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krishnadey30/arima.ipynb

Created May 1, 2019 19:52
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Arima.ipynb
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arima.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Arima.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/krishnadey30/65b0d779b6b199072dafa5293e23ac4b/arima.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"metadata": {
"id": "FJOXeJeR_TxB",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"import pandas as pd\n",
"import pandas as pd\n",
"import numpy as np \n",
"% matplotlib inline\n",
"import matplotlib.pyplot as plt"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "-hOqepFi_0DB",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"data = pd.read_csv('newdelhi.csv', sep=',')\n",
"data.head()\n",
"data['Date_Time'] = pd.to_datetime(data['Date_Time'],format=\"%d-%m-%Y\")\n"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "78ER6zErANks",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"Date_Time = data.groupby('Date_Time').size()"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "_VW1n38hAQr2",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "f6c98597-ba0e-410f-da1b-7d85d0095bf5"
},
"cell_type": "code",
"source": [
"type(Date_Time)"
],
"execution_count": 153,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"pandas.core.series.Series"
]
},
"metadata": {
"tags": []
},
"execution_count": 153
}
]
},
{
"metadata": {
"id": "KQzLXlSpAmVz",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 360
},
"outputId": "4ce0b173-0243-468d-e1f9-610a3b04859b"
},
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"fig = plt.figure(figsize=(10, 5))\n",
"\n",
"Date_Time.plot(title='Number of Datapoints',\n",
" fontsize=15)\n",
"\n",
"plt.xlabel('Date',fontsize=13)\n",
"plt.ylabel('Number of DataPoints collected',fontsize=13)\n",
"plt.savefig('Points.png')\n",
"# plt.ticklabel_format(useOffset=False)"
],
"execution_count": 154,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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AqroRODzpjlTVi3o+CKM+HT+UGsWZwLV1jtvl+E7ec5LaYxiGYXQ21aAoAmB1X2f1orOQ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\nAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "9ekNqkb9A4T1",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 170
},
"outputId": "62a27eae-5265-42b4-b2a8-6a1b22565bc2"
},
"cell_type": "code",
"source": [
"data.info()"
],
"execution_count": 155,
"outputs": [
{
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 9841 entries, 0 to 9840\n",
"Data columns (total 4 columns):\n",
"Date_Time 9841 non-null datetime64[ns]\n",
"Longitude 9841 non-null float64\n",
"Latitude 9841 non-null float64\n",
"Value 9841 non-null float64\n",
"dtypes: datetime64[ns](1), float64(3)\n",
"memory usage: 307.6 KB\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "T6Zt3hHbGPpX",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 297
},
"outputId": "e1029239-f85f-4aca-89d0-52d6a91115a7"
},
"cell_type": "code",
"source": [
"data.describe()"
],
"execution_count": 156,
"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>Longitude</th>\n",
" <th>Latitude</th>\n",
" <th>Value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>9841.000000</td>\n",
" <td>9841.000000</td>\n",
" <td>9.841000e+03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>77.082876</td>\n",
" <td>28.646519</td>\n",
" <td>1.084026e+35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.144775</td>\n",
" <td>0.141761</td>\n",
" <td>1.033988e+36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>76.831650</td>\n",
" <td>28.400310</td>\n",
" <td>-1.545030e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>76.957436</td>\n",
" <td>28.523228</td>\n",
" <td>5.580000e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>77.082794</td>\n",
" <td>28.645940</td>\n",
" <td>8.670000e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>77.207825</td>\n",
" <td>28.769196</td>\n",
