pandas1.ipynb

{
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   "execution_count": 1,
   "id": "87b669ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Päivitetty 2024-07-02 / Aki Taanila\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "print(f'Päivitetty {datetime.now().date()} / Aki Taanila')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa944ac2",
   "metadata": {},
   "source": [
    "# Tutustu Pythoniin ja pandas-kirjastoon\n",
    "\n",
    "Tästä muistiosta voit aloittaa vaikka et olisi koskaan koodannut Pythonilla.\n",
    "\n",
    "Paketti (kutsutaan myös kirjastoksi) on kokoelma valmista ohjelmakoodia, jota voin käyttää omissa ohjelmissani. Data-analytiikan peruspaketti on nimeltään **pandas**. Pandas sisältää muiden muassa valmiin **crosstab**-funktion, jolla voin laskea lukumäärä- ja prosenttiyhteenvetoja sekä **describe**-funktion, jolla voin nopeasti laskea tilastolliset tunnusluvut.\n",
    "\n",
    "Useimmat data-analytiikassa tarvittavat paketit sisältyvät Colabiin ja Anacondaan. Jos käytät Minicondaa, niin sinun pitää asentaa jokainen käytettävä paketti erikseen.\n",
    "\n",
    "Jos paketti on valmiiksi asennettu, niin voin tuoda sen käyttööni **import**-komennolla. Esimerkiksi `import pandas as pd` tuo pandas-paketin käyttööni siten että voin seuraavissa koodisoluissa viitata siihen lyhenteellä **pd**.\n",
    "\n",
    "Muistion solun voit suorittaa näppäinyhdistelmillä **ctrl-enter** (pysyt samassa solussa) tai **shift-enter** (siirryt seuraavaan soluun)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5f1dc1a",
   "metadata": {},
   "source": [
    "## Datan lukeminen\n",
    "\n",
    "Pandas-paketin **read_excel**-funktiolla voin lukea datan Excel-tiedostosta. Jos tiedosto on samassa kansiossa kuin muistio, niin voin viitata siihen pelkällä tiedostonimellä, esimerkiksi **'data1.xlsx'**. Seuraavassa tiedosto on kuitenkin nettiosoitteessa. Luen seuraavassa data-objektin **df**-nimisen (voit käyttää haluamaasi nimeä) muuttujan arvoksi.\n",
    "\n",
    "Merkkijonot kirjoitetaan heittomerkkien tai lainausmerkkien väliin. Heittomerkin saat enter-näppäimen vierestä (samassa näppäimessä kuin tähti eli asteriski)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9970901e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_excel('https://taanila.fi/data1.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f34427d",
   "metadata": {},
   "source": [
    "Pandas-paketissa on määritelty **dataframe**-niminen tietorakenne, jota voit hyvin pitää Excel-taulukon vastineena. Edellä avattu data-olio **df** on dataframe.\n",
    "\n",
    "## Dataan tutustuminen\n",
    "\n",
    "Käsky `df` (tai muu dataframelle annettu nimi) näyttää datan ensimmäisimmät ja viimeisimmät rivit."
   ]
  },
  {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nro</th>\n",
       "      <th>sukup</th>\n",
       "      <th>ikä</th>\n",
       "      <th>perhe</th>\n",
       "      <th>koulutus</th>\n",
       "      <th>palveluv</th>\n",
       "      <th>palkka</th>\n",
       "      <th>johto</th>\n",
       "      <th>työtov</th>\n",
       "      <th>työymp</th>\n",
       "      <th>palkkat</th>\n",
       "      <th>työteht</th>\n",
       "      <th>työterv</th>\n",
       "      <th>lomaosa</th>\n",
       "      <th>kuntosa</th>\n",
       "      <th>hieroja</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>7.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2144</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
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       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>5</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2612</td>\n",
       "      <td>3</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2808</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>81</th>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2183</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    nro  sukup  ikä  perhe  koulutus  palveluv  palkka  johto  työtov  työymp  \\\n",
       "0     1      1   38      1       1.0      22.0    3587      3     3.0       3   \n",
       "1     2      1   29      2       2.0      10.0    2963      1     5.0       2   \n",
       "2     3      1   30      1       1.0       7.0    1989      3     4.0       1   \n",
       "3     4      1   36      2       1.0      14.0    2144      3     3.0       3   \n",
       "4     5      1   24      1       2.0       4.0    2183      2     3.0       2   \n",
       "..  ...    ...  ...    ...       ...       ...     ...    ...     ...     ...   \n",
       "77   78      1   22      1       3.0       0.0    1598      4     4.0       4   \n",
       "78   79      1   33      1       1.0       2.0    1638      1     3.0       2   \n",
       "79   80      1   27      1       2.0       7.0    2612      3     4.0       3   \n",
       "80   81      1   35      2       2.0      16.0    2808      3     4.0       3   \n",
       "81   82      2   35      2       3.0      15.0    2183      3     4.0       4   \n",
       "\n",
       "    palkkat  työteht  työterv  lomaosa  kuntosa  hieroja  \n",
       "0         3        3      NaN      NaN      NaN      NaN  \n",
       "1         1        3      NaN      NaN      NaN      NaN  \n",
       "2         1        3      1.0      NaN      NaN      NaN  \n",
       "3         3        3      1.0      NaN      NaN      NaN  \n",
       "4         1        2      1.0      NaN      NaN      NaN  \n",
       "..      ...      ...      ...      ...      ...      ...  \n",
       "77        3        4      NaN      1.0      1.0      NaN  \n",
       "78        1        2      1.0      NaN      NaN      NaN  \n",
       "79        3        3      1.0      NaN      1.0      NaN  \n",
       "80        3        3      NaN      NaN      NaN      NaN  \n",
       "81        3        4      1.0      NaN      NaN      NaN  \n",
       "\n",
       "[82 rows x 16 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5624560f",
   "metadata": {},
   "source": [
    "Dataframe-oliolla on lukuisia ominaisuuksia ja funktioita, joista jatkossa esimerkkejä. Huomaa, että \n",
    "\n",
    "* ominaisuuden nimen perässä ei ole sulkumerkkejä\n",
    "* funktion nimen perässä on aina sulkumerkit."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd9eacdb-1774-472b-a22e-7c3ce5b16fce",
   "metadata": {},
   "source": [
    "## Kategorinen vai määrällinen muuttuja\n",
    "\n",
    "* **nro** Tämä muuttuja on keinotekoinen järjestysnumero eikä muuttujan tyypillä ole merkitystä.\n",
    "* **sukup** Sukupuoli on kategorinen muuttuja. Tässä arvo 1 tarkoittaa miestä ja 2 naista.\n",
    "* **ika** Ikä on määrällinen muuttuja.\n",
    "* **perhe** Perhesuhde on kategorinen muuttuja. Tässä arvo 1 tarkoittaa perheetöntä ja 2 perheellistä.\n",
    "* **koulutus** Koulutus on kategorinen muuttuja (1 = peruskoulu, 2 = 2. aste, 3 = korkeakoulu, 4 = ylempi korkeakoulu).\n",
    "* **palveluv** Palvelusvuodet on määrällinen muuttuja.\n",
    "* **palkka** Palkka on määrällinen muuttuja.\n",
    "* **johto, työtov, työymp, palkkat, työteht** Nämä ovat tyytyväisyysmuuttujia, joiden arvo on välillä 1 = erittäin tyytymätön - 5 = erittäin tyytyväinen. Lähtökohtaisesti nämä ovat kategorisia muuttujia, mutta voidaan tulkita myös määrällisiksi muuttujiksi. Mielipideasteikot ovat usein rajatapauksia, joille voidaan käyttää sekä kategoristen että määrällisten muuttujien menetelmiä.\n",
