Scenario2-homeworking.ipynb

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   "cell_type": "code",
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   "id": "former-horizontal",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go"
   ]
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  {
   "cell_type": "markdown",
   "id": "miniature-movie",
   "metadata": {},
   "source": [
    "# Mobility Data"
   ]
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   ],
   "source": [
    "mobility = pd.read_csv('data/COVID-19-transport-use-statistics.csv')\n",
    "\n",
    "# mobility plot \n",
    "fig = px.line(mobility, x=mobility.Date, y=mobility.drop(['Date'], axis=1).columns,\n",
    "              title='Mobility during covid')\n",
    "\n",
    "# add baseline\n",
    "fig.add_shape(type=\"line\",\n",
    "        x0=mobility.index.min(),\n",
    "        y0=1,\n",
    "        x1=mobility.index.max(),\n",
    "        y1=1,\n",
    "    name = 'baseline',\n",
    "    line=dict(color='Red'))\n",
    "\n",
    "fig.update_layout(margin={\"r\":0,\"t\":50,\"l\":0,\"b\":30})\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "neutral-digit",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-22-4a5d52eedf89>:3: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# convert strings to datetime\n",
    "for i in range(len(mobility['Date'])):\n",
    "    mobility['Date'][i] = datetime.strptime(mobility['Date'][i], '%d/%m/%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "unsigned-grove",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cars mobility mean during April 2020:  32.0\n",
      "All vehicles mean during April 2020:  41.25\n"
     ]
    }
   ],
   "source": [
    "def mobility_mean(start_date, end_date): \n",
    "    '''Average mobility of cars and total vehicles during specified dates'''\n",
    "    \n",
    "    cars_covid  = mobility[(mobility['Date'] > start_date) & (mobility['Date'] < end_date) ]['Cars']\n",
    "    lcv_covid = mobility[(mobility['Date'] > start_date) & (mobility['Date'] < end_date)]['Light Commercial Vehicles']\n",
    "    hgv_covid = mobility[(mobility['Date'] > start_date) & (mobility['Date'] < end_date)]['Heavy Goods Vehicles']\n",
    "    others_covid = mobility[(mobility['Date'] > start_date) & (mobility['Date'] < end_date)]['All motor vehicles']\n",
    "    sum_vehicles = (cars_covid+lcv_covid+hgv_covid+others_covid)/4\n",
    "\n",
    "    return(np.mean(cars_covid)*100, np.mean(sum_vehicles)*100)\n",
    "    \n",
    "#-------------------------------    \n",
    "car_mean, all_vehicles_mean = mobility_mean(datetime(2020,4,1), datetime(2020,4,3))\n",
    "print('Cars mobility mean during April 2020: ', car_mean)\n",
    "print('All vehicles mean during April 2020: ', all_vehicles_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "parental-ranking",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Car mobility decrese in April 2020: -66.92857142857143\n",
      "All vehicles mobility decrese in April 2020: -57.169642857142854\n"
     ]
    }
   ],
   "source": [
    "def mobibiliy_change(start_date, end_date):\n",
    "    '''Percentage change of mobility between given dates in comparison to baseline'''\n",
    "    \n",
    "    cars_covid  = mobility[(mobility['Date'] > start_date) & (mobility['Date'] < end_date) ]['Cars']\n",
    "    lcv_covid = mobility[(mobility['Date'] > start_date) & (mobility['Date'] < end_date)]['Light Commercial Vehicles']\n",
    "    hgv_covid = mobility[(mobility['Date'] > start_date) & (mobility['Date'] < end_date)]['Heavy Goods Vehicles']\n",
    "    others_covid = mobility[(mobility['Date'] > start_date) & (mobility['Date'] < end_date)]['All motor vehicles']\n",
    "    sum_vehicles = (cars_covid+lcv_covid+hgv_covid+others_covid)/4\n",
    "\n",
    "    change_cars = ((np.mean(cars_covid)-1)/1)*100\n",
    "    change_all = ((np.mean(sum_vehicles)-1)/1)*100\n",
    "    return(change_cars , change_all)\n",
    "    \n",
