7v7UFX9oyS3V9UnVm/s7q6qPp4XHPHfmyTnnHPOCU4PALaOE/qk3d2Hxu/DSd6V5IIkn3/ysvf4fXjsfijJ2asO3zHGvvY1b+zu3d29e2lp6USmBwBbytOOdlU9v6q++cnlJBcn+XiS/UmuGLtdkeTdY3l/kteOu8hfmuSLqy6jAwBrOJHL42ckeVdVPfk6f9Ddf1xVdyW5taquTPLZJD829r8tyauSLCf5cpLXncB7A8C287Sj3d0PJvnuo4z/VZKXH2W8k1z1dN8PALY7T0QDgEmINgBMQrQBYBKiDQCTEG0AmIRoA8AkRBsAJiHaADAJ0QaASYg2AExCtAFgEqINAJMQbQCYhGgDwCREGwAmIdoAMAnRBoBJiDYATEK0AWASog0AkxBtAJiEaAPAJEQbACYh2gAwCdEGgEmINgBMQrQBYBKiDQCTEG0AmIRoA8AkRBsAJiHaADAJ0QaASYg2AExCtAFgEqINAJMQbQCYhGgDwCREGwAmIdoAMAnRBoBJiDYATEK0AWASmx7tqtpTVZ+squWqesNmvz8AzGpTo11VJyX5rSSvTHJekldX1XmbOQcAmNVmf9K+IMlydz/Y3X+X5JYkl27yHABgSpsd7bOSPLRq/eAYAwDWUN29eW9W9SNJ9nT3vx3rr0nyku6+etU+e5PsHavfkeSTmzbBr3d6kr9c4PsvmvN3/tv1/LfzuSfOf9Hn/0+6e+loG07e5IkcSnL2qvUdY+wruvvGJDdu5qSOpaoOdPfuRc9jUZy/89+u57+dzz1x/s/m89/sy+N3JdlVVedW1XOSXJ5k/ybPAQCmtKmftLv78aq6Osl7k5yUZF9337eZcwCAWW325fF0921Jbtvs932anhWX6RfI+W9v2/n8t/O5J87/WXv+m3ojGgDw9HmMKQBMQrSPYTs/brWq9lXV4ar6+KLnstmq6uyquqOq7q+q+6rq9Yue02aqqudV1Z1V9Rfj/H9p0XNahKo6qao+WlXvWfRcNltVfaaqPlZV91TVgUXPZ7NV1alV9faq+kRVPVBV37PoOa3m8vhRjMet/q8kr8jKA2DuSvLq7r5/oRPbJFX1fUm+lOSt3f1di57PZqqqM5Oc2d0fqapvTnJ3ksu20f/2leT53f2lqjolyQeTvL67P7TgqW2qqvr3SXYn+Yfd/YOLns9mqqrPJNnd3dvy77Sr6uYk/7O73zL+yukfdPcXFj2vJ/mkfXTb+nGr3f2BJI8seh6L0N0Pd/dHxvLfJHkg2+ipfb3iS2P1lPGzrf5lX1U7kvxAkrcsei5srqr6liTfl+SmJOnuv3s2BTsR7WPxuFVSVTuTvCjJhxc7k801Lg3fk+Rwktu7e1udf5LfTPKzSf5+0RNZkE7yJ1V193hC5XZybpIjSX5nfD3ylqp6/qIntZpow1FU1TcleUeSn+ruv170fDZTdz/R3edn5YmFF1TVtvmKpKp+MMnh7r570XNZoO/t7hdn5b/GeNX4umy7ODnJi5Pc0N0vSvJ/kzyr7mkS7aNb83GrbF3ju9x3JHlbd79z0fNZlHFZ8I4kexY9l010YZIfGt/r3pLkoqr6/cVOaXN196Hx+3CSd2Xl68Lt4mCSg6uuLr09KxF/1hDto/O41W1q3Ih1U5IHuvs3Fj2fzVZVS1V16lj+xqzcjPmJxc5q83T3Nd29o7t3ZuX/9+/v7h9f8LQ2TVU9f9yAmXFZ+OIk2+avSLr7c0keqqrvGEMvT/Ksugl105+INoPt/rjVqvrDJC9LcnpVHUxybXfftNhZbZoLk7wmycfG97pJ8vPjSX7bwZlJbh5/QfENSW7t7m33Z0/b2BlJ3rXyb9ecnOQPuvuPFzulTfeTSd42PrA9mOR1C57PV/EnXwAwCZfHAWASog0AkxBtAJiEaAPAJEQbACYh2gAwCdEGgEmINgBM4v8BcX7U84SByegAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ABbImJ2hdzBt"
},
"source": [
"## distribution of weeks is nearly same"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 513
},
"id": "wIRYuF6-U9Q7",
"outputId": "a94f9dbf-4076-44cc-fa73-1e8a62308f27"
},
"source": [
"month_variation = train[[\"Month\",\"Radiation\",\"Temperature\",\"Pressure\", \"Humidity\", \"Speed\",]].groupby(\"Month\").mean()\n",
"plt.figure(figsize=(20,8))\n",
"temp = sns.lineplot(data=month_variation).set(title=\"Monthly variation of other numerical variables\")"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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H5XKRkZHBqFGj6nlXGoYSQiIiIiIiIiLSZJVX7BQUFBAXF8f48eMBOHToEDfeeCMbN27EGENJSQkAX375JXfeeScA8fHxxMfHVzvuM888w8KFCwHYtm0bGzdupHPnzjXGsWTJEiZPnkxQUBBdu3Zl7NixrFixgvbt25OcnEyPHj0ASEhIwO12KyEkIiIiIiIiIk1fXSt5Glr5GkLff/89EyZM4LnnnuPOO+/koYce4sILL2ThwoW43W5SUlLqPGZ6ejqffvopWVlZhIWFkZKScsqPdffXqlWriu2goCBKS0vrPVZD0RpCIiIiIiIiItLkhYWF8cwzz/Dkk09SWlrKoUOHiI6OBmDOnDkV7caMGcP8+fMBWL9+PWvXrj1hrEOHDtGpUyfCwsL45ptvWLp0acWxkJCQimojf6NHj+b111+nrKyMPXv28OWXX5KcnNzAV9lwlBASERERERERkWZh6NChxMfH89prr3Hffffx4IMPMnTo0EoVOb/85S8pLCwkLi6OP/7xjwwfPvyEcS699FJKS0uJi4vjgQce4IILLqg4duuttxIfH1+xqHS5q6++mvj4eIYMGcK4ceN4/PHHOeecc87cxZ4m0xhWtk5MTLTZ2dmBDkNERERERERETkFubi5xcXGBDkOo/r0wxqy01iZW115rCImIyFnn8Vg81uKx+H7+sA0Q4nAQHGQIdhiMMQGOVkRERESk+VFCSESaHGst1pc8KPPf9niTCtaXXCjz2Irt8uOV+9Vw7CTjlPmSF9ZaPB4qjVPmKT924pjlfb39LGUV41dzjqpxl4/pqXy93nNUHsc/0VLmF2fVpEvl2Cq3qRRb1b5VEzke/6SO/3jVJHv8+p6KYIchOMj8kCQKchDi8P6sfr8huCKh5CCk6rEa+ocEOXzn8vWp2O83zknalp8n2OE7Xik277bDoeSWiIiIiDQOSghJi2RP+PJay5feGvZb3xfyGsep6Ytx1f0ntPlhLOuXhKgag/VLXlQdx/oSCdUf+yF5UXWcqv1OOFbDOFXjrL5f5YSMx0OlREtdEivlCZHmzhhwGIPDgDGGIN+2wxgcjh+2jTEEOcrbGhx+28bg62cqxgty+I3pt+1weKtyKsb0G99h8LX1jemoMqavf0Xbin4/xOY/jn87h6PyOOXFQCVlltIyDyUe789Sj6WkzENpmaXU4znxeJmttH20tNS7r8xDmcfW2v9Uk1Sn8776Vz+VJ45qTyhVn9CqMUlV6XjNbcvPE+Q4cd+JybQqMal6S0RERKRJU0KoARUVl1Hi8WA9fl94T1Yt4DnJF+C6fjmuWslQ0a/66oWTV0tQkTTwH6dqBULVagn/CoTK1Qv+yYtqKhlqiLNq8uKESoZTSeTUkIxpSQmFoCrJAP9EQ9Uv5+WJgar9qiYhKm37jR3iSxT47z+xn39f/2NV9lVt66jary7tqjl+wrlOTJCcmHT5IXlRNQb/BElt8fgnaKpN3jgqxyNnj8djKfH4EkZlP2yX+JJRpWW+JJJfMqnaJFPF8Zrb/nCeygmvmvoUl3o4Wlzml/iqpr///rOV3aJ+1Vs1VmJVk9gKrq2PqrdERERE6k0JoQZ01XMZbNh1JNBhNJiavkBX98U1qIYv1ZWrCaqpFvAbMzjIUbF9QiVDpQqE6qsXqqtkqJoE8Y+zvHqhLpUMJ0+snDhO+XblaolqxnRUf72Vzuc4xXF8/ZRQEKk7h8PQyhFEq2bwr6K1tiLBVF3yqDxx5Z/wOrF6qvpKqjKPPe3qrUK/6q2qybbK+73xn60EvsP371Bt1Vc1J8HOTPVWXRNaQareEhERkVPUDP7Tt/G4ZUxvDn5fXKlS4ORTPapUFvglX37od2LVwQmVDJUSNnWvXqh2HL/Y9B+TIiJNjzHeypmQIGhDUKDDOW1lntqTR1UTUmUNWb3ll9Cqeu7jpR6OHi+tvL/i+IlxljWj6q0aK7VOSGipektERKSxUkKoAV03vEegQxAREWlWghyGIEfTT2yBt3qrIlFUPk2wgaq36lYRdvLxj5WUVlup1VSqt6qruqq10ussVG/59y2v8BURkYazb98+LrroIgB27txJUFAQUVFRACxfvpzQ0NBAhldJeno6oaGhjBw5MtChAEoIiYiIiJwVxhhCgw2hOAIdSoOoT/VWXdbBqnnMk63jVfncx0o8lJY1zuqtmpJMQSeZjniq1Vud24ZyZUI0ndo2ni9BIiJnSufOnVm9ejUAM2bMIDw8nHvuuSdg8ZSWlhIcXH2qJT09nfDw8FNKCJ1svNOlhJCIiIiInLLmVL3l8fyQVDrpwvGnsDZX1cXoyzwNX71VU+IN4LGPv+HqodGkumLp17VdgO+wiMjZtXLlSu6++24KCwuJjIxkzpw5dOvWjZSUFIYOHcrixYs5evQo8+bN49FHH2XdunVMnDiRhx9+GLfbzaWXXsrw4cPJyclh4MCBzJs3j7CwsJOOm5CQwJIlS5g8eTL9+vXj4Ycfpri4mM6dO/Pqq69SVFTECy+8QFBQEP/4xz+YNWsWL7/8MpdddhnXXXcdAOHh4RQWFpKens5DDz1Ep06d+Oabb8jNzeWBBx4gPT2d48ePc/vtt/OLX/zitO+TEkIiIiIi0qI5HIZQR/Oo3rLW8u2uQuZk5vF2TgGvLd/GqD6RpLqcXNi/i9ZkEpEzL+0n1e9P/cD786MHYOe6E49f+ih0i4dVr8Lq+Sf2qyNrLXfccQfvvvsuUVFRvP766/zhD39g9uzZAISGhpKdnc3MmTO58sorWblyJREREZx77rncddddAGzYsIGXX34Zl8vF9OnT+b//+z9+85vfnHTc4uJisrOzAThw4ABLly7FGMPf//53Hn/8cZ588kluu+22ShVML7/8co3XkZOTw/r164mNjeXFF1+kQ4cOrFixguPHj+NyubjkkkuIjY09pXtTlRJCIiIiIiLNhDGG/ue049Fr4rl3wnm8tnwr87Lc3DQ3G2fnMG4c6eSniT0Jbw6PVRQRqcbx48dZv34948ePB6CsrIxu3bpVHL/iiisAGDx4MAMHDqw41rt3b7Zt20bHjh3p2bMnLpcLgOuvv55nnnmGSy+99KTjTpw4sWI7Pz+fiRMnsmPHDoqLi+uVuElOTq7o98knn7B27VoWLFgAwKFDh9i4caMSQiIiIiIicqKItqHcfmEfbh3Tm4/W7yQtI4//+tfXPPXJt/w0sSfTRjrp1Tks0GGKSHNTW0XPjx47+fGhU7x/6slay8CBA8nKyqr2eKtWrQBwOBwV2+WvS0tLgROfuG2MqXXctm3bVmzfcccd3H333VxxxRWkp6czY8aMavsEBwfj8XgA8Hg8FBcXVzuetZZZs2YxYcKEmi67Xpp+XayIiIiIiNQoJMjBFUO6s/BXLhb+aiQXnteFeVluxj6xiFvmZZO5eS/2bD02TkTkDGvVqhV79uypSNyUlJTw1VdfndIYW7dureg/f/58Ro0aRf/+/es87qFDh4iOjgZg7ty5FfvbtWvHkSNHKl47nU5WrlwJwHvvvUdJSUm1402YMIHnn3++4vi3337L0aNHT+maqqOEkIiIiIhICzG0VyeemTyUJfeP41cp55Lt3s/PX1rGj2Yu5o0V2zhWUhboEEVETovD4WDBggXcf//9DBkyhISEBDIzM09pjP79+/Pcc88RFxfHgQMH+OUvf0loaGidx50xYwY//elPGT58OJGRkRX7L7/8chYuXEhCQgKLFy/mlltu4YsvvmDIkCFkZWVVqgryd/PNNzNgwACGDRvGoEGD+MUvflFRzXQ6TGP4vwGJiYm2fPElERERERE5O46VlPHu6gJmL3GzYdcRItqGMuX8Xlx/QQxd27cOdHgi0gTk5uYSFxcX6DAajNvt5rLLLmP9+vWBDuWUVfdeGGNWWmsTq2uvNYRERERERFqo1iFBTEzqxc8Se5K1eR+zM9w8u2gTz6dv5ifx3Uh1xZLQs2OgwxQRkTNACSERERERkRbOGMPIPpGM7BPJln1HmZPp5s3sfN5dvZ1hvTqS6orl0kHnEBKkFSdEpHlzOp1NsjqoPpQQEhERERGRCjGd2/Knywdy9/h+LFiZz5xMN3e8topuHVpzw4gYJif1olPb0ECHKSIip0kpfhEREREROUG71iGkumL5/Hcp/H1qIr2j2vL4xxsY8dhnPPj2Or7ddaT2QUREpNFShZCIiIiIiNQoyGG4eEBXLh7QlW92HmZOhpu3c/J5bflWRvWJJNXl5ML+XXA4TKBDFRGRU6AKIRERERERqZPzzmnPY9fGk/XgRdw7oT8bdx/hprnZXPTUF8zJyKPw+Ok/BllERM4OJYREREREROSURLQN5fYL+7Dk/nHMnJRAhzYhzPjX14z4y2f89/tfs3Xf94EOUURakKCgIBISEhg0aBA//elP+f57/R1UF0oIiYiIiIhIvYQEObgyIZp3bnex8FcjufC8LszNdDP2iUXcMi+brM37sNYGOkwRaebatGnD6tWrWb9+PaGhobzwwguVjpeWnr3qxbN5rtOlhJCIiIiIiJy2ob068czkoSy5fxy/SjmXbPd+Jr+0lB/NXMwbK7ZxrKQs0CGKSAswevRoNm3aRHp6OqNHj+aKK65gwIABlJWVce+995KUlER8fDx/+9vfANixYwdjxoypqDBavHgxZWVlTJs2jUGDBjF48GCefvppAFJSUsjOzgZg7969OJ1OAObMmcMVV1zBuHHjuOiiizh69CjTp08nOTmZoUOH8u677wbkXtRGCSEREREREWkw53Rozb0TziPrwYv467WDsRbue2strsc+58lPNrDr8LFAhygiZ1Dqx6m8s+mdBt2uq9LSUj766CMGDx4MQE5ODjNnzuTbb7/l5ZdfpkOHDqxYsYIVK1bw0ksvkZeXx/z585kwYQKrV69mzZo1JCQksHr1agoKCli/fj3r1q0jNTW11nPn5OSwYMECvvjiCx555BHGjRvH8uXLWbRoEffeey9Hjx49pWs5G/SUMRERERERaXCtQ4KYmNSLnyX2JGvzPmZn5PHsok08n76Zy+K7keqKZUjPjoEOU0SagaKiIhISEgBvhdBNN91EZmYmycnJxMbGAvDJJ5+wdu1aFixYAMChQ4fYuHEjSUlJTJ8+nZKSEq666ioSEhLo3bs33333HXfccQc/+clPuOSSS2qNYfz48URERFSc67333uOJJ54A4NixY2zdupW4uLgzcfn1poSQiIiIiIicMcYYRvaJZGSfSNx7jzI3y82b2fm8s3o7w3p1ZPqoWCYMPIeQIE1eEGkO0i5Na/Dt2pSvIVRV27ZtK7attcyaNYsJEyac0O7LL7/kgw8+YNq0adx9991MnTqVNWvW8O9//5sXXniBN954g9mzZxMcHIzH4wG8SZ6Tneutt96if//+db6GQNDfuiIiIiIiclY4I9vyp8sHkvXgOP542QD2HS3m1/NXMebxRfxf+iYOHC0OdIgi0kxNmDCB559/npKSEgC+/fZbjh49ypYtW+jatSu33HILN998Mzk5OezduxePx8O1117Lww8/TE5ODgBOp5OVK1cCVFQa1XSuWbNmVSyqv2rVqjN8dfWjCiERERERETmr2rUOYfqoWG4c6WTRN7uZnZHH4x9v4JnPNnL10B6kupz069ou0GGKSDNy880343a7GTZsGNZaoqKieOedd0hPT+d//ud/CAkJITw8nHnz5lFQUEBqampFNdCjjz4KwD333MPPfvYzXnzxRX7yk5/UeK6HHnqI3/72t8THx+PxeIiNjeX9998/K9d5KkxjeAxkYmKiLV+pW0REREREWp5vdh5mToabhasKOF7qYXTfSFJdTlL6dcHhMIEOT0RqkJub2+jWxmmpqnsvjDErrbWJ1bXXlDEREREREQm4885pz2PXxpP14EXcO6E/3+46wvQ52Vz01BfMycij8HhpoEMUEWlWlBASEREREZFGI6JtKLdf2Icl949j5qQEOrQJYca/vmbEXz7jv9//mm37vw90iCIizYLWEBIRERERkUYnJMjBlQnRXJkQTc7WA6RluJmb6SYtI4+L47qS6orlgt4RGKPpZCIi9aGEkIiIiIiINGrDenViWK9O7PxxHK8sdTN/2VY++XoXcS6YNooAACAASURBVN3ak+pycsWQ7rQOCQp0mCIiTYqmjImIiIiISJNwTofW3DvhPLIevIjHrhmMx2O5b8FaXI99zpOfbGD34WOBDlFEpMlQhZCIiIiIiDQprUOCmJTci4lJPcnavI/ZGXk8u2gTL3yxmZ8M7kaqK5YhPTsGOkwRkUZNFUIiIiIiItIkGWMY2SeSv9+YxKLfpTDl/Bg+zd3Nlc9lcO3zmby/djulZZ5AhykiZ1h4eHil13PmzOHXv/51g4z9wgsvMG/evBP2u91uBg0aBEB2djZ33nknAOnp6WRmZjbIuc80VQiJiIiIiEiT54xsy4wrBvK7S/rxZnY+c7Pc/Hr+Krp1aM0NI2KYnNSLTm1DAx2miDQxt912W61tEhMTSUxMBLwJofDwcEaOHHmmQzttqhASEREREZFmo13rEKaPiuXz36Xw0tREYiPb8vjHGxjx2Gc8+PY6Nu46EugQReQsmjZtGgsWLKh4XV5NlJ6eztixY7nyyivp3bs3DzzwAK+++irJyckMHjyYzZs3AzBjxgyeeOIJAFauXMmQIUMYMmQIzz33XMWY6enpXHbZZbjdbl544QWefvppEhISWLx4MbGxsZSUlABw+PDhSq8DTRVCIiIiIiLS7AQ5DOMHdGX8gK7k7jjMnAw3b+Xk89ryrYzuG0mqy0lKvy44HHpsvUhD2nLD1EqvO1x9NR2vuZq9L77E0cWLaTt6NJG33sLBtxdyaOHCSm1jXplH6Z49FNz9OwCin3qS4KioWs9ZVFREQkJCxev9+/dzxRVX1NpvzZo15ObmEhERQe/evbn55ptZvnw5M2fOZNasWfzv//5vpfapqak8++yzjBkzhnvvvfeE8ZxOJ7fddhvh4eHcc889AKSkpPDBBx9w1VVX8c9//pNrrrmGkJCQWmM7G1QhJCIiIiIizVpct/b89bp4lj54EfdO6M+3u44wfU42Fz31BXMz3RQeLw10iCJyGtq0acPq1asr/vz5z3+uU7+kpCS6detGq1atOPfcc7nkkksAGDx4MG63u1LbgwcPcvDgQcaMGQPADTfcUKdz3HzzzaSlpQGQlpZGampqHa/qzFOFkIiIiIiItAgRbUO5/cI+3DqmNx+u20Fahps/vfcVT/x7AxOTenLjSCc9I8ICHaZIkxbzyokLMANE3noLkbfeUvG64zXeyqGqgqOiahyjPoKDg/F4vIvLezweiouLK461atWqYtvhcFS8djgclJY2TKLY5XLhdrtJT0+nrKysYiHqxkAVQiIiIiIi0qKEBDm4MiGad2538favRpJyXhfmZLoZ+z+LuHVeNku/24e1NtBhikgDcDqdrFy5EoD33nuv3uv3dOzYkY4dO7JkyRIAXn311WrbtWvXjiNHKq9VNnXqVH7+8583quogUEJIRERERERasGG9OjFr8lAW338ht409lxXu/Ux6cSk/fmYJb2Rv41hJWaBDFJHTcMstt/DFF18wZMgQsrKyaNu2bb3HSktL4/bbbychIaHGpPHll1/OwoULKxaVBpgyZQoHDhxg8uTJ9T73mWAaQ+Y7MTHRZmdnBzoMERERERFp4Y6VlPHOqgJmZ+Tx7a5COrcNZcr5vbj+ghi6tG8d6PBEGp3c3Fzi4uICHUajtmDBAt59911eeeWVM3qe6t4LY8xKa21ide21hpCIiIiIiIhP65AgJiX3YmJSTzI37yMtI49Zizbx/BebuSy+O6kuJ/E9OgY6TBFpIu644w4++ugjPvzww0CHcgIlhERERERERKowxuDqE4mrTyTuvUeZk+nmzextLFxVwPCYTqS6nFw68ByCg7QKh4jUbNasWYEOoUZKCImIiIiIiJyEM7ItM64YyN2X9OPN7HzmZrr59fxVdO/QmhtGOJmc3JOOYaGBDlNE5JQonS0iIiIiIlIH7VuHcNOoWBbdk8JLUxNxRrblrx9/wwWPfsbvF65j464jtQ8iItJIqEJIRERERETkFAQ5DOMHdGX8gK7k7jjMnAw3C1bmM3/ZVkb3jWS6K5ax/aJwOEygQxURqZEqhEREREREROoprlt7/npdPFkPjOOeS/rx7a4jpM5ZwcVPfcHcTDdHj5cGOkQRkWopISQiIiIiInKaOoe34tfj+rL4vnHMnJRAuzYh/Om9r7jg0c94+P2v2bb/+0CHKNKsPfLIIwwcOJD4+HgSEhJYtmzZGTtXSkoK2dnZZ2z8s0VTxkRERERERBpIaLCDKxOiuTIhmpytB0jLcJOW6WZ2Rh7jB3Ql1RXL+bERGKPpZCINJSsri/fff5+cnBxatWrF3r17KS4uDnRYjZ4qhERERERERM6AYb06MWvyUJbcfyG3jT2XZXn7mfTiUn78zBLezN7GsZKyQIco0izs2LGDyMhIWrVqBUBkZCTdu3fH6XRy3333MXjwYJKTk9m0aRMAe/bs4dprryUpKYmkpCQyMjIAOHr0KNOnTyc5OZmhQ4fy7rvvAlBUVMSkSZOIi4vj6quvpqioKDAX2sBqrRAyxvQE5gFdAQu8aK2daYyJAF4HnIAb+Jm19oDxprpnAj8GvgemWWtzzkz4IiIiIiIijVu3Dm2479LzuGNcX95ZXUBaRh73LljLYx99w5Tze3H9BTF0ad860GGKNIiFT9b89f+8Ed2IG9mNhU/mVNqurX1tLrnkEv785z/Tr18/Lr74YiZOnMjYsWMB6NChA+vWrWPevHn89re/5f333+c3v/kNd911F6NGjWLr1q1MmDCB3NxcHnnkEcaNG8fs2bM5ePAgycnJXHzxxfztb38jLCyM3Nxc1q5dy7Bhw079xjRCdZkyVgr8zlqbY4xpB6w0xvwHmAZ8Zq19zBjzAPAAcD/wI6Cv78/5wPO+nyIiIiIiIi1Wm9AgJif3YlJSTzI372P2kjxmLdrE819s5rL47qS6nMT36BjoMEWanPDwcFauXMnixYtZtGgREydO5LHHHgNg8uTJFT/vuusuAD799FO+/vrriv6HDx+msLCQTz75hPfee48nnngCgGPHjrF161a+/PJL7rzzTgDi4+OJj48/m5d3xtSaELLW7gB2+LaPGGNygWjgSiDF12wukI43IXQlMM9aa4GlxpiOxphuvnFERERERERaNGMMrj6RuPpE4t57lDmZbt7M3sbCVQUkxnQi1RXLhIFdCQ7SCh/S9Fz9u9qrZ/zb1KV9XQQFBZGSkkJKSgqDBw9m7ty5AJXW6yrf9ng8LF26lNatK1fmWWt566236N+/f4PE1Nid0t8wxhgnMBRYBnT1S/LsxDulDLzJom1+3fJ9+6qOdasxJtsYk71nz55TDFtERERERKTpc0a2ZcYVA8n6/UU8dNkAdh85zu3zcxjz+CKeT9/Mwe+1MK5IbTZs2MDGjRsrXq9evZqYmBgAXn/99YqfI0aMALxTzGbNmlWpPcCECROYNWsW3voWWLVqFQBjxoxh/vz5AKxfv561a9ee4Ss6O+qcEDLGhANvAb+11h72P+arBrKncmJr7YvW2kRrbWJUVNSpdBUREREREWlW2rcO4aZRsSy6J4UXbxhOTOe2/PXjb7jg0c/4/cJ1bNx1JNAhijRahYWF3HjjjQwYMID4+Hi+/vprZsyYAcCBAweIj49n5syZPP300wA888wzZGdnEx8fz4ABA3jhhRcAeOihhygpKSE+Pp6BAwfy0EMPAfDLX/6SwsJC4uLi+OMf/8jw4cMDcp0NzZRnvk7ayJgQ4H3g39bap3z7NgAp1todxphuQLq1tr8x5m++7deqtqtp/MTERJudnd0AlyMiIiIiItI85O44TFpGHu+s3k5xqYfRfSOZ7oplbL8oHA49tl4ah9zcXOLi4gIdRrWcTifZ2dlERkYGOpSzorr3whiz0lqbWF37WiuEfE8NexnILU8G+bwH3OjbvhF412//VON1AXBI6weJiIiIiIicmrhu7Xn8uiFkPTCOey7px4adR0ids4KLn/qCeVlujh4vDXSIItKE1VohZIwZBSwG1gEe3+7f411H6A2gF7AF72Pn9/sSSM8Cl+J97Hyqtfak5T+qEBIRERERETm54lIPH63fwewMN2u2HaRd62AmJvbkxpFOekaEBTo8aaEac4VQS3OqFUJ1ecrYEqCmesSLqmlvgdtrD1VERERERETqKjTYwZUJ0VyZEE3O1gPMXpJHWqab2Rl5jB/QlVRXLOfHRlR6qpKISE1qTQiJiIiIiIhI4zKsVyeG/bwTOw4V8UrWFuYv38q/v9rFgG7tSXU5uXxId1qHBAU6TGkhrLVKRAZYXdaHrqpOi0qfaZoyJiIiIiIiUn9FxWW8s7qAtIw8vt1VSOe2oUw5vxfXXxBDl/atAx2eNGN5eXm0a9eOzp07KykUINZa9u3bx5EjR4iNja107GRTxpQQEhERERERaSastWRs2kdaRh6fb9hNsMNwWXx3prtiGdyjQ6DDk2aopKSE/Px8jh07FuhQWrTWrVvTo0cPQkJCKu1XQkhERERERKSFydt7lLmZbt7M3sbR4jISYzqR6oplwsCuBAfV+sBpEWkGlBASERERERFpoQ4fK+HN7HzmZOaxbX8R3Tu0ZupIJ5OSetIxLDTQ4YnIGaSEkIiIiIiISAtX5rF8lruLtAw3Wd/to3WIg2uH9SDV5aRPl3aBDk9EzgAlhERERERERKRC7o7DpGXk8c7q7RSXehjdN5Lpo2IZ2zcKh0MLA4s0F0oIiYiIiIiIyAn2FR7nteVbmZe1hd1HjtM7si3TXE6uHdaDtq2CAx2eiJwmJYRERERERESkRsWlHj5av4PZS/JYk3+Idq2DmZTUk6kjnPSMCAt0eCJST0oIiYiIiIiISK2steRsPUhaRh4frd+JtZZLBpxDqstJcmwExmg6mUhTcrKEkGoARUREREREBABjDMNjOjE8phPbDxbxytItvLZ8Kx9/tZMB3dqT6nJy+ZDutA4JCnSoInKaVCEkIiIiIiIiNSoqLmPhqgLSMvLYuLuQyPBQfn5+DNdf0Isu7VoHOjwROQlNGRMREREREZHTYq0lY9M+0jLy+Oyb3YQEGS6P706qK5bBPToEOjwRqYamjImIiIiIiMhpMcYwqm8ko/pGkrf3KHMz3byZvY23VxWQGNOJ6aNiuWRAV4KDHIEOVUTqQBVCIiIiIiIiUi+Hj5XwxoptzM1ys21/EdEd23DDiBgmJfWkY1hooMMTafE0ZUxERERERETOmDKP5bPcXczOyGPpd/tpExLENcOiSXU56dOlXaDDE2mxlBASERERERGRs+Lr7YeZk5nHO6u3U1zqYUy/KFJdTsb2jcLh0GPrRc4mJYRERERERETkrNpXeJz5y7byytIt7D5ynN5RbUkd6eSaYT1o20rL2YqcDUoIiYiIiIiISEAUl3r4cN0O0jLyWJN/iHatg5mU1JOpI5z0jAgLdHgizZoSQiIiIiIiIhJQ1lpyth5kdkYeH6/fibWWSwacQ6rLSXJsBMZoOplIQ9Nj50VERERERCSgjDEMj+nE8JhObD9YxCtLt/Da8q18/NVOBnZvT6orlsuHdKNVcFCgQxVpEVQhJCIiIiIiIgFRVFzGwlUFpGXksXF3IZHhofz8/Biuv6AXXdq1DnR4Ik2epoyJiIiIiIhIo2WtZcmmvaRluPn8m92EBBkuj+9OqiuWwT06BDo8kSZLU8ZERERERESk0TLGMLpvFKP7RpG39yhzM928mb2Nt1cVkOTsRKorlksGdCU4yBHoUEWaDVUIiYiIiIiISKNz+FgJb6zYxtwsN9v2FxHdsQ1TR8QwKakXHcJCAh2eSJOgKWMiIiIiIiLSJJV5LJ/m7iItI4+l3+2nTUgQ1wyLJtXlpE+XdoEOT6RRU0JIREREREREmryvtx8mLSOPd9dsp7jUw5h+UaS6nIztG4XDocfWi1SlhJCIiIiIiIg0G/sKjzN/2VZeWbqF3UeO0zuqLakjnVwzrAdtW2mpXJFySgiJiIiIiIhIs1Nc6uHDdTuYnZHH2vxDtGsdzKSknkwd4aRnRFigwxMJOCWEREREREREpNmy1pKz9QCzM9x8vH4n1louGXAO00fFkuTshDGaTiYtkx47LyIiIiIiIs2WMYbhMREMj4lg+8EiXlm6hfnLtvLxVzsZ2L09qa5YLh/SjVbBQYEOVaTRUIWQiIiIiIiINDtFxWUsXFVAWkYeG3cXEhkeypTzY5hyQS+6tGsd6PBEzgpNGRMREREREZEWyVrLkk17Sctw8/k3uwkJMlw+pDvTXbEMiu4Q6PBEzihNGRMREREREZEWyRjD6L5RjO4bxXd7Cpmb6ebNlfm8nVNAkrMT012xjB/QleAgR6BDFTmrVCEkIiIiIiIiLcrhYyW8sWIbczLd5B8oIrpjG6aOiGFSUi86hIUEOjyRBqMpYyIiIiIiIiJVlHksn+buIi0jj6Xf7adNSBDXDo9m2shY+nQJD3R4IqdNCSERERERERGRk/hq+yHmZLh5d812iks9jO0XRarLyZi+UTgcemy9NE1KCImIiIiIiIjUwd7C48xftpVXlm5hz5Hj9I5qS+pIJ9cM60HbVlqGV5oWJYRERERERERETkFxqYcP1+1gdkYea/MP0b51MJOSezF1RAw9OoUFOjyROlFCSERERERERKQerLXkbD3A7Aw3H6/fibWWCQPPIdUVS5KzE8ZoOpk0XnrsvIiIiIiIiEg9GGMYHhPB8JgIth8sYl7WFl5bvpWP1u9kYPf2THfFctmQbrQKDgp0qCKnRBVCIiIiIiIiIqegqLiMhasKSMvIY+PuQiLDWzHl/F5MuaAXXdq1DnR4IhU0ZUxERERERESkgVlrWbJpL2kZbj7/ZjehQQ4uG9KN6a5YBkV3CHR4IpoyJiIiIiIiItLQjDGM7hvF6L5RfLenkLmZbt5cmc/bOQUkOyNIdTkZP6ArwUGOQIcqcgJVCImIiIiIiIg0kENFJbyZvY05mW7yDxQR3bENN46MYWJiLzqEhQQ6PGlhNGVMRERERERE5Cwq81g+zd3F7CV5LMvbT5uQIK4dHs20kbH06RIe6PCkhVBCSERERERERCRAvtp+iLQMN++t3k5xmYex/aJIdTkZ0zcKh0OPrZczRwkhERERERERkQDbW3ic+cu28srSLew5cpxzo9oyzRXLtcOiCQvVEr/S8JQQEhEREREREWkkiks9fLBuO2kZbtbmH6J962AmJfdi6ogYenQKC3R40owoISQiIiIiIiLSyFhrydl6gNlL3Hz81U6stUwYeA7TR8WSGNMJYzSdTE6PHjsvIiIiIiIi0sgYYxgeE8HwmAgKDhbxStYWXlu+lY/W72RQdHtSR8Zy2ZButAoOCnSo0gypQkhERERERESkkfi+uJSFqwpIy3CzaXchkeGtuP6CXkw5P4aodq0CHZ40MZoyJiIiIiIiItKEWGtZsmkvs5fksWjDHkKDHFw2pBvTXbEMiu4Q6PCkidCUMREREREREZEmxBjD6L5RjO4bxeY9hczNdLNgZT5v5xSQ7Iwg1eVk/ICuBAc5Ah2qNFGqEBIRERERERFpAg4VlfBm9jbmZLrJP1BEdMc23DgyhomJvegQFhLo8KQR0pQxERERERERkWaizGP5z9e7SMvIY1neftqEBHHt8GimjYylT5fwQIcnjYgSQiIiIiIiIiLN0FfbD5GW4ea91dspLvMwtl8U00fFMqZvpB5bL0oIiYiIiIiIiDRnewuPM3/ZVl5ZuoU9R45zblRbprliuXZYNGGhWj64pVJCSERERERERKQFKC718MG67aRluFmbf4j2rYOZnNyLG0bE0KNTWKDDk7NMCSERERERERGRFsRay8otB0jLcPPxVzux1nLpoHNIdcWSGNNJ08laCD12XkRERERERKQFMcaQ6Iwg0RlBwcEiXsnawmvLt/Lhup0Mim7PdFcsP4nvRqvgoECHKgGiCiERERERERGRFuD74lIWriogLcPNpt2FRIa34voLejHl/Bii2rUKdHhyBmjKmIiIiIiIiIgA3ulkizfuJS0jj0Ub9hAa5ODyId1JdTkZFN0h0OFJA9KUMREREREREREBvNPJxvSLYky/KDbvKWRuppsFK/N5Kyef5NgIprucjB9wDkEOrTPUnKlCSERERERERKSFO1RUwhsrtjE3y03+gSKiO7bhxpExTEzqRYc2IYEOT+pJU8ZEREREREREpFZlHst/vt5FWkYey/L2ExYaxLXDejDN5eTcqPBAhyenSAkhERERERERETklX20/RFqGm/dWb6e4zENK/yhSXbGM6Rupx9Y3EUoIiYiIiIiIiEi97DlynPnLtvKPZVvYc+Q450a1JdUVyzXDogkL1dLEjZkSQiIiIiIiIiJyWopLPXywbjuzl7hZV3CI9q2DmZzci6kjnUR3bBPo8KQaSgiJiIiIiIiISIOw1rJyywHSMtx8/NVOACYM7EqqK5bEmE6aTtaI6LHzIiIiIiIiItIgjDEkOiNIdEZQcLCIeVlu/rl8Gx+u28ng6A6kupz8JL4brYKDAh2qnIQqhERERERERETktHxfXMrbOQXMyXSzaXchkeGtuOGCGH5+fi+i2rUKdHgtlqaMiYiIiIiIiMgZZ61l8ca9zM7II33DHkKDHFw+pDupLieDojsEOrwWR1PGREREREREROSMM8Ywpl8UY/pFsXlPIXMz3SxYmc9bOfkkx0Yw3eVk/IBzCHJonaFAU4WQiIiIiIiIiJwxh4pKeGPFNuZkuik4WESPTm24cYSTnyX1pEObkECH16xpypiIiIiIiIiIBFRpmYdPc3cxO8PN8rz9hIUGce2wHkxzOTk3KjzQ4TVLSgiJiIiIiIiISKOxvuAQczLdvLd6O8VlHlL6RzHdFcvovpF6bH0DUkJIRERERERERBqdPUe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"text/plain": [
"<Figure size 1440x576 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rXtlGALeeRJI"
},
"source": [
"## average radiation gose down in the month of december while humidity gose up"
]
},
{
"cell_type": "code",
"metadata": {
"id": "pfahMbCoU9Oh"
},
"source": [
""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "gHDZ65iLegLU"
},
"source": [
"## Time\n",
"### time of day is quite important for radiation measurement as afternoon will generally have greater radiation"
]
},
{
"cell_type": "code",
"metadata": {
"id": "P_B8tyg3U8jt"
},
"source": [
"train[\"Hour\"] = [int(tm.split(\":\")[0]) for tm in train[\"Time\"]]\n",
"train[\"Mins\"] = [int(tm.split(\":\")[1]) for tm in train[\"Time\"]]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 513
},
"id": "DPQjkXvnUKxG",
"outputId": "d1a1d35c-2805-42f7-a1fb-86abb9df5737"
},
"source": [
"hourly_variation = train[[\"Hour\",\"Radiation\",\"Temperature\",\"Pressure\", \"Humidity\", \"Speed\",]].groupby(\"Hour\").mean()\n",
"plt.figure(figsize=(20,8))\n",
"temp = sns.lineplot(data=hourly_variation).set(title=\"hourly variation of other numerical variables\")"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x576 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Tk15X4DNfwVG"
},
"source": [
"## As Expected afternoon has high Radiation and low humidity with slight increase in temp"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "l2LSmsNEfg_p",
"outputId": "198b733e-98c1-4f27-b326-a7fa1c9617ea"
},
"source": [
"train.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>UNIXTime</th>\n",
" <th>Date</th>\n",
" <th>Time</th>\n",
" <th>Radiation</th>\n",
" <th>Temperature</th>\n",
" <th>Pressure</th>\n",
" <th>Humidity</th>\n",
" <th>WindDirection(Degrees)</th>\n",
" <th>Speed</th>\n",
" <th>TimeSunRise</th>\n",
" <th>TimeSunSet</th>\n",
" <th>Month</th>\n",
" <th>Year</th>\n",
" <th>Day</th>\n",
" <th>WeekDay</th>\n",
" <th>WeekNumber</th>\n",
" <th>Month_plus_day</th>\n",
" <th>Hour</th>\n",
" <th>Mins</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1480107904</td>\n",
" <td>2016-11-25</td>\n",
" <td>11:05:04</td>\n",
" <td>288.44</td>\n",
" <td>46</td>\n",
" <td>30.48</td>\n",
" <td>101</td>\n",
" <td>129.84</td>\n",
" <td>13.50</td>\n",
" <td>06:37:00</td>\n",
" <td>17:42:00</td>\n",
" <td>11</td>\n",
" <td>2016</td>\n",
" <td>25</td>\n",
" <td>4</td>\n",
" <td>47</td>\n",
" <td>135</td>\n",
" <td>11</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1472818508</td>\n",
" <td>2016-09-02</td>\n",
" <td>02:15:08</td>\n",
" <td>2.79</td>\n",
" <td>50</td>\n",
" <td>30.42</td>\n",
" <td>75</td>\n",
" <td>173.90</td>\n",
" <td>6.75</td>\n",
" <td>06:07:00</td>\n",
" <td>18:37:00</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>35</td>\n",
" <td>92</td>\n",
" <td>2</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1475804719</td>\n",
" <td>2016-10-06</td>\n",
" <td>15:45:19</td>\n",
" <td>118.05</td>\n",
" <td>54</td>\n",
" <td>30.42</td>\n",
" <td>100</td>\n",
" <td>7.35</td>\n",
" <td>1.12</td>\n",
" <td>06:15:00</td>\n",
" <td>18:07:00</td>\n",
" <td>10</td>\n",
" <td>2016</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>40</td>\n",
" <td>106</td>\n",
" <td>15</td>\n",
" <td>45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1482533149</td>\n",
" <td>2016-12-23</td>\n",
" <td>12:45:49</td>\n",
" <td>853.17</td>\n",
" <td>58</td>\n",
" <td>30.44</td>\n",
" <td>57</td>\n",
" <td>81.67</td>\n",
" <td>11.25</td>\n",
" <td>06:54:00</td>\n",
" <td>17:50:00</td>\n",
" <td>12</td>\n",
" <td>2016</td>\n",
" <td>23</td>\n",
" <td>4</td>\n",
" <td>51</td>\n",
" <td>143</td>\n",
" <td>12</td>\n",
" <td>45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1481883019</td>\n",
" <td>2016-12-16</td>\n",
" <td>00:10:19</td>\n",
" <td>1.24</td>\n",
" <td>42</td>\n",
" <td>30.24</td>\n",
" <td>103</td>\n",
" <td>171.13</td>\n",
" <td>2.25</td>\n",
" <td>06:50:00</td>\n",
" <td>17:46:00</td>\n",
" <td>12</td>\n",
" <td>2016</td>\n",
" <td>16</td>\n",
" <td>4</td>\n",
" <td>50</td>\n",
" <td>136</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" UNIXTime Date Time ... Month_plus_day Hour Mins\n",
"0 1480107904 2016-11-25 11:05:04 ... 135 11 5\n",
"1 1472818508 2016-09-02 02:15:08 ... 92 2 15\n",
"2 1475804719 2016-10-06 15:45:19 ... 106 15 45\n",
"3 1482533149 2016-12-23 12:45:49 ... 143 12 45\n",
"4 1481883019 2016-12-16 00:10:19 ... 136 0 10\n",
"\n",
"[5 rows x 19 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "J4PCNMtjgCVS"
},
"source": [
""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "7INtWfaagBsE"
},
"source": [
"## Radiation"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 281
},
"id": "cau9YQ0df_K_",
"outputId": "a100b9d7-0c61-454e-ead5-064f37b74543"
},
"source": [
"temp = sns.boxplot(data = train[\"Radiation\"]).set(title=\"Radiation ouliers\")"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEICAYAAACzliQjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAW9UlEQVR4nO3df5ilZX3f8feHXRdFjAvLdCO7i7uVNRZNiHSCNGpKRHHxB0t7GQttw9aQbNMC2mqrYNuQH1f8kdigUGOvFbYuakFqNa4Jga6oQZuADBbll4YJorsbcIddRA0K2eXbP86z8TDMrzNnmDPj835d11zzPPd9n+f5nhU/55n7POfcqSokSe1wyKALkCTNH0NfklrE0JekFjH0JalFDH1JahFDX5JaxNDXopZkbZJKsrTZ/9Mkm2Z5rGOSfD/Jkrmtcvbm8vlJAPE+fc23JPcCK4EDwPeBa4Hzqur7szjWWuAbwFOqav8s6vjVqvpMr+edL/08P2kiXulrUF5bVYcDPwu8ELhwwPX8WDv4l4Jk6Gugqup+4Do64Q9AkguS/FWS7yW5M8k/6epbkuQ9SR5Icg/w6u7jJfl8kl9ttp+T5LNJ9jbjP5pkedP3YeAY4NPNlM5bJ5hKOTrJ9iT7kowm+bWu8/xmkquTXNHUeUeS4cmeZ5KfT3Jzkoea3z/f1XdvkpePO/ZHJjnO3z2/Zv9XktyV5MEk1yV5dldfJTk3yd3A3em4OMmeJN9NcluSF0z+v45+HBn6Gqgkq4HTgNGu5r8CXgo8E/gt4CNJntX0/RrwGjp/HQwDr5vq8MA7gaOBfwCsAX4ToKp+GfgWzV8cVfV7Ezz+KmBX8/jXAe9I8rKu/tObMcuB7cB/m+Q5Hgn8CXAJsAL4A+BPkqyYovZpJdkIvB34p8AQ8AXgynHDzgBeBBwHnAr8AvBcOv+2rwf29lODFh9DX4PyR0m+B+wE9gAXHeyoqv9VVX9dVY9V1ceAu4ETm+7XA++tqp1VtY9OqE+oqkarakdVPVJVY3TC9h/PpLgka4AXA2+rqh9W1a3AZcDZXcO+WFXXVNUB4MPA8ZMc7tXA3VX14araX1VXAl8DXjuTWqbw68A7q+quZr7/HcDPdl/tN/37quoHwN8CzwCeR+f9vLuq6r4+a9AiY+hrUM6oqmcAJ9MJoaMOdiQ5O8mtSb6T5DvAC7r6j6bzQnHQNyc7QZKVSa5KsjvJd4GPdJ9nGkcD+6rqe+POtapr//6u7YeBp04yd370BHWOP9ZsPBt4X9e/0z46f910H/fv/q2q6rN0/hp5P7AnyZYkP9FnDVpkDH0NVFX9GfAh4D0AzVXqB4HzgBVVtRy4nU6YAdxHZ5rmoGOmOPw7gAJ+uqp+AviXXceh6ZvMXwNHJnnGuHPtnuYpTXasZ49r6z7W3wCHdfX95AyPuxP411W1vOvnaVX1511jHvccq+qSqvqHdKZ7ngv8x5k+Cf14MPS1ELwXeEWS44Gn0wmqMYAkb6BzpX/Q1cAbk6xOcgRwwRTHfQadW0IfSrKKJwbct4G/P9EDq2on8OfAO5M8NcnPAOfQ+WuhV9cAz03yz5MsTfLP6ITuHzf9twJnJnlK82bwVO9TdPvvwIVJng+Q5JlJfmmywUl+LsmLkjyFzgvND4HHZvF8tIgZ+hq4Zr79CuA3qupO4L8Cf0EnlH8a+L9dwz9I526frwBfBj4xxaF/CzgBeIjOG6njx74T+M/N9Mh/mODxZwFr6VypfxK4aDb39FfVXjpvPr+FzhunbwVeU1UPNEP+C/Ac4MGm5v85w+N+Eng3cFUzfXU7nTfFJ/MTdP79HqQzvbQX+P1en48WNz+cJUkt4pW+JLWIoS9JLWLoS1KLGPqS1CIL+kuYjjrqqFq7du2gy5CkReWWW255oKqGJupb0KG/du1aRkZGBl2GJC0qSSb9pLrTO5LUIoa+JLWIoS9JLWLoS1KLGPrSLOzdu5c3vvGN7N3rGiRaXKYN/SRbm+XVbh/Xfn6SrzXLxP1eV/uFzdJyX0/yyq72DU3baJKpvhlRWvC2bdvGbbfdxhVXXDHoUqSezORK/0PAhu6GJL8IbASOr6rn86PvQj8OOBN4fvOYP2zWNF1CZ+GG0+h8pexZzVhp0dm7dy/XXnstVcW1117r1b4WlWlDv6puoLMiT7d/A7yrqh5pxuxp2jcCVzXL032DzrqnJzY/o1V1T1U9Smdd0Y1z9BykebVt2zYee6zzNfQHDhzwal+Lymzn9J8LvDTJTUn+LMnPNe2rePxSdruatsnanyDJ5iQjSUbGxsZmWZ705PnMZz7D/v37Adi/fz87duwYcEXSzM029JcCRwIn0VmN6OokmfohM1NVW6pquKqGh4Ym/BSxNFAvf/nLWbq082H2pUuX8opXvGLAFUkzN9vQ3wV8ojq+RGfJtaPorPnZvX7p6qZtsnZp0dm0aROHHNL5v86SJUs4++yzB1yRNHOzDf0/An4RIMlzgWXAA8B2Omt9HppkHbAe+BJwM7A+yboky+i82bu93+KlQVixYgUbNmwgCRs2bGDFihWDLkmasWm/cC3JlcDJwFFJdgEXAVuBrc1tnI8Cm6qz7uIdSa4G7gT2A+dW1YHmOOfRWdt0CbC1qu54Ep6PNC82bdrEvffe61W+Fp0FvUbu8PBw+S2bktSbJLdU1fBEfX4iV5JaxNCXpBYx9CWpRQx9SWoRQ1+SWsTQl6QWMfSlWfD79LVYGfrSLPh9+lqsDH2pR36fvhYzQ1/qkd+nr8XM0Jd65PfpazEz9KUe+X36WswMfalHfp++FjNDX+qR36evxczQl2bh9NNP57DDDuO1r33toEuRemLoS7Owfft2Hn74YT796U8PuhSpJ9OGfpKtSfY0q2SN73tLkkpyVLOfJJckGU3y1SQndI3dlOTu5mfT3D4Naf54n74Ws5lc6X8I2DC+Mcka4FTgW13Np9FZF3c9sBn4QDP2SDrLLL4IOBG4KMkR/RQuDYr36Wsxmzb0q+oGYN8EXRcDbwW611vcCFxRHTcCy5M8C3glsKOq9lXVg8AOJnghkRYD79PXYjarOf0kG4HdVfWVcV2rgJ1d+7uatsnaJzr25iQjSUbGxsZmU570pHrpS1865b60kPUc+kkOA94O/MbclwNVtaWqhqtqeGho6Mk4hdSXqpp+kLRAzeZK/znAOuArSe4FVgNfTvKTwG5gTdfY1U3bZO3SovPFL37xcftf+MIXBlSJ1LueQ7+qbquqv1dVa6tqLZ2pmhOq6n5gO3B2cxfPScBDVXUfcB1wapIjmjdwT23apEXnJS95yeP2nd7RYjKTWzavBP4C+Kkku5KcM8Xwa4B7gFHgg8C/BaiqfcDvADc3P7/dtEmLzqOPPvq4/UceeWRAlUi9WzrdgKo6a5r+tV3bBZw7ybitwNYe65MWnPHTOU7vaDHxE7lSj5JMuS8tZIa+1KPxc/rj96WFzNCXerRs2bLH7R966KEDqkTqnaEv9Wj8HP4NN9wwoEqk3hn6Uo9Wrlw55b60kBn6Uo/uv//+KfelhczQl3p0+OGHT7kvLWSGvtSjBx54YMp9aSEz9CWpRQx9SWoRQ1+SWsTQl6QWMfQlqUUMfUlqEUNfklrE0JekFpnJyllbk+xJcntX2+8n+VqSryb5ZJLlXX0XJhlN8vUkr+xq39C0jSa5YO6fiiRpOjO50v8QsGFc2w7gBVX1M8BfAhcCJDkOOBN4fvOYP0yyJMkS4P3AacBxwFnNWEnSPJo29KvqBmDfuLb/U1X7m90bgdXN9kbgqqp6pKq+QWet3BObn9GquqeqHgWuasZKkubRXMzp/wrwp832KmBnV9+upm2y9idIsjnJSJKRsbGxOShPknRQX6Gf5D8B+4GPzk05UFVbqmq4qoaHhobm6rCSJGDpbB+Y5F8BrwFOqapqmncDa7qGrW7amKJdkjRPZnWln2QD8Fbg9Kp6uKtrO3BmkkOTrAPWA18CbgbWJ1mXZBmdN3u391e6JKlX017pJ7kSOBk4Ksku4CI6d+scCuxIAnBjVf16Vd2R5GrgTjrTPudW1YHmOOcB1wFLgK1VdceT8HwkSVOYNvSr6qwJmi+fYvzvAr87Qfs1wDU9VSdJmlN+IleSWsTQl6QWMfQlqUUMfUlqEUNfklrE0JekFjH0JalFDH1JahFDX5JaxNCXpBYx9CWpRQx9SWoRQ1+SWsTQl6QWMfQlqUWmDf0kW5PsSXJ7V9uRSXYkubv5fUTTniSXJBlN8tUkJ3Q9ZlMz/u4km56cpyNJmspMrvQ/BGwY13YBcH1VrQeub/YBTqOzROJ6YDPwAei8SNBZcetFwInARQdfKCRJ82fa0K+qG4B945o3Atua7W3AGV3tV1THjcDyJM8CXgnsqKp9VfUgsIMnvpBIkp5ks53TX1lV9zXb9wMrm+1VwM6ucbuatsnaJUnzqO83cquqgJqDWgBIsjnJSJKRsbGxuTqsJInZh/63m2kbmt97mvbdwJqucaubtsnan6CqtlTVcFUNDw0NzbI8SdJEZhv624GDd+BsAj7V1X52cxfPScBDzTTQdcCpSY5o3sA9tWmTJM2jpdMNSHIlcDJwVJJddO7CeRdwdZJzgG8Cr2+GXwO8ChgFHgbeAFBV+5L8DnBzM+63q2r8m8OSpCfZtKFfVWdN0nXKBGMLOHeS42wFtvZUnSRpTvmJXElqEUNfklrE0JekFjH0JalFDH1JahFDX5JaZNpbNqWDLr30UkZHRwddxoL0pje9adAlDNSxxx7L+eefP+gyNANe6UtSi6TzeaqFaXh4uEZGRgZdhvQ4J5988hPaPv/5z897HdJkktxSVcMT9XmlL0ktYuhLPRp/Ve9VvhYTQ1+SWsTQl2bh+OOP5/jjj/cqX4uOoS9JLWLoS1KLGPqS1CJ9hX6Sf5/kjiS3J7kyyVOTrEtyU5LRJB9LsqwZe2izP9r0r52LJyBJmrlZh36SVcAbgeGqegGwBDgTeDdwcVUdCzwInNM85Bzgwab94macJGke9Tu9sxR4WpKlwGHAfcDLgI83/duAM5rtjc0+Tf8pSdLn+SVJPZh16FfVbuA9wLfohP1DwC3Ad6pqfzNsF7Cq2V4F7Gweu78Zv2L8cZNsTjKSZGRsbGy25