    "* **työterv, lomaosa, kuntosa, hieroja** Nämä ovat kategorisia muuttujia, jotka kertovat onko vastaaja käyttänyt (**1**) kyseistä etua."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0ee67f11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(82, 16)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape-ominaisuus sisältää rivien ja sarakkeiden lukumäärät\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0b31a86e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 82 entries, 0 to 81\n",
      "Data columns (total 17 columns):\n",
      " #   Column         Non-Null Count  Dtype   \n",
      "---  ------         --------------  -----   \n",
      " 0   nro            82 non-null     int64   \n",
      " 1   sukupuoli      82 non-null     int64   \n",
      " 2   ikä            82 non-null     int64   \n",
      " 3   perhe          82 non-null     int64   \n",
      " 4   koulutus       81 non-null     float64 \n",
      " 5   palveluvuodet  80 non-null     float64 \n",
      " 6   palkka         82 non-null     int64   \n",
      " 7   johto          82 non-null     int64   \n",
      " 8   työtov         81 non-null     float64 \n",
      " 9   työymp         82 non-null     int64   \n",
      " 10  palkkat        82 non-null     int64   \n",
      " 11  työteht        82 non-null     int64   \n",
      " 12  työterv        47 non-null     float64 \n",
      " 13  lomaosa        20 non-null     float64 \n",
      " 14  kuntosa        9 non-null      float64 \n",
      " 15  hieroja        22 non-null     float64 \n",
      " 16  ikäluokka      82 non-null     category\n",
      "dtypes: category(1), float64(7), int64(9)\n",
      "memory usage: 10.7 KB\n"
     ]
    }
   ],
   "source": [
    "# info-funktio tulostaa tietoja datan sarakkeista eli muuttujista\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4b11102",
   "metadata": {},
   "source": [
    "Yllä näkyvästä tulosteesta selviää, että tässä dataframessa on **int64** eli kokonaislukutyyppistä tietoa ja **float64** eli liukulukutyyppistä tietoa. Liukulukuja voit ajatella desimaalilukuina.\n",
    "\n",
    "Jos sarakkeessa olisi tekstimuotoista tietoa, niin se näyttäytyisi **object**-tyyppinä."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ba7b5c4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([38, 29, 30, 36, 24, 31, 49, 55, 40, 33, 39, 35, 58, 53, 42, 26, 47,\n",
       "       44, 43, 56, 21, 45, 59, 37, 28, 50, 32, 51, 22, 34, 27, 41, 25, 61,\n",
       "       20, 52, 46], dtype=int64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# unique-funktio tulostaa sarakkeen ainutkertaiset arvot (järjestys tulee datasta)\n",
    "df['ikä'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed1aa53e",
   "metadata": {},
   "source": [
    "## Suodatuksia\n",
    "\n",
    "Datalle on helppo tehdä erilaisia suodatuksia. Dataframen sarakkeeseen voin viitata kirjoittamalla sarakkeen nimen hakasulkujen sisään, esimerkiksi `df['palkka']`. Suodatuksissa voin käyttää \n",
    "\n",
    "* vertailuoperaattoreita **>, <, >=, <=, ==, !=** \n",
    "* yhdistämisoperaattoreita **&** (ja), **|** (tai) \n",
    "* kielto-operaattoria **~**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d645eab3",
   "metadata": {},
   "outputs": [
    {
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       "      <th>nro</th>\n",
       "      <th>sukup</th>\n",
       "      <th>ikä</th>\n",
       "      <th>perhe</th>\n",
       "      <th>koulutus</th>\n",
       "      <th>palveluv</th>\n",
       "      <th>palkka</th>\n",
       "      <th>johto</th>\n",
       "      <th>työtov</th>\n",
       "      <th>työymp</th>\n",
       "      <th>palkkat</th>\n",
       "      <th>työteht</th>\n",
       "      <th>työterv</th>\n",
       "      <th>lomaosa</th>\n",
       "      <th>kuntosa</th>\n",
       "      <th>hieroja</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2300</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2223</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    nro  sukup  ikä  perhe  koulutus  palveluv  palkka  johto  työtov  työymp  \\\n",
       "2     3      1   30      1       1.0       7.0    1989      3     4.0       1   \n",
       "38   39      1   30      1       2.0      10.0    2300      3     5.0       3   \n",
       "43   44      1   30      1       2.0       7.0    2223      2     3.0       4   \n",
       "\n",
       "    palkkat  työteht  työterv  lomaosa  kuntosa  hieroja  \n",
       "2         1        3      1.0      NaN      NaN      NaN  \n",
       "38        3        4      NaN      NaN      NaN      NaN  \n",
       "43        1        3      1.0      NaN      NaN      1.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 30-vuotiaat\n",
    "df[df['ikä']==30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "69f94572",
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>53</th>\n",
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       "      <td>1</td>\n",
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       "      <td>15.0</td>\n",
       "      <td>1598</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1598</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    nro  sukup  ikä  perhe  koulutus  palveluv  palkka  johto  työtov  työymp  \\\n",
       "25   26      1   26      1       2.0       3.0    1521      2     4.0       2   \n",
       "35   36      1   31      2       3.0       0.0    1559      2     4.0       3   \n",
       "53   54      1   25      1       2.0       1.0    1559      2     4.0       3   \n",
       "75   76      1   37      1       2.0      15.0    1598      1     5.0       1   \n",
       "77   78      1   22      1       3.0       0.0    1598      4     4.0       4   \n",
       "\n",
       "    palkkat  työteht  työterv  lomaosa  kuntosa  hieroja  \n",
       "25        1        3      1.0      NaN      1.0      1.0  \n",
       "35        1        3      1.0      NaN      NaN      NaN  \n",
       "53        1        2      1.0      NaN      NaN      NaN  \n",
       "75        1        1      1.0      NaN      NaN      NaN  \n",
       "77        3        4      NaN      1.0      1.0      NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ne joiden koulutus ei ole 1 ja palkka on korkeintaan 1700 (huomaa sulkumerkit)\n",
    "df[(df['koulutus']!=1) & (df['palkka']<=1700)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e0f5045f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>nro</th>\n",
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       "      <th>perhe</th>\n",
       "      <th>koulutus</th>\n",
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       "      <th>palkka</th>\n",
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       "      <th>hieroja</th>\n",
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       "      <th>45</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <th>62</th>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>51</td>\n",
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       "      <td>10.0</td>\n",
       "      <td>1872</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    nro  sukup  ikä  perhe  koulutus  palveluv  palkka  johto  työtov  työymp  \\\n",