    "#------------------------------- \n",
    "change_cars, change_all = mobibiliy_change(datetime(2020,4,1), datetime(2020,4,30))\n",
    "print('Car mobility decrese in April 2020:' , change_cars)\n",
    "print('All vehicles mobility decrese in April 2020:' , change_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accomplished-modem",
   "metadata": {},
   "source": [
    "# Carbon Emission\n",
    "\n",
    "https://carbonmonitor.org  \n",
    "data filtered for UK, for Ground Transport sector from 1st January 2019 to 31st December 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "palestinian-granny",
   "metadata": {},
   "outputs": [
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "co2 = pd.read_csv('data/carbon-monitor-UK.csv')\n",
    "\n",
    "# plot emissions over time\n",
    "fig = px.line(co2, x=co2.date, y=co2.drop(['date', 'sector'], axis=1).columns,\n",
    "              title='Carbon emission during covid')\n",
    "fig.update_layout(margin={\"r\":0,\"t\":50,\"l\":0,\"b\":30})\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "powerful-liquid",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-26-b2509f916dfd>:3: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# convert strings to datetime\n",
    "for i in range(len(co2['date'])):\n",
    "    co2['date'][i] = datetime.strptime(co2['date'][i], '%d/%m/%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "expected-richmond",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Daily CO2 emission mean pre-covid: 0.31364421374117674 MtCO2\n"
     ]
    }
   ],
   "source": [
    "# average pre covid mean\n",
    "old_co2 = np.mean(co2[co2['date'] < datetime(2020, 3, 1)]['MtCO2 per day'])\n",
    "print(\"Daily CO2 emission mean pre-covid: {} MtCO2\".format(old_co2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "underlying-bicycle",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Carbon mean in April 2020: 0.1827151192857143\n",
      "Carbon change in April 2020: -41.74446354158054\n"
     ]
    }
   ],
   "source": [
    "def carbon_change(start_date, end_date):\n",
    "    # average pre covid\n",
    "    old_co2 = np.mean(co2[co2['date'] < datetime(2020, 3, 1)]['MtCO2 per day'])\n",
    "    #average over given time\n",
    "    new_co2 = np.mean(co2[(co2['date'] > start_date) & (co2['date'] < end_date)]['MtCO2 per day'])\n",
    "\n",
    "    percentage_change = ((new_co2-old_co2)/old_co2)*100\n",
    "\n",
    "    return new_co2, percentage_change\n",
    "\n",
    "# -----------\n",
    "\n",
    "mean_april, change_april =  carbon_change(datetime(2020, 4, 1), datetime(2020, 4, 30))\n",
    "print('Carbon mean in April 2020:' , mean_april)\n",
    "print('Carbon change in April 2020:' , change_april)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "urban-pakistan",
   "metadata": {},
   "source": [
    "# Homeworking\n",
    "https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/bulletins/coronavirustheukeconomyandsocietyfasterindicators/1october2020#measuring-the-data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "metropolitan-satin",
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "homeworking_ons = pd.read_csv('data/homework_ons.csv')\n",
    "\n",
    "fig = px.line(homeworking_ons, x=homeworking_ons.Date, y=homeworking_ons.drop(['Date'], axis=1).columns,\n",
    "              title='Homeworking')\n",
    "fig.update_layout(margin={\"r\":0,\"t\":50,\"l\":0,\"b\":30})\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "legitimate-consumption",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get dates from homeworking ONS dataset\n",
    "dates = []\n",
    "for i in homeworking_ons['Date']:\n",
    "    split = i.strip().split(' ')\n",
    "    if split[-1] == 'May':\n",
    "        dates.append( (datetime(2020,5,int(split[0])), datetime(2020,5,int(split[2]))) )\n",
    "    if split[-1] == 'June':\n",
    "        dates.append( (datetime(2020,6,int(split[0])), datetime(2020,6,int(split[2]))) )\n",