" <td>1.468670e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>77.332500</td>\n",
" <td>28.891346</td>\n",
" <td>9.970000e+36</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Longitude Latitude Value\n",
"count 9841.000000 9841.000000 9.841000e+03\n",
"mean 77.082876 28.646519 1.084026e+35\n",
"std 0.144775 0.141761 1.033988e+36\n",
"min 76.831650 28.400310 -1.545030e-04\n",
"25% 76.957436 28.523228 5.580000e-05\n",
"50% 77.082794 28.645940 8.670000e-05\n",
"75% 77.207825 28.769196 1.468670e-04\n",
"max 77.332500 28.891346 9.970000e+36"
]
},
"metadata": {
"tags": []
},
"execution_count": 156
}
]
},
{
"metadata": {
"id": "Q1FG1NCDGgIs",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "b56ebb37-1671-41e8-b31e-43624aaf81bc"
},
"cell_type": "code",
"source": [
"data.index"
],
"execution_count": 157,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"RangeIndex(start=0, stop=9841, step=1)"
]
},
"metadata": {
"tags": []
},
"execution_count": 157
}
]
},
{
"metadata": {
"id": "mMmyFoaGGnL4",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"data.set_index(\"Date_Time\", inplace=True)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "YmwE2NGWGyIA",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"data.sort_index(inplace=True)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "em1NTlBLHQmr",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"# test = data.index.to_pydatetime()\n",
"# DateTime = []\n",
"# for x in test:\n",
"# DateTime.append(str(x.month)+'-'+str(x.year))\n",
"# data['newDate_Time']=pd.to_datetime(DateTime, format='%m-%Y')\n",
"# data.set_index(\"newDate_Time\", inplace=True)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "-A6Fel_BHdZw",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 2000
},
"outputId": "2de5a000-9a8d-4155-dc06-e15f9a6bcbfb"
},
"cell_type": "code",
"source": [
"data"
],
"execution_count": 161,
"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",
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" }\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>Longitude</th>\n",
" <th>Latitude</th>\n",
" <th>Value</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date_Time</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>77.305020</td>\n",
" <td>28.878668</td>\n",
" <td>0.000046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>76.921520</td>\n",
" <td>28.612926</td>\n",
" <td>0.000043</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>76.838630</td>\n",
" <td>28.609959</td>\n",
" <td>0.000047</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>77.275345</td>\n",
" <td>28.561213</td>\n",
" <td>0.000097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>77.188255</td>\n",
" <td>28.558483</td>\n",
" <td>0.000068</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>77.102264</td>\n",
" <td>28.555690</td>\n",
" <td>0.000066</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>77.017360</td>\n",
" <td>28.552835</td>\n",
" <td>0.000053</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>76.933495</td>\n",
" <td>28.549917</td>\n",
" <td>0.000038</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>76.850655</td>\n",
" <td>28.546942</td>\n",
" <td>0.000047</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>77.287056</td>\n",
" <td>28.498203</td>\n",
" <td>0.000103</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>77.005430</td>\n",
" <td>28.615833</td>\n",
" <td>0.000050</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>77.200030</td>\n",
" <td>28.495464</td>\n",
" <td>0.000067</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>77.029236</td>\n",
" <td>28.489798</td>\n",
" <td>0.000067</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>76.945420</td>\n",
" <td>28.486874</td>\n",
" <td>0.000046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>76.862630</td>\n",
" <td>28.483889</td>\n",
" <td>0.000046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>77.298870</td>\n",
" <td>28.435251</td>\n",
" <td>0.000084</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>77.211880</td>\n",