       "45   46      2   51      2       1.0      28.0    1989      3     3.0       2   \n",
       "62   63      2   51      2       2.0      10.0    1872      4     3.0       2   \n",
       "\n",
       "    palkkat  työteht  työterv  lomaosa  kuntosa  hieroja  \n",
       "45        2        3      1.0      NaN      NaN      1.0  \n",
       "62        2        3      1.0      NaN      NaN      NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Yli 50-vuotiaat alle 2000 palkalla (huomaa sulkumerkit)\n",
    "df[(df['ikä']>50) & (df['palkka']<2000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "17683e90",
   "metadata": {},
   "outputs": [
    {
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       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1598</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    nro  sukup  ikä  perhe  koulutus  palveluv  palkka  johto  työtov  työymp  \\\n",
       "46   47      2   22      1       3.0      21.0    1872      3     3.0       4   \n",
       "77   78      1   22      1       3.0       0.0    1598      4     4.0       4   \n",
       "\n",
       "    palkkat  työteht  työterv  lomaosa  kuntosa  hieroja  \n",
       "46        1        3      NaN      NaN      1.0      NaN  \n",
       "77        3        4      NaN      1.0      1.0      NaN  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ne, jotka eivät ole käyttäneet työterveyshuoltoa ja joiden palkka alle 2000\n",
    "df[~(df['työterv']==1) & (df['palkka']<2000)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2cfc34d",
   "metadata": {},
   "source": [
    "## Funktion parametrit ja argumentit\n",
    "\n",
    "Funktioille voidaan antaa lisätietoa parametreinä. Esimerkiksi **nsmallest**-funktiolle mahdolliset parametrit löytyvät funktion ohjesivulta https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.nsmallest.html. Jos annat parametrien arvot funktion ohjesivun mukaisessa järjestyksessä, niin parametreja ei tarvitse nimetä. Parametrien nimeäminen on kuitenkin hyvä tapa. Esimerkiksi seuraavassa solussa voisin käyttää nimettyjä parametreja: `df.nsmallest(n=3, columns='palkka')`\n",
    "\n",
    "Terminologiaa: **n** ja **columns** ovat nsmallest-funktion parametrejä, joille voidaan antaa arvo. Parametrille annettavaa arvoa kutsutaan argumentiksi. Seuraavassa **3** on parametrin **n** arvo eli argumentti."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "56d030d0",
   "metadata": {},
   "outputs": [
    {
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       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1559</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1559</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    nro  sukup  ikä  perhe  koulutus  palveluv  palkka  johto  työtov  työymp  \\\n",
       "25   26      1   26      1       2.0       3.0    1521      2     4.0       2   \n",
       "35   36      1   31      2       3.0       0.0    1559      2     4.0       3   \n",
       "53   54      1   25      1       2.0       1.0    1559      2     4.0       3   \n",
       "\n",
       "    palkkat  työteht  työterv  lomaosa  kuntosa  hieroja  \n",
       "25        1        3      1.0      NaN      1.0      1.0  \n",
       "35        1        3      1.0      NaN      NaN      NaN  \n",
       "53        1        2      1.0      NaN      NaN      NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rivit, joilla kolmen pienintä palkkaa\n",
    "df.nsmallest(3, 'palkka')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "752ee27c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nro</th>\n",
       "      <th>sukup</th>\n",
       "      <th>ikä</th>\n",
       "      <th>perhe</th>\n",
       "      <th>koulutus</th>\n",
       "      <th>palveluv</th>\n",
       "      <th>palkka</th>\n",
       "      <th>johto</th>\n",
       "      <th>työtov</th>\n",
       "      <th>työymp</th>\n",
       "      <th>palkkat</th>\n",
       "      <th>työteht</th>\n",
       "      <th>työterv</th>\n",
       "      <th>lomaosa</th>\n",
       "      <th>kuntosa</th>\n",
       "      <th>hieroja</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>3119</td>\n",
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       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6278</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3587</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2223</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>56</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2729</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    nro  sukup  ikä  perhe  koulutus  palveluv  palkka  johto  työtov  työymp  \\\n",
       "56   57      1   61      2       2.0      36.0    3119      2     NaN       2   \n",
       "32   33      1   59      2       3.0      15.0    6278      4     4.0       5   \n",
       "13   14      1   58      2       3.0      21.0    3587      4     5.0       4   \n",
       "27   28      2   56      1       1.0      15.0    2223      3     4.0       3   \n",
       "36   37      2   56      2       2.0      17.0    2729      5     5.0       5   \n",
       "\n",
       "    palkkat  työteht  työterv  lomaosa  kuntosa  hieroja  \n",
       "56        1        5      1.0      NaN      NaN      1.0  \n",
       "32        4        4      NaN      1.0      NaN      NaN  \n",
       "13        1        3      NaN      NaN      NaN      NaN  \n",
       "27        2        4      1.0      NaN      NaN      1.0  \n",
       "36        5        5      NaN      NaN      NaN      1.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rivit, joilla viisi korkeinta ikää\n",
    "df.nlargest(5, 'ikä')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dcaf5cf",
   "metadata": {},
   "source": [
    "## Tilastolliset tunnusluvut\n",
    "\n",
    "**Describe**-funktio laskee tilastolliset tunnusluvut:\n",
    "\n",
    "* arvojen lukumäärä (count)\n",
    "* keskiarvo (mean)\n",
    "* keskihajonta (std)\n",
    "* pienin (min)\n",
    "* alaneljännes (25%); neljäsosa arvoista on korkeintaan alaneljänneksen suuruisia\n",
    "* mediaani (50%)\n",
    "* yläneljännes (75%); kolme neljäsosaa arvoista on korkeintaan yläneljänneksen suuruisia\n",
    "* suurin (max)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5dec3bd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nro</th>\n",
       "      <th>sukup</th>\n",
       "      <th>ikä</th>\n",
       "      <th>perhe</th>\n",
       "      <th>koulutus</th>\n",
       "      <th>palveluv</th>\n",
       "      <th>palkka</th>\n",
       "      <th>johto</th>\n",
       "      <th>työtov</th>\n",
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       "      <th>lomaosa</th>\n",
       "      <th>kuntosa</th>\n",
       "      <th>hieroja</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>82.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>47.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>41.500000</td>\n",
       "      <td>1.231707</td>\n",
       "      <td>37.951220</td>\n",
       "      <td>1.621951</td>\n",
       "      <td>1.987654</td>\n",
       "      <td>12.175000</td>\n",
       "      <td>2563.878049</td>\n",
       "      <td>3.060976</td>\n",
