From 3bde54b95aa22dc87060b5cf87f6feda959f44b0 Mon Sep 17 00:00:00 2001
From: db4d42e543ac9826803271b6e416a5b1
 <db4d42e543ac9826803271b6e416a5b1@app-learninglab.inria.fr>
Date: Wed, 14 Feb 2024 09:59:03 +0000
Subject: [PATCH] moocrr-reproducibility-study

---
 module3/data_shuttle.csv                   |   24 +
 module3/exo3.ipynb                         | 3067 --------------------
 module3/moocrr-reproducibility-study.ipynb |  708 +++++
 3 files changed, 732 insertions(+), 3067 deletions(-)
 create mode 100644 module3/data_shuttle.csv
 delete mode 100644 module3/exo3.ipynb
 create mode 100644 module3/moocrr-reproducibility-study.ipynb

diff --git a/module3/data_shuttle.csv b/module3/data_shuttle.csv
new file mode 100644
index 0000000..9412ab3
--- /dev/null
+++ b/module3/data_shuttle.csv
@@ -0,0 +1,24 @@
+Date,Count,Temperature,Pressure,Malfunction
+4/12/81,6,66,50,0
+11/12/81,6,70,50,1
+3/22/82,6,69,50,0
+11/11/82,6,68,50,0
+4/04/83,6,67,50,0
+6/18/82,6,72,50,0
+8/30/83,6,73,100,0
+11/28/83,6,70,100,0
+2/03/84,6,57,200,1
+4/06/84,6,63,200,1
+8/30/84,6,70,200,1
+10/05/84,6,78,200,0
+11/08/84,6,67,200,0
+1/24/85,6,53,200,2
+4/12/85,6,67,200,0
+4/29/85,6,75,200,0
+6/17/85,6,70,200,0
+7/2903/85,6,81,200,0
+8/27/85,6,76,200,0
+10/03/85,6,79,200,0
+10/30/85,6,75,200,2
+11/26/85,6,76,200,0
+1/12/86,6,58,200,1
diff --git a/module3/exo3.ipynb b/module3/exo3.ipynb
deleted file mode 100644
index b56f73d..0000000
--- a/module3/exo3.ipynb
+++ /dev/null
@@ -1,3067 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# CO2 concentration in the atmosphere since 1958\n",
-    "\n",
-    "## Scientific methodology Project : CO2 concentration in the atmosphere since 1958\n",
-    "\n",
-    "### Introduction\n",
-    "\n",
-    "Each month, CO2 athmospheric level is measured in the Mauna Loa observatory, in Hawaii. Data provided here combines measurements since 1958. In the context of this study, I’ll take the dataset of 15/01/20 (17h).\n",
-    "\n",
-    "### About data\n",
-    "\n",
-    "The provided data file consists of 10 columns. The first four columns (Columns 1-4) contain dates represented in various redundant formats. Column 5 displays monthly Mauna Loa CO2 concentrations, measured in micro-mol CO2 per mole (ppm), using the 2008A SIO manometric mole fraction scale—this is the commonly sought standard version of the data. The monthly values are adjusted to 24:00 hours on the 15th day of each month. In Column 6, the same data is presented after undergoing a seasonal adjustment to eliminate the quasi-regular seasonal cycle. This adjustment entails subtracting a 4-harmonic fit with a linear gain factor from the data.\n",
-    "\n",
-    "Moving on, Column 7 showcases a smoothed version of the data, derived from a stiff cubic spline function along with 4-harmonic functions incorporating a linear gain. Column 8 is identical to Column 7's smoothed version but with the seasonal cycle removed. Column 9 replicates the content of Column 5, with the distinction that missing values in Column 5 are filled with corresponding values from Column 7. Similarly, Column 10 mirrors the data in Column 6, with missing values replaced by values from Column 8. It's worth noting that missing values are indicated by -99.99.\n",
-    "\n",
-    "### The (accepted) mission\n",
-    "\n",
-    "    1. Make a plot that shows the superposition of a periodic oscillation and a slower systematic evolution.Separate these two phenomena. Characterize the periodic oscillation.\n",
-    "    2. Find a simple model for the slow contribution, estimate its parameters, and attempt an extrapolation until 2025 (for validating the model using future observations).\n",
-    "    \n",
-    "### How to use Jupyter/Python to clean data\n",
-    "\n",
-    "We’ll start with downloading dataset from Scripps CO2 Program."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_url= \"https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/weekly/weekly_in_situ_co2_mlo.csv\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "import pandas to read and analyse data.\n",
-    "as first 44 rows are comment so we skip them. \n",
-    "File has no header so header=None."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "raw_data = pd.read_csv(data_url, encoding = 'iso-8859-1', skiprows=44, header=None, names=[\"date\",\"CO2\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "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>date</th>\n",
-       "      <th>CO2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1958-03-29</td>\n",
-       "      <td>316.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1958-04-05</td>\n",
-       "      <td>317.31</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1958-04-12</td>\n",
-       "      <td>317.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1958-04-19</td>\n",
-       "      <td>317.58</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1958-04-26</td>\n",
-       "      <td>316.48</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1958-05-03</td>\n",
-       "      <td>316.95</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1958-05-17</td>\n",
-       "      <td>317.56</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>1958-05-24</td>\n",
-       "      <td>317.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>1958-07-05</td>\n",
-       "      <td>315.85</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>1958-07-12</td>\n",
-       "      <td>315.85</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>1958-07-19</td>\n",
-       "      <td>315.46</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>1958-07-26</td>\n",
-       "      <td>315.59</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>1958-08-02</td>\n",
-       "      <td>315.64</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>1958-08-09</td>\n",
-       "      <td>315.10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>1958-08-16</td>\n",
-       "      <td>315.09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>1958-08-30</td>\n",
-       "      <td>314.14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>1958-09-06</td>\n",
-       "      <td>313.54</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>1958-11-08</td>\n",
-       "      <td>313.05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>1958-11-15</td>\n",
-       "      <td>313.26</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>1958-11-22</td>\n",
-       "      <td>313.57</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>1958-11-29</td>\n",
-       "      <td>314.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>1958-12-06</td>\n",
-       "      <td>314.56</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1958-12-13</td>\n",
-       "      <td>314.41</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>1958-12-20</td>\n",
-       "      <td>314.77</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>1958-12-27</td>\n",
-       "      <td>315.21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>1959-01-03</td>\n",
-       "      <td>315.24</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>1959-01-10</td>\n",
-       "      <td>315.50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>1959-01-17</td>\n",
-       "      <td>315.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>1959-01-24</td>\n",
-       "      <td>315.86</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>1959-01-31</td>\n",
-       "      <td>315.42</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3317</th>\n",
-       "      <td>2023-03-25</td>\n",
-       "      <td>420.87</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3318</th>\n",
-       "      <td>2023-04-01</td>\n",
-       "      <td>421.33</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3319</th>\n",
-       "      <td>2023-04-08</td>\n",
-       "      <td>422.20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3320</th>\n",
-       "      <td>2023-04-15</td>\n",
-       "      <td>423.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3321</th>\n",
-       "      <td>2023-04-22</td>\n",
-       "      <td>422.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3322</th>\n",
-       "      <td>2023-04-29</td>\n",
-       "      <td>423.95</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3323</th>\n",
-       "      <td>2023-05-06</td>\n",
-       "      <td>423.76</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3324</th>\n",
-       "      <td>2023-05-13</td>\n",
-       "      <td>423.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3325</th>\n",
-       "      <td>2023-05-20</td>\n",
-       "      <td>422.77</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3326</th>\n",
-       "      <td>2023-05-27</td>\n",
-       "      <td>424.44</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3327</th>\n",
-       "      <td>2023-06-03</td>\n",
-       "      <td>424.40</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3328</th>\n",
-       "      <td>2023-06-10</td>\n",
-       "      <td>424.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3329</th>\n",
-       "      <td>2023-06-17</td>\n",
-       "      <td>422.93</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3330</th>\n",
-       "      <td>2023-06-24</td>\n",
-       "      <td>422.21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3331</th>\n",
-       "      <td>2023-07-01</td>\n",
-       "      <td>422.80</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3332</th>\n",
-       "      <td>2023-07-08</td>\n",
-       "      <td>422.32</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3333</th>\n",
-       "      <td>2023-07-15</td>\n",
-       "      <td>421.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3334</th>\n",
-       "      <td>2023-07-22</td>\n",
-       "      <td>420.74</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3335</th>\n",
-       "      <td>2023-07-29</td>\n",
-       "      <td>420.88</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3336</th>\n",
-       "      <td>2023-08-05</td>\n",
-       "      <td>420.39</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3337</th>\n",
-       "      <td>2023-08-12</td>\n",
-       "      <td>420.30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3338</th>\n",
-       "      <td>2023-08-19</td>\n",
-       "      <td>418.96</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3339</th>\n",
-       "      <td>2023-08-26</td>\n",
-       "      <td>418.84</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3340</th>\n",
-       "      <td>2023-09-02</td>\n",
-       "      <td>418.50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3341</th>\n",
-       "      <td>2023-09-09</td>\n",
-       "      <td>418.28</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3342</th>\n",
-       "      <td>2023-09-16</td>\n",
-       "      <td>418.52</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3343</th>\n",
-       "      <td>2023-09-23</td>\n",
-       "      <td>417.77</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3344</th>\n",
-       "      <td>2023-09-30</td>\n",
-       "      <td>417.89</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3345</th>\n",
-       "      <td>2023-10-07</td>\n",
-       "      <td>418.10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3346</th>\n",
-       "      <td>2023-10-14</td>\n",
-       "      <td>418.82</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>3347 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            date     CO2\n",