" <td>28.432503</td>\n",
" <td>0.000069</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>77.126000</td>\n",
" <td>28.429691</td>\n",
" <td>0.000062</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>77.041190</td>\n",
" <td>28.426819</td>\n",
" <td>0.000059</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>76.957436</td>\n",
" <td>28.423885</td>\n",
" <td>0.000047</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>76.874695</td>\n",
" <td>28.420893</td>\n",
" <td>0.000045</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>77.114090</td>\n",
" <td>28.492662</td>\n",
" <td>0.000071</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>77.090390</td>\n",
" <td>28.618681</td>\n",
" <td>0.000070</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>77.216530</td>\n",
" <td>28.876050</td>\n",
" <td>0.000052</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>77.263580</td>\n",
" <td>28.624186</td>\n",
" <td>0.000087</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>77.176440</td>\n",
" <td>28.621466</td>\n",
" <td>0.000097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>77.129166</td>\n",
" <td>28.873370</td>\n",
" <td>0.000047</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>76.957750</td>\n",
" <td>28.867810</td>\n",
" <td>0.000036</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>76.873630</td>\n",
" <td>28.864935</td>\n",
" <td>0.000029</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-07-07</th>\n",
" <td>77.316730</td>\n",
" <td>28.815700</td>\n",
" <td>0.000046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>76.958620</td>\n",
" <td>28.842907</td>\n",
" <td>0.000024</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>77.004990</td>\n",
" <td>28.848171</td>\n",
" <td>0.000033</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>77.051605</td>\n",
" <td>28.853415</td>\n",
" <td>0.000021</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>77.098480</td>\n",
" <td>28.858637</td>\n",
" <td>0.000047</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>77.145615</td>\n",
" <td>28.863836</td>\n",
" <td>0.000036</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>77.193010</td>\n",
" <td>28.869015</td>\n",
" <td>0.000047</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>77.240670</td>\n",
" <td>28.874172</td>\n",
" <td>0.000051</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>76.927210</td>\n",
" <td>28.774378</td>\n",
" <td>0.000027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>76.956550</td>\n",
" <td>28.647840</td>\n",
" <td>0.000051</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>76.881350</td>\n",
" <td>28.769070</td>\n",
" <td>0.000022</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>77.317500</td>\n",
" <td>28.752796</td>\n",
" <td>0.000058</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>77.048874</td>\n",
" <td>28.658396</td>\n",
" <td>0.000052</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>77.095406</td>\n",
" <td>28.663641</td>\n",
" <td>0.000084</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>77.142200</td>\n",
" <td>28.668865</td>\n",
" <td>0.000112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>77.189240</td>\n",
" <td>28.674068</td>\n",
" <td>0.000130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>77.236540</td>\n",
" <td>28.679249</td>\n",
" <td>0.000093</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>77.284120</td>\n",
" <td>28.684408</td>\n",
" <td>0.000081</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>77.331960</td>\n",
" <td>28.689545</td>\n",
" <td>0.000089</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>76.850440</td>\n",
" <td>28.700460</td>\n",
" <td>0.000031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>76.896034</td>\n",
" <td>28.705790</td>\n",
" <td>0.000034</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>76.941864</td>\n",
" <td>28.711098</td>\n",
" <td>0.000033</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>76.987930</td>\n",
" <td>28.716387</td>\n",