UmSJtDP9M4RdK7e1wFHA08HNvRbUFVtqarhqhoeGhrq93CSpC79TO+8HPhGVY1V1d8CnwBeDCxvpnsAVgO7m+3dwBqApv+ZwN4+zi9J6lE/of8t4KQkhzVz86cAdwKfA17XjNkEfKrZ3t7s0/R/thbyV3xK0o+hfub0b6LzhuyXgduaY20B3ga8OckonTn7y5uHXA6saNrfDFzQR92SpFnoa+WsqroIuGhc8z3AiROM/SHwS/2cT5LUHz+RK0ktYuhLUosY+pLUIoa+JLWIoS9JLWLoS1KLGPqS1CKGviS1iKEvSS1i6EtSixj6ktQihr4ktYihL0ktYuhLUosY+pLUIoa+JLVIX6GfZHmSjyf5WpK7kvyjJEcm2ZHk7ub3Ec3YJLkkyWiSryY5YW6egiRppvq90n8fcG1VPQ84HriLzjKI11fVeuB6frQs4mnA+uZnM/CBPs8tSerRrEM/yTOBX6BZA7eqHq2q7wAbgW3NsG3AGc32RuCK6rgRWJ7kWbOuXJLUs36u9NcBY8D/SPL/klyW5OnAyqq6rxlzP7Cy2V4F7Ox6/K6m7XGSbE4ykmRkbGysj/IkSeP1E/pLgROAD1TVC4G/4UdTOQBUVQHVy0GraktVDVfV8NDQUB/lSZLG6yf0dwG7quqmZv/jdF4Evn1w2qb5vafp3w2s6Xr86qZNkjRPZh36VXU/sDPJTzVNpwB3AtuBTU3bJuBTzfZ24OzmLp6TgIe6poEkSfNgaZ+PPx/4aJJlwD3AG+i8kFyd5Bzgm8Drm7HXAK8CRoGHm7GSpHnUV+hX1a3A8ARdp0wwtoBz+zmfJKk/fiJXklrE0JekFjH0JalFDH1JahFDX5JaxNCXpBYx9CWpRQx9SWoRQ1+SWsTQl6QWMfQlqUUMfUlqEUNfklrE0JekFjH0JalF+g79JEuahdH/uNlfl+SmJKNJPtYssEKSQ5v90aZ/bb/nliT1Zi6u9N8E3NW1/27g4qo6FngQOKdpPwd4sGm/uBknSZpHfYV+ktXAq4HLmv0AL6OzSDrANuCMZntjs0/Tf0ozXpI0T/q90n8v8FbgsWZ/BfCdqtrf7O8CVjXbq4CdAE3/Q814SdI8mXXoJ3kNsKeqbpnDekiyOclIkpGxsbG5PLQktV4/V/ovBk5Pci9wFZ1pnfcBy5McXHB9NbC72d4NrAFo+p8J7B1/0KraUlXDVTU8NDTUR3mSpPFmHfpVdWFVra6qtcCZwGer6l8AnwNe1wzbBHyq2d7e7NP0f7aqarbnlyT17sm4T/9twJuTjNKZs7+8ab8cWNG0vxm44Ek4tyRpClnIF9vDw8M1MjIy0BouvfRSRkdHB1qDFp6D/00ce+yxA65EC82xxx7L+eefP9AaktxSVcMT9S2dqFE/Mjo6yq2338WBw44cdClaQA55tHOxdMs93x5wJVpIljy8b9AlTMvQn4EDhx3JD573qkGXIWmBe9rXrhl0CdPyu3ckqUUMfUlqEUNfklrE0JekFjH0JalFDH1JahFDX5JaxNCXpBYx9CWpRQx9SWoRQ1+SWsTQl6QWMfQlqUUMfUlqkX4WRl+T5HNJ7kxyR5I3Ne1HJtmR5O7m9xFNe5JckmQ0yVeTnDBXT0KSNDP9XOnvB95SVccBJwHnJjmOzjKI11fVeuB6frQs4mnA+uZnM/CBPs4tSZqFfhZGv6+qvtxsfw+4C1gFbAS2NcO2AWc02xuBK6rjRmB5kmfNunJJUs/mZE4/yVrghcBNwMqquq/puh9Y2WyvAnZ2PWxX0zb+WJuTjCQZGRsbm4vyJEmNvkM/yeHA/wb+XVV9t7uvOquu97TyelVtqarhqhoeGhrqtzxJUpe+Qj/JU+gE/ker6hNN87cPTts0v/c07buBNV0PX920SZLmST937wS4HLirqv6gq2s7sKnZ3gR8qqv97OYunpOAh7qmgSRJ82BpH499MfDLwG1Jbm3a3g68C7g6yTnAN4HXN33XAK8CRoGHgTf0cW5J0izMOvSr6otAJuk+ZYLxBZw72/NJkvrnJ3IlqUUMfUlqEUNfklrE0JekFjH0JalFDH1JahFDX5JaxNCXpBYx9CWpRQx9SWoRQ1+SWsTQl6QWMfQlqUUMfUlqEUNfklrE0JekFpn30E+yIcnXk4wmuWC+zy9JbTavoZ9kCfB+4DTgOOCsJMfNZw2S1Gb9rJE7GycCo1V1D0CSq4CNwJ3zXMeM7d69myXf28vhX/7woEsZvMcOQNWgq9BClMAhSwZdxeAd2M/u3fsHXcWU5jv0VwE7u/Z3AS/qHpBkM7AZ4Jhjjpm/yiaxfPlyfvCDHwy6jAXhkUce4bHHHht0GVqADjnkEA49dNmgy1gAlrF8+fJBFzGl+Q79aVXVFmALwPDw8MAvKy+77LJBlyBJc2a+38jdDazp2l/dtEmS5sF8h/7NwPok65IsA84Ets9zDZLUWvM6vVNV+5OcB1wHLAG2VtUd81mDJLXZvM/pV9U1wDXzfV5Jkp/IlaRWMfQlqUUMfUlqEUNfkloktYA/Vp9kDPjmoOuQJnEU8MCgi5Am8OyqGpqoY0GHvrSQJRmpquFB1yH1wukdSWoRQ1+SWsTQl2Zvy6ALkHrlnL4ktYhX+pLUIoa+JLWIoS/1KMmGJF9PMprkgkHXI/XCOX2pB0mWAH8JvILOcp83A2dV1YJd51nq5pW+1JsTgdGquqeqHgWuAjYOuCZpxgx9qTergJ1d+7uaNmlRMPQlqUUMfak3u4E1XfurmzZpUTD0pd7cDKxPsi7JMuBMYPuAa5JmbN7XyJUWs6ran+Q84DpgCbC1qu4YcFnSjHnLpiS1iNM7ktQihr4ktYihL0ktYuhLUosY+pLUIoa+JLWIoS9JLfL/AceVsRNXHTziAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lNk-UK_5gXRm"
},
"source": [
"## Lots of very high values in Radiaiton "
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "Zgr83jKzgVYr",
"outputId": "6e3203c2-2b3f-479f-bceb-95625a54e7f3"
},
"source": [
"train[train[\"Radiation\"]>1000]"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>UNIXTime</th>\n",
" <th>Date</th>\n",
" <th>Time</th>\n",
" <th>Radiation</th>\n",
" <th>Temperature</th>\n",
" <th>Pressure</th>\n",
" <th>Humidity</th>\n",
" <th>WindDirection(Degrees)</th>\n",
" <th>Speed</th>\n",
" <th>TimeSunRise</th>\n",
" <th>TimeSunSet</th>\n",
" <th>Month</th>\n",
" <th>Year</th>\n",
" <th>Day</th>\n",
" <th>WeekDay</th>\n",
" <th>WeekNumber</th>\n",
" <th>Month_plus_day</th>\n",
" <th>Hour</th>\n",
" <th>Mins</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>1476051626</td>\n",
" <td>2016-10-09</td>\n",
" <td>12:20:26</td>\n",
" <td>1056.42</td>\n",
" <td>65</td>\n",
" <td>30.42</td>\n",
" <td>59</td>\n",
" <td>29.62</td>\n",
" <td>3.37</td>\n",
" <td>06:16:00</td>\n",
" <td>18:04:00</td>\n",
" <td>10</td>\n",
" <td>2016</td>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" <td>40</td>\n",
" <td>109</td>\n",
" <td>12</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>106</th>\n",
" <td>1474408204</td>\n",
" <td>2016-09-20</td>\n",
" <td>11:50:04</td>\n",
" <td>1061.11</td>\n",
" <td>63</td>\n",
" <td>30.42</td>\n",
" <td>52</td>\n",
" <td>24.46</td>\n",
" <td>4.50</td>\n",
" <td>06:11:00</td>\n",
" <td>18:21:00</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>20</td>\n",
" <td>1</td>\n",
" <td>38</td>\n",
" <td>110</td>\n",
" <td>11</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120</th>\n",
" <td>1474841721</td>\n",
" <td>2016-09-25</td>\n",
" <td>12:15:21</td>\n",
" <td>1047.31</td>\n",
" <td>64</td>\n",
" <td>30.48</td>\n",
" <td>49</td>\n",
" <td>359.92</td>\n",
" <td>5.62</td>\n",
" <td>06:12:00</td>\n",
" <td>18:16:00</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>25</td>\n",
" <td>6</td>\n",
" <td>38</td>\n",
" <td>115</td>\n",
" <td>12</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>144</th>\n",
" <td>1474581921</td>\n",
" <td>2016-09-22</td>\n",
" <td>12:05:21</td>\n",
" <td>1053.17</td>\n",
" <td>71</td>\n",
" <td>30.43</td>\n",
" <td>32</td>\n",
" <td>84.45</td>\n",
" <td>3.37</td>\n",
" <td>06:11:00</td>\n",
" <td>18:19:00</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>22</td>\n",
" <td>3</td>\n",
" <td>38</td>\n",
" <td>112</td>\n",
" <td>12</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>160</th>\n",
" <td>1474326004</td>\n",
" <td>2016-09-19</td>\n",
" <td>13:00:04</td>\n",
" <td>1053.71</td>\n",
" <td>66</td>\n",
" <td>30.43</td>\n",
" <td>27</td>\n",
" <td>25.43</td>\n",
" <td>7.87</td>\n",
" <td>06:11:00</td>\n",
" <td>18:22:00</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>19</td>\n",
" <td>0</td>\n",
" <td>38</td>\n",
" <td>109</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25923</th>\n",
" <td>1476398420</td>\n",
" <td>2016-10-13</td>\n",
" <td>12:40:20</td>\n",
" <td>1004.33</td>\n",
" <td>64</td>\n",
" <td>30.45</td>\n",
" <td>42</td>\n",
" <td>75.25</td>\n",
" <td>11.25</td>\n",
" <td>06:17:00</td>\n",
" <td>18:01:00</td>\n",
" <td>10</td>\n",
" <td>2016</td>\n",
" <td>13</td>\n",
" <td>3</td>\n",
" <td>41</td>\n",
" <td>113</td>\n",
" <td>12</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26021</th>\n",
" <td>1477087818</td>\n",
" <td>2016-10-21</td>\n",
" <td>12:10:18</td>\n",
" <td>1002.59</td>\n",
" <td>51</td>\n",
" <td>30.44</td>\n",
" <td>95</td>\n",
" <td>8.58</td>\n",
" <td>3.37</td>\n",
" <td>06:19:00</td>\n",
" <td>17:55:00</td>\n",
" <td>10</td>\n",
" <td>2016</td>\n",
" <td>21</td>\n",
" <td>4</td>\n",
" <td>42</td>\n",
" <td>121</td>\n",
" <td>12</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26055</th>\n",
" <td>1473802505</td>\n",
" <td>2016-09-13</td>\n",
" <td>11:35:05</td>\n",
" <td>1051.82</td>\n",
" <td>67</td>\n",
" <td>30.46</td>\n",
" <td>49</td>\n",
" <td>0.83</td>\n",
" <td>6.75</td>\n",
" <td>06:10:00</td>\n",
" <td>18:27:00</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>13</td>\n",
" <td>1</td>\n",
" <td>37</td>\n",
" <td>103</td>\n",
" <td>11</td>\n",
" <td>35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26101</th>\n",
" <td>1474930520</td>\n",
" <td>2016-09-26</td>\n",
" <td>12:55:20</td>\n",
" <td>1026.57</td>\n",
" <td>60</td>\n",
" <td>30.41</td>\n",
" <td>69</td>\n",
" <td>34.13</td>\n",
" <td>11.25</td>\n",
" <td>06:12:00</td>\n",
" <td>18:15:00</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>26</td>\n",
" <td>0</td>\n",
" <td>39</td>\n",
" <td>116</td>\n",
" <td>12</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26105</th>\n",
" <td>1474752322</td>\n",
" <td>2016-09-24</td>\n",
" <td>11:25:22</td>\n",
" <td>1088.79</td>\n",
" <td>63</td>\n",
" <td>30.48</td>\n",
" <td>81</td>\n",
" <td>320.94</td>\n",
" <td>3.37</td>\n",
" <td>06:12:00</td>\n",
" <td>18:17:00</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>24</td>\n",
" <td>5</td>\n",
" <td>38</td>\n",
" <td>114</td>\n",
" <td>11</td>\n",
" <td>25</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>509 rows × 19 columns</p>\n",
"</div>"
],
"text/plain": [
" UNIXTime Date Time ... Month_plus_day Hour Mins\n",
"51 1476051626 2016-10-09 12:20:26 ... 109 12 20\n",
"106 1474408204 2016-09-20 11:50:04 ... 110 11 50\n",
"120 1474841721 2016-09-25 12:15:21 ... 115 12 15\n",
"144 1474581921 2016-09-22 12:05:21 ... 112 12 5\n",
"160 1474326004 2016-09-19 13:00:04 ... 109 13 0\n",
"... ... ... ... ... ... ... ...\n",
"25923 1476398420 2016-10-13 12:40:20 ... 113 12 40\n",
"26021 1477087818 2016-10-21 12:10:18 ... 121 12 10\n",
"26055 1473802505 2016-09-13 11:35:05 ... 103 11 35\n",
"26101 1474930520 2016-09-26 12:55:20 ... 116 12 55\n",
"26105 1474752322 2016-09-24 11:25:22 ... 114 11 25\n",
"\n",
"[509 rows x 19 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 21
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "eq9BUpH2huen",
"outputId": "9d0ed66d-8007-4ea4-d941-e5a9916930a4"
},
"source": [
"train.iloc[train[\"Radiation\"].idxmax()]"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"UNIXTime 1473027304\n",
"Date 2016-09-04\n",
"Time 12:15:04\n",
"Radiation 1601.26\n",
"Temperature 61\n",
"Pressure 30.47\n",
"Humidity 93\n",
"WindDirection(Degrees) 3.56\n",
"Speed 9\n",
"TimeSunRise 06:08:00\n",
"TimeSunSet 18:35:00\n",
"Month 9\n",
"Year 2016\n",
"Day 4\n",
"WeekDay 6\n",
"WeekNumber 35\n",
"Month_plus_day 94\n",
"Hour 12\n",
"Mins 15\n",
"Name: 22312, dtype: object"
]
},
"metadata": {
"tags": []
},
"execution_count": 22
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81
},
"id": "ppikgyw7oW6g",
"outputId": "202f7287-1526-4a8e-b8c9-73d100d856fb"
},
"source": [
"train.loc[lambda df:df[\"Radiation\"] == df[\"Radiation\"].max()]"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>UNIXTime</th>\n",
" <th>Date</th>\n",
" <th>Time</th>\n",
" <th>Radiation</th>\n",
" <th>Temperature</th>\n",
" <th>Pressure</th>\n",
" <th>Humidity</th>\n",
" <th>WindDirection(Degrees)</th>\n",
" <th>Speed</th>\n",
" <th>TimeSunRise</th>\n",
" <th>TimeSunSet</th>\n",
" <th>Month</th>\n",
" <th>Year</th>\n",
" <th>Day</th>\n",
" <th>WeekDay</th>\n",
" <th>WeekNumber</th>\n",
" <th>Month_plus_day</th>\n",
" <th>Hour</th>\n",
" <th>Mins</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>22312</th>\n",
" <td>1473027304</td>\n",
" <td>2016-09-04</td>\n",
" <td>12:15:04</td>\n",
" <td>1601.26</td>\n",
" <td>61</td>\n",
" <td>30.47</td>\n",
" <td>93</td>\n",
" <td>3.56</td>\n",
" <td>9.0</td>\n",
" <td>06:08:00</td>\n",
" <td>18:35:00</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>35</td>\n",
" <td>94</td>\n",
" <td>12</td>\n",
" <td>15</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" UNIXTime Date Time ... Month_plus_day Hour Mins\n",
"22312 1473027304 2016-09-04 12:15:04 ... 94 12 15\n",
"\n",
"[1 rows x 19 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 23
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "IORhs0g6oc-y",
"outputId": "068c5ce1-d209-46bc-bae7-6709ec15ea17"
},
"source": [
"top10_radiations = sorted(list(train[\"Radiation\"]))[::-1][:10]\n",
"top10_radiations"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[1601.26,\n",
" 1475.4,\n",
" 1410.52,\n",
" 1387.17,\n",
" 1359.79,\n",
" 1349.59,\n",
" 1328.5,\n",
" 1315.46,\n",
" 1298.4,\n",
" 1288.17]"
]
},
"metadata": {
"tags": []
},
"execution_count": 24
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "F-Nwbv35iNSG"
},
"source": [
"### This could be a possible value"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fKyk8riCg60_"
},
"source": [
"## all the radiation values above 1000 are for afternoon time"
]
},
{
"cell_type": "code",
"metadata": {
"id": "i5gRH5y8g2qp"
},
"source": [
""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "-xgoKBxdhFrR"
},
"source": [
"## Temprature"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 281
},
"id": "UZ-Yn1p0hF-p",
"outputId": "b388c372-1b85-4326-8443-e26b34ddf523"
},
"source": [
"temp = sns.boxplot(data = train[\"Temperature\"]).set(title=\"Temperature ouliers\")"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 520
},
"id": "TpuEiklDhG6x",
"outputId": "148e89e6-fc3f-4a26-9c56-b1f9f39ca7cb"
},
"source": [
"train[train[\"Temperature\"]>70]"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>UNIXTime</th>\n",
" <th>Date</th>\n",
" <th>Time</th>\n",
" <th>Radiation</th>\n",
" <th>Temperature</th>\n",
" <th>Pressure</th>\n",
" <th>Humidity</th>\n",
" <th>WindDirection(Degrees)</th>\n",
" <th>Speed</th>\n",
" <th>TimeSunRise</th>\n",
" <th>TimeSunSet</th>\n",
" <th>Month</th>\n",
" <th>Year</th>\n",
" <th>Day</th>\n",
" <th>WeekDay</th>\n",
" <th>WeekNumber</th>\n",
" <th>Month_plus_day</th>\n",
" <th>Hour</th>\n",
" <th>Mins</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>144</th>\n",
" <td>1474581921</td>\n",
" <td>2016-09-22</td>\n",
" <td>12:05:21</td>\n",
" <td>1053.17</td>\n",
" <td>71</td>\n",
" <td>30.43</td>\n",
" <td>32</td>\n",
" <td>84.45</td>\n",
" <td>3.37</td>\n",
" <td>06:11:00</td>\n",
" <td>18:19:00</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>22</td>\n",
" <td>3</td>\n",
" <td>38</td>\n",
" <td>112</td>\n",
" <td>12</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5186</th>\n",
" <td>1474578620</td>\n",
" <td>2016-09-22</td>\n",
" <td>11:10:20</td>\n",
" <td>1012.71</td>\n",
" <td>71</td>\n",
" <td>30.44</td>\n",
" <td>28</td>\n",
" <td>125.68</td>\n",
" <td>10.12</td>\n",
" <td>06:11:00</td>\n",
" <td>18:19:00</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>22</td>\n",
" <td>3</td>\n",
" <td>38</td>\n",
" <td>112</td>\n",
" <td>11</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6213</th>\n",
" <td>1474579519</td>\n",
" <td>2016-09-22</td>\n",
" <td>11:25:19</td>\n",
" <td>1028.63</td>\n",
" <td>71</td>\n",