-       "0     1958-03-29  316.19\n",
-       "1     1958-04-05  317.31\n",
-       "2     1958-04-12  317.69\n",
-       "3     1958-04-19  317.58\n",
-       "4     1958-04-26  316.48\n",
-       "5     1958-05-03  316.95\n",
-       "6     1958-05-17  317.56\n",
-       "7     1958-05-24  317.99\n",
-       "8     1958-07-05  315.85\n",
-       "9     1958-07-12  315.85\n",
-       "10    1958-07-19  315.46\n",
-       "11    1958-07-26  315.59\n",
-       "12    1958-08-02  315.64\n",
-       "13    1958-08-09  315.10\n",
-       "14    1958-08-16  315.09\n",
-       "15    1958-08-30  314.14\n",
-       "16    1958-09-06  313.54\n",
-       "17    1958-11-08  313.05\n",
-       "18    1958-11-15  313.26\n",
-       "19    1958-11-22  313.57\n",
-       "20    1958-11-29  314.01\n",
-       "21    1958-12-06  314.56\n",
-       "22    1958-12-13  314.41\n",
-       "23    1958-12-20  314.77\n",
-       "24    1958-12-27  315.21\n",
-       "25    1959-01-03  315.24\n",
-       "26    1959-01-10  315.50\n",
-       "27    1959-01-17  315.69\n",
-       "28    1959-01-24  315.86\n",
-       "29    1959-01-31  315.42\n",
-       "...          ...     ...\n",
-       "3317  2023-03-25  420.87\n",
-       "3318  2023-04-01  421.33\n",
-       "3319  2023-04-08  422.20\n",
-       "3320  2023-04-15  423.02\n",
-       "3321  2023-04-22  422.99\n",
-       "3322  2023-04-29  423.95\n",
-       "3323  2023-05-06  423.76\n",
-       "3324  2023-05-13  423.78\n",
-       "3325  2023-05-20  422.77\n",
-       "3326  2023-05-27  424.44\n",
-       "3327  2023-06-03  424.40\n",
-       "3328  2023-06-10  424.01\n",
-       "3329  2023-06-17  422.93\n",
-       "3330  2023-06-24  422.21\n",
-       "3331  2023-07-01  422.80\n",
-       "3332  2023-07-08  422.32\n",
-       "3333  2023-07-15  421.43\n",
-       "3334  2023-07-22  420.74\n",
-       "3335  2023-07-29  420.88\n",
-       "3336  2023-08-05  420.39\n",
-       "3337  2023-08-12  420.30\n",
-       "3338  2023-08-19  418.96\n",
-       "3339  2023-08-26  418.84\n",
-       "3340  2023-09-02  418.50\n",
-       "3341  2023-09-09  418.28\n",
-       "3342  2023-09-16  418.52\n",
-       "3343  2023-09-23  417.77\n",
-       "3344  2023-09-30  417.89\n",
-       "3345  2023-10-07  418.10\n",
-       "3346  2023-10-14  418.82\n",
-       "\n",
-       "[3347 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>CO2</th>\n",
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-       "Empty DataFrame\n",
-       "Columns: [date, CO2]\n",
-       "Index: []"
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-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
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-    "raw_data[raw_data.isnull().any(axis=1)]"
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-   "cell_type": "code",
-   "execution_count": 6,
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-     "data": {
-      "text/plain": [
-       "0       316.19\n",
-       "1       317.31\n",
-       "2       317.69\n",
-       "3       317.58\n",
-       "4       316.48\n",
-       "5       316.95\n",
-       "6       317.56\n",
-       "7       317.99\n",
-       "8       315.85\n",
-       "9       315.85\n",
-       "10      315.46\n",
-       "11      315.59\n",
-       "12      315.64\n",
-       "13      315.10\n",
-       "14      315.09\n",
-       "15      314.14\n",
-       "16      313.54\n",
-       "17      313.05\n",
-       "18      313.26\n",
-       "19      313.57\n",
-       "20      314.01\n",
-       "21      314.56\n",
-       "22      314.41\n",
-       "23      314.77\n",
-       "24      315.21\n",
-       "25      315.24\n",
-       "26      315.50\n",
-       "27      315.69\n",
-       "28      315.86\n",
-       "29      315.42\n",
-       "         ...  \n",
-       "3317    420.87\n",
-       "3318    421.33\n",
-       "3319    422.20\n",
-       "3320    423.02\n",
-       "3321    422.99\n",
-       "3322    423.95\n",
-       "3323    423.76\n",
-       "3324    423.78\n",
-       "3325    422.77\n",
-       "3326    424.44\n",
-       "3327    424.40\n",
-       "3328    424.01\n",
-       "3329    422.93\n",
-       "3330    422.21\n",
-       "3331    422.80\n",
-       "3332    422.32\n",
-       "3333    421.43\n",
-       "3334    420.74\n",
-       "3335    420.88\n",
-       "3336    420.39\n",
-       "3337    420.30\n",
-       "3338    418.96\n",
-       "3339    418.84\n",
-       "3340    418.50\n",
-       "3341    418.28\n",
-       "3342    418.52\n",
-       "3343    417.77\n",
-       "3344    417.89\n",
-       "3345    418.10\n",
-       "3346    418.82\n",
-       "Name: CO2, Length: 3347, dtype: float64"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_data['CO2']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>CO2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1958-03-29</td>\n",
-       "      <td>316.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1958-04-05</td>\n",
-       "      <td>317.31</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1958-04-12</td>\n",
-       "      <td>317.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1958-04-19</td>\n",
-       "      <td>317.58</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1958-04-26</td>\n",
-       "      <td>316.48</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1958-05-03</td>\n",
-       "      <td>316.95</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1958-05-17</td>\n",
-       "      <td>317.56</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>1958-05-24</td>\n",
-       "      <td>317.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>1958-07-05</td>\n",
-       "      <td>315.85</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>1958-07-12</td>\n",
-       "      <td>315.85</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>1958-07-19</td>\n",
-       "      <td>315.46</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>1958-07-26</td>\n",
-       "      <td>315.59</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>1958-08-02</td>\n",
-       "      <td>315.64</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>1958-08-09</td>\n",
-       "      <td>315.10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>1958-08-16</td>\n",
-       "      <td>315.09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>1958-08-30</td>\n",
-       "      <td>314.14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>1958-09-06</td>\n",
-       "      <td>313.54</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>1958-11-08</td>\n",
-       "      <td>313.05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>1958-11-15</td>\n",
-       "      <td>313.26</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>1958-11-22</td>\n",
-       "      <td>313.57</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>1958-11-29</td>\n",
-       "      <td>314.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>1958-12-06</td>\n",
-       "      <td>314.56</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1958-12-13</td>\n",
-       "      <td>314.41</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>1958-12-20</td>\n",
-       "      <td>314.77</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>1958-12-27</td>\n",
-       "      <td>315.21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>1959-01-03</td>\n",
-       "      <td>315.24</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>1959-01-10</td>\n",
-       "      <td>315.50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>1959-01-17</td>\n",
-       "      <td>315.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>1959-01-24</td>\n",
-       "      <td>315.86</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>1959-01-31</td>\n",
-       "      <td>315.42</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3317</th>\n",
-       "      <td>2023-03-25</td>\n",
-       "      <td>420.87</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3318</th>\n",
-       "      <td>2023-04-01</td>\n",
-       "      <td>421.33</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3319</th>\n",
-       "      <td>2023-04-08</td>\n",
-       "      <td>422.20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3320</th>\n",
-       "      <td>2023-04-15</td>\n",
-       "      <td>423.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3321</th>\n",
-       "      <td>2023-04-22</td>\n",
-       "      <td>422.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3322</th>\n",
-       "      <td>2023-04-29</td>\n",
-       "      <td>423.95</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3323</th>\n",
-       "      <td>2023-05-06</td>\n",
-       "      <td>423.76</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3324</th>\n",
-       "      <td>2023-05-13</td>\n",
-       "      <td>423.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3325</th>\n",
-       "      <td>2023-05-20</td>\n",
-       "      <td>422.77</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3326</th>\n",
-       "      <td>2023-05-27</td>\n",
-       "      <td>424.44</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3327</th>\n",
-       "      <td>2023-06-03</td>\n",
-       "      <td>424.40</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3328</th>\n",
-       "      <td>2023-06-10</td>\n",
-       "      <td>424.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3329</th>\n",
-       "      <td>2023-06-17</td>\n",
-       "      <td>422.93</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3330</th>\n",
-       "      <td>2023-06-24</td>\n",
-       "      <td>422.21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3331</th>\n",
-       "      <td>2023-07-01</td>\n",
-       "      <td>422.80</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3332</th>\n",
-       "      <td>2023-07-08</td>\n",
-       "      <td>422.32</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3333</th>\n",
-       "      <td>2023-07-15</td>\n",
-       "      <td>421.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3334</th>\n",
-       "      <td>2023-07-22</td>\n",
-       "      <td>420.74</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3335</th>\n",
-       "      <td>2023-07-29</td>\n",
-       "      <td>420.88</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3336</th>\n",
-       "      <td>2023-08-05</td>\n",
-       "      <td>420.39</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3337</th>\n",
-       "      <td>2023-08-12</td>\n",
-       "      <td>420.30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3338</th>\n",
-       "      <td>2023-08-19</td>\n",
-       "      <td>418.96</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3339</th>\n",
-       "      <td>2023-08-26</td>\n",
-       "      <td>418.84</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3340</th>\n",
-       "      <td>2023-09-02</td>\n",
-       "      <td>418.50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3341</th>\n",
-       "      <td>2023-09-09</td>\n",
-       "      <td>418.28</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3342</th>\n",
-       "      <td>2023-09-16</td>\n",
-       "      <td>418.52</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3343</th>\n",
-       "      <td>2023-09-23</td>\n",
-       "      <td>417.77</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3344</th>\n",
-       "      <td>2023-09-30</td>\n",