    "    if split[-1] == 'July':\n",
    "        dates.append( (datetime(2020,7,int(split[0])), datetime(2020,7,int(split[2]))) )\n",
    "    if split[-1] == 'August':\n",
    "        dates.append( (datetime(2020,8,int(split[0])), datetime(2020,8,int(split[2]))) )\n",
    "    if split[-1] == 'September':\n",
    "        dates.append( (datetime(2020,9,int(split[0])), datetime(2020,9,int(split[2]))) )\n",
    "        \n",
    "dates[11] = (datetime(2020, 7, 29, 0, 0), datetime(2020, 8, 2, 0, 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "angry-seeker",
   "metadata": {},
   "source": [
    "# Homeworking baseline\n",
    "https://data.europa.eu/euodp/en/data/dataset/orJJzGDF3cnXimvsoKDfXw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "quiet-blond",
   "metadata": {},
   "outputs": [
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       "      <td>5.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.8 b</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>4.4</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25851</th>\n",
       "      <td>PC</td>\n",
       "      <td>M</td>\n",
       "      <td>NVR</td>\n",
       "      <td>Y20-64</td>\n",
       "      <td>EMP</td>\n",
       "      <td>UK</td>\n",
       "      <td>72.8</td>\n",
       "      <td>75.7</td>\n",
       "      <td>75.3</td>\n",
       "      <td>75.6</td>\n",
       "      <td>...</td>\n",
       "      <td>71.5</td>\n",
       "      <td>71.5</td>\n",
       "      <td>71.2 b</td>\n",
       "      <td>71.2</td>\n",
       "      <td>66.8</td>\n",
       "      <td>67.8</td>\n",
       "      <td>68.1</td>\n",
       "      <td>68.1</td>\n",
       "      <td>67.7</td>\n",
       "      <td>66.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33698</th>\n",
       "      <td>PC</td>\n",
       "      <td>M</td>\n",
       "      <td>SMT</td>\n",
       "      <td>Y20-64</td>\n",
       "      <td>EMP</td>\n",
       "      <td>UK</td>\n",
       "      <td>23.1</td>\n",
       "      <td>20.5</td>\n",
       "      <td>21.4</td>\n",
       "      <td>20.9</td>\n",
       "      <td>...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.6 b</td>\n",
       "      <td>27.4</td>\n",
       "      <td>31.9</td>\n",
       "      <td>30.8</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.4</td>\n",
       "      <td>30.9</td>\n",
       "      <td>24.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      unit sex frequenc     age wstatus geo  2019   2018   2017   2016   ...  \\\n",
       "2382    PC   F      NVR  Y20-64     EMP  UK  73.0   75.4   75.6   75.8   ...   \n",
       "10227   PC   F      SMT  Y20-64     EMP  UK  21.5   19.3   19.3   19.1   ...   \n",
       "18016   PC   F      USU  Y20-64     EMP  UK   5.5    5.2    5.1    5.1   ...   \n",
       "25851   PC   M      NVR  Y20-64     EMP  UK  72.8   75.7   75.3   75.6   ...   \n",
       "33698   PC   M      SMT  Y20-64     EMP  UK  23.1   20.5   21.4   20.9   ...   \n",
       "\n",
       "       2001   2000    1999   1998   1997   1996   1995   1994   1993   1992   \n",
       "2382   75.6   75.6   76.1 b  75.5   72.1   72.9   73.7   73.8   73.2   74.5   \n",
       "10227  20.6   20.5   20.1 b  20.4   23.8   22.9   22.2   21.8   22.3   18.5   \n",
       "18016   3.8    3.9    3.8 b   4.1    4.0    4.2    4.1    4.4    4.4    7.0   \n",
       "25851  71.5   71.5   71.2 b  71.2   66.8   67.8   68.1   68.1   67.7   66.7   \n",
       "33698  27.0   27.0   27.6 b  27.4   31.9   30.8   30.5   30.4   30.9   24.5   \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# clean the file as its messy\n",
    "\n",
    "homeworking1 = pd.read_csv('data/homeworking.tsv')\n",
    "name = 'geo\\\\time\\t2019 \\t2018 \\t2017 \\t2016 \\t2015 \\t2014 \\t2013 \\t2012 \\t2011 \\t2010 \\t2009 \\t2008 \\t2007 \\t2006 \\t2005 \\t2004 \\t2003 \\t2002 \\t2001 \\t2000 \\t1999 \\t1998 \\t1997 \\t1996 \\t1995 \\t1994 \\t1993 \\t1992 '\n",
    "\n",
    "# get geo location\n",
    "geo = []\n",