" <td>0.000039</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>77.034240</td>\n",
" <td>28.721653</td>\n",
" <td>0.000039</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>77.080800</td>\n",
" <td>28.726896</td>\n",
" <td>0.000053</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>77.127620</td>\n",
" <td>28.732120</td>\n",
" <td>0.000068</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>77.174690</td>\n",
" <td>28.737322</td>\n",
" <td>0.000072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>77.222020</td>\n",
" <td>28.742502</td>\n",
" <td>0.000058</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>77.269630</td>\n",
" <td>28.747660</td>\n",
" <td>0.000043</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>76.835730</td>\n",
" <td>28.763740</td>\n",
" <td>0.000033</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-03-21</th>\n",
" <td>77.107320</td>\n",
" <td>28.405360</td>\n",
" <td>0.000098</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>9841 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" Longitude Latitude Value\n",
"Date_Time \n",
"2018-07-07 77.305020 28.878668 0.000046\n",
"2018-07-07 76.921520 28.612926 0.000043\n",
"2018-07-07 76.838630 28.609959 0.000047\n",
"2018-07-07 77.275345 28.561213 0.000097\n",
"2018-07-07 77.188255 28.558483 0.000068\n",
"2018-07-07 77.102264 28.555690 0.000066\n",
"2018-07-07 77.017360 28.552835 0.000053\n",
"2018-07-07 76.933495 28.549917 0.000038\n",
"2018-07-07 76.850655 28.546942 0.000047\n",
"2018-07-07 77.287056 28.498203 0.000103\n",
"2018-07-07 77.005430 28.615833 0.000050\n",
"2018-07-07 77.200030 28.495464 0.000067\n",
"2018-07-07 77.029236 28.489798 0.000067\n",
"2018-07-07 76.945420 28.486874 0.000046\n",
"2018-07-07 76.862630 28.483889 0.000046\n",
"2018-07-07 77.298870 28.435251 0.000084\n",
"2018-07-07 77.211880 28.432503 0.000069\n",
"2018-07-07 77.126000 28.429691 0.000062\n",
"2018-07-07 77.041190 28.426819 0.000059\n",
"2018-07-07 76.957436 28.423885 0.000047\n",
"2018-07-07 76.874695 28.420893 0.000045\n",
"2018-07-07 77.114090 28.492662 0.000071\n",
"2018-07-07 77.090390 28.618681 0.000070\n",
"2018-07-07 77.216530 28.876050 0.000052\n",
"2018-07-07 77.263580 28.624186 0.000087\n",
"2018-07-07 77.176440 28.621466 0.000097\n",
"2018-07-07 77.129166 28.873370 0.000047\n",
"2018-07-07 76.957750 28.867810 0.000036\n",
"2018-07-07 76.873630 28.864935 0.000029\n",
"2018-07-07 77.316730 28.815700 0.000046\n",
"... ... ... ...\n",
"2019-03-21 76.958620 28.842907 0.000024\n",
"2019-03-21 77.004990 28.848171 0.000033\n",
"2019-03-21 77.051605 28.853415 0.000021\n",
"2019-03-21 77.098480 28.858637 0.000047\n",
"2019-03-21 77.145615 28.863836 0.000036\n",
"2019-03-21 77.193010 28.869015 0.000047\n",
"2019-03-21 77.240670 28.874172 0.000051\n",
"2019-03-21 76.927210 28.774378 0.000027\n",
"2019-03-21 76.956550 28.647840 0.000051\n",
"2019-03-21 76.881350 28.769070 0.000022\n",
"2019-03-21 77.317500 28.752796 0.000058\n",
"2019-03-21 77.048874 28.658396 0.000052\n",
"2019-03-21 77.095406 28.663641 0.000084\n",
"2019-03-21 77.142200 28.668865 0.000112\n",
"2019-03-21 77.189240 28.674068 0.000130\n",
"2019-03-21 77.236540 28.679249 0.000093\n",
"2019-03-21 77.284120 28.684408 0.000081\n",
"2019-03-21 77.331960 28.689545 0.000089\n",
"2019-03-21 76.850440 28.700460 0.000031\n",
"2019-03-21 76.896034 28.705790 0.000034\n",
"2019-03-21 76.941864 28.711098 0.000033\n",
"2019-03-21 76.987930 28.716387 0.000039\n",
"2019-03-21 77.034240 28.721653 0.000039\n",
"2019-03-21 77.080800 28.726896 0.000053\n",
"2019-03-21 77.127620 28.732120 0.000068\n",
"2019-03-21 77.174690 28.737322 0.000072\n",
"2019-03-21 77.222020 28.742502 0.000058\n",
"2019-03-21 77.269630 28.747660 0.000043\n",
"2019-03-21 76.835730 28.763740 0.000033\n",
"2019-03-21 77.107320 28.405360 0.000098\n",
"\n",
"[9841 rows x 3 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 161
}
]
},
{
"metadata": {
"id": "zd7reirUHiKv",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
},