-       "      <td>417.89</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3345</th>\n",
-       "      <td>2023-10-07</td>\n",
-       "      <td>418.10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3346</th>\n",
-       "      <td>2023-10-14</td>\n",
-       "      <td>418.82</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>3347 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            date     CO2\n",
-       "0     1958-03-29  316.19\n",
-       "1     1958-04-05  317.31\n",
-       "2     1958-04-12  317.69\n",
-       "3     1958-04-19  317.58\n",
-       "4     1958-04-26  316.48\n",
-       "5     1958-05-03  316.95\n",
-       "6     1958-05-17  317.56\n",
-       "7     1958-05-24  317.99\n",
-       "8     1958-07-05  315.85\n",
-       "9     1958-07-12  315.85\n",
-       "10    1958-07-19  315.46\n",
-       "11    1958-07-26  315.59\n",
-       "12    1958-08-02  315.64\n",
-       "13    1958-08-09  315.10\n",
-       "14    1958-08-16  315.09\n",
-       "15    1958-08-30  314.14\n",
-       "16    1958-09-06  313.54\n",
-       "17    1958-11-08  313.05\n",
-       "18    1958-11-15  313.26\n",
-       "19    1958-11-22  313.57\n",
-       "20    1958-11-29  314.01\n",
-       "21    1958-12-06  314.56\n",
-       "22    1958-12-13  314.41\n",
-       "23    1958-12-20  314.77\n",
-       "24    1958-12-27  315.21\n",
-       "25    1959-01-03  315.24\n",
-       "26    1959-01-10  315.50\n",
-       "27    1959-01-17  315.69\n",
-       "28    1959-01-24  315.86\n",
-       "29    1959-01-31  315.42\n",
-       "...          ...     ...\n",
-       "3317  2023-03-25  420.87\n",
-       "3318  2023-04-01  421.33\n",
-       "3319  2023-04-08  422.20\n",
-       "3320  2023-04-15  423.02\n",
-       "3321  2023-04-22  422.99\n",
-       "3322  2023-04-29  423.95\n",
-       "3323  2023-05-06  423.76\n",
-       "3324  2023-05-13  423.78\n",
-       "3325  2023-05-20  422.77\n",
-       "3326  2023-05-27  424.44\n",
-       "3327  2023-06-03  424.40\n",
-       "3328  2023-06-10  424.01\n",
-       "3329  2023-06-17  422.93\n",
-       "3330  2023-06-24  422.21\n",
-       "3331  2023-07-01  422.80\n",
-       "3332  2023-07-08  422.32\n",
-       "3333  2023-07-15  421.43\n",
-       "3334  2023-07-22  420.74\n",
-       "3335  2023-07-29  420.88\n",
-       "3336  2023-08-05  420.39\n",
-       "3337  2023-08-12  420.30\n",
-       "3338  2023-08-19  418.96\n",
-       "3339  2023-08-26  418.84\n",
-       "3340  2023-09-02  418.50\n",
-       "3341  2023-09-09  418.28\n",
-       "3342  2023-09-16  418.52\n",
-       "3343  2023-09-23  417.77\n",
-       "3344  2023-09-30  417.89\n",
-       "3345  2023-10-07  418.10\n",
-       "3346  2023-10-14  418.82\n",
-       "\n",
-       "[3347 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = raw_data.dropna().copy()\n",
-    "data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data1 = raw_data.dropna().copy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plot CO2 Evolution\n",
-    " \n",
-    " So now, we’ll use matplotlib to represent more clearly evolution of CO2 concentration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1500x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_df(df, x, y, title=\"\", xlabel='Date', ylabel='CO2 concentration (ppm)', dpi=100):\n",
-    "    plt.figure(figsize=(15,4), dpi=dpi)\n",
-    "    plt.plot(x, y, color='tab:red')\n",
-    "    plt.gca().set(title=title, xlabel=xlabel, ylabel=ylabel)\n",
-    "    plt.show()\n",
-    "    \n",
-    "\n",
-    "plot_df(data, x=data['date'], y=data['CO2'], title='Evolution of CO2 concentration in Hawaii 1958-2025')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The curve that I have is the same as the Wikipedia’s one. I guess it’s a good sign. This figure show the global evolution, but as we can see, we have some periodic evolution too. It can be fun to show that to complete first mission. But i Don't know HOW?\n",
-    "So I tried different grouping method.\n",
-    "I grouped the data month wise to plot oscilation more precisely."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "date_df=pd.to_datetime(data[\"date\"], yearfirst=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data.insert(2, \"month\", date_df.dt.month)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data.insert(3, \"year\", date_df.dt.year)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "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>date</th>\n",
-       "      <th>CO2</th>\n",
-       "      <th>month</th>\n",
-       "      <th>year</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1958-03-29</td>\n",
-       "      <td>316.19</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1958-04-05</td>\n",
-       "      <td>317.31</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1958-04-12</td>\n",
-       "      <td>317.69</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1958-04-19</td>\n",
-       "      <td>317.58</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1958-04-26</td>\n",
-       "      <td>316.48</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1958-05-03</td>\n",
-       "      <td>316.95</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1958-05-17</td>\n",
-       "      <td>317.56</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>1958-05-24</td>\n",
-       "      <td>317.99</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>1958-07-05</td>\n",
-       "      <td>315.85</td>\n",
-       "      <td>7</td>\n",
-       "      <td>1958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>1958-07-12</td>\n",
-       "      <td>315.85</td>\n",
-       "      <td>7</td>\n",
-       "      <td>1958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>1958-07-19</td>\n",
-       "      <td>315.46</td>\n",
-       "      <td>7</td>\n",
-       "      <td>1958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>1958-07-26</td>\n",
-       "      <td>315.59</td>\n",
-       "      <td>7</td>\n",
-       "      <td>1958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>1958-08-02</td>\n",
-       "      <td>315.64</td>\n",
-       "      <td>8</td>\n",
-       "      <td>1958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>1958-08-09</td>\n",
-       "      <td>315.10</td>\n",
-       "      <td>8</td>\n",
-       "      <td>1958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>1958-08-16</td>\n",
-       "      <td>315.09</td>\n",
-       "      <td>8</td>\n",
-       "      <td>1958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>1958-08-30</td>\n",
-       "      <td>314.14</td>\n",
-       "      <td>8</td>\n",
-       "      <td>1958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>1958-09-06</td>\n",
-       "      <td>313.54</td>\n",
-       "      <td>9</td>\n",
-       "      <td>1958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>1958-11-08</td>\n",
-       "      <td>313.05</td>\n",
-       "      <td>11</td>\n",
-       "      <td>1958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>1958-11-15</td>\n",
-       "      <td>313.26</td>\n",
-       "      <td>11</td>\n",
-       "      <td>1958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>1958-11-22</td>\n",
-       "      <td>313.57</td>\n",
-       "      <td>11</td>\n",
-       "      <td>1958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>1958-11-29</td>\n",
-       "      <td>314.01</td>\n",
-       "      <td>11</td>\n",
-       "      <td>1958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>1958-12-06</td>\n",
-       "      <td>314.56</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1958-12-13</td>\n",
-       "      <td>314.41</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>1958-12-20</td>\n",
-       "      <td>314.77</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>1958-12-27</td>\n",
-       "      <td>315.21</td>\n",
-       "      <td>12</td>\n",
-       "      <td>1958</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>1959-01-03</td>\n",
-       "      <td>315.24</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1959</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>1959-01-10</td>\n",
-       "      <td>315.50</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1959</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>1959-01-17</td>\n",
-       "      <td>315.69</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1959</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>1959-01-24</td>\n",
-       "      <td>315.86</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1959</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>1959-01-31</td>\n",
-       "      <td>315.42</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1959</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3317</th>\n",
-       "      <td>2023-03-25</td>\n",
-       "      <td>420.87</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3318</th>\n",
-       "      <td>2023-04-01</td>\n",
-       "      <td>421.33</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3319</th>\n",
-       "      <td>2023-04-08</td>\n",
-       "      <td>422.20</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3320</th>\n",
-       "      <td>2023-04-15</td>\n",
-       "      <td>423.02</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3321</th>\n",
-       "      <td>2023-04-22</td>\n",
-       "      <td>422.99</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3322</th>\n",
-       "      <td>2023-04-29</td>\n",
-       "      <td>423.95</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3323</th>\n",
-       "      <td>2023-05-06</td>\n",
-       "      <td>423.76</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3324</th>\n",
-       "      <td>2023-05-13</td>\n",
-       "      <td>423.78</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3325</th>\n",
-       "      <td>2023-05-20</td>\n",
-       "      <td>422.77</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3326</th>\n",
-       "      <td>2023-05-27</td>\n",
-       "      <td>424.44</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3327</th>\n",
-       "      <td>2023-06-03</td>\n",
-       "      <td>424.40</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3328</th>\n",
-       "      <td>2023-06-10</td>\n",
-       "      <td>424.01</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3329</th>\n",
-       "      <td>2023-06-17</td>\n",
-       "      <td>422.93</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3330</th>\n",
-       "      <td>2023-06-24</td>\n",
-       "      <td>422.21</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3331</th>\n",
-       "      <td>2023-07-01</td>\n",
-       "      <td>422.80</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3332</th>\n",
-       "      <td>2023-07-08</td>\n",
-       "      <td>422.32</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3333</th>\n",
-       "      <td>2023-07-15</td>\n",
-       "      <td>421.43</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3334</th>\n",
-       "      <td>2023-07-22</td>\n",
-       "      <td>420.74</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3335</th>\n",
-       "      <td>2023-07-29</td>\n",
-       "      <td>420.88</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3336</th>\n",
-       "      <td>2023-08-05</td>\n",
-       "      <td>420.39</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3337</th>\n",
-       "      <td>2023-08-12</td>\n",
