Kysyttyä.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Esille tulleita ongelmia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tiedostopolut \n",
    "\n",
    "Jos esimerkissä käytetään suhteellisia tiedostopolkuja esim.\n",
    "\n",
    "    kaksoismyonit = pd.read_csv('../Data/Dimuon_DoubleMu.csv')\n",
    "    \n",
    "colab ei löydä niitä ja ne pitää lukea suoraan github-hakemistosta seuraavasti:\n",
    "\n",
    "    kaksoismyonit = pd.read_csv('https://raw.githubusercontent.com/cms-opendata-education/cms-jupyter-materials-finnish/master/Data/Dimuon_DoubleMu.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jos haluaa lukea tiedoston drivesta, se pitää ensin tallentaa taulukkomuotoisena, sitten avata google-sheetsissä, mistä löytyy \"Julkaise verkossa\"-nappi. Siitä saa linkin, jolla tiedoston voi lukea suoraan read_csv-funktiolla."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lisärivit tiedoston alussa\n",
    "\n",
    "Tiedostossa ei sarakkeiden nimiä tai lisärivejä ennen sarakkeiden nimiä:\n",
    "    \n",
    "    dataset = pd.read_csv(\"tiedosto.csv\", header=None)\n",
    "    \n",
    "tai (riippuen lisärivien määrästä):\n",
    "    \n",
    "    dataset = pd.read_csv(\"tiedosto.csv\", header=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoding\n",
    "\n",
    "Jos tiedostossa on erityismerkkejä (skandit, muut), ne saa auki lisäämällä parametrin encoding read_csv-funktioon. Koodit löytyvät osoitteesta https://docs.python.org/2.4/lib/standard-encodings.html esim.\n",
    "\n",
    "    dataset = pd.read_csv(\"tiedosto.csv\", encoding=\"iso8859_10\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tiedoston nimi\n",
    "\n",
    "Ääkköset tiedoston nimessä & sarakkeiden erottajana jokin muu merkki kuin pilkku:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kati\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2717: DtypeWarning: Columns (110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"http://www.espoo.fi/materiaalit/Asioi_verkossa/Avoindata/Espoon_py%C3%B6railijamaarat.csv\", sep=\";\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Päivä</th>\n",
       "      <th>Päivämäärä</th>\n",
       "      <th>Aika</th>\n",
       "      <th>Espoonlahti (Eco-Counter)</th>\n",
       "      <th>Gallen-Kallelan tie (DSL10)</th>\n",
       "      <th>Kalevalantien alikulku (DSL10)</th>\n",
       "      <th>Kehä I, Laajalahti (DSL10)</th>\n",
       "      <th>Keskuspuisto, etelähaara (Viacount 2)</th>\n",
       "      <th>Keskuspuisto, etelähaara, itä (Viacount 2)</th>\n",
       "      <th>Keskuspuisto, etelähaara, länsi (Viacount 2)</th>\n",
       "      <th>...</th>\n",
       "      <th>Unnamed: 121</th>\n",
       "      <th>Unnamed: 122</th>\n",
       "      <th>Unnamed: 123</th>\n",
       "      <th>Unnamed: 124</th>\n",
       "      <th>Unnamed: 125</th>\n",
       "      <th>Unnamed: 126</th>\n",
       "      <th>Unnamed: 127</th>\n",
       "      <th>Unnamed: 128</th>\n",
       "      <th>Unnamed: 129</th>\n",
       "      <th>Unnamed: 130</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pe</td>\n",
       "      <td>3.1.2014</td>\n",
       "      <td>0:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pe</td>\n",
       "      <td>3.1.2014</td>\n",
       "      <td>1:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pe</td>\n",
       "      <td>3.1.2014</td>\n",
       "      <td>2:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pe</td>\n",
       "      <td>3.1.2014</td>\n",
       "      <td>3:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pe</td>\n",
       "      <td>3.1.2014</td>\n",
       "      <td>4:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 131 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Päivä Päivämäärä  Aika  Espoonlahti (Eco-Counter)  \\\n",
       "0    pe   3.1.2014  0:00                        NaN   \n",
       "1    pe   3.1.2014  1:00                        NaN   \n",
       "2    pe   3.1.2014  2:00                        NaN   \n",
       "3    pe   3.1.2014  3:00                        NaN   \n",
       "4    pe   3.1.2014  4:00                        NaN   \n",
       "\n",
       "   Gallen-Kallelan tie (DSL10)  Kalevalantien alikulku (DSL10)  \\\n",
       "0                          NaN                             NaN   \n",
       "1                          NaN                             NaN   \n",
       "2                          NaN                             NaN   \n",
       "3                          NaN                             NaN   \n",
       "4                          NaN                             NaN   \n",
       "\n",
       "   Kehä I, Laajalahti (DSL10)  Keskuspuisto, etelähaara (Viacount 2)  \\\n",
       "0                         NaN                                    NaN   \n",
       "1                         NaN                                    NaN   \n",