       "      <td>4.061728</td>\n",
       "      <td>3.219512</td>\n",
       "      <td>2.109756</td>\n",
       "      <td>3.195122</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>23.815261</td>\n",
       "      <td>0.424519</td>\n",
       "      <td>9.773866</td>\n",
       "      <td>0.487884</td>\n",
       "      <td>0.844006</td>\n",
       "      <td>8.807038</td>\n",
       "      <td>849.350302</td>\n",
       "      <td>1.058155</td>\n",
       "      <td>0.826826</td>\n",
       "      <td>1.154961</td>\n",
       "      <td>1.111179</td>\n",
       "      <td>1.047502</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>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1521.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>2027.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>41.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>2320.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>18.250000</td>\n",
       "      <td>2808.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>82.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>6278.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             nro      sukup        ikä      perhe   koulutus   palveluv  \\\n",
       "count  82.000000  82.000000  82.000000  82.000000  81.000000  80.000000   \n",
       "mean   41.500000   1.231707  37.951220   1.621951   1.987654  12.175000   \n",
       "std    23.815261   0.424519   9.773866   0.487884   0.844006   8.807038   \n",
       "min     1.000000   1.000000  20.000000   1.000000   1.000000   0.000000   \n",
       "25%    21.250000   1.000000  31.000000   1.000000   1.000000   3.750000   \n",
       "50%    41.500000   1.000000  37.500000   2.000000   2.000000  12.500000   \n",
       "75%    61.750000   1.000000  44.000000   2.000000   3.000000  18.250000   \n",
       "max    82.000000   2.000000  61.000000   2.000000   4.000000  36.000000   \n",
       "\n",
       "            palkka      johto     työtov     työymp    palkkat    työteht  \\\n",
       "count    82.000000  82.000000  81.000000  82.000000  82.000000  82.000000   \n",
       "mean   2563.878049   3.060976   4.061728   3.219512   2.109756   3.195122   \n",
       "std     849.350302   1.058155   0.826826   1.154961   1.111179   1.047502   \n",
       "min    1521.000000   1.000000   2.000000   1.000000   1.000000   1.000000   \n",
       "25%    2027.000000   2.000000   4.000000   3.000000   1.000000   3.000000   \n",
       "50%    2320.000000   3.000000   4.000000   3.000000   2.000000   3.000000   \n",
       "75%    2808.000000   4.000000   5.000000   4.000000   3.000000   4.000000   \n",
       "max    6278.000000   5.000000   5.000000   5.000000   5.000000   5.000000   \n",
       "\n",
       "       työterv  lomaosa  kuntosa  hieroja  \n",
       "count     47.0     20.0      9.0     22.0  \n",
       "mean       1.0      1.0      1.0      1.0  \n",
       "std        0.0      0.0      0.0      0.0  \n",
       "min        1.0      1.0      1.0      1.0  \n",
       "25%        1.0      1.0      1.0      1.0  \n",
       "50%        1.0      1.0      1.0      1.0  \n",
       "75%        1.0      1.0      1.0      1.0  \n",
       "max        1.0      1.0      1.0      1.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "17a8295f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      82.000000\n",
       "mean     2563.878049\n",
       "std       849.350302\n",
       "min      1521.000000\n",
       "25%      2027.000000\n",
       "50%      2320.000000\n",
       "75%      2808.000000\n",
       "max      6278.000000\n",
       "Name: palkka, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# palkan tunnusluvut\n",
    "df['palkka'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b619baad",
   "metadata": {},
   "source": [
    "## Frekvenssitaulukko\n",
    "\n",
    "Seuraavassa sijoitan **crosstab**-funktion antaman tuloksen **df1**-nimisen muuttujan arvoksi. Tällöin tulosta ei näytetä ellen anna erikseen käskyä `df1`.\n",
    "\n",
    "Lisätietoa **crosstab**-funktion parametreista https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.crosstab.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5fe68a5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>f</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>koulutus</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0      f\n",
       "koulutus    \n",
       "1.0       27\n",
       "2.0       30\n",
       "3.0       22\n",
       "4.0        2"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.crosstab(df['koulutus'], 'f')\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e11984e",
   "metadata": {},
   "source": [
    "Crosstab laskee frekvenssit eli esiintymiskertojen lukumäärät. Esimerkiksi koulutuksen 1 on suorittanut 27 henkilöä.\n",
    "\n",
    "Crosstab-funktion tuottama taulukko on dataframe kuten alkuperäinen datakin. Dataframe koostuu aina kolmesta osasta:\n",
    "\n",
    "* indeksi (**index**), joka näkyy vasemmassa reunassa\n",
    "* sarakeotsikot (**columns**), jotka näkyvät yläreunassa\n",
    "* varsinainen data; yhdessä sarakkeessa voi olla vain yhdentyyppistä dataa (esimerkiksi kokonaislukuja).\n",
    "\n",
    "Yllä indeksinä ovat koulutus-muuttujassa esiintyvät arvot 1, 2, 3 ja 4. Sarakeotsikoina on ainoastaan f (minulla on tapana käyttää f:ää frekvenssin tunnuksena). Indeksillä on nimi (koulutus) ja sarakeotsikoilla on nimi (col_0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d2bfb111",
   "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>f</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>koulutus</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           f\n",
       "koulutus    \n",
       "1.0       27\n",
       "2.0       30\n",
       "3.0       22\n",
       "4.0        2"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vaihdan sarakeotsikoiden nimeksi tyhjän merkkijonon (peräkkäiset heittomerkit)\n",
    "df1.columns.name = ''\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72984f91",
   "metadata": {},
   "source": [
    "**Lista** on Pythonin tietorakenne, joka kirjoitetaan hakasulkujen sisään. Seuraavassa määrittelen listan ja sijoitan sen dataframen indeksiin (**index**)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "40577477",
   "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>f</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Peruskoulu</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2. aste</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Korkeakoulu</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ylempi korkeakoulu</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     f\n",
       "Peruskoulu          27\n",
       "2. aste             30\n",
       "Korkeakoulu         22\n",
       "Ylempi korkeakoulu   2"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "koulutus = ['Peruskoulu', '2. aste', 'Korkeakoulu', 'Ylempi korkeakoulu']\n",
    "df1.index = koulutus\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b57e8f2d",
   "metadata": {},
   "source": [
    "Jos viittaan sarakkeeseen, jota ei ole vielä olemassa, niin sellainen luodaan. Seuraavassa luon sarakkeen, johon lasken prosentit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c518273d",