-       "      <td>420.30</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3338</th>\n",
-       "      <td>2023-08-19</td>\n",
-       "      <td>418.96</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3339</th>\n",
-       "      <td>2023-08-26</td>\n",
-       "      <td>418.84</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3340</th>\n",
-       "      <td>2023-09-02</td>\n",
-       "      <td>418.50</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3341</th>\n",
-       "      <td>2023-09-09</td>\n",
-       "      <td>418.28</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3342</th>\n",
-       "      <td>2023-09-16</td>\n",
-       "      <td>418.52</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3343</th>\n",
-       "      <td>2023-09-23</td>\n",
-       "      <td>417.77</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3344</th>\n",
-       "      <td>2023-09-30</td>\n",
-       "      <td>417.89</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3345</th>\n",
-       "      <td>2023-10-07</td>\n",
-       "      <td>418.10</td>\n",
-       "      <td>10</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3346</th>\n",
-       "      <td>2023-10-14</td>\n",
-       "      <td>418.82</td>\n",
-       "      <td>10</td>\n",
-       "      <td>2023</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>3347 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            date     CO2  month  year\n",
-       "0     1958-03-29  316.19      3  1958\n",
-       "1     1958-04-05  317.31      4  1958\n",
-       "2     1958-04-12  317.69      4  1958\n",
-       "3     1958-04-19  317.58      4  1958\n",
-       "4     1958-04-26  316.48      4  1958\n",
-       "5     1958-05-03  316.95      5  1958\n",
-       "6     1958-05-17  317.56      5  1958\n",
-       "7     1958-05-24  317.99      5  1958\n",
-       "8     1958-07-05  315.85      7  1958\n",
-       "9     1958-07-12  315.85      7  1958\n",
-       "10    1958-07-19  315.46      7  1958\n",
-       "11    1958-07-26  315.59      7  1958\n",
-       "12    1958-08-02  315.64      8  1958\n",
-       "13    1958-08-09  315.10      8  1958\n",
-       "14    1958-08-16  315.09      8  1958\n",
-       "15    1958-08-30  314.14      8  1958\n",
-       "16    1958-09-06  313.54      9  1958\n",
-       "17    1958-11-08  313.05     11  1958\n",
-       "18    1958-11-15  313.26     11  1958\n",
-       "19    1958-11-22  313.57     11  1958\n",
-       "20    1958-11-29  314.01     11  1958\n",
-       "21    1958-12-06  314.56     12  1958\n",
-       "22    1958-12-13  314.41     12  1958\n",
-       "23    1958-12-20  314.77     12  1958\n",
-       "24    1958-12-27  315.21     12  1958\n",
-       "25    1959-01-03  315.24      1  1959\n",
-       "26    1959-01-10  315.50      1  1959\n",
-       "27    1959-01-17  315.69      1  1959\n",
-       "28    1959-01-24  315.86      1  1959\n",
-       "29    1959-01-31  315.42      1  1959\n",
-       "...          ...     ...    ...   ...\n",
-       "3317  2023-03-25  420.87      3  2023\n",
-       "3318  2023-04-01  421.33      4  2023\n",
-       "3319  2023-04-08  422.20      4  2023\n",
-       "3320  2023-04-15  423.02      4  2023\n",
-       "3321  2023-04-22  422.99      4  2023\n",
-       "3322  2023-04-29  423.95      4  2023\n",
-       "3323  2023-05-06  423.76      5  2023\n",
-       "3324  2023-05-13  423.78      5  2023\n",
-       "3325  2023-05-20  422.77      5  2023\n",
-       "3326  2023-05-27  424.44      5  2023\n",
-       "3327  2023-06-03  424.40      6  2023\n",
-       "3328  2023-06-10  424.01      6  2023\n",
-       "3329  2023-06-17  422.93      6  2023\n",
-       "3330  2023-06-24  422.21      6  2023\n",
-       "3331  2023-07-01  422.80      7  2023\n",
-       "3332  2023-07-08  422.32      7  2023\n",
-       "3333  2023-07-15  421.43      7  2023\n",
-       "3334  2023-07-22  420.74      7  2023\n",
-       "3335  2023-07-29  420.88      7  2023\n",
-       "3336  2023-08-05  420.39      8  2023\n",
-       "3337  2023-08-12  420.30      8  2023\n",
-       "3338  2023-08-19  418.96      8  2023\n",
-       "3339  2023-08-26  418.84      8  2023\n",
-       "3340  2023-09-02  418.50      9  2023\n",
-       "3341  2023-09-09  418.28      9  2023\n",
-       "3342  2023-09-16  418.52      9  2023\n",
-       "3343  2023-09-23  417.77      9  2023\n",
-       "3344  2023-09-30  417.89      9  2023\n",
-       "3345  2023-10-07  418.10     10  2023\n",
-       "3346  2023-10-14  418.82     10  2023\n",
-       "\n",
-       "[3347 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data1[\"date\"] = pd.to_datetime(data1[\"date\"])\n",
-    "df2 = data1.groupby(data1['date'].dt.to_period('M')).sum()\n",
-    "df1 = df2.resample('M').asfreq().dropna()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "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>CO2</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>date</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1958-03</th>\n",
-       "      <td>316.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-04</th>\n",
-       "      <td>1269.06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-05</th>\n",
-       "      <td>952.50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-07</th>\n",
-       "      <td>1262.75</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-08</th>\n",
-       "      <td>1259.97</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-09</th>\n",
-       "      <td>313.54</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-11</th>\n",
-       "      <td>1253.89</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-12</th>\n",
-       "      <td>1258.95</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-01</th>\n",
-       "      <td>1577.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-02</th>\n",
-       "      <td>950.17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-03</th>\n",
-       "      <td>950.25</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-04</th>\n",
-       "      <td>1270.80</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-05</th>\n",
-       "      <td>1273.53</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-06</th>\n",
-       "      <td>1272.31</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-07</th>\n",
-       "      <td>1266.30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-08</th>\n",
-       "      <td>1259.68</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-09</th>\n",
-       "      <td>1255.49</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-10</th>\n",
-       "      <td>1567.20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-11</th>\n",
-       "      <td>1259.61</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-12</th>\n",
-       "      <td>1262.33</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1960-01</th>\n",
-       "      <td>1582.10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1960-02</th>\n",
-       "      <td>1268.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1960-03</th>\n",
-       "      <td>1270.56</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1960-04</th>\n",
-       "      <td>1595.76</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1960-05</th>\n",
-       "      <td>1279.88</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1960-06</th>\n",
-       "      <td>1278.05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1960-07</th>\n",
-       "      <td>1590.47</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1960-08</th>\n",
-       "      <td>1263.20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1960-09</th>\n",
-       "      <td>1256.91</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1960-10</th>\n",
-       "      <td>1569.40</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-05</th>\n",
-       "      <td>2094.30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-06</th>\n",
-       "      <td>1674.67</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-07</th>\n",
-       "      <td>2082.55</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-08</th>\n",
-       "      <td>1656.32</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-09</th>\n",
-       "      <td>1651.87</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-10</th>\n",
-       "      <td>2068.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-11</th>\n",
-       "      <td>1659.59</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-12</th>\n",
-       "      <td>1665.62</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2022-01</th>\n",
-       "      <td>2089.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2022-02</th>\n",
-       "      <td>1675.98</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2022-03</th>\n",
-       "      <td>1673.86</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2022-04</th>\n",
-       "      <td>2099.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2022-05</th>\n",
-       "      <td>1682.89</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2022-06</th>\n",
-       "      <td>1682.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2022-07</th>\n",
-       "      <td>2092.94</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2022-08</th>\n",
-       "      <td>1666.61</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2022-09</th>\n",
-       "      <td>1662.16</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2022-10</th>\n",
-       "      <td>2076.95</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2022-11</th>\n",
-       "      <td>1668.25</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2022-12</th>\n",
-       "      <td>1256.62</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-01</th>\n",
-       "      <td>1677.25</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-02</th>\n",
-       "      <td>1681.26</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-03</th>\n",
-       "      <td>1682.14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-04</th>\n",
-       "      <td>2113.49</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-05</th>\n",
-       "      <td>1694.75</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-06</th>\n",
-       "      <td>1693.55</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-07</th>\n",
-       "      <td>2108.17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-08</th>\n",
-       "      <td>1678.49</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-09</th>\n",
-       "      <td>2090.96</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-10</th>\n",
-       "      <td>836.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>783 rows × 1 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             CO2\n",
-       "date            \n",
-       "1958-03   316.19\n",
-       "1958-04  1269.06\n",
-       "1958-05   952.50\n",
-       "1958-07  1262.75\n",
-       "1958-08  1259.97\n",
-       "1958-09   313.54\n",
-       "1958-11  1253.89\n",
-       "1958-12  1258.95\n",
-       "1959-01  1577.71\n",
-       "1959-02   950.17\n",
-       "1959-03   950.25\n",
-       "1959-04  1270.80\n",
-       "1959-05  1273.53\n",
-       "1959-06  1272.31\n",
-       "1959-07  1266.30\n",
-       "1959-08  1259.68\n",
-       "1959-09  1255.49\n",
-       "1959-10  1567.20\n",
-       "1959-11  1259.61\n",
-       "1959-12  1262.33\n",
-       "1960-01  1582.10\n",
-       "1960-02  1268.01\n",
-       "1960-03  1270.56\n",
-       "1960-04  1595.76\n",