"outputId": "0270a53c-1e01-493d-ad2f-35420a187996"
},
"cell_type": "code",
"source": [
"data.dtypes"
],
"execution_count": 162,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Longitude float64\n",
"Latitude float64\n",
"Value float64\n",
"dtype: object"
]
},
"metadata": {
"tags": []
},
"execution_count": 162
}
]
},
{
"metadata": {
"id": "RWZV09MUHm6z",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 297
},
"outputId": "387bb0da-e0dd-4046-ecdf-fc00843e5ed7"
},
"cell_type": "code",
"source": [
"data.describe()"
],
"execution_count": 163,
"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>Longitude</th>\n",
" <th>Latitude</th>\n",
" <th>Value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>9841.000000</td>\n",
" <td>9841.000000</td>\n",
" <td>9.841000e+03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>77.082876</td>\n",
" <td>28.646519</td>\n",
" <td>1.084026e+35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.144775</td>\n",
" <td>0.141761</td>\n",
" <td>1.033988e+36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>76.831650</td>\n",
" <td>28.400310</td>\n",
" <td>-1.545030e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>76.957436</td>\n",
" <td>28.523228</td>\n",
" <td>5.580000e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>77.082794</td>\n",
" <td>28.645940</td>\n",
" <td>8.670000e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>77.207825</td>\n",
" <td>28.769196</td>\n",
" <td>1.468670e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>77.332500</td>\n",
" <td>28.891346</td>\n",
" <td>9.970000e+36</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Longitude Latitude Value\n",
"count 9841.000000 9841.000000 9.841000e+03\n",
"mean 77.082876 28.646519 1.084026e+35\n",
"std 0.144775 0.141761 1.033988e+36\n",
"min 76.831650 28.400310 -1.545030e-04\n",
"25% 76.957436 28.523228 5.580000e-05\n",
"50% 77.082794 28.645940 8.670000e-05\n",
"75% 77.207825 28.769196 1.468670e-04\n",
"max 77.332500 28.891346 9.970000e+36"
]
},
"metadata": {
"tags": []
},
"execution_count": 163
}
]
},
{
"metadata": {
"id": "vtYR5N57HtMR",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"No2_data = data['Value']"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "iGArD8zaIGff",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 461
},
"outputId": "0a29d0b4-c73a-4116-b989-d41bf36224ea"
},
"cell_type": "code",
"source": [
"fig, ax = plt.subplots(figsize=(10,7))\n",
"\n",
"ax.plot(No2_data)\n",
"ax.set_title ('No2 Data')"
],
"execution_count": 165,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Text(0.5, 1.0, 'No2 Data')"
]
},
"metadata": {
"tags": []
},
"execution_count": 165
},
{
"output_type": "display_data",
"data": {
"image/png": 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EsmRKXAHJChkAALkQyJIpskcYeQwAgFQIZMmUWLxihQwAgFwIZMlwc3EAANqHQJZMkZ36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\nD9zvqHP297ncAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 720x504 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "2KhIXlxuIP-y",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
""
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "rRK4M8k2JBSB",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 153
},
"outputId": "86136018-2b6c-47f2-84f5-60fcbf4324b7"
},
"cell_type": "code",
"source": [
"data.info()"
],
"execution_count": 166,
"outputs": [
{
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"DatetimeIndex: 9841 entries, 2018-07-07 to 2019-03-21\n",
"Data columns (total 3 columns):\n",
"Longitude 9841 non-null float64\n",
"Latitude 9841 non-null float64\n",
"Value 9841 non-null float64\n",
"dtypes: float64(3)\n",
"memory usage: 307.5 KB\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "bLU4Gwbphvc_",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 461
},
"outputId": "6dc3493f-b025-40ad-80a4-5a57037f2aad"
},
"cell_type": "code",
"source": [
"fig, ax = plt.subplots(figsize=(10,7))\n",
"\n",
"ax.plot(No2_data['2018-7-7':'2018-7-15'])\n",
"ax.set_title ('No2 Data')"
],