   "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>f</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Peruskoulu</th>\n",
       "      <td>27</td>\n",
       "      <td>33.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2. aste</th>\n",
       "      <td>30</td>\n",
       "      <td>37.037037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Korkeakoulu</th>\n",
       "      <td>22</td>\n",
       "      <td>27.160494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ylempi korkeakoulu</th>\n",
       "      <td>2</td>\n",
       "      <td>2.469136</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     f          %\n",
       "Peruskoulu          27  33.333333\n",
       "2. aste             30  37.037037\n",
       "Korkeakoulu         22  27.160494\n",
       "Ylempi korkeakoulu   2   2.469136"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Havaintojen lukumäärä\n",
    "n = df1['f'].sum()\n",
    "\n",
    "# Lisään %-sarakkeen\n",
    "df1['%'] = df1['f']/n*100\n",
    "\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1c53e70",
   "metadata": {},
   "source": [
    "Uuden rivin luomiseksi viittaan uuteen riviin **loc**-ominaisuudella. Seuraavassa lasken **sum**-funktiolla Yhteensä-rivin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "07dbf422",
   "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>f</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Peruskoulu</th>\n",
       "      <td>27.0</td>\n",
       "      <td>33.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2. aste</th>\n",
       "      <td>30.0</td>\n",
       "      <td>37.037037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Korkeakoulu</th>\n",
       "      <td>22.0</td>\n",
       "      <td>27.160494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ylempi korkeakoulu</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.469136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yhteensä</th>\n",
       "      <td>81.0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       f           %\n",
       "Peruskoulu          27.0   33.333333\n",
       "2. aste             30.0   37.037037\n",
       "Korkeakoulu         22.0   27.160494\n",
       "Ylempi korkeakoulu   2.0    2.469136\n",
       "Yhteensä            81.0  100.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.loc['Yhteensä'] = df1.sum()\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d96a545",
   "metadata": {},
   "source": [
    "**VAROITUS!** Kokeile mitä tapahtuu Yhteensä-rivillä, jos suoritat yllä olevan solun uudelleen? Tämän jälkeen kannattaa suorittaa kaikki muistion solut uudelleen **Cell**-valikon komennolla **Run All**.\n",
    "\n",
    "Yllä mainitun varoituksen mukaisen vaaran voit välttää käyttämällä `df1.sum()` sijasta `df1.loc['Peruskoulu':'Ylempi korkeakoulu'].sum()`\n",
    "\n",
    "## Ristiintaulukointi\n",
    "\n",
    "Crosstab-funktiolla voin laskea myös ristiintaulukoinnin. Seuraavassa lasken koulutuksen ja sukupuolen välisen ristiintaulukoinnin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3740f468",
   "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>sukup</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>koulutus</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sukup      1  2\n",
       "koulutus       \n",
       "1.0       22  5\n",
       "2.0       23  7\n",
       "3.0       15  7\n",
       "4.0        2  0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.crosstab(df['koulutus'], df['sukup'])\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48748766",
   "metadata": {},
   "source": [
    "Voin sijoittaa koulutuksen tekstimuotoiset arvot (aiemmin määrittelemäni lista) indeksiin. Luon myös sukupuoli-listan ja sijoitan sen arvot sarakeotsikoihin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "df02561c",
   "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>Mies</th>\n",
       "      <th>Nainen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Peruskoulu</th>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2. aste</th>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Korkeakoulu</th>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ylempi korkeakoulu</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Mies  Nainen\n",
       "Peruskoulu            22       5\n",
       "2. aste               23       7\n",
       "Korkeakoulu           15       7\n",
       "Ylempi korkeakoulu     2       0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sukupuoli = ['Mies', 'Nainen']\n",
    "\n",
    "df2.index = koulutus\n",
    "df2.columns = sukupuoli\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3abb729f",
   "metadata": {},
   "source": [
    "Dataframen indeksille voin antaa nimen. Samoin sarakeotsikoille voin antaa nimen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e2317967",
   "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>Sukupuoli</th>\n",
       "      <th>Mies</th>\n",
       "      <th>Nainen</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koulutus</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Peruskoulu</th>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2. aste</th>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Korkeakoulu</th>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ylempi korkeakoulu</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sukupuoli           Mies  Nainen\n",
       "Koulutus                        \n",
       "Peruskoulu            22       5\n",
       "2. aste               23       7\n",
       "Korkeakoulu           15       7\n",
       "Ylempi korkeakoulu     2       0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.index.name = 'Koulutus'\n",
    "df2.columns.name = 'Sukupuoli'\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c64e74a8",
   "metadata": {},
   "source": [
    "## Dataframen käsittelyä"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e673b360",
   "metadata": {},
   "source": [
    "Listan ohella **sanakirja** (**dictionary**) on toinen keskeinen pythonin tietorakenne. Sanakirja kirjoitetaan aaltosulkujen sisään ja se koostuu pareista. Parin jäsenet erotetaan toisistaan kaksoispisteellä.\n",
    "\n",
    "Seuraavassa käytän sanakirjaa **rename**-funktion yhteydessä kahden muuttujan (sarakkeen) uudelleen nimeämiseen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "eb27b5a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['nro', 'sukupuoli', 'ikä', 'perhe', 'koulutus', 'palveluvuodet',\n",
       "       'palkka', 'johto', 'työtov', 'työymp', 'palkkat', 'työteht', 'työterv',\n",
       "       'lomaosa', 'kuntosa', 'hieroja'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.rename(columns={'sukup':'sukupuoli', 'palveluv':'palveluvuodet'})\n",
    "\n",
    "# Tarkistan onnistuiko uudelleen nimeäminen\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2642b5fe",
   "metadata": {},
   "source": [
    "**VAROITUS!** Jos muuttujien uudelleen nimeämisen jälkeen palaat sellaiseen aiempaan soluun, jossa viitataan sukup- tai palveluv-muuttujaan, niin solun suorittaminen tuottaa virheilmoituksen. Tällaisessa tilanteessa kannattaa suorittaa kaikkien solujen koodit uudelleen **Cell**-valikon komennolla **Run All**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f7474aac",