-       "1960-05  1279.88\n",
-       "1960-06  1278.05\n",
-       "1960-07  1590.47\n",
-       "1960-08  1263.20\n",
-       "1960-09  1256.91\n",
-       "1960-10  1569.40\n",
-       "...          ...\n",
-       "2021-05  2094.30\n",
-       "2021-06  1674.67\n",
-       "2021-07  2082.55\n",
-       "2021-08  1656.32\n",
-       "2021-09  1651.87\n",
-       "2021-10  2068.19\n",
-       "2021-11  1659.59\n",
-       "2021-12  1665.62\n",
-       "2022-01  2089.99\n",
-       "2022-02  1675.98\n",
-       "2022-03  1673.86\n",
-       "2022-04  2099.78\n",
-       "2022-05  1682.89\n",
-       "2022-06  1682.69\n",
-       "2022-07  2092.94\n",
-       "2022-08  1666.61\n",
-       "2022-09  1662.16\n",
-       "2022-10  2076.95\n",
-       "2022-11  1668.25\n",
-       "2022-12  1256.62\n",
-       "2023-01  1677.25\n",
-       "2023-02  1681.26\n",
-       "2023-03  1682.14\n",
-       "2023-04  2113.49\n",
-       "2023-05  1694.75\n",
-       "2023-06  1693.55\n",
-       "2023-07  2108.17\n",
-       "2023-08  1678.49\n",
-       "2023-09  2090.96\n",
-       "2023-10   836.92\n",
-       "\n",
-       "[783 rows x 1 columns]"
-      ]
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f49f95f2400>"
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df1.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "I plot a graph with 6 years data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "yearly_5_data1=df1.head(60)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "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>CO2</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>date</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1958-03</th>\n",
-       "      <td>316.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-04</th>\n",
-       "      <td>1269.06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-05</th>\n",
-       "      <td>952.50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-07</th>\n",
-       "      <td>1262.75</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-08</th>\n",
-       "      <td>1259.97</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-09</th>\n",
-       "      <td>313.54</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-11</th>\n",
-       "      <td>1253.89</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1958-12</th>\n",
-       "      <td>1258.95</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-01</th>\n",
-       "      <td>1577.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-02</th>\n",
-       "      <td>950.17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-03</th>\n",
-       "      <td>950.25</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-04</th>\n",
-       "      <td>1270.80</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-05</th>\n",
-       "      <td>1273.53</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-06</th>\n",
-       "      <td>1272.31</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-07</th>\n",
-       "      <td>1266.30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-08</th>\n",
-       "      <td>1259.68</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-09</th>\n",
-       "      <td>1255.49</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-10</th>\n",
-       "      <td>1567.20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-11</th>\n",
-       "      <td>1259.61</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959-12</th>\n",
-       "      <td>1262.33</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1960-01</th>\n",
-       "      <td>1582.10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1960-02</th>\n",
-       "      <td>1268.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1960-03</th>\n",
-       "      <td>1270.56</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1960-04</th>\n",
-       "      <td>1595.76</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1960-05</th>\n",
-       "      <td>1279.88</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1960-06</th>\n",
-       "      <td>1278.05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1960-07</th>\n",
-       "      <td>1590.47</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1960-08</th>\n",
-       "      <td>1263.20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1960-09</th>\n",
-       "      <td>1256.91</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1960-10</th>\n",
-       "      <td>1569.40</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1960-11</th>\n",
-       "      <td>1260.11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1960-12</th>\n",
-       "      <td>1581.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1961-01</th>\n",
-       "      <td>1267.79</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1961-02</th>\n",
-       "      <td>1270.75</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1961-03</th>\n",
-       "      <td>1274.18</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1961-04</th>\n",
-       "      <td>1597.27</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1961-05</th>\n",
-       "      <td>1282.10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1961-06</th>\n",
-       "      <td>1279.22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1961-07</th>\n",
-       "      <td>1591.95</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1961-08</th>\n",
-       "      <td>1267.26</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1961-09</th>\n",
-       "      <td>1575.45</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1961-10</th>\n",
-       "      <td>1261.60</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1961-11</th>\n",
-       "      <td>1264.28</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1961-12</th>\n",
-       "      <td>1585.12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1962-01</th>\n",
-       "      <td>1271.96</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1962-02</th>\n",
-       "      <td>1274.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1962-03</th>\n",
-       "      <td>1598.74</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1962-04</th>\n",
-       "      <td>1282.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1962-05</th>\n",
-       "      <td>1283.88</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1962-06</th>\n",
-       "      <td>1602.68</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1962-07</th>\n",
-       "      <td>1277.81</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1962-08</th>\n",
-       "      <td>953.40</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1962-09</th>\n",
-       "      <td>948.29</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1962-10</th>\n",
-       "      <td>1262.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1962-11</th>\n",
-       "      <td>1266.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1962-12</th>\n",
-       "      <td>1270.34</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1963-01</th>\n",
-       "      <td>1274.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1963-02</th>\n",
-       "      <td>957.09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1963-03</th>\n",
-       "      <td>1599.48</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1963-04</th>\n",
-       "      <td>1285.31</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             CO2\n",
-       "date            \n",
-       "1958-03   316.19\n",
-       "1958-04  1269.06\n",
-       "1958-05   952.50\n",
-       "1958-07  1262.75\n",
-       "1958-08  1259.97\n",
-       "1958-09   313.54\n",
-       "1958-11  1253.89\n",
-       "1958-12  1258.95\n",
-       "1959-01  1577.71\n",
-       "1959-02   950.17\n",
-       "1959-03   950.25\n",
-       "1959-04  1270.80\n",
-       "1959-05  1273.53\n",
-       "1959-06  1272.31\n",
-       "1959-07  1266.30\n",
-       "1959-08  1259.68\n",
-       "1959-09  1255.49\n",
-       "1959-10  1567.20\n",
-       "1959-11  1259.61\n",
-       "1959-12  1262.33\n",
-       "1960-01  1582.10\n",
-       "1960-02  1268.01\n",
-       "1960-03  1270.56\n",
-       "1960-04  1595.76\n",
-       "1960-05  1279.88\n",
-       "1960-06  1278.05\n",
-       "1960-07  1590.47\n",
-       "1960-08  1263.20\n",
-       "1960-09  1256.91\n",
-       "1960-10  1569.40\n",
-       "1960-11  1260.11\n",
-       "1960-12  1581.19\n",
-       "1961-01  1267.79\n",
-       "1961-02  1270.75\n",
-       "1961-03  1274.18\n",
-       "1961-04  1597.27\n",
-       "1961-05  1282.10\n",
-       "1961-06  1279.22\n",
-       "1961-07  1591.95\n",
-       "1961-08  1267.26\n",
-       "1961-09  1575.45\n",
-       "1961-10  1261.60\n",
-       "1961-11  1264.28\n",
-       "1961-12  1585.12\n",
-       "1962-01  1271.96\n",
-       "1962-02  1274.73\n",
-       "1962-03  1598.74\n",
-       "1962-04  1282.43\n",
-       "1962-05  1283.88\n",
-       "1962-06  1602.68\n",
-       "1962-07  1277.81\n",
-       "1962-08   953.40\n",
-       "1962-09   948.29\n",
-       "1962-10  1262.00\n",
-       "1962-11  1266.43\n",
-       "1962-12  1270.34\n",
-       "1963-01  1274.99\n",
-       "1963-02   957.09\n",
-       "1963-03  1599.48\n",
-       "1963-04  1285.31"
-      ]
-     },
-     "execution_count": 88,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "yearly_5_data1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f49f960ce48>"
-      ]
-     },
-     "execution_count": 89,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "yearly_5_data1.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then I grouped the data year wise and plot a graph for all data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df3 = data1.groupby(data1['date'].dt.to_period('Y')).sum()\n",
-    "df3 = df3.resample('Y').asfreq().dropna()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f49f998af28>"
-      ]
-     },
-     "execution_count": 91,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df3.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "I plot a graph with 5 years data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "yearly_5_data=df3.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "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>CO2</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>date</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1958</th>\n",
-       "      <td>7886.85</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959</th>\n",
-       "      <td>15165.38</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1960</th>\n",
-       "      <td>16795.64</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1961</th>\n",
-       "      <td>16516.97</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1962</th>\n",
-       "      <td>15292.69</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           CO2\n",
-       "date          \n",
-       "1958   7886.85\n",
-       "1959  15165.38\n",
-       "1960  16795.64\n",
-       "1961  16516.97\n",
-       "1962  15292.69"
-      ]
-     },
-     "execution_count": 94,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "yearly_5_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f49f9a61cc0>"