"execution_count": 174,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Text(0.5, 1.0, 'No2 Data')"
]
},
"metadata": {
"tags": []
},
"execution_count": 174
},
{
"output_type": "display_data",
"data": {
"image/png": 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biIhIObD1QBajpi+nWWhl3hzQCV9NlrqWypuIiEgZl3Ykn2Gxy/D1MUyO6kpwoL/TkeQM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\nJkmSlJD/A8Iad/VRb9BgAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 720x504 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "g3VZ8X9okIlC",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1088
},
"outputId": "78eb5577-e038-4461-ad1a-1ded0990ffb0"
},
"cell_type": "code",
"source": [
"No2_data['2018-7-10':'2018-7-12']"
],
"execution_count": 177,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Date_Time\n",
"2018-07-10 0.000105\n",
"2018-07-10 0.000168\n",
"2018-07-10 0.000142\n",
"2018-07-10 0.000054\n",
"2018-07-10 0.000057\n",
"2018-07-10 0.000080\n",
"2018-07-10 0.000209\n",
"2018-07-10 0.000089\n",
"2018-07-10 0.000176\n",
"2018-07-10 0.000151\n",
"2018-07-10 0.000149\n",
"2018-07-10 0.000136\n",
"2018-07-10 0.000063\n",
"2018-07-10 0.000097\n",
"2018-07-10 0.000166\n",
"2018-07-10 0.000163\n",
"2018-07-10 0.000091\n",
"2018-07-10 0.000051\n",
"2018-07-10 0.000046\n",
"2018-07-10 0.000154\n",
"2018-07-10 0.000118\n",
"2018-07-10 0.000068\n",
"2018-07-10 0.000119\n",
"2018-07-10 0.000148\n",
"2018-07-10 0.000108\n",
"2018-07-10 0.000086\n",
"2018-07-10 0.000081\n",
"2018-07-10 0.000080\n",
"2018-07-10 0.000082\n",
"2018-07-10 0.000081\n",
" ... \n",
"2018-07-12 0.000047\n",
"2018-07-12 0.000059\n",
"2018-07-12 0.000048\n",
"2018-07-12 0.000044\n",
"2018-07-12 0.000042\n",
"2018-07-12 0.000055\n",
"2018-07-12 0.000064\n",
"2018-07-12 0.000048\n",
"2018-07-12 0.000058\n",
"2018-07-12 0.000045\n",
"2018-07-12 0.000063\n",
"2018-07-12 0.000059\n",
"2018-07-12 0.000041\n",
"2018-07-12 0.000078\n",
"2018-07-12 0.000071\n",
"2018-07-12 0.000061\n",
"2018-07-12 0.000081\n",
"2018-07-12 0.000062\n",
"2018-07-12 0.000070\n",
"2018-07-12 0.000090\n",
"2018-07-12 0.000105\n",
"2018-07-12 0.000062\n",
"2018-07-12 0.000080\n",
"2018-07-12 0.000125\n",
"2018-07-12 0.000086\n",
"2018-07-12 0.000089\n",
"2018-07-12 0.000061\n",
"2018-07-12 0.000077\n",
"2018-07-12 0.000062\n",
"2018-07-12 0.000073\n",
"Name: Value, Length: 138, dtype: float64"
]
},
"metadata": {
"tags": []
},
"execution_count": 177
}
]
},
{
"metadata": {
"id": "tRQSLfgckcgr",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 307
},
"outputId": "d457f1f3-ac62-4da4-a23a-e71f902c45ba"
},
"cell_type": "code",
"source": [
"No2_data_lagged = No2_data.shift()\n",
"plt.scatter(No2_data, No2_data_lagged)\n",
"plt.xlabel('No2')\n",
"plt.ylabel('No2_lagged')"
],
"execution_count": 183,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Text(0, 0.5, 'No2_lagged')"
]
},
"metadata": {
"tags": []
},
"execution_count": 183
},
{
"output_type": "display_data",
"data": {
"image/png": 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gSWoxGCRJLQaDJKnFYJAktRgMkqQWg0GS1GIwSJJaDAZJUovBIElqWd31ByTZBPwPYBXw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\nRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "46epyJ87lIv-",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 111
},
"outputId": "20ae7a78-481f-4be1-cddb-10956e682b41"
},
"cell_type": "code",
"source": [
"pd.DataFrame({'real': No2_data, 'lagged': No2_data_lagged}).corr()"
],
"execution_count": 180,
"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>real</th>\n",
" <th>lagged</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>real</th>\n",
" <td>1.000000</td>\n",
" <td>0.508676</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lagged</th>\n",
" <td>0.508676</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" real lagged\n",
"real 1.000000 0.508676\n",
"lagged 0.508676 1.000000"
]
},
"metadata": {
"tags": []
},
"execution_count": 180
}
]
},
{
"metadata": {
"id": "OLUC1_s-5BdS",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1088
},