" <td>30.44</td>\n",
" <td>30</td>\n",
" <td>190.66</td>\n",
" <td>7.87</td>\n",
" <td>06:11:00</td>\n",
" <td>18:19:00</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>22</td>\n",
" <td>3</td>\n",
" <td>38</td>\n",
" <td>112</td>\n",
" <td>11</td>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7483</th>\n",
" <td>1474587319</td>\n",
" <td>2016-09-22</td>\n",
" <td>13:35:19</td>\n",
" <td>994.54</td>\n",
" <td>71</td>\n",
" <td>30.41</td>\n",
" <td>32</td>\n",
" <td>121.87</td>\n",
" <td>19.12</td>\n",
" <td>06:11:00</td>\n",
" <td>18:19:00</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>22</td>\n",
" <td>3</td>\n",
" <td>38</td>\n",
" <td>112</td>\n",
" <td>13</td>\n",
" <td>35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9512</th>\n",
" <td>1474589125</td>\n",
" <td>2016-09-22</td>\n",
" <td>14:05:25</td>\n",
" <td>934.18</td>\n",
" <td>71</td>\n",
" <td>30.41</td>\n",
" <td>37</td>\n",
" <td>151.88</td>\n",
" <td>12.37</td>\n",
" <td>06:11:00</td>\n",
" <td>18:19:00</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>22</td>\n",
" <td>3</td>\n",
" <td>38</td>\n",
" <td>112</td>\n",
" <td>14</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9566</th>\n",
" <td>1474579826</td>\n",
" <td>2016-09-22</td>\n",
" <td>11:30:26</td>\n",
" <td>1035.48</td>\n",
" <td>71</td>\n",
" <td>30.44</td>\n",
" <td>27</td>\n",
" <td>113.85</td>\n",
" <td>18.00</td>\n",
" <td>06:11:00</td>\n",
" <td>18:19:00</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>22</td>\n",
" <td>3</td>\n",
" <td>38</td>\n",
" <td>112</td>\n",
" <td>11</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10863</th>\n",
" <td>1474581024</td>\n",
" <td>2016-09-22</td>\n",
" <td>11:50:24</td>\n",
" <td>1046.05</td>\n",
" <td>71</td>\n",
" <td>30.43</td>\n",
" <td>31</td>\n",
" <td>151.69</td>\n",
" <td>12.37</td>\n",
" <td>06:11:00</td>\n",
" <td>18:19:00</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>22</td>\n",
" <td>3</td>\n",
" <td>38</td>\n",
" <td>112</td>\n",
" <td>11</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12502</th>\n",
" <td>1474587023</td>\n",
" <td>2016-09-22</td>\n",
" <td>13:30:23</td>\n",
" <td>990.52</td>\n",
" <td>71</td>\n",
" <td>30.41</td>\n",
" <td>37</td>\n",
" <td>160.76</td>\n",
" <td>6.75</td>\n",
" <td>06:11:00</td>\n",
" <td>18:19:00</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>22</td>\n",
" <td>3</td>\n",
" <td>38</td>\n",
" <td>112</td>\n",
" <td>13</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13258</th>\n",
" <td>1474578323</td>\n",
" <td>2016-09-22</td>\n",
" <td>11:05:23</td>\n",
" <td>1000.86</td>\n",
" <td>71</td>\n",
" <td>30.43</td>\n",
" <td>29</td>\n",
" <td>134.92</td>\n",
" <td>13.50</td>\n",
" <td>06:11:00</td>\n",
" <td>18:19:00</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>22</td>\n",
" <td>3</td>\n",
" <td>38</td>\n",
" <td>112</td>\n",
" <td>11</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14687</th>\n",
" <td>1474578920</td>\n",
" <td>2016-09-22</td>\n",
" <td>11:15:20</td>\n",
" <td>1013.01</td>\n",
" <td>71</td>\n",
" <td>30.44</td>\n",
" <td>25</td>\n",
" <td>126.69</td>\n",
" <td>6.75</td>\n",
" <td>06:11:00</td>\n",
" <td>18:19:00</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>22</td>\n",
" <td>3</td>\n",
" <td>38</td>\n",
" <td>112</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16841</th>\n",
" <td>1474583719</td>\n",
" <td>2016-09-22</td>\n",
" <td>12:35:19</td>\n",
" <td>1047.28</td>\n",
" <td>71</td>\n",
" <td>30.42</td>\n",
" <td>36</td>\n",
" <td>129.04</td>\n",
" <td>13.50</td>\n",
" <td>06:11:00</td>\n",
" <td>18:19:00</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>22</td>\n",
" <td>3</td>\n",
" <td>38</td>\n",
" <td>112</td>\n",
" <td>12</td>\n",
" <td>35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17697</th>\n",
" <td>1474589419</td>\n",
" <td>2016-09-22</td>\n",
" <td>14:10:19</td>\n",
" <td>925.65</td>\n",
" <td>71</td>\n",
" <td>30.40</td>\n",
" <td>34</td>\n",
" <td>105.30</td>\n",
" <td>13.50</td>\n",
" <td>06:11:00</td>\n",
" <td>18:19:00</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>22</td>\n",
" <td>3</td>\n",
" <td>38</td>\n",
" <td>112</td>\n",
" <td>14</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19822</th>\n",
" <td>1474587626</td>\n",
" <td>2016-09-22</td>\n",
" <td>13:40:26</td>\n",
" <td>982.27</td>\n",
" <td>71</td>\n",
" <td>30.41</td>\n",
" <td>36</td>\n",
" <td>105.24</td>\n",
" <td>10.12</td>\n",
" <td>06:11:00</td>\n",
" <td>18:19:00</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>22</td>\n",
" <td>3</td>\n",
" <td>38</td>\n",
" <td>112</td>\n",
" <td>13</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21383</th>\n",
" <td>1474580121</td>\n",
" <td>2016-09-22</td>\n",
" <td>11:35:21</td>\n",
" <td>1039.27</td>\n",
" <td>71</td>\n",
" <td>30.44</td>\n",
" <td>30</td>\n",
" <td>134.89</td>\n",
" <td>15.75</td>\n",
" <td>06:11:00</td>\n",
" <td>18:19:00</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>22</td>\n",
" <td>3</td>\n",
" <td>38</td>\n",
" <td>112</td>\n",
" <td>11</td>\n",
" <td>35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24799</th>\n",
" <td>1474586722</td>\n",
" <td>2016-09-22</td>\n",
" <td>13:25:22</td>\n",
" <td>997.00</td>\n",
" <td>71</td>\n",
" <td>30.41</td>\n",
" <td>35</td>\n",
" <td>203.60</td>\n",
" <td>5.62</td>\n",
" <td>06:11:00</td>\n",
" <td>18:19:00</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>22</td>\n",
" <td>3</td>\n",
" <td>38</td>\n",
" <td>112</td>\n",
" <td>13</td>\n",
" <td>25</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" UNIXTime Date Time ... Month_plus_day Hour Mins\n",
"144 1474581921 2016-09-22 12:05:21 ... 112 12 5\n",
"5186 1474578620 2016-09-22 11:10:20 ... 112 11 10\n",
"6213 1474579519 2016-09-22 11:25:19 ... 112 11 25\n",
"7483 1474587319 2016-09-22 13:35:19 ... 112 13 35\n",
"9512 1474589125 2016-09-22 14:05:25 ... 112 14 5\n",
"9566 1474579826 2016-09-22 11:30:26 ... 112 11 30\n",
"10863 1474581024 2016-09-22 11:50:24 ... 112 11 50\n",
"12502 1474587023 2016-09-22 13:30:23 ... 112 13 30\n",
"13258 1474578323 2016-09-22 11:05:23 ... 112 11 5\n",
"14687 1474578920 2016-09-22 11:15:20 ... 112 11 15\n",
"16841 1474583719 2016-09-22 12:35:19 ... 112 12 35\n",
"17697 1474589419 2016-09-22 14:10:19 ... 112 14 10\n",
"19822 1474587626 2016-09-22 13:40:26 ... 112 13 40\n",
"21383 1474580121 2016-09-22 11:35:21 ... 112 11 35\n",
"24799 1474586722 2016-09-22 13:25:22 ... 112 13 25\n",
"\n",
"[15 rows x 19 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 26
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RNhpWymghj3q"
},
"source": [
"### nothing unusual here"
]
},
{
"cell_type": "code",
"metadata": {
"id": "tns94a8DJRlK"
},
"source": [
"cal_last_temp = train.sort_values(by=[\"Date\",\"Time\"],ascending=[True,True])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "2wr9ZAnAJRg3"
},
"source": [
"daily_temp_dict = {}\n",
"for row in cal_last_temp.iterrows():\n",
" try:\n",
" temp_list = daily_temp_dict[row[1][\"Date\"]]\n",
" except KeyError:\n",
" temp_list = list([])\n",
" temp_list.append(row[1][\"Temperature\"])\n",
" daily_temp_dict[row[1][\"Date\"]] = temp_list"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "iaGic1OFJRTd"
},
"source": [
"average_daytemp={}\n",
"max_temp = {}\n",
"for k,v in zip(daily_temp_dict.keys(), daily_temp_dict.values()):\n",
" average_daytemp[k] = sum(v)/len(v)\n",
" max_temp[k] = max(v)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "9rf1iAdaWdaX"
},
"source": [
"train[\"Average_dailytemp\"] = [average_daytemp[dt] for dt in train[\"Date\"]]\n",
"train[\"temp_dif\"] = train[\"Temperature\"] - train[\"Average_dailytemp\"]\n",
"train[\"Day_max_temp\"] = [max_temp[dt] for dt in train[\"Date\"]]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "BS18FMbMc7mf"
},
"source": [
"train[\"afternoon\"] = [1 if h>8 and h<13 else 0 for h in train[\"Hour\"]]\n",
"train[\"night\"] = [1 if h>17 or h<5 else 0 for h in train[\"Hour\"]]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "kC4-4ci9WpHO",
"outputId": "63f63e02-ebd4-4320-889a-8f40412c9d84"
},
"source": [
"train.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>UNIXTime</th>\n",
" <th>Date</th>\n",
" <th>Time</th>\n",
" <th>Radiation</th>\n",
" <th>Temperature</th>\n",
" <th>Pressure</th>\n",
" <th>Humidity</th>\n",
" <th>WindDirection(Degrees)</th>\n",
" <th>Speed</th>\n",
" <th>TimeSunRise</th>\n",
" <th>TimeSunSet</th>\n",
" <th>Month</th>\n",
" <th>Year</th>\n",
" <th>Day</th>\n",
" <th>WeekDay</th>\n",
" <th>WeekNumber</th>\n",
" <th>Month_plus_day</th>\n",
" <th>Hour</th>\n",
" <th>Mins</th>\n",
" <th>Average_dailytemp</th>\n",
" <th>temp_dif</th>\n",
" <th>Day_max_temp</th>\n",
" <th>afternoon</th>\n",
" <th>night</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1480107904</td>\n",
" <td>2016-11-25</td>\n",
" <td>11:05:04</td>\n",
" <td>288.44</td>\n",
" <td>46</td>\n",
" <td>30.48</td>\n",
" <td>101</td>\n",
" <td>129.84</td>\n",
" <td>13.50</td>\n",
" <td>06:37:00</td>\n",
" <td>17:42:00</td>\n",
" <td>11</td>\n",
" <td>2016</td>\n",
" <td>25</td>\n",
" <td>4</td>\n",
" <td>47</td>\n",
" <td>135</td>\n",
" <td>11</td>\n",
" <td>5</td>\n",
" <td>47.306034</td>\n",
" <td>-1.306034</td>\n",
" <td>53</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1472818508</td>\n",
" <td>2016-09-02</td>\n",
" <td>02:15:08</td>\n",
" <td>2.79</td>\n",
" <td>50</td>\n",
" <td>30.42</td>\n",
" <td>75</td>\n",
" <td>173.90</td>\n",
" <td>6.75</td>\n",
" <td>06:07:00</td>\n",
" <td>18:37:00</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>35</td>\n",
" <td>92</td>\n",
" <td>2</td>\n",
" <td>15</td>\n",
" <td>56.118182</td>\n",
" <td>-6.118182</td>\n",
" <td>65</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1475804719</td>\n",
" <td>2016-10-06</td>\n",
" <td>15:45:19</td>\n",
" <td>118.05</td>\n",
" <td>54</td>\n",
" <td>30.42</td>\n",
" <td>100</td>\n",
" <td>7.35</td>\n",
" <td>1.12</td>\n",
" <td>06:15:00</td>\n",
" <td>18:07:00</td>\n",
" <td>10</td>\n",
" <td>2016</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>40</td>\n",
" <td>106</td>\n",
" <td>15</td>\n",
" <td>45</td>\n",
" <td>52.215517</td>\n",
" <td>1.784483</td>\n",
" <td>60</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1482533149</td>\n",
" <td>2016-12-23</td>\n",
" <td>12:45:49</td>\n",
" <td>853.17</td>\n",
" <td>58</td>\n",
" <td>30.44</td>\n",
" <td>57</td>\n",
" <td>81.67</td>\n",
" <td>11.25</td>\n",
" <td>06:54:00</td>\n",
" <td>17:50:00</td>\n",
" <td>12</td>\n",
" <td>2016</td>\n",
" <td>23</td>\n",
" <td>4</td>\n",
" <td>51</td>\n",
" <td>143</td>\n",
" <td>12</td>\n",
" <td>45</td>\n",
" <td>51.030043</td>\n",
" <td>6.969957</td>\n",
" <td>61</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1481883019</td>\n",
" <td>2016-12-16</td>\n",
" <td>00:10:19</td>\n",
" <td>1.24</td>\n",
" <td>42</td>\n",
" <td>30.24</td>\n",
" <td>103</td>\n",
" <td>171.13</td>\n",
" <td>2.25</td>\n",
" <td>06:50:00</td>\n",
" <td>17:46:00</td>\n",
" <td>12</td>\n",
" <td>2016</td>\n",
" <td>16</td>\n",
" <td>4</td>\n",
" <td>50</td>\n",
" <td>136</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>46.775424</td>\n",
" <td>-4.775424</td>\n",
" <td>62</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" UNIXTime Date Time ... Day_max_temp afternoon night\n",
"0 1480107904 2016-11-25 11:05:04 ... 53 1 0\n",
"1 1472818508 2016-09-02 02:15:08 ... 65 0 1\n",
"2 1475804719 2016-10-06 15:45:19 ... 60 0 0\n",
"3 1482533149 2016-12-23 12:45:49 ... 61 1 0\n",
"4 1481883019 2016-12-16 00:10:19 ... 62 0 1\n",
"\n",
"[5 rows x 24 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 32
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NLN6zu3liU07"
},
"source": [
"## Pressure"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 281
},
"id": "loOGd9cehG3y",
"outputId": "b57469fa-3919-4b30-b03d-6ea72b52e707"
},
"source": [
"temp = sns.boxplot(data = train[\"Pressure\"]).set(title=\"Pressure ouliers\")"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 513
},
"id": "ss3xMALThG06",
"outputId": "2f2875f4-8e8b-4cb8-cc48-385600e92c85"
},
"source": [
"hourly_variation_ofpressure = train[[\"Hour\",\"Pressure\",]].groupby(\"Hour\").mean()\n",
"plt.figure(figsize=(20,8))\n",
"temp = sns.lineplot(data=hourly_variation_ofpressure).set(title=\"hourly variation of other Pressure variables\")"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x576 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FFzoqQP-ixYD"
},
"source": [
"## very slight variations in pressure "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2IBP_ua1i67q"
},
"source": [
"## Humidity"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 281
},
"id": "FJRahTS5hGxo",
"outputId": "4b3db0c4-9f2c-4c80-9b2b-a5e6f86ebf72"
},
"source": [
"temp = sns.boxplot(data = train[\"Humidity\"]).set(title=\"Humidity ouliers\")"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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JapBxl6QGGXdJapBxl6QGGXdJapBxl6QGparGPQNJZoHd455DmscG4DvjHkJawFOramK+O1ZE3KWVKsl0VW0e9xzSqFyWkaQGGXdJapBxlx7djnEPIC2Fa+6S1CCv3CWpQcZdkhpk3KUFJDkzyTeSzCS5eNzzSKNwzV2aR5I1wDeBXwf2AF8Czq+qm8Y6mLRIXrlL8zsVmKmq26rqQeCjwDljnklaNOMuzW8SuGNoe0+3T1oVjLskNci4S/PbCzxlaPvEbp+0Khh3aX5fAk5OclKSI4HzgKvGPJO0aGvHPYC0ElXVQ0n+GPhXYA3w/qq6ccxjSYvmSyElqUEuy0hSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg/4Xp6iDWUOjzlsAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 513
},
"id": "wBdxjTkohGu2",
"outputId": "5f010495-1a43-4316-eb9e-6a9f74a903b0"
},
"source": [
"hourly_variation_ofhumidity = train[[\"Hour\",\"Humidity\",]].groupby(\"Hour\").mean()\n",
"plt.figure(figsize=(20,8))\n",
"temp = sns.lineplot(data=hourly_variation_ofhumidity).set(title=\"hourly variation of Humidity variables\")"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