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "yearly_5_data.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let’s make an interval. We need first the maximum and minimum value of CO2 concentration for each month. We can translate that with this SQL request :"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pip install pandasql"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pandasql import sqldf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newdata_df=sqldf('SELECT year, MIN(CO2),MAX(CO2) FROM data GROUP BY year')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newdata_df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "make a graph forminimum and maximum CO2 concentration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_df(x1, x2,y, title=\"\", xlabel='Date', ylabel='CO2 concentration (ppm)'):\n",
-    "    plt.plot(x1, x2,y, color='tab:red')\n",
-    "    plt.gca().set(title=title, xlabel=xlabel, ylabel=ylabel)\n",
-    "    plt.show()\n",
-    "    \n",
-    "\n",
-    "plot_df( x1=newdata_df['MIN(CO2)'],x2=newdata_df['MAX(CO2)'],y=newdata_df['year'], title='Evolution of CO2 concentration in Hawaii 1958-2025')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Still I'm not sure it's the right oscilation or not so i draw it using sine curve for complete data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "amplitude   = np.sin(data[\"CO2\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(data[\"year\"], amplitude)\n",
-    "plt.title('superposition of a periodic oscillation')\n",
-    "plt.xlabel('year')\n",
-    "plt.ylabel('CO2 concentration 1958-2020')\n",
-    "plt.grid(True, which='both')\n",
-    "plt.axhline(y=0, color='R')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now I draw the sine curve for 7 years"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sin_data=data.head(300)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "amplitude_sin   = np.sin(sin_data[\"CO2\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(sin_data[\"year\"], amplitude_sin)\n",
-    "plt.title('slower systematic evolution')\n",
-    "plt.xlabel('year')\n",
-    "plt.ylabel('CO2 concentration 1958-1964')\n",
-    "plt.grid(True, which='both')\n",
-    "plt.axhline(y=0, color='R')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# make some regressions for prediction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "features=np.array([data[\"month\"],data[\"year\"]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "feature=np.array(data[\"year\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1958, 1958, 1958, ..., 2023, 2023, 2023])"
-      ]
-     },
-     "execution_count": 138,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "feature"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 141,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "feature=feature.reshape(-1,1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[   3,    4,    4, ...,    9,   10,   10],\n",
-       "       [1958, 1958, 1958, ..., 2023, 2023, 2023]])"
-      ]
-     },
-     "execution_count": 134,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 143,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.linear_model import LinearRegression\n",
-    "\n",
-    "# Training data\n",
-    "X = (feature)  # features\n",
-    "y = data[\"CO2\"]  # target\n",
-    "\n",
-    "# Train the model\n",
-    "model = LinearRegression()\n",
-    "model.fit(X, y)\n",
-    "\n",
-    "# Store the fitted values as a time series with the same time index as\n",
-    "# the training data\n",
-    "y_pred = pd.Series(model.predict(X), index=data[\"year\"].index)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0       305.214859\n",
-       "1       305.214859\n",
-       "2       305.214859\n",
-       "3       305.214859\n",
-       "4       305.214859\n",
-       "5       305.214859\n",
-       "6       305.214859\n",
-       "7       305.214859\n",
-       "8       305.214859\n",
-       "9       305.214859\n",
-       "10      305.214859\n",
-       "11      305.214859\n",
-       "12      305.214859\n",
-       "13      305.214859\n",
-       "14      305.214859\n",
-       "15      305.214859\n",
-       "16      305.214859\n",
-       "17      305.214859\n",
-       "18      305.214859\n",
-       "19      305.214859\n",
-       "20      305.214859\n",
-       "21      305.214859\n",
-       "22      305.214859\n",
-       "23      305.214859\n",
-       "24      305.214859\n",
-       "25      306.846076\n",
-       "26      306.846076\n",
-       "27      306.846076\n",
-       "28      306.846076\n",
-       "29      306.846076\n",
-       "           ...    \n",
-       "3317    411.243981\n",
-       "3318    411.243981\n",
-       "3319    411.243981\n",
-       "3320    411.243981\n",
-       "3321    411.243981\n",
-       "3322    411.243981\n",
-       "3323    411.243981\n",
-       "3324    411.243981\n",
-       "3325    411.243981\n",
-       "3326    411.243981\n",
-       "3327    411.243981\n",
-       "3328    411.243981\n",
-       "3329    411.243981\n",
-       "3330    411.243981\n",
-       "3331    411.243981\n",
-       "3332    411.243981\n",
-       "3333    411.243981\n",
-       "3334    411.243981\n",
-       "3335    411.243981\n",
-       "3336    411.243981\n",
-       "3337    411.243981\n",
-       "3338    411.243981\n",
-       "3339    411.243981\n",
-       "3340    411.243981\n",
-       "3341    411.243981\n",
-       "3342    411.243981\n",
-       "3343    411.243981\n",
-       "3344    411.243981\n",
-       "3345    411.243981\n",
-       "3346    411.243981\n",
-       "Length: 3347, dtype: float64"
-      ]
-     },
-     "execution_count": 144,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y_pred"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 145,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([414.50641544])"
-      ]
-     },
-     "execution_count": 145,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.predict(2025)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/module3/moocrr-reproducibility-study.ipynb b/module3/moocrr-reproducibility-study.ipynb
new file mode 100644
index 0000000..c7f23de
--- /dev/null
+++ b/module3/moocrr-reproducibility-study.ipynb
@@ -0,0 +1,708 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.6.4 |Anaconda, Inc.| (default, Mar 13 2018, 01:15:57) \n",
+      "[GCC 7.2.0]\n",
+      "uname_result(system='Linux', node='98ca55aa610e', release='4.15.0-142-generic', version='#146~16.04.1-Ubuntu SMP Tue Apr 13 09:27:15 UTC 2021', machine='x86_64', processor='x86_64')\n",
+      "IPython 7.12.0\n",
+      "IPython.core.release 7.12.0\n",
+      "PIL 7.0.0\n",
+      "PIL.Image 7.0.0\n",
+      "PIL._version 7.0.0\n",
+      "_csv 1.0\n",
+      "_ctypes 1.1.0\n",
+      "_curses b'2.2'\n",
+      "decimal 1.70\n",
+      "argparse 1.1\n",
+      "backcall 0.1.0\n",
+      "cffi 1.13.2\n",
+      "csv 1.0\n",
+      "ctypes 1.1.0\n",
+      "cycler 0.10.0\n",
+      "dateutil 2.8.1\n",
+      "decimal 1.70\n",
+      "decorator 4.4.1\n",
+      "distutils 3.6.4\n",
+      "ipaddress 1.0\n",
+      "ipykernel 5.1.4\n",
+      "ipykernel._version 5.1.4\n",
+      "ipython_genutils 0.2.0\n",
+      "ipython_genutils._version 0.2.0\n",
+      "ipywidgets 7.2.1\n",
+      "ipywidgets._version 7.2.1\n",
+      "jedi 0.16.0\n",
+      "json 2.0.9\n",
+      "jupyter_client 6.0.0\n",
+      "jupyter_client._version 6.0.0\n",
+      "jupyter_core 4.6.3\n",
+      "jupyter_core.version 4.6.3\n",
+      "kiwisolver 1.1.0\n",
+      "logging 0.5.1.2\n",
+      "matplotlib 2.2.3\n",
+      "matplotlib.backends.backend_agg 2.2.3\n",
+      "numpy 1.15.2\n",
+      "numpy.core 1.15.2\n",
+      "numpy.core.multiarray 3.1\n",
+      "numpy.lib 1.15.2\n",
+      "numpy.linalg._umath_linalg b'0.1.5'\n",
+      "numpy.matlib 1.15.2\n",
+      "optparse 1.5.3\n",
+      "pandas 0.22.0\n",
+      "_libjson 1.33\n",
+      "parso 0.6.0\n",
+      "patsy 0.5.1\n",
+      "patsy.version 0.5.1\n",
+      "pexpect 4.8.0\n",
+      "pickleshare 0.7.5\n",
+      "platform 1.0.8\n",
+      "prompt_toolkit 3.0.3\n",
+      "ptyprocess 0.6.0\n",
+      "pygments 2.5.2\n",
+      "pyparsing 2.4.6\n",
+      "pytz 2019.3\n",
+      "re 2.2.1\n",
+      "scipy 1.1.0\n",
+      "scipy._lib.decorator 4.0.5\n",
+      "scipy._lib.six 1.2.0\n",
+      "scipy.fftpack._fftpack b'$Revision: $'\n",
+      "scipy.fftpack.convolve b'$Revision: $'\n",
+      "scipy.integrate._dop b'$Revision: $'\n",
+      "scipy.integrate._ode $Id$\n",
+      "scipy.integrate._odepack  1.9 \n",
+      "scipy.integrate._quadpack  1.13 \n",
+      "scipy.integrate.lsoda b'$Revision: $'\n",
+      "scipy.integrate.vode b'$Revision: $'\n",
+      "scipy.interpolate._fitpack  1.7 \n",
+      "scipy.interpolate.dfitpack b'$Revision: $'\n",
+      "scipy.linalg 0.4.9\n",
+      "scipy.linalg._fblas b'$Revision: $'\n",
+      "scipy.linalg._flapack b'$Revision: $'\n",
+      "scipy.linalg._flinalg b'$Revision: $'\n",
+      "scipy.ndimage 2.0\n",
+      "scipy.optimize._cobyla b'$Revision: $'\n",
+      "scipy.optimize._lbfgsb b'$Revision: $'\n",
+      "scipy.optimize._minpack  1.10 \n",
+      "scipy.optimize._nnls b'$Revision: $'\n",
+      "scipy.optimize._slsqp b'$Revision: $'\n",
+      "scipy.optimize.minpack2 b'$Revision: $'\n",
+      "scipy.signal.spline 0.2\n",
+      "scipy.sparse.linalg.eigen.arpack._arpack b'$Revision: $'\n",
+      "scipy.sparse.linalg.isolve._iterative b'$Revision: $'\n",
+      "scipy.special.specfun b'$Revision: $'\n",
+      "scipy.stats.mvn b'$Revision: $'\n",
+      "scipy.stats.statlib b'$Revision: $'\n",
+      "seaborn 0.8.1\n",
+      "seaborn.external.husl 2.1.0\n",
+      "seaborn.external.six 1.10.0\n",
+      "six 1.14.0\n",
+      "statsmodels 0.9.0\n",
+      "statsmodels.__init__ 0.9.0\n",
+      "traitlets 4.3.3\n",
+      "traitlets._version 4.3.3\n",
+      "urllib.request 3.6\n",
+      "zlib 1.0\n",
+      "zmq 17.1.2\n",
+      "zmq.sugar 17.1.2\n",
+      "zmq.sugar.version 17.1.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "def print_imported_modules():\n",
+    "    import sys\n",
+    "    for name, val in sorted(sys.modules.items()):\n",
+    "        if(hasattr(val, '__version__')): \n",
+    "            print(val.__name__, val.__version__)\n",
+    "#        else:\n",
+    "#            print(val.__name__, \"(unknown version)\")\n",
+    "def print_sys_info():\n",
+    "    import sys\n",
+    "    import platform\n",
+    "    print(sys.version)\n",
+    "    print(platform.uname())\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import statsmodels.api as sm\n",
+    "import seaborn as sns\n",
+    "\n",
+    "print_sys_info()\n",
+    "print_imported_modules()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading and inspecting data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>Date</th>\n",
+       "      <th>Count</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Pressure</th>\n",
+       "      <th>Malfunction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4/12/81</td>\n",
+       "      <td>6</td>\n",
+       "      <td>66</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>11/12/81</td>\n",
+       "      <td>6</td>\n",
+       "      <td>70</td>\n",
+       "      <td>50</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3/22/82</td>\n",
+       "      <td>6</td>\n",
+       "      <td>69</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11/11/82</td>\n",
+       "      <td>6</td>\n",
+       "      <td>68</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4/04/83</td>\n",
+       "      <td>6</td>\n",
+       "      <td>67</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6/18/82</td>\n",
+       "      <td>6</td>\n",