   "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>Sukupuoli</th>\n",
       "      <th>Mies</th>\n",
       "      <th>Nainen</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koulutus</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Peruskoulu</th>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ylempi korkeakoulu</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Korkeakoulu</th>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2. aste</th>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sukupuoli           Mies  Nainen\n",
       "Koulutus                        \n",
       "Peruskoulu            22       5\n",
       "Ylempi korkeakoulu     2       0\n",
       "Korkeakoulu           15       7\n",
       "2. aste               23       7"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Indeksin järjestyksen vaihtaminen reindex-funktiolla\n",
    "df2.reindex(['Peruskoulu', 'Ylempi korkeakoulu', 'Korkeakoulu', '2. aste'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be7a369d",
   "metadata": {},
   "source": [
    "Huomaa, että uusi järjestys ei tallennu dataframeen ellen varta vasten sijoita sitä df2:n arvoksi!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "96f657dd",
   "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>Sukupuoli</th>\n",
       "      <th>Nainen</th>\n",
       "      <th>Mies</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koulutus</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Peruskoulu</th>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2. aste</th>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Korkeakoulu</th>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ylempi korkeakoulu</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sukupuoli           Nainen  Mies\n",
       "Koulutus                        \n",
       "Peruskoulu               5    22\n",
       "2. aste                  7    23\n",
       "Korkeakoulu              7    15\n",
       "Ylempi korkeakoulu       0     2"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sarakkeiden järjestyksen vaihtamiseksi annan axis-parametrille arvon 1\n",
    "df2.reindex(['Nainen', 'Mies'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4b36aa79",
   "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>Sukupuoli</th>\n",
       "      <th>Koulutus</th>\n",
       "      <th>Mies</th>\n",
       "      <th>Nainen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Peruskoulu</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2. aste</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Korkeakoulu</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ylempi korkeakoulu</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sukupuoli            Koulutus  Mies  Nainen\n",
       "0                  Peruskoulu    22       5\n",
       "1                     2. aste    23       7\n",
       "2                 Korkeakoulu    15       7\n",
       "3          Ylempi korkeakoulu     2       0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Indeksin siirtäminen pois indeksistä; tilalle oletusindeksi 0, 1,...\n",
    "df2 = df2.reset_index()\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "46edbe5c",
   "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>Sukupuoli</th>\n",
       "      <th>Mies</th>\n",
       "      <th>Nainen</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koulutus</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Peruskoulu</th>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2. aste</th>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Korkeakoulu</th>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ylempi korkeakoulu</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sukupuoli           Mies  Nainen\n",
       "Koulutus                        \n",
       "Peruskoulu            22       5\n",
       "2. aste               23       7\n",
       "Korkeakoulu           15       7\n",
       "Ylempi korkeakoulu     2       0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Koulutuksen palauttaminen indeksiin\n",
    "df2 = df2.set_index('Koulutus')\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99a2f73a",
   "metadata": {},
   "source": [
    "**Loc**-ominaisuudella voin viitata datan \"viipaleisiin\" indeksin arvoja ja sarakeotsikoita käyttäen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e92d8e5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Kuinka monta 2. asteen koulutuksen suorittanutta miestä?\n",
    "df2.loc['2. aste', 'Mies']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5dfdbcd",
   "metadata": {},
   "source": [
    "Pelkkä kaksoispiste tarkoittaa kaikkia rivejä tai kaikkia sarakkeita."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "be04c21a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Koulutus\n",
       "Peruskoulu            5\n",
       "2. aste               7\n",
       "Korkeakoulu           7\n",
       "Ylempi korkeakoulu    0\n",
       "Name: Nainen, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df2:n 'Nainen'-sarakkeen kaikki rivit\n",
    "df2.loc[:, 'Nainen']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40e1b0e0",
   "metadata": {},
   "source": [
    "Edellä dataa on vain yhdessä sarakkeessa ja sen vuoksi tulos ei ole tyypiltään dataframe, vaan **series**. Voin kuitenkin vaihtaa tuloksen dataframeksi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ba5dd14d",
   "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>Nainen</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koulutus</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Peruskoulu</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2. aste</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Korkeakoulu</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ylempi korkeakoulu</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Nainen\n",
       "Koulutus                  \n",
       "Peruskoulu               5\n",
       "2. aste                  7\n",
       "Korkeakoulu              7\n",
       "Ylempi korkeakoulu       0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.loc[:, 'Nainen'].to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "63ca5998",
   "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>Sukupuoli</th>\n",
       "      <th>Mies</th>\n",
       "      <th>Nainen</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koulutus</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Peruskoulu</th>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2. aste</th>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Korkeakoulu</th>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sukupuoli    Mies  Nainen\n",
       "Koulutus                 \n",
       "Peruskoulu     22       5\n",
       "2. aste        23       7\n",
       "Korkeakoulu    15       7"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Kaikki sarakkeet, mutta koulutuksesta jätetään Ylempi korkeakoulu pois\n",
    "df2.loc['Peruskoulu':'Korkeakoulu', :]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dce9bf9",
   "metadata": {},
   "source": [
    "## Luokiteltu jakauma\n",
    "\n",
    "Luokkarajojen määrittämistä varten tarkistan pienimmän ja suurimman iän."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "baa9f66e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    82.000000\n",