    "for i in range(len(homeworking1[name])):\n",
    "    geo.append(homeworking1[name][i][0:2])\n",
    "\n",
    "homeworking2 = pd.read_csv('data/homeworking.tsv', delimiter='\\t')\n",
    "\n",
    "# first 5 columns from here\n",
    "homeworking = homeworking1.iloc[:, :5]\n",
    "# add geo location\n",
    "homeworking['geo'] = geo\n",
    "# add year columns\n",
    "homeworking = pd.concat([homeworking, homeworking2.iloc[:,1:]], axis=1, join=\"inner\")\n",
    "\n",
    "# filter data for for employed in UK\n",
    "employed_homeworking = homeworking[(homeworking['geo']=='UK') & (homeworking['wstatus']=='EMP') & (homeworking['age'] =='Y20-64')]\n",
    "employed_homeworking.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "organized-carrier",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UK average of people going to work is 75.15357142857144 %\n"
     ]
    }
   ],
   "source": [
    "# get baseline \n",
    "homeworking_year_stat = []\n",
    "for i in np.array(employed_homeworking)[0, 6::]:\n",
    "    homeworking_year_stat.append(float(i[0:4]))\n",
    "       \n",
    "homeorking_mean_old =  np.mean(homeworking_years)\n",
    "print('UK average of people going to work is {} %'.format(homeorking_mean_old))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "concrete-return",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get change over years\n",
    "homeworking_change = []\n",
    "for i in homeworking_ons['Travelled to work']:\n",
    "    change = ((i-homeorking_mean_old)/homeorking_mean_old)*100\n",
    "    homeworking_change.append(change) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "underlying-shoot",
   "metadata": {},
   "source": [
    "# Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "informal-atlanta",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_mobility_means = []\n",
    "all_mobility_means = []\n",
    "car_mobility_changes = []\n",
    "all_mobility_changes = []\n",
    "co2_change = []\n",
    "co2_means = []\n",
    "\n",
    "\n",
    "for date in dates:\n",
    "    car_mean, all_vehicles_mean = mobility_mean(date[0], date[1])\n",
    "    change_cars, change_all =  mobibiliy_change(date[0], date[1])\n",
    "    mean_co2, change_co2 = carbon_change(date[0], date[1])\n",
    "    \n",
    "    car_mobility_means.append(car_mean)\n",
    "    all_mobility_means.append(all_vehicles_mean)\n",
    "    car_mobility_changes.append(change_cars)\n",
    "    all_mobility_changes.append(change_all)\n",
    "    co2_change.append(change_co2)\n",
    "    co2_means.append(mean_co2)\n",
    "    \n",
    "# combine above into dataframe    \n",
    "statistics_df = pd.DataFrame({'period':homeworking_ons['Date'],\n",
    "                             'car_mobility_mean': car_mobility_means,\n",
    "                             'all_vehicles_mobility_mean':all_mobility_means,\n",
    "                             'car_mobility_baseline_change':car_mobility_changes,\n",
    "                             'all_mobility_baseline_change':all_mobility_changes,\n",
    "                             'co2_change': co2_change,\n",
    "                             'co2 means':co2_means, \n",
    "                             'travelled to work':(homeworking_ons['Travelled to work']),\n",
    "                             'travelled to work change':homeworking_change})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "advanced-passenger",
   "metadata": {},
   "outputs": [
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   "source": [
    "# plot mobility and emisison over time\n",
    "\n",
    "fig = go.Figure()\n",
    "\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=statistics_df.period,\n",
    "    y=statistics_df['car_mobility_mean'],\n",
    "    mode=\"lines+markers+text\",\n",
    "    name=\"Car mobility\",\n",
    "    text=round(statistics_df['car_mobility_mean'], 2),\n",
    "    textposition=\"bottom center\"\n",
    "))\n",
    "\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=statistics_df.period,\n",