+       "      <td>72</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>8/30/83</td>\n",
+       "      <td>6</td>\n",
+       "      <td>73</td>\n",
+       "      <td>100</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>11/28/83</td>\n",
+       "      <td>6</td>\n",
+       "      <td>70</td>\n",
+       "      <td>100</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2/03/84</td>\n",
+       "      <td>6</td>\n",
+       "      <td>57</td>\n",
+       "      <td>200</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>4/06/84</td>\n",
+       "      <td>6</td>\n",
+       "      <td>63</td>\n",
+       "      <td>200</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>8/30/84</td>\n",
+       "      <td>6</td>\n",
+       "      <td>70</td>\n",
+       "      <td>200</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>10/05/84</td>\n",
+       "      <td>6</td>\n",
+       "      <td>78</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>11/08/84</td>\n",
+       "      <td>6</td>\n",
+       "      <td>67</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1/24/85</td>\n",
+       "      <td>6</td>\n",
+       "      <td>53</td>\n",
+       "      <td>200</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>4/12/85</td>\n",
+       "      <td>6</td>\n",
+       "      <td>67</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>4/29/85</td>\n",
+       "      <td>6</td>\n",
+       "      <td>75</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>6/17/85</td>\n",
+       "      <td>6</td>\n",
+       "      <td>70</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>7/2903/85</td>\n",
+       "      <td>6</td>\n",
+       "      <td>81</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>8/27/85</td>\n",
+       "      <td>6</td>\n",
+       "      <td>76</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>10/03/85</td>\n",
+       "      <td>6</td>\n",
+       "      <td>79</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>10/30/85</td>\n",
+       "      <td>6</td>\n",
+       "      <td>75</td>\n",
+       "      <td>200</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>11/26/85</td>\n",
+       "      <td>6</td>\n",
+       "      <td>76</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>1/12/86</td>\n",
+       "      <td>6</td>\n",
+       "      <td>58</td>\n",
+       "      <td>200</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Date  Count  Temperature  Pressure  Malfunction\n",
+       "0     4/12/81      6           66        50            0\n",
+       "1    11/12/81      6           70        50            1\n",
+       "2     3/22/82      6           69        50            0\n",
+       "3    11/11/82      6           68        50            0\n",
+       "4     4/04/83      6           67        50            0\n",
+       "5     6/18/82      6           72        50            0\n",
+       "6     8/30/83      6           73       100            0\n",
+       "7    11/28/83      6           70       100            0\n",
+       "8     2/03/84      6           57       200            1\n",
+       "9     4/06/84      6           63       200            1\n",
+       "10    8/30/84      6           70       200            1\n",
+       "11   10/05/84      6           78       200            0\n",
+       "12   11/08/84      6           67       200            0\n",
+       "13    1/24/85      6           53       200            2\n",
+       "14    4/12/85      6           67       200            0\n",
+       "15    4/29/85      6           75       200            0\n",
+       "16    6/17/85      6           70       200            0\n",
+       "17  7/2903/85      6           81       200            0\n",
+       "18    8/27/85      6           76       200            0\n",
+       "19   10/03/85      6           79       200            0\n",
+       "20   10/30/85      6           75       200            2\n",
+       "21   11/26/85      6           76       200            0\n",
+       "22    1/12/86      6           58       200            1"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(\"data_shuttle.csv\")\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "pd.set_option('mode.chained_assignment',None) # this removes a useless warning from pandas\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "data[\"Frequency\"]=data.Malfunction/data.Count\n",
+    "data.plot(x=\"Temperature\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n",
+    "plt.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###  Logistic regression\n",
+    "Let's assume O-rings independently fail with the same probability which solely depends on temperature. A logistic regression should allow us to estimate the influence of temperature."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>Generalized Linear Model Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>      <td>Frequency</td>    <th>  No. Observations:  </th>  <td>    23</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                 <td>GLM</td>       <th>  Df Residuals:      </th>  <td>    21</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model Family:</th>       <td>Binomial</td>     <th>  Df Model:          </th>  <td>     1</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Link Function:</th>        <td>logit</td>      <th>  Scale:             </th> <td>  1.0000</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>               <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -3.9210</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>           <td>Wed, 14 Feb 2024</td> <th>  Deviance:          </th> <td>  3.0144</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>               <td>09:42:06</td>     <th>  Pearson chi2:      </th>  <td>  5.00</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Iterations:</th>         <td>6</td>        <th>  Covariance Type:   </th> <td>nonrobust</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "       <td></td>          <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Intercept</th>   <td>    5.0850</td> <td>    7.477</td> <td>    0.680</td> <td> 0.496</td> <td>   -9.570</td> <td>   19.740</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Temperature</th> <td>   -0.1156</td> <td>    0.115</td> <td>   -1.004</td> <td> 0.316</td> <td>   -0.341</td> <td>    0.110</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                 Generalized Linear Model Regression Results                  \n",
+       "==============================================================================\n",
+       "Dep. Variable:              Frequency   No. Observations:                   23\n",
+       "Model:                            GLM   Df Residuals:                       21\n",
+       "Model Family:                Binomial   Df Model:                            1\n",
+       "Link Function:                  logit   Scale:                          1.0000\n",
+       "Method:                          IRLS   Log-Likelihood:                -3.9210\n",
+       "Date:                Wed, 14 Feb 2024   Deviance:                       3.0144\n",
+       "Time:                        09:42:06   Pearson chi2:                     5.00\n",
+       "No. Iterations:                     6   Covariance Type:             nonrobust\n",
+       "===============================================================================\n",
+       "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "-------------------------------------------------------------------------------\n",
+       "Intercept       5.0850      7.477      0.680      0.496      -9.570      19.740\n",
+       "Temperature    -0.1156      0.115     -1.004      0.316      -0.341       0.110\n",
+       "===============================================================================\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import statsmodels.api as sm\n",
+    "\n",
+    "data[\"Success\"]=data.Count-data.Malfunction\n",
+    "data[\"Intercept\"]=1\n",
+    "\n",
+    "logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], \n",
+    "                family=sm.families.Binomial(sm.families.links.logit)).fit()\n",
+    "\n",
+    "logmodel.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>Generalized Linear Model Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>      <td>Frequency</td>    <th>  No. Observations:  </th>  <td>    23</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                 <td>GLM</td>       <th>  Df Residuals:      </th>  <td>    21</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model Family:</th>       <td>Binomial</td>     <th>  Df Model:          </th>  <td>     1</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Link Function:</th>        <td>logit</td>      <th>  Scale:             </th> <td>  1.0000</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>               <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -23.526</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>           <td>Wed, 14 Feb 2024</td> <th>  Deviance:          </th> <td>  18.086</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>               <td>09:42:25</td>     <th>  Pearson chi2:      </th>  <td>  30.0</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Iterations:</th>         <td>6</td>        <th>  Covariance Type:   </th> <td>nonrobust</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "       <td></td>          <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Intercept</th>   <td>    5.0850</td> <td>    3.052</td> <td>    1.666</td> <td> 0.096</td> <td>   -0.898</td> <td>   11.068</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Temperature</th> <td>   -0.1156</td> <td>    0.047</td> <td>   -2.458</td> <td> 0.014</td> <td>   -0.208</td> <td>   -0.023</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                 Generalized Linear Model Regression Results                  \n",
+       "==============================================================================\n",
+       "Dep. Variable:              Frequency   No. Observations:                   23\n",
+       "Model:                            GLM   Df Residuals:                       21\n",
+       "Model Family:                Binomial   Df Model:                            1\n",
+       "Link Function:                  logit   Scale:                          1.0000\n",
+       "Method:                          IRLS   Log-Likelihood:                -23.526\n",
+       "Date:                Wed, 14 Feb 2024   Deviance:                       18.086\n",
+       "Time:                        09:42:25   Pearson chi2:                     30.0\n",
+       "No. Iterations:                     6   Covariance Type:             nonrobust\n",
+       "===============================================================================\n",
+       "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "-------------------------------------------------------------------------------\n",
+       "Intercept       5.0850      3.052      1.666      0.096      -0.898      11.068\n",
+       "Temperature    -0.1156      0.047     -2.458      0.014      -0.208      -0.023\n",
+       "===============================================================================\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], \n",
+    "                family=sm.families.Binomial(sm.families.links.logit),\n",
+    "                var_weights=data['Count']).fit()\n",
+    "\n",