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"text/plain": [
"<Figure size 1440x576 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dGoKUnm5jh0R"
},
"source": [
"### generally high humidity at evening and low at peak hours"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "XPI7AlK0hGrz",
"outputId": "ecfc7441-75f8-43c9-f15b-abad3803a7e7"
},
"source": [
"train.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>UNIXTime</th>\n",
" <th>Date</th>\n",
" <th>Time</th>\n",
" <th>Radiation</th>\n",
" <th>Temperature</th>\n",
" <th>Pressure</th>\n",
" <th>Humidity</th>\n",
" <th>WindDirection(Degrees)</th>\n",
" <th>Speed</th>\n",
" <th>TimeSunRise</th>\n",
" <th>TimeSunSet</th>\n",
" <th>Month</th>\n",
" <th>Year</th>\n",
" <th>Day</th>\n",
" <th>WeekDay</th>\n",
" <th>WeekNumber</th>\n",
" <th>Month_plus_day</th>\n",
" <th>Hour</th>\n",
" <th>Mins</th>\n",
" <th>Average_dailytemp</th>\n",
" <th>temp_dif</th>\n",
" <th>Day_max_temp</th>\n",
" <th>afternoon</th>\n",
" <th>night</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1480107904</td>\n",
" <td>2016-11-25</td>\n",
" <td>11:05:04</td>\n",
" <td>288.44</td>\n",
" <td>46</td>\n",
" <td>30.48</td>\n",
" <td>101</td>\n",
" <td>129.84</td>\n",
" <td>13.50</td>\n",
" <td>06:37:00</td>\n",
" <td>17:42:00</td>\n",
" <td>11</td>\n",
" <td>2016</td>\n",
" <td>25</td>\n",
" <td>4</td>\n",
" <td>47</td>\n",
" <td>135</td>\n",
" <td>11</td>\n",
" <td>5</td>\n",
" <td>47.306034</td>\n",
" <td>-1.306034</td>\n",
" <td>53</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1472818508</td>\n",
" <td>2016-09-02</td>\n",
" <td>02:15:08</td>\n",
" <td>2.79</td>\n",
" <td>50</td>\n",
" <td>30.42</td>\n",
" <td>75</td>\n",
" <td>173.90</td>\n",
" <td>6.75</td>\n",
" <td>06:07:00</td>\n",
" <td>18:37:00</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>35</td>\n",
" <td>92</td>\n",
" <td>2</td>\n",
" <td>15</td>\n",
" <td>56.118182</td>\n",
" <td>-6.118182</td>\n",
" <td>65</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1475804719</td>\n",
" <td>2016-10-06</td>\n",
" <td>15:45:19</td>\n",
" <td>118.05</td>\n",
" <td>54</td>\n",
" <td>30.42</td>\n",
" <td>100</td>\n",
" <td>7.35</td>\n",
" <td>1.12</td>\n",
" <td>06:15:00</td>\n",
" <td>18:07:00</td>\n",
" <td>10</td>\n",
" <td>2016</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>40</td>\n",
" <td>106</td>\n",
" <td>15</td>\n",
" <td>45</td>\n",
" <td>52.215517</td>\n",
" <td>1.784483</td>\n",
" <td>60</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1482533149</td>\n",
" <td>2016-12-23</td>\n",
" <td>12:45:49</td>\n",
" <td>853.17</td>\n",
" <td>58</td>\n",
" <td>30.44</td>\n",
" <td>57</td>\n",
" <td>81.67</td>\n",
" <td>11.25</td>\n",
" <td>06:54:00</td>\n",
" <td>17:50:00</td>\n",
" <td>12</td>\n",
" <td>2016</td>\n",
" <td>23</td>\n",
" <td>4</td>\n",
" <td>51</td>\n",
" <td>143</td>\n",
" <td>12</td>\n",
" <td>45</td>\n",
" <td>51.030043</td>\n",
" <td>6.969957</td>\n",
" <td>61</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1481883019</td>\n",
" <td>2016-12-16</td>\n",
" <td>00:10:19</td>\n",
" <td>1.24</td>\n",
" <td>42</td>\n",
" <td>30.24</td>\n",
" <td>103</td>\n",
" <td>171.13</td>\n",
" <td>2.25</td>\n",
" <td>06:50:00</td>\n",
" <td>17:46:00</td>\n",
" <td>12</td>\n",
" <td>2016</td>\n",
" <td>16</td>\n",
" <td>4</td>\n",
" <td>50</td>\n",
" <td>136</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>46.775424</td>\n",
" <td>-4.775424</td>\n",
" <td>62</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" UNIXTime Date Time ... Day_max_temp afternoon night\n",
"0 1480107904 2016-11-25 11:05:04 ... 53 1 0\n",
"1 1472818508 2016-09-02 02:15:08 ... 65 0 1\n",
"2 1475804719 2016-10-06 15:45:19 ... 60 0 0\n",
"3 1482533149 2016-12-23 12:45:49 ... 61 1 0\n",
"4 1481883019 2016-12-16 00:10:19 ... 62 0 1\n",
"\n",
"[5 rows x 24 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 37
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mfgkiHzbj16w"
},
"source": [
"## WindDirection \n",
"# We can divide windDirection into Four categories according to the angle\n",
" ne winds\n",
" nw \n",
" sw \n",
" se "
]
},
{
"cell_type": "code",
"metadata": {
"id": "NWo00YHehGpH"
},
"source": [
"type_of_wind = []\n",
"for h in train.iterrows():\n",
" if h[1][\"WindDirection(Degrees)\"]>0 and h[1][\"WindDirection(Degrees)\"]<90:\n",
" type_of_wind.append(0)\n",
" elif h[1][\"WindDirection(Degrees)\"]>=90 and h[1][\"WindDirection(Degrees)\"]<180:\n",
" type_of_wind.append(1)\n",
" elif h[1][\"WindDirection(Degrees)\"]>=180 and h[1][\"WindDirection(Degrees)\"]<270:\n",
" type_of_wind.append(2)\n",
" else :\n",
" type_of_wind.append(3)\n",
"\n",
"train[\"type_of_wind\"] = type_of_wind"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 336
},
"id": "o1LxMGDKlj8d",
"outputId": "683d16c2-b3db-4bbe-9a6a-ece7fbae78f7"
},
"source": [
"freq_wind = dict(train[\"type_of_wind\"].value_counts())\n",
"plt.figure(figsize=(18,5))\n",
"temp = sns.barplot(x =list(freq_wind.keys()),y = list(freq_wind.values())).set(title=\"freq count of different wind type\")"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1296x360 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 639
},
"id": "j-hf1sMkhGjl",
"outputId": "bda05e58-80ac-46b4-ce84-416f8927cfa4"
},
"source": [
"plt.figure(figsize=(20,10))\n",
"sns.boxplot(x=\"type_of_wind\",y=\"Radiation\", data = train,).set(title=\"radiation variation with winds\")"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[Text(0.5, 1.0, 'radiation variation with winds')]"
]
},
"metadata": {
"tags": []
},
"execution_count": 40
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "u0Q6nDR2hGhd"
},
"source": [
""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Wl7rBWP6l51r"
},
"source": [
"## Speed"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 281
},
"id": "fVP1AllthGe_",
"outputId": "30c3696e-6141-425b-c9d6-e7c57822fb7e"
},
"source": [
"temp = sns.boxplot(data = train[\"Speed\"]).set(title=\"Speed ouliers\")"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 513
},
"id": "AWyWL_OshGcZ",
"outputId": "997c5337-4d00-4ee2-a7e9-a063a991c058"
},
"source": [
"hourly_variation_ofSpeed = train[[\"Hour\",\"Speed\",]].groupby(\"Hour\").mean()\n",
"plt.figure(figsize=(8,8))\n",
"temp = sns.lineplot(data=hourly_variation_ofSpeed).set(title=\"hourly variation of Speed variables\")"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 296
},
"id": "KZf8JZz-hGZ_",
"outputId": "a40bc123-0e5f-4668-f4cf-12ae78312521"
},
"source": [
"sns.scatterplot(x=train[\"Speed\"],y=train[\"Radiation\"])"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f02c2c86c18>"
]
},
"metadata": {
"tags": []
},
"execution_count": 43
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OR_7Ji7AmiZD"
},
"source": [
"## Timerise and TimeSet\n",
"It can give us the length of the day which might have some relation with radiation"
]
},
{
"cell_type": "code",
"metadata": {
"id": "fuVMRNvXhGXN"
},
"source": [
"train[\"Sunrise_h\"] = [int(time.split(\":\")[0]) for time in train[\"TimeSunRise\"]]\n",
"train[\"Sunset_h\"] = [int(time.split(\":\")[0]) for time in train[\"TimeSunSet\"]] \n",
"train[\"Daylength\"] = train[\"Sunset_h\"] - train[\"Sunrise_h\"]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 296
},
"id": "pUq7jomFhGUW",
"outputId": "ed4ec788-98a0-4793-fc84-53d32b1f4804"
},
"source": [
"sns.lineplot(x=train[\"Daylength\"],y=train[\"Radiation\"])"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f02c2bfd7b8>"
]
},
"metadata": {
"tags": []
},
"execution_count": 45
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "7LBhMpsX-XQ6"
},
"source": [
"from collections import defaultdict\n",
"def cal_temprange(df):\n",
" dailytemp = defaultdict(list)\n",
" for day,value in zip(df[\"Date\"],df[\"Temperature\"]):\n",
" dailytemp[day].append(value)\n",
" ranges = dict()\n",
" for day, values in dailytemp.items():\n",
" ranges[day] = (max(values) - min(values))\n",
"\n",
" return ranges\n",
"\n",
"ranges = cal_temprange(train)\n",
"train[\"Temp_range\"] = [ranges[day] for day in train[\"Date\"]]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 854
},
"id": "oa3RqgDTncbn",
"outputId": "6805bce7-aee5-4cfa-94aa-3072485b0091"
},
"source": [
"train.corr()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>UNIXTime</th>\n",
" <th>Radiation</th>\n",
" <th>Temperature</th>\n",
" <th>Pressure</th>\n",
" <th>Humidity</th>\n",
" <th>WindDirection(Degrees)</th>\n",
" <th>Speed</th>\n",
" <th>Month</th>\n",
" <th>Year</th>\n",
" <th>Day</th>\n",
" <th>WeekDay</th>\n",
" <th>WeekNumber</th>\n",
" <th>Month_plus_day</th>\n",
" <th>Hour</th>\n",
" <th>Mins</th>\n",
" <th>Average_dailytemp</th>\n",
" <th>temp_dif</th>\n",
" <th>Day_max_temp</th>\n",
" <th>afternoon</th>\n",
" <th>night</th>\n",
" <th>type_of_wind</th>\n",
" <th>Sunrise_h</th>\n",
" <th>Sunset_h</th>\n",
" <th>Daylength</th>\n",
" <th>Temp_range</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>UNIXTime</th>\n",
" <td>1.000000</td>\n",
" <td>-0.077825</td>\n",
" <td>-0.366321</td>\n",
" <td>-0.329805</td>\n",
" <td>-0.061278</td>\n",
" <td>0.151159</td>\n",
" <td>0.177101</td>\n",
" <td>9.683265e-01</td>\n",
" <td>NaN</td>\n",
" <td>2.854281e-01</td>\n",
" <td>1.912980e-02</td>\n",
" <td>9.982712e-01</td>\n",
" <td>9.194980e-01</td>\n",
" <td>0.001843</td>\n",
" <td>0.001403</td>\n",
" <td>-6.483497e-01</td>\n",
" <td>1.838028e-03</td>\n",
" <td>-5.328182e-01</td>\n",
" <td>0.011186</td>\n",
" <td>-0.014058</td>\n",
" <td>0.148752</td>\n",
" <td>NaN</td>\n",
" <td>-8.191229e-01</td>\n",
" <td>-8.191229e-01</td>\n",
" <td>-1.856826e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Radiation</th>\n",
" <td>-0.077825</td>\n",
" <td>1.000000</td>\n",
" <td>0.735391</td>\n",
" <td>0.118741</td>\n",
" <td>-0.225779</td>\n",
" <td>-0.229311</td>\n",
" <td>0.072798</td>\n",
" <td>-9.283088e-02</td>\n",
" <td>NaN</td>\n",
" <td>4.390106e-02</td>\n",
" <td>-2.661752e-02</td>\n",
" <td>-7.627795e-02</td>\n",
" <td>-4.465120e-02</td>\n",
" <td>0.005687</td>\n",
" <td>-0.000304</td>\n",
" <td>1.487876e-01</td>\n",
" <td>7.905170e-01</td>\n",
" <td>1.650128e-01</td>\n",
" <td>0.647856</td>\n",
" <td>-0.602130</td>\n",
" <td>-0.203012</td>\n",
" <td>NaN</td>\n",
" <td>4.487787e-02</td>\n",
" <td>4.487787e-02</td>\n",
" <td>1.472929e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Temperature</th>\n",
" <td>-0.366321</td>\n",
" <td>0.735391</td>\n",
" <td>1.000000</td>\n",
" <td>0.307348</td>\n",
" <td>-0.286960</td>\n",
" <td>-0.256470</td>\n",
" <td>-0.031535</td>\n",
" <td>-3.524557e-01</td>\n",
" <td>NaN</td>\n",
" <td>-1.201129e-01</td>\n",
" <td>-4.931515e-03</td>\n",
" <td>-3.674540e-01</td>\n",
" <td>-3.445843e-01</td>\n",
" <td>0.198480</td>\n",
" <td>-0.002293</td>\n",
" <td>5.673393e-01</td>\n",
" <td>8.234841e-01</td>\n",
" <td>4.895803e-01</td>\n",
" <td>0.418214</td>\n",
" <td>-0.494964</td>\n",
" <td>-0.231982</td>\n",
" <td>NaN</td>\n",
" <td>2.977681e-01</td>\n",
" <td>2.977681e-01</td>\n",
" <td>2.616901e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pressure</th>\n",
" <td>-0.329805</td>\n",
" <td>0.118741</td>\n",
" <td>0.307348</td>\n",
" <td>1.000000</td>\n",
" <td>-0.223938</td>\n",
" <td>-0.227982</td>\n",
" <td>-0.087306</td>\n",
" <td>-3.403184e-01</td>\n",
" <td>NaN</td>\n",
" <td>-2.102070e-02</td>\n",
" <td>-2.617088e-02</td>\n",
" <td>-3.288528e-01</td>\n",
" <td>-2.747584e-01</td>\n",
" <td>0.092667</td>\n",
" <td>0.002089</td>\n",
" <td>5.241970e-01</td>\n",
" <td>1.208316e-02</td>\n",
" <td>4.151285e-01</td>\n",
" <td>0.156741</td>\n",
" <td>0.044702</td>\n",
" <td>-0.253564</td>\n",
" <td>NaN</td>\n",
" <td>1.492182e-01</td>\n",
" <td>1.492182e-01</td>\n",
" <td>1.287324e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Humidity</th>\n",
" <td>-0.061278</td>\n",
" <td>-0.225779</td>\n",
" <td>-0.286960</td>\n",
" <td>-0.223938</td>\n",
" <td>1.000000</td>\n",
" <td>0.001035</td>\n",
" <td>-0.210001</td>\n",
" <td>-6.680905e-02</td>\n",
" <td>NaN</td>\n",
" <td>1.433116e-02</td>\n",
" <td>5.765932e-02</td>\n",
" <td>-6.518818e-02</td>\n",
" <td>-4.267238e-02</td>\n",
" <td>0.071997</td>\n",
" <td>-0.000976</td>\n",
" <td>-3.586414e-01</td>\n",
" <td>-1.013852e-01</td>\n",
" <td>-4.794937e-01</td>\n",
" <td>-0.087897</td>\n",
" <td>0.057255</td>\n",
" <td>0.010805</td>\n",
" <td>NaN</td>\n",
" <td>1.438555e-01</td>\n",
" <td>1.438555e-01</td>\n",
" <td>-5.075996e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>WindDirection(Degrees)</th>\n",
" <td>0.151159</td>\n",
" <td>-0.229311</td>\n",
" <td>-0.256470</td>\n",
" <td>-0.227982</td>\n",
" <td>0.001035</td>\n",
" <td>1.000000</td>\n",
" <td>0.073848</td>\n",
" <td>1.800732e-01</td>\n",
" <td>NaN</td>\n",
" <td>-8.312769e-02</td>\n",
" <td>-1.458513e-02</td>\n",
" <td>1.525442e-01</td>\n",
" <td>8.785430e-02</td>\n",
" <td>-0.076015</td>\n",
" <td>0.000996</td>\n",
" <td>-1.325908e-01</td>\n",
" <td>-2.200967e-01</td>\n",
" <td>-1.011888e-01</td>\n",
" <td>-0.105122</td>\n",
" <td>0.154576</td>\n",
" <td>0.946086</td>\n",
" <td>NaN</td>\n",
" <td>-7.766456e-02</td>\n",
" <td>-7.766456e-02</td>\n",
" <td>-2.169983e-02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Speed</th>\n",
" <td>0.177101</td>\n",
" <td>0.072798</td>\n",
" <td>-0.031535</td>\n",
" <td>-0.087306</td>\n",
" <td>-0.210001</td>\n",
" <td>0.073848</td>\n",
" <td>1.000000</td>\n",
" <td>1.539738e-01</td>\n",
" <td>NaN</td>\n",
" <td>1.182764e-01</td>\n",
" <td>-7.177590e-03</td>\n",
" <td>1.778927e-01</td>\n",
" <td>1.907008e-01</td>\n",
" <td>-0.058085</td>\n",
" <td>0.000003</td>\n",
" <td>-7.285138e-02</td>\n",
" <td>1.189638e-02</td>\n",
" <td>1.044180e-02</td>\n",
" <td>0.025807</td>\n",
" <td>-0.048990</td>\n",
" <td>0.083819</td>\n",
" <td>NaN</td>\n",
" <td>-1.632895e-01</td>\n",