       "2                         NaN                                    NaN   \n",
       "3                         NaN                                    NaN   \n",
       "4                         NaN                                    NaN   \n",
       "\n",
       "   Keskuspuisto, etelähaara, itä (Viacount 2)  \\\n",
       "0                                         NaN   \n",
       "1                                         NaN   \n",
       "2                                         NaN   \n",
       "3                                         NaN   \n",
       "4                                         NaN   \n",
       "\n",
       "   Keskuspuisto, etelähaara, länsi (Viacount 2)      ...       Unnamed: 121  \\\n",
       "0                                           NaN      ...                NaN   \n",
       "1                                           NaN      ...                NaN   \n",
       "2                                           NaN      ...                NaN   \n",
       "3                                           NaN      ...                NaN   \n",
       "4                                           NaN      ...                NaN   \n",
       "\n",
       "   Unnamed: 122  Unnamed: 123  Unnamed: 124  Unnamed: 125  Unnamed: 126  \\\n",
       "0           NaN           NaN           NaN           NaN           NaN   \n",
       "1           NaN           NaN           NaN           NaN           NaN   \n",
       "2           NaN           NaN           NaN           NaN           NaN   \n",
       "3           NaN           NaN           NaN           NaN           NaN   \n",
       "4           NaN           NaN           NaN           NaN           NaN   \n",
       "\n",
       "   Unnamed: 127  Unnamed: 128  Unnamed: 129  Unnamed: 130  \n",
       "0           NaN           NaN           NaN           NaN  \n",
       "1           NaN           NaN           NaN           NaN  \n",
       "2           NaN           NaN           NaN           NaN  \n",
       "3           NaN           NaN           NaN           NaN  \n",
       "4           NaN           NaN           NaN           NaN  \n",
       "\n",
       "[5 rows x 131 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tyhjät sarakkeet NaN-arvoina \n",
    "\n",
    "Niiden muuttaminen nolliksi:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset = dataset.replace(np.nan, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Päivä</th>\n",
       "      <th>Päivämäärä</th>\n",
       "      <th>Aika</th>\n",
       "      <th>Espoonlahti (Eco-Counter)</th>\n",
       "      <th>Gallen-Kallelan tie (DSL10)</th>\n",
       "      <th>Kalevalantien alikulku (DSL10)</th>\n",
       "      <th>Kehä I, Laajalahti (DSL10)</th>\n",
       "      <th>Keskuspuisto, etelähaara (Viacount 2)</th>\n",
       "      <th>Keskuspuisto, etelähaara, itä (Viacount 2)</th>\n",
       "      <th>Keskuspuisto, etelähaara, länsi (Viacount 2)</th>\n",
       "      <th>...</th>\n",
       "      <th>Unnamed: 121</th>\n",
       "      <th>Unnamed: 122</th>\n",
       "      <th>Unnamed: 123</th>\n",
       "      <th>Unnamed: 124</th>\n",
       "      <th>Unnamed: 125</th>\n",
       "      <th>Unnamed: 126</th>\n",
       "      <th>Unnamed: 127</th>\n",
       "      <th>Unnamed: 128</th>\n",
       "      <th>Unnamed: 129</th>\n",
       "      <th>Unnamed: 130</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pe</td>\n",
       "      <td>3.1.2014</td>\n",
       "      <td>0:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pe</td>\n",
       "      <td>3.1.2014</td>\n",
       "      <td>1:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pe</td>\n",
       "      <td>3.1.2014</td>\n",
       "      <td>2:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pe</td>\n",
       "      <td>3.1.2014</td>\n",
       "      <td>3:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pe</td>\n",
       "      <td>3.1.2014</td>\n",
       "      <td>4:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 131 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Päivä Päivämäärä  Aika  Espoonlahti (Eco-Counter)  \\\n",
       "0    pe   3.1.2014  0:00                        0.0   \n",
       "1    pe   3.1.2014  1:00                        0.0   \n",
       "2    pe   3.1.2014  2:00                        0.0   \n",
       "3    pe   3.1.2014  3:00                        0.0   \n",
       "4    pe   3.1.2014  4:00                        0.0   \n",
       "\n",
       "   Gallen-Kallelan tie (DSL10)  Kalevalantien alikulku (DSL10)  \\\n",
       "0                          0.0                             0.0   \n",
       "1                          0.0                             0.0   \n",
       "2                          0.0                             0.0   \n",
       "3                          0.0                             0.0   \n",
       "4                          0.0                             0.0   \n",