    "    y=statistics_df['travelled to work'],\n",
    "    mode=\"lines+markers+text\",\n",
    "    name=\"Travelled to work\",\n",
    "    text=round(statistics_df['travelled to work'], 2),\n",
    "    textposition=\"bottom center\"\n",
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    "\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=statistics_df.period,\n",
    "    y=statistics_df['co2 means'],\n",
    "    mode=\"lines+markers+text\",\n",
    "    name=\"CO2 emission\",\n",
    "    text=round(statistics_df['co2 means'], 2),\n",
    "    textposition=\"bottom center\"\n",
    "))\n",
    "\n",
    "\n",
    "fig.update_layout(margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "amino-richards",
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "# plot change in statistics over time\n",
    "\n",
    "fig = go.Figure()\n",
    "\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=statistics_df.period,\n",
    "    y=statistics_df['co2_change'],\n",
    "    mode=\"lines+markers+text\",\n",
    "    name=\"CO2 emission (change)\",\n",
    "    text=round(statistics_df['co2_change'], 2),\n",
    "    textposition=\"top center\"\n",
    "))\n",
    "\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=statistics_df.period,\n",
    "    y=statistics_df['car_mobility_baseline_change'],\n",
    "    mode=\"lines+markers+text\",\n",
    "    name=\"Car mobility (change)\",\n",
    "    text=round(statistics_df['car_mobility_baseline_change'], 2),\n",
    "    textposition=\"top center\"\n",
    "))\n",
    "\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=statistics_df.period,\n",
    "    y=statistics_df['travelled to work change'],\n",
    "    mode=\"lines+markers+text\",\n",
    "    name=\"Travelled to work (change)\",\n",
    "    text=round(statistics_df['travelled to work change'], 2),\n",
    "    textposition=\"bottom center\"\n",
    "))\n",
    "\n",
    "\n",
    "fig.update_yaxes(title=\"percentage %\")\n",
    "\n",
    "fig.update_layout(margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "complimentary-garbage",
   "metadata": {},
   "source": [
    "# July (50/50 scheme) statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "associate-prince",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_monthly_stats(month_name):\n",
    "    '''Month name from a list [May, June, July, August, September]'''\n",
    "    \n",
    "    if month_name=='May':\n",
    "        for i in statistics_df.columns[1::]:\n",
    "            print(i, \"----\", np.mean(statistics_df[i][:3]))\n",
    "            \n",
    "    if month_name=='June':\n",
    "        for i in statistics_df.columns[1::]:\n",
    "            print(i, \"----\", np.mean(statistics_df[i][3:7]))\n",
    "    \n",
    "    if month_name=='July':\n",
    "        for i in statistics_df.columns[1::]:\n",
    "            print(i, \"----\", np.mean(statistics_df[i][7:11]))\n",
    "            \n",
    "    if month_name=='August':\n",
    "        for i in statistics_df.columns[1::]:\n",
    "            print(i, \"----\", np.mean(statistics_df[i][11:15]))\n",
    "    \n",
    "    if month_name=='September':\n",
    "        for i in statistics_df.columns[1::]:\n",
    "            print(i, \"----\", np.mean(statistics_df[i][-3::]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "primary-assault",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "car_mobility_mean ---- 83.45833333333333\n",
      "all_vehicles_mobility_mean ---- 90.84375\n",
      "car_mobility_baseline_change ---- -16.54166666666667\n",
      "all_mobility_baseline_change ---- -9.156249999999998\n",
      "co2_change ---- -4.8403557787417135\n",
      "co2 means ---- 0.2984627179166667\n",
      "travelled to work ---- 51.25\n",
      "travelled to work change ---- -31.80630138288268\n"
     ]
    }
   ],
   "source": [
    "print_monthly_stats('July')"
   ]
  }
 ],
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