" <td>-1.632895e-01</td>\n",
" <td>1.262556e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Month</th>\n",
" <td>0.968326</td>\n",
" <td>-0.092831</td>\n",
" <td>-0.352456</td>\n",
" <td>-0.340318</td>\n",
" <td>-0.066809</td>\n",
" <td>0.180073</td>\n",
" <td>0.153974</td>\n",
" <td>1.000000e+00</td>\n",
" <td>NaN</td>\n",
" <td>3.731715e-02</td>\n",
" <td>3.220937e-02</td>\n",
" <td>9.659006e-01</td>\n",
" <td>7.924326e-01</td>\n",
" <td>-0.003529</td>\n",
" <td>0.002081</td>\n",
" <td>-6.212433e-01</td>\n",
" <td>2.582824e-17</td>\n",
" <td>-5.383509e-01</td>\n",
" <td>0.010419</td>\n",
" <td>-0.012178</td>\n",
" <td>0.178077</td>\n",
" <td>NaN</td>\n",
" <td>-7.852101e-01</td>\n",
" <td>-7.852101e-01</td>\n",
" <td>-2.242639e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Year</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Day</th>\n",
" <td>0.285428</td>\n",
" <td>0.043901</td>\n",
" <td>-0.120113</td>\n",
" <td>-0.021021</td>\n",
" <td>0.014331</td>\n",
" <td>-0.083128</td>\n",
" <td>0.118276</td>\n",
" <td>3.731715e-02</td>\n",
" <td>NaN</td>\n",
" <td>1.000000e+00</td>\n",
" <td>-4.646074e-02</td>\n",
" <td>2.880574e-01</td>\n",
" <td>6.391060e-01</td>\n",
" <td>-0.011958</td>\n",
" <td>-0.003464</td>\n",
" <td>-2.117126e-01</td>\n",
" <td>-4.721019e-17</td>\n",
" <td>-7.107560e-02</td>\n",
" <td>0.007109</td>\n",
" <td>-0.012559</td>\n",
" <td>-0.085023</td>\n",
" <td>NaN</td>\n",
" <td>-2.624346e-01</td>\n",
" <td>-2.624346e-01</td>\n",
" <td>1.102231e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>WeekDay</th>\n",
" <td>0.019130</td>\n",
" <td>-0.026618</td>\n",
" <td>-0.004932</td>\n",
" <td>-0.026171</td>\n",
" <td>0.057659</td>\n",
" <td>-0.014585</td>\n",
" <td>-0.007178</td>\n",
" <td>3.220937e-02</td>\n",
" <td>NaN</td>\n",
" <td>-4.646074e-02</td>\n",
" <td>1.000000e+00</td>\n",
" <td>-3.908323e-02</td>\n",
" <td>-3.568833e-03</td>\n",
" <td>0.008250</td>\n",
" <td>0.000093</td>\n",
" <td>-8.692355e-03</td>\n",
" <td>-5.027455e-17</td>\n",
" <td>-6.383226e-02</td>\n",
" <td>0.000620</td>\n",
" <td>0.003553</td>\n",
" <td>-0.008659</td>\n",
" <td>NaN</td>\n",
" <td>-2.188097e-02</td>\n",
" <td>-2.188097e-02</td>\n",
" <td>-1.253335e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>WeekNumber</th>\n",
" <td>0.998271</td>\n",
" <td>-0.076278</td>\n",
" <td>-0.367454</td>\n",
" <td>-0.328853</td>\n",
" <td>-0.065188</td>\n",
" <td>0.152544</td>\n",
" <td>0.177893</td>\n",
" <td>9.659006e-01</td>\n",
" <td>NaN</td>\n",
" <td>2.880574e-01</td>\n",
" <td>-3.908323e-02</td>\n",
" <td>1.000000e+00</td>\n",
" <td>9.192358e-01</td>\n",
" <td>-0.006903</td>\n",
" <td>0.001101</td>\n",
" <td>-6.476795e-01</td>\n",
" <td>-7.171216e-18</td>\n",
" <td>-5.289698e-01</td>\n",
" <td>0.011708</td>\n",
" <td>-0.014976</td>\n",
" <td>0.149808</td>\n",
" <td>NaN</td>\n",
" <td>-8.174268e-01</td>\n",
" <td>-8.174268e-01</td>\n",
" <td>-1.783877e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Month_plus_day</th>\n",
" <td>0.919498</td>\n",
" <td>-0.044651</td>\n",
" <td>-0.344584</td>\n",
" <td>-0.274758</td>\n",
" <td>-0.042672</td>\n",
" <td>0.087854</td>\n",
" <td>0.190701</td>\n",
" <td>7.924326e-01</td>\n",
" <td>NaN</td>\n",
" <td>6.391060e-01</td>\n",
" <td>-3.568833e-03</td>\n",
" <td>9.192358e-01</td>\n",
" <td>1.000000e+00</td>\n",
" <td>-0.010015</td>\n",
" <td>-0.000513</td>\n",
" <td>-6.073690e-01</td>\n",
" <td>3.649577e-17</td>\n",
" <td>-4.577278e-01</td>\n",
" <td>0.012358</td>\n",
" <td>-0.017038</td>\n",
" <td>0.085161</td>\n",
" <td>NaN</td>\n",
" <td>-7.645268e-01</td>\n",
" <td>-7.645268e-01</td>\n",
" <td>-1.053273e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hour</th>\n",
" <td>0.001843</td>\n",
" <td>0.005687</td>\n",
" <td>0.198480</td>\n",
" <td>0.092667</td>\n",
" <td>0.071997</td>\n",
" <td>-0.076015</td>\n",
" <td>-0.058085</td>\n",
" <td>-3.529289e-03</td>\n",
" <td>NaN</td>\n",
" <td>-1.195821e-02</td>\n",
" <td>8.250181e-03</td>\n",
" <td>-6.902766e-03</td>\n",
" <td>-1.001544e-02</td>\n",
" <td>1.000000</td>\n",
" <td>-0.005825</td>\n",
" <td>2.704026e-02</td>\n",
" <td>2.223955e-01</td>\n",
" <td>2.203471e-02</td>\n",
" <td>-0.068222</td>\n",
" <td>0.086867</td>\n",
" <td>-0.077594</td>\n",
" <td>NaN</td>\n",
" <td>8.167525e-03</td>\n",
" <td>8.167525e-03</td>\n",
" <td>1.252750e-02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mins</th>\n",
" <td>0.001403</td>\n",
" <td>-0.000304</td>\n",
" <td>-0.002293</td>\n",
" <td>0.002089</td>\n",
" <td>-0.000976</td>\n",
" <td>0.000996</td>\n",
" <td>0.000003</td>\n",
" <td>2.080769e-03</td>\n",
" <td>NaN</td>\n",
" <td>-3.463940e-03</td>\n",
" <td>9.348091e-05</td>\n",
" <td>1.100586e-03</td>\n",
" <td>-5.128623e-04</td>\n",
" <td>-0.005825</td>\n",
" <td>1.000000</td>\n",
" <td>2.378895e-03</td>\n",
" <td>-4.423946e-03</td>\n",
" <td>1.123544e-03</td>\n",
" <td>-0.001853</td>\n",
" <td>0.005939</td>\n",
" <td>-0.000832</td>\n",
" <td>NaN</td>\n",
" <td>-1.854266e-05</td>\n",
" <td>-1.854266e-05</td>\n",
" <td>-1.008471e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Average_dailytemp</th>\n",
" <td>-0.648350</td>\n",
" <td>0.148788</td>\n",
" <td>0.567339</td>\n",
" <td>0.524197</td>\n",
" <td>-0.358641</td>\n",
" <td>-0.132591</td>\n",
" <td>-0.072851</td>\n",
" <td>-6.212433e-01</td>\n",
" <td>NaN</td>\n",
" <td>-2.117126e-01</td>\n",
" <td>-8.692355e-03</td>\n",
" <td>-6.476795e-01</td>\n",
" <td>-6.073690e-01</td>\n",
" <td>0.027040</td>\n",
" <td>0.002379</td>\n",
" <td>1.000000e+00</td>\n",
" <td>1.552478e-18</td>\n",
" <td>8.629409e-01</td>\n",
" <td>0.007510</td>\n",
" <td>-0.013004</td>\n",
" <td>-0.133251</td>\n",
" <td>NaN</td>\n",
" <td>5.248501e-01</td>\n",
" <td>5.248501e-01</td>\n",
" <td>4.612585e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>temp_dif</th>\n",
" <td>0.001838</td>\n",
" <td>0.790517</td>\n",
" <td>0.823484</td>\n",
" <td>0.012083</td>\n",
" <td>-0.101385</td>\n",
" <td>-0.220097</td>\n",
" <td>0.011896</td>\n",
" <td>2.582824e-17</td>\n",
" <td>NaN</td>\n",
" <td>-4.721019e-17</td>\n",
" <td>-5.027455e-17</td>\n",
" <td>-7.171216e-18</td>\n",
" <td>3.649577e-17</td>\n",
" <td>0.222395</td>\n",
" <td>-0.004424</td>\n",
" <td>1.552478e-18</td>\n",
" <td>1.000000e+00</td>\n",
" <td>2.074037e-17</td>\n",
" <td>0.502685</td>\n",
" <td>-0.592102</td>\n",
" <td>-0.189904</td>\n",
" <td>NaN</td>\n",
" <td>-1.073792e-16</td>\n",
" <td>-1.284200e-17</td>\n",
" <td>5.706098e-17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Day_max_temp</th>\n",
" <td>-0.532818</td>\n",
" <td>0.165013</td>\n",
" <td>0.489580</td>\n",
" <td>0.415128</td>\n",
" <td>-0.479494</td>\n",
" <td>-0.101189</td>\n",
" <td>0.010442</td>\n",
" <td>-5.383509e-01</td>\n",
" <td>NaN</td>\n",
" <td>-7.107560e-02</td>\n",
" <td>-6.383226e-02</td>\n",
" <td>-5.289698e-01</td>\n",
" <td>-4.577278e-01</td>\n",
" <td>0.022035</td>\n",
" <td>0.001124</td>\n",
" <td>8.629409e-01</td>\n",
" <td>2.074037e-17</td>\n",
" <td>1.000000e+00</td>\n",
" <td>-0.002424</td>\n",
" <td>0.001529</td>\n",
" <td>-0.097496</td>\n",
" <td>NaN</td>\n",
" <td>3.900034e-01</td>\n",
" <td>3.900034e-01</td>\n",
" <td>8.212296e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>afternoon</th>\n",
" <td>0.011186</td>\n",
" <td>0.647856</td>\n",
" <td>0.418214</td>\n",
" <td>0.156741</td>\n",
" <td>-0.087897</td>\n",
" <td>-0.105122</td>\n",
" <td>0.025807</td>\n",
" <td>1.041920e-02</td>\n",
" <td>NaN</td>\n",
" <td>7.109125e-03</td>\n",
" <td>6.201905e-04</td>\n",
" <td>1.170759e-02</td>\n",
" <td>1.235849e-02</td>\n",
" <td>-0.068222</td>\n",
" <td>-0.001853</td>\n",
" <td>7.509756e-03</td>\n",
" <td>5.026853e-01</td>\n",
" <td>-2.424311e-03</td>\n",
" <td>1.000000</td>\n",
" <td>-0.407557</td>\n",
" <td>-0.087752</td>\n",
" <td>NaN</td>\n",
" <td>-1.190067e-02</td>\n",
" <td>-1.190067e-02</td>\n",
" <td>-6.890064e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>night</th>\n",
" <td>-0.014058</td>\n",
" <td>-0.602130</td>\n",
" <td>-0.494964</td>\n",
" <td>0.044702</td>\n",
" <td>0.057255</td>\n",
" <td>0.154576</td>\n",
" <td>-0.048990</td>\n",
" <td>-1.217762e-02</td>\n",
" <td>NaN</td>\n",
" <td>-1.255869e-02</td>\n",
" <td>3.552916e-03</td>\n",
" <td>-1.497646e-02</td>\n",
" <td>-1.703820e-02</td>\n",
" <td>0.086867</td>\n",
" <td>0.005939</td>\n",
" <td>-1.300394e-02</td>\n",
" <td>-5.921015e-01</td>\n",
" <td>1.529421e-03</td>\n",
" <td>-0.407557</td>\n",
" <td>1.000000</td>\n",
" <td>0.130619</td>\n",
" <td>NaN</td>\n",
" <td>1.252629e-02</td>\n",
" <td>1.252629e-02</td>\n",
" <td>6.751213e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>type_of_wind</th>\n",
" <td>0.148752</td>\n",
" <td>-0.203012</td>\n",
" <td>-0.231982</td>\n",
" <td>-0.253564</td>\n",
" <td>0.010805</td>\n",
" <td>0.946086</td>\n",
" <td>0.083819</td>\n",
" <td>1.780770e-01</td>\n",
" <td>NaN</td>\n",
" <td>-8.502336e-02</td>\n",
" <td>-8.659298e-03</td>\n",
" <td>1.498083e-01</td>\n",
" <td>8.516087e-02</td>\n",
" <td>-0.077594</td>\n",
" <td>-0.000832</td>\n",
" <td>-1.332511e-01</td>\n",
" <td>-1.899042e-01</td>\n",
" <td>-9.749566e-02</td>\n",
" <td>-0.087752</td>\n",
" <td>0.130619</td>\n",
" <td>1.000000</td>\n",
" <td>NaN</td>\n",
" <td>-7.036614e-02</td>\n",
" <td>-7.036614e-02</td>\n",
" <td>-1.357569e-02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sunrise_h</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sunset_h</th>\n",
" <td>-0.819123</td>\n",
" <td>0.044878</td>\n",
" <td>0.297768</td>\n",
" <td>0.149218</td>\n",
" <td>0.143856</td>\n",
" <td>-0.077665</td>\n",
" <td>-0.163290</td>\n",
" <td>-7.852101e-01</td>\n",
" <td>NaN</td>\n",
" <td>-2.624346e-01</td>\n",
" <td>-2.188097e-02</td>\n",
" <td>-8.174268e-01</td>\n",
" <td>-7.645268e-01</td>\n",
" <td>0.008168</td>\n",
" <td>-0.000019</td>\n",
" <td>5.248501e-01</td>\n",
" <td>-1.073792e-16</td>\n",
" <td>3.900034e-01</td>\n",
" <td>-0.011901</td>\n",
" <td>0.012526</td>\n",
" <td>-0.070366</td>\n",
" <td>NaN</td>\n",
" <td>1.000000e+00</td>\n",
" <td>1.000000e+00</td>\n",
" <td>1.040639e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Daylength</th>\n",
" <td>-0.819123</td>\n",
" <td>0.044878</td>\n",
" <td>0.297768</td>\n",
" <td>0.149218</td>\n",
" <td>0.143856</td>\n",
" <td>-0.077665</td>\n",
" <td>-0.163290</td>\n",
" <td>-7.852101e-01</td>\n",
" <td>NaN</td>\n",
" <td>-2.624346e-01</td>\n",
" <td>-2.188097e-02</td>\n",
" <td>-8.174268e-01</td>\n",
" <td>-7.645268e-01</td>\n",
" <td>0.008168</td>\n",
" <td>-0.000019</td>\n",
" <td>5.248501e-01</td>\n",
" <td>-1.284200e-17</td>\n",
" <td>3.900034e-01</td>\n",
" <td>-0.011901</td>\n",
" <td>0.012526</td>\n",
" <td>-0.070366</td>\n",
" <td>NaN</td>\n",
" <td>1.000000e+00</td>\n",
" <td>1.000000e+00</td>\n",
" <td>1.040639e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Temp_range</th>\n",
" <td>-0.185683</td>\n",
" <td>0.147293</td>\n",
" <td>0.261690</td>\n",
" <td>0.128732</td>\n",
" <td>-0.507600</td>\n",
" <td>-0.021700</td>\n",
" <td>0.126256</td>\n",
" <td>-2.242639e-01</td>\n",
" <td>NaN</td>\n",
" <td>1.102231e-01</td>\n",
" <td>-1.253335e-01</td>\n",
" <td>-1.783877e-01</td>\n",
" <td>-1.053273e-01</td>\n",
" <td>0.012528</td>\n",
" <td>-0.001008</td>\n",
" <td>4.612585e-01</td>\n",
" <td>5.706098e-17</td>\n",
" <td>8.212296e-01</td>\n",
" <td>-0.006890</td>\n",
" <td>0.006751</td>\n",
" <td>-0.013576</td>\n",
" <td>NaN</td>\n",
" <td>1.040639e-01</td>\n",
" <td>1.040639e-01</td>\n",
" <td>1.000000e+00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" UNIXTime Radiation ... Daylength Temp_range\n",
"UNIXTime 1.000000 -0.077825 ... -8.191229e-01 -1.856826e-01\n",
"Radiation -0.077825 1.000000 ... 4.487787e-02 1.472929e-01\n",
"Temperature -0.366321 0.735391 ... 2.977681e-01 2.616901e-01\n",
"Pressure -0.329805 0.118741 ... 1.492182e-01 1.287324e-01\n",
"Humidity -0.061278 -0.225779 ... 1.438555e-01 -5.075996e-01\n",
"WindDirection(Degrees) 0.151159 -0.229311 ... -7.766456e-02 -2.169983e-02\n",
"Speed 0.177101 0.072798 ... -1.632895e-01 1.262556e-01\n",
"Month 0.968326 -0.092831 ... -7.852101e-01 -2.242639e-01\n",
"Year NaN NaN ... NaN NaN\n",
"Day 0.285428 0.043901 ... -2.624346e-01 1.102231e-01\n",
"WeekDay 0.019130 -0.026618 ... -2.188097e-02 -1.253335e-01\n",
"WeekNumber 0.998271 -0.076278 ... -8.174268e-01 -1.783877e-01\n",
"Month_plus_day 0.919498 -0.044651 ... -7.645268e-01 -1.053273e-01\n",
"Hour 0.001843 0.005687 ... 8.167525e-03 1.252750e-02\n",
"Mins 0.001403 -0.000304 ... -1.854266e-05 -1.008471e-03\n",
"Average_dailytemp -0.648350 0.148788 ... 5.248501e-01 4.612585e-01\n",
"temp_dif 0.001838 0.790517 ... -1.284200e-17 5.706098e-17\n",
"Day_max_temp -0.532818 0.165013 ... 3.900034e-01 8.212296e-01\n",
"afternoon 0.011186 0.647856 ... -1.190067e-02 -6.890064e-03\n",
"night -0.014058 -0.602130 ... 1.252629e-02 6.751213e-03\n",
"type_of_wind 0.148752 -0.203012 ... -7.036614e-02 -1.357569e-02\n",
"Sunrise_h NaN NaN ... NaN NaN\n",
"Sunset_h -0.819123 0.044878 ... 1.000000e+00 1.040639e-01\n",
"Daylength -0.819123 0.044878 ... 1.000000e+00 1.040639e-01\n",
"Temp_range -0.185683 0.147293 ... 1.040639e-01 1.000000e+00\n",
"\n",
"[25 rows x 25 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 47
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "upzay-cThGRs",
"outputId": "32eca01d-555a-4006-b553-730b314b9b4f"
},
"source": [
"train.corr()[\"Radiation\"]"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"UNIXTime -0.077825\n",
"Radiation 1.000000\n",
"Temperature 0.735391\n",
"Pressure 0.118741\n",
"Humidity -0.225779\n",
"WindDirection(Degrees) -0.229311\n",
"Speed 0.072798\n",
"Month -0.092831\n",
"Year NaN\n",
"Day 0.043901\n",
"WeekDay -0.026618\n",
"WeekNumber -0.076278\n",
"Month_plus_day -0.044651\n",
"Hour 0.005687\n",
"Mins -0.000304\n",
"Average_dailytemp 0.148788\n",
