Metodo de interpolação.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0c052469",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ab3d4833",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [0.5, None, None, 0.52,0.54, None, None, 0.59,0.6, None,0.7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ecea61a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.50\n",
       "1      NaN\n",
       "2      NaN\n",
       "3     0.52\n",
       "4     0.54\n",
       "5      NaN\n",
       "6      NaN\n",
       "7     0.59\n",
       "8     0.60\n",
       "9      NaN\n",
       "10    0.70\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(data)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6d7f46b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.50\n",
       "1     0.00\n",
       "2     0.00\n",
       "3     0.52\n",
       "4     0.54\n",
       "5     0.00\n",
       "6     0.00\n",
       "7     0.59\n",
       "8     0.60\n",
       "9     0.00\n",
       "10    0.70\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dbe7800f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.50\n",
       "1     0.50\n",
       "2     0.50\n",
       "3     0.52\n",
       "4     0.54\n",
       "5     0.54\n",
       "6     0.54\n",
       "7     0.59\n",
       "8     0.60\n",
       "9     0.60\n",
       "10    0.70\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.fillna(method = 'ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "caf84a2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.50\n",
       "1     0.52\n",
       "2     0.52\n",
       "3     0.52\n",
       "4     0.54\n",
       "5     0.59\n",
       "6     0.59\n",
       "7     0.59\n",
       "8     0.60\n",
       "9     0.70\n",
       "10    0.70\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.fillna(method = 'bfill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8837bfcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.500\n",
       "1     0.575\n",
       "2     0.575\n",
       "3     0.520\n",
       "4     0.540\n",
       "5     0.575\n",
       "6     0.575\n",
       "7     0.590\n",
       "8     0.600\n",
       "9     0.575\n",
       "10    0.700\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.fillna(s.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "baaddb81",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.50\n",
       "1     0.50\n",
       "2      NaN\n",
       "3     0.52\n",
       "4     0.54\n",
       "5     0.54\n",
       "6      NaN\n",
       "7     0.59\n",
       "8     0.60\n",
       "9     0.60\n",
       "10    0.70\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = s.fillna(method = 'ffill' , limit = 1)\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8722ecaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.50\n",
       "1     0.50\n",
       "2     0.52\n",
       "3     0.52\n",
       "4     0.54\n",
       "5     0.54\n",
       "6     0.59\n",
       "7     0.59\n",
       "8     0.60\n",
       "9     0.60\n",
       "10    0.70\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.fillna(method = 'bfill' , limit = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4bb0044d",
   "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>Corredor</th>\n",
       "      <th>Melhor Tempo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Marcos</td>\n",
       "      <td>9.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pedro</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>João</td>\n",
       "      <td>9.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Beto</td>\n",
       "      <td>9.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sandro</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Denis</td>\n",
       "      <td>9.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Carlos</td>\n",
       "      <td>9.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "  Corredor  Melhor Tempo\n",
       "0   Marcos          9.62\n",
       "1    Pedro           NaN\n",
       "2     João          9.69\n",
       "3     Beto          9.72\n",
       "4   Sandro           NaN\n",
       "5    Denis          9.69\n",
       "6      Ary           NaN\n",
       "7   Carlos          9.74"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#Exercício\n",
    "atletas = pd.DataFrame([['Marcos', 9.62], ['Pedro', None], ['João', 9.69], \n",
    "                        ['Beto', 9.72], ['Sandro', None], ['Denis', 9.69], \n",
    "                        ['Ary', None], ['Carlos', 9.74]], \n",
    "                        columns = ['Corredor', 'Melhor Tempo'])\n",
    "atletas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c3bcf987",
   "metadata": {},
   "outputs": [],
   "source": [
    " atletas.fillna(atletas['Melhor Tempo'].mean(), inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f70c42dc",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Corredor</th>\n",
       "      <th>Melhor Tempo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Marcos</td>\n",
       "      <td>9.620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pedro</td>\n",
       "      <td>9.692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>João</td>\n",
       "      <td>9.690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Beto</td>\n",
       "      <td>9.720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sandro</td>\n",
       "      <td>9.692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Denis</td>\n",
       "      <td>9.690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ary</td>\n",
       "      <td>9.692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Carlos</td>\n",
       "      <td>9.740</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "  Corredor  Melhor Tempo\n",
       "0   Marcos         9.620\n",
       "1    Pedro         9.692\n",
       "2     João         9.690\n",
       "3     Beto         9.720\n",
       "4   Sandro         9.692\n",
       "5    Denis         9.690\n",
       "6      Ary         9.692\n",
       "7   Carlos         9.740"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "atletas"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "e2bfac35",
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "id": "5e511ea5",
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