       "\n",
       "   Kehä I, Laajalahti (DSL10)  Keskuspuisto, etelähaara (Viacount 2)  \\\n",
       "0                         0.0                                    0.0   \n",
       "1                         0.0                                    0.0   \n",
       "2                         0.0                                    0.0   \n",
       "3                         0.0                                    0.0   \n",
       "4                         0.0                                    0.0   \n",
       "\n",
       "   Keskuspuisto, etelähaara, itä (Viacount 2)  \\\n",
       "0                                         0.0   \n",
       "1                                         0.0   \n",
       "2                                         0.0   \n",
       "3                                         0.0   \n",
       "4                                         0.0   \n",
       "\n",
       "   Keskuspuisto, etelähaara, länsi (Viacount 2)      ...       Unnamed: 121  \\\n",
       "0                                           0.0      ...                  0   \n",
       "1                                           0.0      ...                  0   \n",
       "2                                           0.0      ...                  0   \n",
       "3                                           0.0      ...                  0   \n",
       "4                                           0.0      ...                  0   \n",
       "\n",
       "   Unnamed: 122  Unnamed: 123  Unnamed: 124  Unnamed: 125  Unnamed: 126  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             0             0   \n",
       "2             0             0             0             0             0   \n",
       "3             0             0             0             0             0   \n",
       "4             0             0             0             0             0   \n",
       "\n",
       "   Unnamed: 127  Unnamed: 128  Unnamed: 129  Unnamed: 130  \n",
       "0           0.0           0.0           0.0           0.0  \n",
       "1           0.0           0.0           0.0           0.0  \n",
       "2           0.0           0.0           0.0           0.0  \n",
       "3           0.0           0.0           0.0           0.0  \n",
       "4           0.0           0.0           0.0           0.0  \n",
       "\n",
       "[5 rows x 131 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aikaformaatit\n",
    "\n",
    "string-formaatissa luetun ajan muuntaminen datetime-formaattiin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset[\"Aika\"] = pd.to_datetime(dataset['Aika'], format='%H:%M')\n",
    "t = dataset['Aika'].dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x261e62940b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Tunnit x-akselilla\n",
    "plt.scatter(t,dataset['Gallen-Kallelan tie (DSL10)'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Päivämäärä "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#muunna päivämäärä \"datetime\"-formaattiin, josta sitten voidaan ottaa osat lukuna (ja käyttää ongelmitta kuvaajissa)\n",
    "dataset[\"Päivämäärä\"] = pd.to_datetime(dataset['Päivämäärä'], format='%d.%m.%Y')\n",
    "kk = dataset['Päivämäärä'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2oAAAD8CAYAAAAG/FfIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x261e62912b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#kuukausi käytössä valintakriteereissä\n",
    "datajanwe = dataset[((dataset['Päivä']==\"la\") | (dataset['Päivä']==\"su\")) & (dataset['Päivämäärä'].dt.month==1)]\n",
    "datajan = dataset[(dataset['Päivämäärä'].dt.month==1)]\n",
    "datajunwe = dataset[((dataset['Päivä']==\"la\") | (dataset['Päivä']==\"su\")) & (dataset['Päivämäärä'].dt.month==6)]\n",
    "datajun = dataset[(dataset['Päivämäärä'].dt.month==6)]\n",
    "\n",
    "fig = plt.figure(figsize=(15, 4)) #kuvan koko\n",
    "\n",
    "plt.subplot(1, 2, 1) #kaksi kuvaa vierekkäin (kuvia: 1 = yksi rivi, 2=kaksi saraketta, 1 = tämä kuva ens. kohtaan)\n",
    "plt.scatter(datajan[\"Aika\"].dt.hour,datajan['Gallen-Kallelan tie (DSL10)'], label='Tammikuu, viikonloppu')\n",
    "\n",
    "plt.scatter(datajanwe[\"Aika\"].dt.hour,datajanwe['Gallen-Kallelan tie (DSL10)'], label='Tammikuu, koko viikko')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.scatter(datajun[\"Aika\"].dt.hour,datajun['Gallen-Kallelan tie (DSL10)'], label='Kesäkuu, viikonloppu')\n",
    "\n",
    "plt.scatter(datajunwe[\"Aika\"].dt.hour,datajunwe['Gallen-Kallelan tie (DSL10)'], label='Kesäkuu, koko viikko')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Desimaalipilkku\n",
    "\n",
    "Python tunnistaa luvut stringeinä, jos käytetään desimaalipilkkua. Jos datassa on desimaalipilkkuja, sen voi lukea numeeriseen muotoon read_csv-funktiolla:\n",
    "\n",
    "      df = pd.read_csv(\"tiedosto.csv\", decimal=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}