"temp_dif 0.790517\n",
"Day_max_temp 0.165013\n",
"afternoon 0.647856\n",
"night -0.602130\n",
"type_of_wind -0.203012\n",
"Sunrise_h NaN\n",
"Sunset_h 0.044878\n",
"Daylength 0.044878\n",
"Temp_range 0.147293\n",
"Name: Radiation, dtype: float64"
]
},
"metadata": {
"tags": []
},
"execution_count": 48
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "F9eQtefShGPO",
"outputId": "7b6c6586-027e-4d88-de5d-c54115ee06d2"
},
"source": [
"train.dtypes"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"UNIXTime int64\n",
"Date object\n",
"Time object\n",
"Radiation float64\n",
"Temperature int64\n",
"Pressure float64\n",
"Humidity int64\n",
"WindDirection(Degrees) float64\n",
"Speed float64\n",
"TimeSunRise object\n",
"TimeSunSet object\n",
"Month int64\n",
"Year int64\n",
"Day int64\n",
"WeekDay int64\n",
"WeekNumber int64\n",
"Month_plus_day int64\n",
"Hour int64\n",
"Mins int64\n",
"Average_dailytemp float64\n",
"temp_dif float64\n",
"Day_max_temp int64\n",
"afternoon int64\n",
"night int64\n",
"type_of_wind int64\n",
"Sunrise_h int64\n",
"Sunset_h int64\n",
"Daylength int64\n",
"Temp_range int64\n",
"dtype: object"
]
},
"metadata": {
"tags": []
},
"execution_count": 49
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "l7anp-Ruqysw"
},
"source": [
"test = pd.read_csv(\"TEST.csv\")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "xYKdmPmNq28u"
},
"source": [
"test.rename(columns = {\"Data\":\"Date\"}, inplace=True)\n",
"test[\"Date\"] = [datetime.strptime(dt, '%m/%d/%Y %H:%M:%S AM').date() for dt in test[\"Date\"]]\n",
"\n",
"test[\"Month\"] = [dt.month for dt in test[\"Date\"]]\n",
"test[\"Year\"] = [dt.year for dt in test[\"Date\"]]\n",
"test[\"Day\"] = [dt.day for dt in test[\"Date\"]]\n",
"test[\"WeekDay\"] = [dt.weekday() for dt in test[\"Date\"]]\n",
"test[\"WeekNumber\"] = [dt.isocalendar()[1] for dt in test[\"Date\"]]\n",
"test[\"Month_plus_day\"] = test[\"Month\"]*10 + test[\"Day\"]\n",
"\n",
"test[\"Hour\"] = [int(tm.split(\":\")[0]) for tm in test[\"Time\"]]\n",
"test[\"Mins\"] = [int(tm.split(\":\")[1]) for tm in test[\"Time\"]]\n",
"\n",
"type_of_wind = []\n",
"for h in test.iterrows():\n",
" if h[1][\"WindDirection(Degrees)\"]>0 and h[1][\"WindDirection(Degrees)\"]<90:\n",
" type_of_wind.append(0)\n",
" elif h[1][\"WindDirection(Degrees)\"]>=90 and h[1][\"WindDirection(Degrees)\"]<180:\n",
" type_of_wind.append(1)\n",
" elif h[1][\"WindDirection(Degrees)\"]>=180 and h[1][\"WindDirection(Degrees)\"]<270:\n",
" type_of_wind.append(2)\n",
" else :\n",
" type_of_wind.append(3)\n",
"\n",
"test[\"type_of_wind\"] = type_of_wind\n",
"\n",
"test[\"Sunrise_h\"] = [int(time.split(\":\")[0]) for time in test[\"TimeSunRise\"]]\n",
"test[\"Sunset_h\"] = [int(time.split(\":\")[0]) for time in test[\"TimeSunSet\"]] \n",
"test[\"Daylength\"] = test[\"Sunset_h\"] - test[\"Sunrise_h\"]\n",
"ranges = cal_temprange(test)\n",
"test[\"Temp_range\"] = [ranges[day] for day in test[\"Date\"]]\n",
"\n",
"\n",
"daily_temp_dict = {}\n",
"for row in test.iterrows():\n",
" try:\n",
" temp_list = daily_temp_dict[row[1][\"Date\"]]\n",
" except KeyError:\n",
" temp_list = list([])\n",
" temp_list.append(row[1][\"Temperature\"])\n",
" daily_temp_dict[row[1][\"Date\"]] = temp_list\n",
"\n",
"average_daytemp={}\n",
"max_temp = {}\n",
"for k,v in zip(daily_temp_dict.keys(), daily_temp_dict.values()):\n",
" average_daytemp[k] = sum(v)/len(v)\n",
" max_temp[k] = max(v)\n",
" \n",
"test[\"Average_dailytemp\"] = [average_daytemp[dt] for dt in test[\"Date\"]]\n",
"test[\"temp_dif\"] = test[\"Temperature\"] - test[\"Average_dailytemp\"]\n",
"test[\"Day_max_temp\"] = [max_temp[dt] for dt in test[\"Date\"]]\n",
"\n",
"test[\"afternoon\"] = [1 if h>8 and h<13 else 0 for h in test[\"Hour\"]]\n",
"test[\"night\"] = [1 if h>17 or h<5 else 0 for h in test[\"Hour\"]]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 226
},
"id": "jrRMIcD9q264",
"outputId": "8d70e142-f07e-47b6-a044-4b533362a60f"
},
"source": [
"test.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>idx</th>\n",
" <th>UNIXTime</th>\n",
" <th>Date</th>\n",
" <th>Time</th>\n",
" <th>Temperature</th>\n",
" <th>Pressure</th>\n",
" <th>Humidity</th>\n",
" <th>WindDirection(Degrees)</th>\n",
" <th>Speed</th>\n",
" <th>TimeSunRise</th>\n",
" <th>TimeSunSet</th>\n",
" <th>Month</th>\n",
" <th>Year</th>\n",
" <th>Day</th>\n",
" <th>WeekDay</th>\n",
" <th>WeekNumber</th>\n",
" <th>Month_plus_day</th>\n",
" <th>Hour</th>\n",
" <th>Mins</th>\n",
" <th>type_of_wind</th>\n",
" <th>Sunrise_h</th>\n",
" <th>Sunset_h</th>\n",
" <th>Daylength</th>\n",
" <th>Temp_range</th>\n",
" <th>Average_dailytemp</th>\n",
" <th>temp_dif</th>\n",
" <th>Day_max_temp</th>\n",
" <th>afternoon</th>\n",
" <th>night</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>1482775250</td>\n",
" <td>2016-12-26</td>\n",
" <td>08:00:50</td>\n",
" <td>48</td>\n",
" <td>30.47</td>\n",
" <td>101</td>\n",
" <td>187.78</td>\n",
" <td>4.50</td>\n",
" <td>06:55:00</td>\n",
" <td>17:51:00</td>\n",
" <td>12</td>\n",
" <td>2016</td>\n",
" <td>26</td>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" <td>146</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>17</td>\n",
" <td>11</td>\n",
" <td>9</td>\n",
" <td>43.319588</td>\n",
" <td>4.680412</td>\n",
" <td>48</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1482774940</td>\n",
" <td>2016-12-26</td>\n",
" <td>07:55:40</td>\n",
" <td>48</td>\n",
" <td>30.47</td>\n",
" <td>101</td>\n",
" <td>133.40</td>\n",
" <td>10.12</td>\n",
" <td>06:55:00</td>\n",
" <td>17:51:00</td>\n",
" <td>12</td>\n",
" <td>2016</td>\n",
" <td>26</td>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" <td>146</td>\n",
" <td>7</td>\n",
" <td>55</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>17</td>\n",
" <td>11</td>\n",
" <td>9</td>\n",
" <td>43.319588</td>\n",
" <td>4.680412</td>\n",
" <td>48</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>1482774649</td>\n",
" <td>2016-12-26</td>\n",
" <td>07:50:49</td>\n",
" <td>48</td>\n",
" <td>30.47</td>\n",
" <td>101</td>\n",
" <td>160.69</td>\n",
" <td>5.62</td>\n",
" <td>06:55:00</td>\n",
" <td>17:51:00</td>\n",
" <td>12</td>\n",
" <td>2016</td>\n",
" <td>26</td>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" <td>146</td>\n",
" <td>7</td>\n",
" <td>50</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>17</td>\n",
" <td>11</td>\n",
" <td>9</td>\n",
" <td>43.319588</td>\n",
" <td>4.680412</td>\n",
" <td>48</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>1482774351</td>\n",
" <td>2016-12-26</td>\n",
" <td>07:45:51</td>\n",
" <td>48</td>\n",
" <td>30.47</td>\n",
" <td>101</td>\n",
" <td>144.56</td>\n",
" <td>7.87</td>\n",
" <td>06:55:00</td>\n",
" <td>17:51:00</td>\n",
" <td>12</td>\n",
" <td>2016</td>\n",
" <td>26</td>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" <td>146</td>\n",
" <td>7</td>\n",
" <td>45</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>17</td>\n",
" <td>11</td>\n",
" <td>9</td>\n",
" <td>43.319588</td>\n",
" <td>4.680412</td>\n",
" <td>48</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>1482774039</td>\n",
" <td>2016-12-26</td>\n",
" <td>07:40:39</td>\n",
" <td>48</td>\n",
" <td>30.47</td>\n",
" <td>101</td>\n",
" <td>169.05</td>\n",
" <td>10.12</td>\n",
" <td>06:55:00</td>\n",
" <td>17:51:00</td>\n",
" <td>12</td>\n",
" <td>2016</td>\n",
" <td>26</td>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" <td>146</td>\n",
" <td>7</td>\n",
" <td>40</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>17</td>\n",
" <td>11</td>\n",
" <td>9</td>\n",
" <td>43.319588</td>\n",
" <td>4.680412</td>\n",
" <td>48</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" idx UNIXTime Date ... Day_max_temp afternoon night\n",
"0 0 1482775250 2016-12-26 ... 48 0 0\n",
"1 1 1482774940 2016-12-26 ... 48 0 0\n",
"2 2 1482774649 2016-12-26 ... 48 0 0\n",
"3 3 1482774351 2016-12-26 ... 48 0 0\n",
"4 4 1482774039 2016-12-26 ... 48 0 0\n",
"\n",
"[5 rows x 29 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 52
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "NP7K7f9bpLXC"
},
"source": [
""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zGf5PvJdr_g1",
"outputId": "553cf9d5-3333-4352-f50c-fc7bff8b8170"
},
"source": [
"!pip install catboost"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Requirement already satisfied: catboost in /usr/local/lib/python3.6/dist-packages (0.24.3)\n",
"Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from catboost) (1.4.1)\n",
"Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.19.4)\n",
"Requirement already satisfied: plotly in /usr/local/lib/python3.6/dist-packages (from catboost) (4.4.1)\n",
"Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from catboost) (3.2.2)\n",
"Requirement already satisfied: graphviz in /usr/local/lib/python3.6/dist-packages (from catboost) (0.10.1)\n",
"Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.1.5)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from catboost) (1.15.0)\n",
"Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.6/dist-packages (from plotly->catboost) (1.3.3)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (1.3.1)\n",
"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (2.4.7)\n",
"Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (2.8.1)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (0.10.0)\n",
"Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24.0->catboost) (2018.9)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "cpZ9d1b0r_Zb"
},
"source": [
"from sklearn.model_selection import train_test_split\n",
"import xgboost as xgb\n",
"from sklearn.preprocessing import StandardScaler,PolynomialFeatures\n",
"from sklearn.metrics import mean_squared_error\n",
"from sklearn.model_selection import RandomizedSearchCV\n",
"from math import sqrt \n",
"from lightgbm import LGBMRegressor\n",
"from xgboost import XGBRegressor, XGBRFRegressor\n",
"from catboost import CatBoostRegressor\n",
"from sklearn.linear_model import LinearRegression,SGDRegressor\n",
"from sklearn.linear_model import Ridge,ElasticNet\n",
"from sklearn.svm import LinearSVR \n",
"from sklearn.ensemble import RandomForestRegressor,ExtraTreesRegressor,GradientBoostingRegressor\n",
"from sklearn.tree import DecisionTreeRegressor\n",
"from collections import defaultdict\n",
"from sklearn.neighbors import KNeighborsRegressor\n",
"from sklearn.preprocessing import StandardScaler\n",
"sc = StandardScaler()\n",
"from sklearn.ensemble import AdaBoostRegressor"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "b8Ubgvh1pLUY"
},
"source": [
"train_columns = [\"UNIXTime\",\"Temperature\",\"Pressure\",\"Humidity\",\"WindDirection(Degrees)\",\"Speed\",\"Month\",\"Day\",\"WeekDay\",\"WeekNumber\",\n",
" \"Hour\",\"Mins\",\"type_of_wind\",\"Daylength\",\"Temp_range\",\"temp_dif\",\"Average_dailytemp\",\"Day_max_temp\",\"afternoon\",\"night\"]\n",
"train_x = train[train_columns]\n",
"train_y = train[\"Radiation\"]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Mt6_-nzuuWVb"
},
"source": [
"train_X,val_x,train_y,val_y = train_test_split(train_x,train_y,test_size=0.33)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "P8pHToMjuWTC",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "8d1faaa7-31f5-4ff4-f577-dedd10005225"
},
"source": [
"dt = DecisionTreeRegressor()\n",
"dt.fit(train_X,train_y)\n",
"pred = dt.predict(val_x)\n",
"sqrt(mean_squared_error(pred,val_y))"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"106.99381845601351"
]
},
"metadata": {
"tags": []
},
"execution_count": 57
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "VzWghGQSuWQJ"
},
"source": [
"params = {\"max_depth\":[50,200,300,500],\"min_samples_leaf\":[7,9,11,13,15],\"max_features\":[5,7,8,9,10,11,12,13,14]}\n",
"rscv = RandomizedSearchCV(estimator = dt,\n",
" param_distributions = params,\n",
" n_jobs = -1,)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "3vRQVWvGuWNq",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "140b7b49-9265-41f7-eefd-b91988775441"
},
"source": [
"rscv.fit(train_X,train_y)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"RandomizedSearchCV(cv=None, error_score=nan,\n",
" estimator=DecisionTreeRegressor(ccp_alpha=0.0,\n",
" criterion='mse',\n",
" max_depth=None,\n",
" max_features=None,\n",
" max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0,\n",
" min_impurity_split=None,\n",
" min_samples_leaf=1,\n",
" min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0,\n",
" presort='deprecated',\n",
" random_state=None,\n",
" splitter='best'),\n",
" iid='deprecated', n_iter=10, n_jobs=-1,\n",
" param_distributions={'max_depth': [50, 200, 300, 500],\n",
" 'max_features': [5, 7, 8, 9, 10, 11, 12,\n",
" 13, 14],\n",
" 'min_samples_leaf': [7, 9, 11, 13, 15]},\n",
" pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
" return_train_score=False, scoring=None, verbose=0)"
]
},
"metadata": {
"tags": []
},
"execution_count": 59
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "BwlP1-GFuWKz",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "077e1e57-7794-4d03-c886-47e6e627b0ca"
},
"source": [
"rscv.best_estimator_"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=200,\n",
" max_features=9, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=13, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, presort='deprecated',\n",
" random_state=None, splitter='best')"
]
},
"metadata": {
"tags": []
},
"execution_count": 60
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "J-kRVNENw_5N",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "09ca33cd-ee21-4f7c-c6eb-2bf4fbeb5c38"
},
"source": [
"rscv.cv_results_"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'mean_fit_time': array([0.13092322, 0.12221589, 0.06039004, 0.07425528, 0.10454345,\n",
" 0.07692604, 0.09559422, 0.05550165, 0.06103644, 0.09496999]),\n",