       "mean     37.951220\n",
       "std       9.773866\n",
       "min      20.000000\n",
       "25%      31.000000\n",
       "50%      37.500000\n",
       "75%      44.000000\n",
       "max      61.000000\n",
       "Name: ikä, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ikä'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "987c8350",
   "metadata": {},
   "source": [
    "Voin muodostaa luokkia pandas-kirjaston **cut**-funktiolla. Ensin määritän luokkarajat listana ja annan listan **bins**-parametrin arvoksi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ba4d4220",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nro</th>\n",
       "      <th>sukupuoli</th>\n",
       "      <th>ikä</th>\n",
       "      <th>perhe</th>\n",
       "      <th>koulutus</th>\n",
       "      <th>palveluvuodet</th>\n",
       "      <th>palkka</th>\n",
       "      <th>johto</th>\n",
       "      <th>työtov</th>\n",
       "      <th>työymp</th>\n",
       "      <th>palkkat</th>\n",
       "      <th>työteht</th>\n",
       "      <th>työterv</th>\n",
       "      <th>lomaosa</th>\n",
       "      <th>kuntosa</th>\n",
       "      <th>hieroja</th>\n",
       "      <th>ikäluokka</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(29, 39]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2963</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(19, 29]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(29, 39]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2144</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(29, 39]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2183</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(19, 29]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1598</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(19, 29]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1638</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(29, 39]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2612</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(19, 29]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2808</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(29, 39]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2183</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(29, 39]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    nro  sukupuoli  ikä  perhe  koulutus  palveluvuodet  palkka  johto  \\\n",
       "0     1          1   38      1       1.0           22.0    3587      3   \n",
       "1     2          1   29      2       2.0           10.0    2963      1   \n",
       "2     3          1   30      1       1.0            7.0    1989      3   \n",
       "3     4          1   36      2       1.0           14.0    2144      3   \n",
       "4     5          1   24      1       2.0            4.0    2183      2   \n",
       "..  ...        ...  ...    ...       ...            ...     ...    ...   \n",
       "77   78          1   22      1       3.0            0.0    1598      4   \n",
       "78   79          1   33      1       1.0            2.0    1638      1   \n",
       "79   80          1   27      1       2.0            7.0    2612      3   \n",
       "80   81          1   35      2       2.0           16.0    2808      3   \n",
       "81   82          2   35      2       3.0           15.0    2183      3   \n",
       "\n",
       "    työtov  työymp  palkkat  työteht  työterv  lomaosa  kuntosa  hieroja  \\\n",
       "0      3.0       3        3        3      NaN      NaN      NaN      NaN   \n",
       "1      5.0       2        1        3      NaN      NaN      NaN      NaN   \n",
       "2      4.0       1        1        3      1.0      NaN      NaN      NaN   \n",
       "3      3.0       3        3        3      1.0      NaN      NaN      NaN   \n",
       "4      3.0       2        1        2      1.0      NaN      NaN      NaN   \n",
       "..     ...     ...      ...      ...      ...      ...      ...      ...   \n",
       "77     4.0       4        3        4      NaN      1.0      1.0      NaN   \n",
       "78     3.0       2        1        2      1.0      NaN      NaN      NaN   \n",
       "79     4.0       3        3        3      1.0      NaN      1.0      NaN   \n",
       "80     4.0       3        3        3      NaN      NaN      NaN      NaN   \n",
       "81     4.0       4        3        4      1.0      NaN      NaN      NaN   \n",
       "\n",
       "   ikäluokka  \n",
       "0   (29, 39]  \n",
       "1   (19, 29]  \n",
       "2   (29, 39]  \n",
       "3   (29, 39]  \n",
       "4   (19, 29]  \n",
       "..       ...  \n",
       "77  (19, 29]  \n",
       "78  (29, 39]  \n",
       "79  (19, 29]  \n",
       "80  (29, 39]  \n",
       "81  (29, 39]  \n",
       "\n",
       "[82 rows x 17 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = [19, 29, 39, 49, 59, 69]\n",
    "\n",
    "df['ikäluokka'] = pd.cut(df['ikä'], bins=bins)\n",
    "df\n",
    "# Ikäluokat löytyvät viimeisestä sarakkeesta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b757d40c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>f</th>\n",
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       "    <tr>\n",
       "      <th>ikäluokka</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(19, 29]</th>\n",
       "      <td>17</td>\n",
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       "    <tr>\n",
       "      <th>(29, 39]</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(39, 49]</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(49, 59]</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(59, 69]</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "            f\n",
       "ikäluokka    \n",
       "(19, 29]   17\n",
       "(29, 39]   30\n",
       "(39, 49]   23\n",
       "(49, 59]   11\n",
       "(59, 69]    1"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Luokiteltu jakauma:\n",
    "df3 = pd.crosstab(df['ikäluokka'], 'f')\n",
    "df3.columns.name = ''\n",
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50f96c95",
   "metadata": {},
   "source": [
    "## Ryhmittely\n",
    "\n",
    "Seuraavassa lasken palkalle tilastollisia tunnuslukuja koulutuksen määräämissä ryhmissä. Datan jakaminen ryhmiin sujuu **groupby**-funktiolla.\n",
    "\n",
    "Seuraavaa komentoon on hyvä perehtyä tarkemminkin. Koodissa operoidaan enimmäkseen **olio**illa (voidaan kutsua myös objekteiksi). \n",
    "\n",
    "Esimerkiksi **df** on **DataFrame**-luokan **olio**. DataFrame-luokan oliot tunnistavat **groupby**-funktion. Groupby-funktiolle pitää antaa parametrina sen muuttujan nimi, jonka mukaan ryhmitellään. Huomaa, että olion nimen ja funktion nimen välissä on aina **piste** (**.**). Samoin käytetään pistettä olion nimen ja ominaisuuden välissä (esimerkiksi **df.shape**).\n",
    "\n",
    "Groupby-funktion käytön seurauksena syntyy uusi olio, jonka 'palkka'-muuttujaan koodi viittaa. Tälle oliolle toteutetaan **describe**-funktio, joka laskee tilastolliset tunnusluvut.\n",
    "\n",
    "Komennot siis usein muodostuvat oliosta, funktioista (funktion kohdistaminen olioon muodostaa uuden olion) ja ominaisuuksista, jotka aina erotetaan toisistaan pisteellä."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "40634165",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Peruskoulu</th>\n",