"outputId": "516c6b60-799d-4aa8-f026-a2333219ce40"
},
"cell_type": "code",
"source": [
"No2_data_lagged"
],
"execution_count": 181,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Date_Time\n",
"2018-07-07 NaN\n",
"2018-07-07 0.000046\n",
"2018-07-07 0.000043\n",
"2018-07-07 0.000047\n",
"2018-07-07 0.000097\n",
"2018-07-07 0.000068\n",
"2018-07-07 0.000066\n",
"2018-07-07 0.000053\n",
"2018-07-07 0.000038\n",
"2018-07-07 0.000047\n",
"2018-07-07 0.000103\n",
"2018-07-07 0.000050\n",
"2018-07-07 0.000067\n",
"2018-07-07 0.000067\n",
"2018-07-07 0.000046\n",
"2018-07-07 0.000046\n",
"2018-07-07 0.000084\n",
"2018-07-07 0.000069\n",
"2018-07-07 0.000062\n",
"2018-07-07 0.000059\n",
"2018-07-07 0.000047\n",
"2018-07-07 0.000045\n",
"2018-07-07 0.000071\n",
"2018-07-07 0.000070\n",
"2018-07-07 0.000052\n",
"2018-07-07 0.000087\n",
"2018-07-07 0.000097\n",
"2018-07-07 0.000047\n",
"2018-07-07 0.000036\n",
"2018-07-07 0.000029\n",
" ... \n",
"2019-03-21 0.000025\n",
"2019-03-21 0.000024\n",
"2019-03-21 0.000033\n",
"2019-03-21 0.000021\n",
"2019-03-21 0.000047\n",
"2019-03-21 0.000036\n",
"2019-03-21 0.000047\n",
"2019-03-21 0.000051\n",
"2019-03-21 0.000027\n",
"2019-03-21 0.000051\n",
"2019-03-21 0.000022\n",
"2019-03-21 0.000058\n",
"2019-03-21 0.000052\n",
"2019-03-21 0.000084\n",
"2019-03-21 0.000112\n",
"2019-03-21 0.000130\n",
"2019-03-21 0.000093\n",
"2019-03-21 0.000081\n",
"2019-03-21 0.000089\n",
"2019-03-21 0.000031\n",
"2019-03-21 0.000034\n",
"2019-03-21 0.000033\n",
"2019-03-21 0.000039\n",
"2019-03-21 0.000039\n",
"2019-03-21 0.000053\n",
"2019-03-21 0.000068\n",
"2019-03-21 0.000072\n",
"2019-03-21 0.000058\n",
"2019-03-21 0.000043\n",
"2019-03-21 0.000033\n",
"Name: Value, Length: 9841, dtype: float64"
]
},
"metadata": {
"tags": []
},
"execution_count": 181
}
]
},
{
"metadata": {
"id": "cjMc7BHQ5OQH",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"from statsmodels.tsa.stattools import adfuller\n",
"def test_stationarity(timeseries):\n",
" #Determing rolloing statistics\n",
" rolmean = timeseries.rolling(window=24).mean()\n",
" rolstd = timeseries.rolling(window=24).std()\n",
" \n",
" #Plot rolling statistics:\n",
" plt.figure(figsize=(20,10))\n",
" orig = plt.plot (timeseries, color='blue', label='Original')\n",
" mean = plt.plot (rolmean, color='red', label = 'Rolling Mean')\n",
" std = plt.plot (rolstd, color='black', label='Rolling Std')\n",
" plt.legend(loc='best')\n",
" plt.title('rolling Mean & Standard Deviation')\n",
"# plt.show()\n",
" plt.savefig('RollingMeanAndStandDev.png')\n",
" #Perform Dickey-Fuller test:\n",
" print('Results of Dickey-Fuller Test:')\n",
" dftest = adfuller(timeseries)\n",
" dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])\n",
" for key,value in dftest[4].items():\n",
" dfoutput['Critical Value (%s)'%key] = value\n",
" print (dfoutput)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "WPr4tnPG5fug",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 780
},
"outputId": "ef989c12-ceba-4618-c525-0b03c06a9fac"
},
"cell_type": "code",
"source": [
"test_stationarity(No2_data['2019-1-1':'2019-1-8'])"
],
"execution_count": 236,
"outputs": [
{
"output_type": "stream",
"text": [
"Results of Dickey-Fuller Test:\n",
"Test Statistic -3.619548\n",
"p-value 0.005398\n",
"#Lags Used 7.000000\n",
"Number of Observations Used 482.000000\n",
"Critical Value (1%) -3.443990\n",
"Critical Value (5%) -2.867555\n",
"Critical Value (10%) -2.569974\n",
"dtype: float64\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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JkqTaYrkkqdp07gwnnwx33AGffZZ2GkmSJElSbbBcklStRoxIiqU770w7iSRJ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\ncYKMtdfumLoefRQmTICnn4bhw+P4iWPH+iVRKnRlZTH8ac6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tKm+4JBWpRYsW8eGHHzYa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\ny6G98wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "2hAkggk95ich",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
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