" 'mean_score_time': array([0.00379429, 0.00401125, 0.00402765, 0.00366578, 0.00386529,\n",
" 0.00386105, 0.00383778, 0.00352125, 0.00370207, 0.00341301]),\n",
" 'mean_test_score': array([0.89670442, 0.89788534, 0.88359159, 0.88514427, 0.89147801,\n",
" 0.88759493, 0.89812607, 0.88033192, 0.8763117 , 0.89059468]),\n",
" 'param_max_depth': masked_array(data=[50, 300, 300, 200, 50, 50, 200, 50, 500, 300],\n",
" mask=[False, False, False, False, False, False, False, False,\n",
" False, False],\n",
" fill_value='?',\n",
" dtype=object),\n",
" 'param_max_features': masked_array(data=[13, 12, 5, 7, 10, 7, 9, 5, 5, 10],\n",
" mask=[False, False, False, False, False, False, False, False,\n",
" False, False],\n",
" fill_value='?',\n",
" dtype=object),\n",
" 'param_min_samples_leaf': masked_array(data=[13, 13, 7, 15, 11, 11, 13, 15, 7, 15],\n",
" mask=[False, False, False, False, False, False, False, False,\n",
" False, False],\n",
" fill_value='?',\n",
" dtype=object),\n",
" 'params': [{'max_depth': 50, 'max_features': 13, 'min_samples_leaf': 13},\n",
" {'max_depth': 300, 'max_features': 12, 'min_samples_leaf': 13},\n",
" {'max_depth': 300, 'max_features': 5, 'min_samples_leaf': 7},\n",
" {'max_depth': 200, 'max_features': 7, 'min_samples_leaf': 15},\n",
" {'max_depth': 50, 'max_features': 10, 'min_samples_leaf': 11},\n",
" {'max_depth': 50, 'max_features': 7, 'min_samples_leaf': 11},\n",
" {'max_depth': 200, 'max_features': 9, 'min_samples_leaf': 13},\n",
" {'max_depth': 50, 'max_features': 5, 'min_samples_leaf': 15},\n",
" {'max_depth': 500, 'max_features': 5, 'min_samples_leaf': 7},\n",
" {'max_depth': 300, 'max_features': 10, 'min_samples_leaf': 15}],\n",
" 'rank_test_score': array([ 3, 2, 8, 7, 4, 6, 1, 9, 10, 5], dtype=int32),\n",
" 'split0_test_score': array([0.89510523, 0.89516934, 0.88507947, 0.8875092 , 0.89304022,\n",
" 0.89762341, 0.89918461, 0.89151184, 0.87032941, 0.89150939]),\n",
" 'split1_test_score': array([0.90085504, 0.90269482, 0.87891999, 0.88728172, 0.89718151,\n",
" 0.88122332, 0.89575944, 0.87170541, 0.88898183, 0.89037981]),\n",
" 'split2_test_score': array([0.89846606, 0.8994393 , 0.8950331 , 0.88193831, 0.90013485,\n",
" 0.89081818, 0.9078102 , 0.88422296, 0.86269524, 0.8935026 ]),\n",
" 'split3_test_score': array([0.89357525, 0.89339732, 0.88197819, 0.90604807, 0.88985121,\n",
" 0.89053355, 0.89802661, 0.88094403, 0.87280547, 0.8882176 ]),\n",
" 'split4_test_score': array([0.89552053, 0.89872594, 0.87694721, 0.86294407, 0.87718224,\n",
" 0.87777622, 0.88984949, 0.87327537, 0.88674654, 0.88936403]),\n",
" 'std_fit_time': array([0.00203654, 0.00649471, 0.00195477, 0.0066191 , 0.00352971,\n",
" 0.0035838 , 0.00577497, 0.00201453, 0.00344726, 0.01046358]),\n",
" 'std_score_time': array([3.81321211e-04, 4.34578346e-04, 3.49832562e-04, 3.78314608e-04,\n",
" 2.67807277e-04, 1.44746792e-04, 6.69174995e-05, 1.50606992e-04,\n",
" 1.55896992e-04, 4.58791379e-04]),\n",
" 'std_test_score': array([0.00261081, 0.00327937, 0.00635132, 0.01378592, 0.00796149,\n",
" 0.00716379, 0.00581404, 0.00727622, 0.01002903, 0.0018168 ])}"
]
},
"metadata": {
"tags": []
},
"execution_count": 61
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "PIPSyn-YuWBz"
},
"source": [
"rscv.best_estimator_.fit(train[train_columns],train[\"Radiation\"])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "LqfNoV-8xx2z"
},
"source": [
"rscv.best_estimator_.feature_importances_"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "7D1scu4Jm_98"
},
"source": [
"import math\n",
"train[\"log_Temp\"] = [math.log(tem) for tem in train[\"Temperature\"]]\n",
"test[\"log_Temp\"] = [math.log(tem) for tem in test[\"Temperature\"]]\n",
"\n",
"train[\"sqr_Temp\"] = [tem*tem for tem in train[\"Temperature\"]]\n",
"test[\"sqr_Temp\"] = [tem*tem for tem in test[\"Temperature\"]]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Uc8aeHWSx1aK"
},
"source": [
"new_train_columns = [\"Temperature\",\"Hour\",\"UNIXTime\",\"Temp_range\",\"WeekDay\",]\n",
"\n",
"\n",
"tr_col = [\"UNIXTime\",\"Temperature\",\"Pressure\",\"Humidity\",\"WindDirection(Degrees)\",\"Speed\",\"Month\",\"Day\",\"WeekDay\",\"WeekNumber\",\"Hour\",\"Mins\",\"type_of_wind\",\"Daylength\",\"Temp_range\"]\n",
"train_xx = train[new_train_columns]\n",
"train_yy = train[\"Radiation\"]\n",
"test_xx = test[new_train_columns]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "GgghqJ6xx1Tp",
"outputId": "b24de61a-d3e8-4f0f-cdf1-cf01e4ef7c08"
},
"source": [
"dt = DecisionTreeRegressor(max_depth=300)\n",
"dt.fit(train_xx,train_yy)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=300,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, presort='deprecated',\n",
" random_state=None, splitter='best')"
]
},
"metadata": {
"tags": []
},
"execution_count": 66
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "iv0MngA4cOlV",
"outputId": "dfee47d2-75d8-457c-dbf4-b7716375855a"
},
"source": [
"score_cal(dt)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"21.285053064006817"
]
},
"metadata": {
"tags": []
},
"execution_count": 67
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1Q4SXwoLb2Pv",
"outputId": "517467b7-d387-47d4-da99-fe549d3ad8c8"
},
"source": [
"dt.feature_importances_"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([0.00347503, 0.28610618, 0.09446134, 0.0260534 , 0.01533394,\n",
" 0.01170669, 0.56286342])"
]
},
"metadata": {
"tags": []
},
"execution_count": 95
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "aRJhdUmNzM-V"
},
"source": [
"rt = RandomForestRegressor(n_estimators=500)\n",
"rt.fit(train_xx,train_yy)\n",
"score_cal(rt)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "QDytHMxMhZN-"
},
"source": [
"cb = CatBoostRegressor(iterations=2000,learning_rate=0.7,depth=10)\n",
"cb.fit(train_xx,train_yy)\n",
"score_cal(cb)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1qcKjXYK0Fsb",
"outputId": "f801c55b-e2a3-4e76-c27a-ad176422e363"
},
"source": [
"neigh = KNeighborsRegressor(n_neighbors=1,p=2,n_jobs=-1)\n",
"neigh.fit(train_xx,train_yy)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
" metric_params=None, n_jobs=-1, n_neighbors=1, p=2,\n",
" weights='uniform')"
]
},
"metadata": {
"tags": []
},
"execution_count": 68
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QFo4PdVfoYIx",
"outputId": "6485ca58-f88d-4513-c16a-60fd51ce452f"
},
"source": [
"score_cal(neigh)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"21.845847487686413"
]
},
"metadata": {
"tags": []
},
"execution_count": 69
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "nY-t-bfQzfyb",
"outputId": "bfed0463-9dbd-492c-e908-5d28ef31c9ab"
},
"source": [
"et_r = ExtraTreesRegressor(n_jobs=-1,n_estimators=300,max_depth=100,max_leaf_nodes=20000000,)\n",
"et_r.fit(train_xx,train_yy)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"ExtraTreesRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mse',\n",
" max_depth=100, max_features='auto', max_leaf_nodes=20000000,\n",
" max_samples=None, min_impurity_decrease=0.0,\n",
" min_impurity_split=None, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" n_estimators=300, n_jobs=-1, oob_score=False,\n",
" random_state=None, verbose=0, warm_start=False)"
]
},
"metadata": {
"tags": []
},
"execution_count": 70
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "DLLIuQxfob-L",
"outputId": "d4dbd6da-5a92-437f-f0be-c7bb02485fcb"
},
"source": [
"score_cal(et_r)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"21.74464412382463"
]
},
"metadata": {
"tags": []
},
"execution_count": 71
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "6s1A1NjDr_Tr"
},
"source": [
"lgm = LGBMRegressor(num_leaves=980,learning_rate=0.7,n_estimators=800)\n",
"lgm.fit(train_xx,train_yy)\n",
"score_cal(lgm)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "7fOsOAvFdxhk",
"outputId": "9d46c85d-bde1-447b-8046-3fcbfbf47289"
},
"source": [
"xg_b = xgb.XGBRegressor(base_score=1,booster='gbtree',max_depth=100,n_estimators=400,learning_rate=0.7,objective=\"reg:tweedie\")\n",
"xg_b.fit(train_xx,train_yy)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"XGBRegressor(base_score=1, booster='gbtree', colsample_bylevel=1,\n",
" colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
" importance_type='gain', learning_rate=0.7, max_delta_step=0,\n",
" max_depth=100, min_child_weight=1, missing=None, n_estimators=400,\n",
" n_jobs=1, nthread=None, objective='reg:tweedie', random_state=0,\n",
" reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
" silent=None, subsample=1, verbosity=1)"
]
},
"metadata": {
"tags": []
},
"execution_count": 87
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xlG0c3DkpZ95",
"outputId": "f7bb88d2-d937-4d0f-9008-d779dba7a163"
},
"source": [
"score_cal(xg_b)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"21.58403178161132"
]
},
"metadata": {
"tags": []
},
"execution_count": 88
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Ci-SScJieAe4",
"outputId": "7cfe440c-457a-4679-dc94-6f7867740d64"
},
"source": [
"ad_dt = AdaBoostRegressor(base_estimator=dt,learning_rate=0.7,n_estimators=100)\n",
"ad_dt.fit(train_xx,train_yy)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"AdaBoostRegressor(base_estimator=DecisionTreeRegressor(ccp_alpha=0.0,\n",
" criterion='mse',\n",
" max_depth=300,\n",
" max_features=None,\n",
" max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0,\n",
" min_impurity_split=None,\n",
" min_samples_leaf=1,\n",
" min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0,\n",
" presort='deprecated',\n",
" random_state=None,\n",
" splitter='best'),\n",
" learning_rate=0.7, loss='linear', n_estimators=100,\n",
" random_state=None)"
]
},
"metadata": {
"tags": []
},
"execution_count": 89
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JqNvJArZp85S",
"outputId": "a2d6806f-007d-4114-e727-220fff6f2428"
},
"source": [
"score_cal(ad_dt)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"21.429437103918314"
]
},
"metadata": {
"tags": []
},
"execution_count": 90
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5bWrzEGRepzQ",
"outputId": "d6fa2238-906e-4bc5-c5a5-29fa3d84fcfc"
},
"source": [
"gbrt = GradientBoostingRegressor(alpha=0.3,max_depth=400,n_estimators=500, learning_rate=0.7,max_leaf_nodes=2000,subsample=1)\n",
"gbrt.fit(train_xx,train_yy)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"GradientBoostingRegressor(alpha=0.3, ccp_alpha=0.0, criterion='friedman_mse',\n",
" init=None, learning_rate=0.7, loss='ls',\n",
" max_depth=400, max_features=None, max_leaf_nodes=2000,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=500,\n",
" n_iter_no_change=None, presort='deprecated',\n",
" random_state=None, subsample=1, tol=0.0001,\n",
" validation_fraction=0.1, verbose=0, warm_start=False)"
]
},
"metadata": {
"tags": []
},
"execution_count": 99
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "X07yQlDKfLd0",
"outputId": "5dde3878-333f-466d-a5c2-6bbe14865247"
},
"source": [
"score_cal(gbrt)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"21.699746671829224"
]
},
"metadata": {
"tags": []
},
"execution_count": 100
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "3xKkWpcteqHW"
},
"source": [
"ad_et = AdaBoostRegressor(base_estimator=et_r,learning_rate=0.7,n_estimators=100)\n",
"ad_et.fit(train_xx,train_yy)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "3QzZM_MyE1ln"
},
"source": [
"score_cal(ad_et)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "1lN7jZ7bs7cL"
},
"source": [
"ad_et = et_r"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "jBqSBgeTrYsI"
},
"source": [
"pred1 = dt.predict(test_xx)\n",
"pred2 = neigh.predict(test_xx)\n",
"pred3 = xg_b.predict(test_xx)\n",
"pred4 = gbrt.predict(test_xx)\n",
"#pred5 = ad_et.predict(test_xx)\n",
"pred6 = et_r.predict(test_xx)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "mZ3XpyTIrYpO"
},
"source": [
"n = pred1+pred2+pred3+pred4+pred6\n",
"n =n/5"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ZzG5IQ0frYlz"
},
"source": [
"result = pd.DataFrame(list(zip(list(test_xx.index),n)),columns=[\"idx\",\"Radiation\"])\n",
"result.to_csv(\"results.csv\",index=False)\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "f270x9CGrYYT",
"outputId": "6af2cfd2-3cdd-463a-9392-0cde68ff1492"
},
"source": [
"result\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>idx</th>\n",
" <th>Radiation</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>208.134321</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>76.822794</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>75.703447</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>65.648480</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>58.439636</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6532</th>\n",
" <td>6532</td>\n",
" <td>1.219568</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6533</th>\n",
" <td>6533</td>\n",
" <td>1.170674</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6534</th>\n",
" <td>6534</td>\n",
" <td>1.200008</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6535</th>\n",
" <td>6535</td>\n",
" <td>1.229282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6536</th>\n",
" <td>6536</td>\n",
" <td>1.200311</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>6537 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" idx Radiation\n",
"0 0 208.134321\n",
"1 1 76.822794\n",
"2 2 75.703447\n",
"3 3 65.648480\n",
"4 4 58.439636\n",
"... ... ...\n",
"6532 6532 1.219568\n",
"6533 6533 1.170674\n",
"6534 6534 1.200008\n",
"6535 6535 1.229282\n",
"6536 6536 1.200311\n",
"\n",
"[6537 rows x 2 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 110
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "6cB7gNOls_O-"
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
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