       "      <td>27.0</td>\n",
       "      <td>2310.481481</td>\n",
       "      <td>473.108653</td>\n",
       "      <td>1638.0</td>\n",
       "      <td>2008.00</td>\n",
       "      <td>2144.0</td>\n",
       "      <td>2534.0</td>\n",
       "      <td>3587.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2. aste</th>\n",
       "      <td>30.0</td>\n",
       "      <td>2403.100000</td>\n",
       "      <td>533.984395</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>2008.25</td>\n",
       "      <td>2378.5</td>\n",
       "      <td>2729.0</td>\n",
       "      <td>3510.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Korkeakoulu</th>\n",
       "      <td>22.0</td>\n",
       "      <td>2887.227273</td>\n",
       "      <td>1108.404384</td>\n",
       "      <td>1559.0</td>\n",
       "      <td>2222.25</td>\n",
       "      <td>2710.0</td>\n",
       "      <td>2925.0</td>\n",
       "      <td>6278.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ylempi korkeakoulu</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5147.000000</td>\n",
       "      <td>110.308658</td>\n",
       "      <td>5069.0</td>\n",
       "      <td>5108.00</td>\n",
       "      <td>5147.0</td>\n",
       "      <td>5186.0</td>\n",
       "      <td>5225.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count         mean          std     min      25%     50%  \\\n",
       "Peruskoulu           27.0  2310.481481   473.108653  1638.0  2008.00  2144.0   \n",
       "2. aste              30.0  2403.100000   533.984395  1521.0  2008.25  2378.5   \n",
       "Korkeakoulu          22.0  2887.227273  1108.404384  1559.0  2222.25  2710.0   \n",
       "Ylempi korkeakoulu    2.0  5147.000000   110.308658  5069.0  5108.00  5147.0   \n",
       "\n",
       "                       75%     max  \n",
       "Peruskoulu          2534.0  3587.0  \n",
       "2. aste             2729.0  3510.0  \n",
       "Korkeakoulu         2925.0  6278.0  \n",
       "Ylempi korkeakoulu  5186.0  5225.0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df.groupby('koulutus')['palkka'].describe()\n",
    "\n",
    "# Aiemmin määritelty koulutus-lista sisältää koulutusten tekstimuotoiset arvot\n",
    "df4.index = koulutus\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0e534dda",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Peruskoulu</th>\n",
       "      <th>2. aste</th>\n",
       "      <th>Korkeakoulu</th>\n",
       "      <th>Ylempi korkeakoulu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2310.481481</td>\n",
       "      <td>2403.100000</td>\n",
       "      <td>2887.227273</td>\n",
       "      <td>5147.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>473.108653</td>\n",
       "      <td>533.984395</td>\n",
       "      <td>1108.404384</td>\n",
       "      <td>110.308658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1638.000000</td>\n",
       "      <td>1521.000000</td>\n",
       "      <td>1559.000000</td>\n",
       "      <td>5069.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2008.000000</td>\n",
       "      <td>2008.250000</td>\n",
       "      <td>2222.250000</td>\n",
       "      <td>5108.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2144.000000</td>\n",
       "      <td>2378.500000</td>\n",
       "      <td>2710.000000</td>\n",
       "      <td>5147.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2534.000000</td>\n",
       "      <td>2729.000000</td>\n",
       "      <td>2925.000000</td>\n",
       "      <td>5186.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3587.000000</td>\n",
       "      <td>3510.000000</td>\n",
       "      <td>6278.000000</td>\n",
       "      <td>5225.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Peruskoulu      2. aste  Korkeakoulu  Ylempi korkeakoulu\n",
       "count    27.000000    30.000000    22.000000            2.000000\n",
       "mean   2310.481481  2403.100000  2887.227273         5147.000000\n",
       "std     473.108653   533.984395  1108.404384          110.308658\n",
       "min    1638.000000  1521.000000  1559.000000         5069.000000\n",
       "25%    2008.000000  2008.250000  2222.250000         5108.000000\n",
       "50%    2144.000000  2378.500000  2710.000000         5147.000000\n",
       "75%    2534.000000  2729.000000  2925.000000         5186.000000\n",
       "max    3587.000000  3510.000000  6278.000000         5225.000000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Voin halutessani transponoida dataframen\n",
    "df4.T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e84fc38",
   "metadata": {},
   "source": [
    "## Luettelo muistiossa käytetyistä funktioista ja ominaisuuksista\n",
    "\n",
    "\n",
    "* Merkkijono (**string**) kirjoitetaan heittomerkkien '' väliin (myös lainausmerkit \"\" käyvät). Merkkijonoja ovat esimerkiksi tiedostonimet, dataframen sarakeotsikot (muuttujien nimet), muuttujien tekstimuotoiset arvot. Huomaa kuitenkin, että Pythonin muuttujien nimiä ei kirjoitetan heittomerkkien väliin (esimerkiksi df, n jne.) \n",
    "\n",
    "* Paketin tuonti **import**-toiminnolla; esimerkkinä **pandas**-paketin tuonti\n",
    "\n",
    "* Pythonin tietorakenne **lista**, joka kirjoitetaan hakasulkujen sisään\n",
    "* Pythonin tietorakenne **sanakirja** (**dictionary**), joka kirjoitetaan aaltosulkujen sisään\n",
    "* Pandas-kirjaston tietorakenteet **dataframe** ja **series**\n",
    "\n",
    "* **pd.read_excel()** datan lukeminen Excel-tiedostosta\n",
    "* **df.shape** datan rivien ja sarakkeiden lukumäärät\n",
    "* **df.info()** arvojen lukumäärät ja tietotyypit\n",
    "* **df.describe()** tilastolliset tunnusluvut\n",
    "* **df.nsmallest()** n pienintä nimetyn sarakkeen suhteen\n",
    "* **df.nlargest()** n suurinta nimetyn sarakkeen suhteen\n",
    "* **df[].unique()** nimetyn sarakkeen ainutkertaisten arvojen luettelo\n",
    "* **pd.crosstab()** lukumäärä- ja prosenttiyhteenvedot \n",
    "* **df.columns** sarakeotsikot\n",
    "* **df.index** riviotsikot\n",
    "* **df.columns.name** sarakeotsikoiden nimi\n",
    "* **df.index.name** riviotsikoiden nimi\n",
    "* **df.rename()** sarakeotsikoiden uudelleen nimeämiseen\n",
    "* **df.reindex()** sarkeotsikoiden tai indeksin uudelleen järjestäminen\n",
    "* **df.reset_index()** indeksin siirtäminen tavalliseksi sarakkeeksi (tilalle oletusindeksi)\n",
    "* **df.set_index()** sarakkeen siirtäminen indeksiin\n",
    "* **df.loc[]** viittaaminen dataframen \"viipaleeseen\"\n",
    "* **df.to_frame()** series dataframeksi\n",
    "* **pd.cut()** arvojen luokittelu\n",
    "* **df.groupby()** ryhmittely\n",
    "* **df.T** dataframen transponointi (rivien ja sarakkeiden vaihtaminen päittäin)\n",
    "\n",
    "Virhetilanteissa auttaa usein koodisolujen suoritus uudelleen **Run**-valikon komennolla **Run All**. Jos avaat aiemmin aloittamasi muistion, niin ensimmäiseksi kannattaa suorittaa koodisolut **Run**-valikon komennolla **Run All**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8a421a2",
   "metadata": {},
   "source": [
    "## Lisätietoa\n",
    "\n",
    "\n",
    "Data-analytiikka Pythonilla https://tilastoapu.wordpress.com/python/"
   ]
  }
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