+    "logmodel.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Predicting failure probability\n",
+    "The temperature when launching the shuttle was 31°F. Let's try to estimate the failure probability for such temperature using our model.:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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NHDlygXNuUE3tAjlbprqkOfQTIQbIAk4DEoHPzewL59yqgxZy7gngCYBBgwa5ESNGBLD5n8rOzqa2y4ab7OxsbvrFCJq0Wc9dby8jp6Q1t47uEeqyaiXa3pdo6Eu09APUl6MVSLjnAu2qTGcCm6pps905txfYa2YfA/2AVUhAfnlCB1ZvLWRq9lq6NE/hgiyNIikitRfIMfccoKuZdTSzOOBi4K1D2rwJnGRmMWaWBAwFlge31OhmZtx1Ti9O6NyU219fwoLvdoa6JBGJYDWGu3OuHLgBmIMvsF92zi01s0lmNsnfZjnwLrAY+Ap4yjn3bf2VHZ1ivR4evWwgrdMTuO75BeTt1hk0IlI7AZ3n7pyb7Zzr5pzr7Jy7xz/vMefcY1XaTHHO9XTO9XbOPVhfBUe79KQ4pv1yECVllUx4VmfQiEjt6ArVMNSlRSoPXzqAlVvy+a+XdaNtETl6CvcwNbJ7C/77rON4d+kWHv7P6lCXIyIRRgOHhbFrTuzIii0FPPj+arq3TOXMPq1DXZKIRAjtuYcxM+Oe83ozsH06v3v5G5Zt0m36RCQwCvcwt/8+rGmJsVz73Hx2FJaEuiQRiQAK9wjQIjWBJ67IYnthCb/+59e6i5OI1EjhHiH6ZqZz/wV9+XL9Tv749tJQlyMiYU5fqEaQMQPasnxzPo9/vI6erdO4dGj7UJckImFKe+4R5veje3BKt+bc+da3zN+gIQpEpHoK9wjj9RgPXzyAzGOSmPTCAjZpiAIRqYbCPQKlJcXy5BVZFJdVct3zCyguqwh1SSISZhTuEapLi1T+dlF/luTt4fbXl1DTTVdEpHFRuEew03u25Hend+ONhXlM+3R9qMsRkTCicI9wN4zswuherfjz7OV8snpbqMsRkTChcI9wHo/xwLh+dGmRwg0vLmTjjn2hLklEwoDCPQqkxMfw5BW+++Ve+5zGgBcRhXvUOLZpMv+4dACrtxZwyyvf6AtWkUZO4R5FTuranNvO7MG/v93CIx+uCXU5IhJCCvcoc+1JnRjTvw1/eW8V7y/7IdTliEiIKNyjjJlx3wV96dWmCTfPXMSarYWhLklEQkDhHoUSYr08Pn4Q8TEeJj43nz1FZaEuSUQamMI9SrVNT2Tq5Vls3LmP38xYSIVusi3SqCjco9iQjhnceU4vsldu44G5K0Ndjog0II3nHuUuH9qeZZvymZq9lp6tm3B2vzahLklEGoD23KOcmfHHc3ox6NhjmPzqN3ybtyfUJYlIA1C4NwJxMR6mXp7FMUlxTHxuPtt1k22RqKdwbySap8bz5BWD2LG3lF+9sIDSct1kWySaKdwbkd5t0/i/sX3J2bCLO99aqiEKRKKYvlBtZM7t35blmwt47KO19GydyvhhHUJdkojUA+25N0KTR3Xn1B4t+OPby/hs7fZQlyMi9UDh3gh5PcZDF/enQ7Nkrv/n1xoDXiQKKdwbqdSEWJ66YhCVDiY8l0NBsYYoEIkmCvdGrEOzZB69bCBrt+3l5hmLNESBSBRRuDdyw7s0486ze/LBiq1MmaMhCkSihc6WEcYffywrt/jOoOnWMoXzB2aGuiQRqaOA9tzNbLSZrTSzNWZ22xHaDTazCjMbG7wSpb6ZGXed04thnZpy22tLWPDdzlCXJCJ1VGO4m5kXeAQ4E+gJXGJmPQ/T7n5gTrCLlPoX6/Uw9fKBtElPYOJzC8jdpTNoRCJZIHvuQ4A1zrl1zrlSYAZwbjXtbgReA7YGsT5pQOlJcUy7cjClFZVMeHa+zqARiWBW0yXo/kMso51zE/zT44GhzrkbqrRpC7wInApMA95xzr1azbomAhMBWrZsmTVjxoxaFV1YWEhKSkqtlg034diXpdsr+MuCYno383LzwHg8ZgEtF459qa1o6Uu09APUl/1Gjhy5wDk3qKZ2gXyhWt3/7EM/ER4EbnXOVdgRgsA59wTwBMCgQYPciBEjAtj8T2VnZ1PbZcNNOPZlBJCW+R1/mPUtnxS24M6zewW0XDj2pbaipS/R0g9QX45WIOGeC7SrMp0JbDqkzSBghj/YmwFnmVm5c25WUKqUBnf58ceybttenp63nk7NkjUGjUiECSTcc4CuZtYRyAMuBi6t2sA513H/czObju+wjII9wv2/nx/Hdzv2cudbS8nMSGJk9xahLklEAlTjF6rOuXLgBnxnwSwHXnbOLTWzSWY2qb4LlNDxeoyHLxnAca2bcMM/v2bZpvxQlyQiAQroPHfn3GznXDfnXGfn3D3+eY855x6rpu2V1X2ZKpEpOT6Gab8cTGpCLNc8m8OWPcWhLklEAqDhB6RGrdISmHblIPKLyrh6eg6FJeWhLklEaqBwl4D0apPGI5cNZOUPBdzw4teUV+g2fSLhTOEuARvRvQV/Orc32Su3cceb3+o2fSJhTAOHyVG5dGh7cnft49HstbRNT+SGU7uGuiQRqYbCXY7a5FHd2bKnmAfmrqJVWiJjszSKpEi4UbjLUTMz7rugL1sLSrjttcU0T43nlG7Na7WuWQvzmDJnJZt2F9EmPZHJo7ozZkDbIFcs9UXvX/jSMXeplbgY3yiS3Vqm8qsXFvDN97uPeh2zFuZx++tLyNtdhAPydhdx++tLmLUwL/gFS9Dp/QtvCneptdSEWKZfPZimKXFcPT2HLXuP7gyaKXNWUlRWcdC8orIK3REqQuj9C28Kd6mTFqkJPHvVEBzwwPxifsgP/CKnTbuLjmq+hBe9f+FN4S511ql5CtOvGkxhqeOKaV+xZ19g48C3SU88qvkSXvT+hTeFuwRF38x0bhqYwPrte7n62Rz2ldZ8FevkUd1JjPUeNC8x1svkUd3rq0wJIr1/4U3hLkHTs6mXBy/uz8KNu5j0wteUlh/5GPyYAW259/w+tE1PxIC26Ynce34fnW0RIfT+hTedCilBdVaf1tx3fl9+/9pifjtzEQ9fMgCv5/A3cBkzoK3CIILp/QtfCncJunGD25FfXMbd/1pOcryX+87vi+cIAS8iwadwl3ox4aROFBSX89AHq0mM9XLXOb040i0YRSS4FO5Sb27+WVf2lZbz5CfrSYjzctvoHgp4kQaicJd6Y2b891nHsa+0gsc/Wkd8jJffnd4t1GWJNAoKd6lXZsafzu1NaXklD3+wmliPceNpGklSpL4p3KXeeTy+gcYqKh1/eW8VMV4PvxrROdRliUQ1hbs0CK/HmHJhP8orHfe/uwKH49cjuoS6LJGopXCXBuP1GH8d1w8z+L93V+IcXD9SAS9SHxTu0qBivB7+Oq4/hm9UwfIKx02nddFZNCJBpnCXBuf1GH8Z1x+vx8Pf3l9FSXkFk0d1V8CLBJHCXULC6zGmjO1LXIyHR7PXUlxWyR2/OE4BLxIkCncJGY/H+PN5vYmP8fD0vPXsKy3nnvP6HHEsGhEJjMJdQsrMuPPsnqQmxPD3/6yhsKScv47rT1yMBiwVqQuFu4ScmfFfZ3QnJT6Ge/+9goLicqZePpCkOP3zFKkt7R5J2LjulM7cd34fPlm9jUuf/JJde0tDXZJIxFK4S1i5eEh7Hr0si2Wb87nw8c/J0/04RWpF4S5hZ3TvVjx71RB+yC/mvEfmsWxTfqhLEok4CncJS8M6N+WVScPwmDHu8c/5ZPW2UJckElEU7hK2erRqwhvXn0DmMYlc9UwOM3M2hrokkYihcJew1jotkVcmDWNY56bc+toS7n93BZWVLtRliYQ9hbuEvdSEWJ6+cjCXDGnP1Oy1/OqfC9hXWh7qskTCWkDhbmajzWylma0xs9uqef0yM1vs//nMzPoFv1RpzGK9Hv58Xm/u+EVP3lv2A2Onfs4mnUkjclg1hruZeYFHgDOBnsAlZtbzkGbrgVOcc32BPwFPBLtQETPjmhM7Mu3KwXy/cx9n//1Tvlq/M9RliYSlQPbchwBrnHPrnHOlwAzg3KoNnHOfOed2+Se/ADKDW6bIj0Z2b8Eb1w8nLTGWS5/8guc/34BzOg4vUpXV9J/CzMYCo51zE/zT44GhzrkbDtP+FqDH/vaHvDYRmAjQsmXLrBkzZtSq6MLCQlJSUmq1bLhRX2pvX5nj8cUlfLOtguFtYriiVxzx3uAMOhYt70u09APUl/1Gjhy5wDk3qMaGzrkj/gAXAk9VmR4P/P0wbUcCy4GmNa03KyvL1daHH35Y62XDjfpSNxUVle5v7610HW57x43620du/bbCoKw3Wt6XaOmHc+rLfsB8V0O+OucCOiyTC7SrMp0JbDq0kZn1BZ4CznXO7QhgvSJ15vEYN/+sG89cOZjNe4o5+++f8q/Fm0NdlkjIBRLuOUBXM+toZnHAxcBbVRuYWXvgdWC8c25V8MsUObIR3Vvwr5tOpHOLFK5/8WvumPUtxWUVoS5LJGRqDHfnXDlwAzAH3yGXl51zS81skplN8jf7H6Ap8KiZLTKz+fVWschhZB6TxMvXDePakzry/BffMeaReaz+oSDUZYmEREADZjvnZgOzD5n3WJXnE4CffIEq0tBmL9nM7CVbAFj1QwFnPfwJY/q3Zd6a7WzeU0yb9EQmj+rOmAFtg77tWQvzmDJnJZt2F9XrdgLxh1lLeOnL77m5dxnX3D6bS4a24+4xfUJSi4SG7oYgUWPWwjxuf30JRf7DMZUOXIXjlQW5B9rk7S7i9teXAAQ1eA/ddn1tJxB/mLWEF774cRyeCucOTCvgGw8NPyBRY8qclQfCdb/qTvQtKqtgypyV9b7t+thOIF768vujmi/RSeEuUeNohiMI9k1ADrftUAyRUHGYa1cON1+ik8Jdokab9MSA23oM3lyUF7QrWw+37aOpKVi8Vv2FXIebL9FJ4S5RY/Ko7iTGeg+aF+sxYg+5ajU+xkPmMUn8ZsYiJjw7n9xd++pl24mxXiaP6l7ndR+tS4a2O6r5Ep0U7hI1xgxoy73n96FteiIGtE1PZMqF/Zgytt9B8+6/oC8f3jKCP/z8OD5bu4PT//oxT3y8lrKKyqBu+97z+4TkbJm7x/Th8uPbH9hT95px+fHt9WVqI6OzZSSqjBnQttpArW7ehJM6Mbp3K+56ayl/nr2C1xbk8cdze3F8p6ZB3XYo3D2mD3eP6UN2djZrLxsR6nIkBLTnLo1a5jFJPPXLwTwxPovCknIufuILbnppITuLa78XLxIOtOcuApzRqxUndW3O1I/W8thHa3m3spLvvKu47pROJMXpv4lEHu25i/glxnn53end+OB3p9C/hZeHPljNyAeymZmzkQrdt1UijMJd5BDtMpL4df8EXpk0jNZpidz62hLOfOhj3lv2g24KIhFD4S5yGIM7ZPDGr0/g0csGUlbhuPa5+Yx59DM+Xb1dIS9hT+EucgRmxll9WvPeb0/m/gv6sC2/mMunfcm4xz9n3hqFvIQvhbtIAGK8Hi4a3J7/3DKC/z23F9/vLOKyp77kgqmf8Z8VOlwj4UfhLnIUEmK9XDGsA9mTR/Cnc3vxQ34JV0+fz1kPf8obC3PrdCGUSDAp3EVqISHWy3h/yD9wYT/KKir57cxvOPn/PuTxj9ayZ19ZqEuURk4n8IrUQazXw9isTM4f0JbsVVt54uN13PvvFTz4/mouyGrLFcM60K1laqjLlEZI4S4SBB6PcWqPlpzaoyVLN+1h+rwNvDw/lxe+2MjQjhlcfvyxnNGrJfEx3ppXJhIECneRIOvVJo0pF/bj9rOO4+X53/PCF99x40sLyUiO44KBbRk3qB1dtTcv9UzhLlJPMpLjmHRKZyae1IlP12znpa828sy8DTz5yXr6ZaYxNiuTn/dtQ0ZyXKhLlSikcBepZx6PcXK35pzcrTnbC0uYtTCPVxfkcsebS/nj28sY0b05Z/drw8+Oa0lyvP5LSnDoX5JIA2qWEs+Ekzox4aROLN+cz6yFeby5aBPvL99KQqyHU3u04MzerRnZowUpCnqpA/3rEQmR41o34bjWTbh1dA/mf7eLt7/ZxL+/3cLsJVuIi/FwUpdmnN6zJacd15LmqfGhLlcijMJdJMQ8HmNIxwyGdMzgrnN6seC7Xcxespn3lv3AByu2YraEvpnpnNajBSO6N6d3mzQ8Ht0PVY5M4S4SRrxVgv7Os3uyfHMBHyz3hfzf3l/FX99bRUZyHCd2acaJXZtxYpc9/m12AAANA0lEQVRmIbkJt4Q/hbtImDIzerZpQs82TbjxtK5sLyzh09XbyV65lU/XbOetbzYB0LFZMsd3yuD4Tk0Z0jGD1mkKe1G4i0SMZinxB+7T6pxj5Q8FfLp6O5+v3cE732zmpa++B6BdRiKDj81g4LHH4Aoqqah0eHUYp9FRuItEIDOjR6sm9GjVhAkndaK8opLlmwv4asNOvlq/g49Xb+P1hXkA3Jczh76Z6fRrl06/zDT6tkunTVoCZgr8aKZwF4kCMV4PfTLT6JOZxjUndsQ5x8ad+3jh3c8oSWnNwo27mfbpOsoqfEMTH5MUS++2afRs7Tvsc1zrJnRslkysV2MJRguFu0gUMjOObZrM8LaxjBjRG4CS8gpWbC5gce5ulm7K59tNe3hm3gZK/cMUx3k9dGqeTPdWqXRtkULXlql0aZFC+4wkhX4EUriLNBLxMV7foZl26QfmlVVUsm7bXpZvzmfFlgJWbMln/oZdvLlo04E2sV6jfUYSHZul0Kl5Msc2TaJj02TaN02idVqijueHKYW7SCMW6/XQvVUq3VsdPJBZYUk5a7YWsnZrIWu2FbJ+217Wb9/Lx6u3UVpeWWV5o216Iu0yksg8JonMYxJpm55I22MSaZOeSMvUeGK01x8SCncR+YmU+Bj6t0unf5W9fIDKSseW/GI2bN/Lhh37+H7XPjbu2Efurn3M3bSFHXtLD2rvMWiRmkCrtARaNfE9tmgST4vUBFqkxtOiSTzNUuLJSIrThVlBpnAXkYB5PEabdN9e+Qldfvr6vtJyNu0uIndXEZv3FLN5dxGb9hTzQ34xa7YVMm/tdgqKy3+ynNdjZCTH0TQ5jmYp8TRNiSMjOY6MpDgyUuI4JimO73ZU0HJzPsckxZGWGEtCrEdn/ByBwl1EgiYpLoYuLVLp0uLw49XvKy1na34JWwtK2FpQzPaCErYXlrKtoIQde0vZXljCxp372LW3lIKSgz8I7s/55MDzOK+HtKRYmiTEkJYYS5PEWJokxJKaEEPqgccYUuJ//En2//iee0mKi4na7wwCCnczGw08BHiBp5xz9x3yuvlfPwvYB1zpnPs6yLWKRK1ZC/OYMmclm3YX0SY9kcmjuvPK/I3MW7vzQJvhnTO4cFD7n7QDfjJv/nc7eenL77m5dxnX3D6bS4a24+4xfQLa7pgBbQ87P5Dl92+7wjm8Zj/ZdlJcDB2axbDo+9019uXOs3tycrfm7NxXyofzcujQrSe79pXx2drtZK/cxraCEgqKy/B6jLIKx4bte8kvLqeguOzAaZ81iY/xkBTnC/qkOC+JcV4SY70kxXlJiPU9T4jzkhDjJSHWQ0Lsj4/xMR7iY/yPsT8+j9v/4/3xebzXS2yMEev14FxgtdVFjeFuZl7gEeB0IBfIMbO3nHPLqjQ7E+jq/xkKTPU/ikgNZi3M4/bXl1BUVgFA3u4ibp656Cft5q3deVDY5+0uYvKr34CDskp3YN7vZi6isspyFc7xwhcbAQ4K2eq2e/vrS5j/3U5eW5D3k/nAQQFf3fJ12fbkV74B40Ao5+0u4o43l3Lv+X0YM6AtW5p6GdGnNbMW5vHB8q0Hli0uq+T7nUUH2u1XXFZBYUk5hcXlFBSXU1hSzt6ScvaWlrO3pIK9JeXsK61gb2k5+0p9z4tKKw487thbemC6pLyC4rJKisoqqKisezCf2TGWkSPrvJojCmTPfQiwxjm3DsDMZgDnAlXD/VzgOef7OPrCzNLNrLVzbnPQKxaJMlPmrDwQVEerur3TymraAbz05fcHBWx12y0qqziw133o/ClzVh4UntUtX5dtl1UTmoFut7p2vj1sL81SgjtccllFJcVlFZSUV1JS7nte6n/ue6ygpKyS0grf9IHH8krKKnw/nl0bg1pTdaymPw/MbCww2jk3wT89HhjqnLuhSpt3gPucc5/6pz8AbnXOzT9kXROBif7J7sDKWtbdDNhey2XDjfoSnhqsL3GtumTV17or9u3Bm5R2YLp0y5oFddluXZYPwrLNgO1HWrbqNsJcXf59Heuca15To0D23Kv7tuHQT4RA2uCcewJ4IoBtHrkgs/nOuUF1XU84UF/CU7T0xczml+/ZGvH9gOh5T6Bh+hLI1QW5QLsq05nAplq0ERGRBhJIuOcAXc2so5nFARcDbx3S5i3gCvM5Htij4+0iIqFT42EZ51y5md0AzMF3KuTTzrmlZjbJ//pjwGx8p0GuwXcq5FX1VzIQhEM7YUR9CU/R0pdo6QeoL0elxi9URUQk8mhEHxGRKKRwFxGJQmEf7maWYGZfmdk3ZrbUzP7on59hZu+Z2Wr/4zGhrjUQZuY1s4X+awMiuR8bzGyJmS0ys/n+eZHal3Qze9XMVpjZcjMbFol9MbPu/vdj/0++md0coX35rf//+7dm9pI/ByKuHwBm9ht/P5aa2c3+efXel7APd6AEONU51w/oD4z2n5FzG/CBc64r8IF/OhL8BlheZTpS+wEw0jnXv8r5upHal4eAd51zPYB++N6fiOuLc26l//3oD2ThO7nhDSKsL2bWFrgJGOSc643vRI6LibB+AJhZb+BafFf69wN+YWZdaYi+OOci5gdIAr7GN27NSqC1f35rYGWo6wug/kz/G3kq8I5/XsT1w1/rBqDZIfMiri9AE2A9/pMLIrkvh9R/BjAvEvsCtAW+BzLwndH3jr8/EdUPf50X4htscf/0HcDvG6IvkbDnvv9QxiJgK/Cec+5LoKXzn0vvf2wRyhoD9CC+N7bqEByR2A/wXYE818wW+IeVgMjsSydgG/CM/3DZU2aWTGT2paqLgZf8zyOqL865POABYCOwGd91M3OJsH74fQucbGZNzSwJ3ynj7WiAvkREuDvnKpzvT81MYIj/T52IYma/ALY65yJl7IuaDHfODcQ3Iuj1ZnZyqAuqpRhgIDDVOTcA2EsE/Ll/JP6LDc8BXgl1LbXhP/58LtARaAMkm9nloa2qdpxzy4H7gfeAd4FvgJ/eraQeRES47+ec2w1kA6OBH8ysNYD/cWsISwvEcOAcM9sAzABONbMXiLx+AOCc2+R/3IrvuO4QIrMvuUCu/69BgFfxhX0k9mW/M4GvnXM/+KcjrS8/A9Y757Y558qA14ETiLx+AOCcm+acG+icOxnYCaymAfoS9uFuZs3NLN3/PBHfG78C35AHv/Q3+yXwZmgqDIxz7nbnXKZzrgO+P5n/45y7nAjrB4CZJZtZ6v7n+I6HfksE9sU5twX43sy6+2edhm8464jrSxWX8OMhGYi8vmwEjjezJDMzfO/JciKvHwCYWQv/Y3vgfHzvTb33JeyvUDWzvsCz+L4x9wAvO+f+18yaAi8D7fH9Y7jQObfz8GsKH2Y2ArjFOfeLSOyHmXXCt7cOvsMaLzrn7onEvgCYWX/gKSAOWIdv+AwPkdmXJHxfRnZyzu3xz4u498V/yvNF+A5hLAQmAClEWD8AzOwToClQBvzOOfdBQ7wnYR/uIiJy9ML+sIyIiBw9hbuISBRSuIuIRCGFu4hIFFK4i4hEoUBukC3SoPyniX3gn2wFVOAbIgBgiHOuNCSFHYGZXQ3M9p83LxJyOhVSwpqZ3QUUOuceCINavM65isO89ilwg3Nu0VGsL8Y51yCXokvjo8MyElHM7JfmG99/kZk9amYeM4sxs91mNsXMvjazOWY21Mw+MrN1ZnaWf9kJZvaG//WVZvaHANd7t5l9hW9coz+aWY5/fO7HzOcifMNRz/QvH2dmuVWurD7ezN73P7/bzB43s/fwDVYWY2Z/9W97sZlNaPjfqkQjhbtEDP+AcecBJ/gHkovBN5QDQBow1z+YWSlwF77L1i8E/rfKaob4lxkIXGpm/QNY79fOuSHOuc+Bh5xzg4E+/tdGO+dmAouAi5xvPPWaDhsNAM52zo0HJuIbUG4IMBjfIGzta/P7EalKx9wlkvwMXwDO9w05QiK+S+0Bipxz7/mfL8E3TGy5mS0BOlRZxxzn3C4AM5sFnIjv/8Hh1lvKj0MtAJxmZpOBBKAZsAD491H2403nXLH/+RnAcWZW9cOkK75L0kVqTeEukcSAp51zdxw00ywGXwjvV4nvDl77n1f9d37ol0yuhvUWOf8XU/5xW/4BDHTO5ZnZ3fhCvjrl/PiX8aFt9h7Sp1875z5AJIh0WEYiyfvAODNrBr6zampxCOMM890zNQnfmOHzjmK9ifg+LLb7R8W8oMprBUBqlekN+G51xyHtDjUH+LX/g2T/fVATj7JPIj+hPXeJGM65Jf7RAt83Mw++UfYmAZuOYjWfAi8CnYHn95/dEsh6nXM7zOxZfMMbfwd8WeXlZ4CnzKwI33H9u4AnzWwL8NUR6nkc38iAi/yHhLbi+9ARqROdCimNhv9MlN7OuZtDXYtIfdNhGRGRKKQ9dxGRKKQ9dxGRKKRwFxGJQgp3EZEopHAXEYlCCncRkSj0/wHRUJwHFwSFegAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "data_pred = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=121), 'Intercept': 1})\n",
+    "data_pred['Frequency'] = logmodel.predict(data_pred)\n",
+    "data_pred.plot(x=\"Temperature\",y=\"Frequency\",kind=\"line\",ylim=[0,1])\n",
+    "plt.scatter(x=data[\"Temperature\"],y=data[\"Frequency\"])\n",
+    "plt.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set(color_codes=True)\n",
+    "plt.xlim(30,90)\n",
+    "plt.ylim(0,1)\n",
+    "sns.regplot(x='Temperature', y='Frequency', data=data, logistic=True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
-- 
2.18.1