python-data-5-pandas.ipynb

       "</div>"
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     "execution_count": 92,
     "metadata": {},
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   "source": [
    "data.dropna()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
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   "source": [
    "data.dropna(how=\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 11\n",
    "That's fine if we want to remove missing data, what if we want to fill in missing data? Do you know of a way? Try to fill in all of the missing values from the data below with **0s**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame([[1., 6.5, 3.], [2., None, None],\n",
    "                    [None, None, None], [None, 1.5, 9.]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "pandas also allows us to interpolate the data instead of just filling it with a constant. The easiest way to do that is shown below, but there are more complex ones that are not covered in this course."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
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    "data.fillna(method=\"ffill\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want you can explore the other capabilities of [`fillna`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.fillna.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Transformation  <a name=\"transformation\"></a>\n",
    "### Removing duplicates\n",
    "Duplicate data can be a serious issue, luckily pandas offers a simple way to remove duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
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   "source": [
    "data = pd.DataFrame([1, 2, 3, 4, 3, 2, 1])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
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    "data.drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also select which rows to keep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
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    "data.drop_duplicates(keep=\"last\")"
   ]
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   "metadata": {},
   "source": [
    "### Replacing data\n",
    "You've already seen how you can fill in missing data with `fillna`. That is actually a special case of more general value replacement. That is done via the `replace` method.\n",
    "\n",
    "Let's consider an example where the dataset given to us had `-999` as sentinel values for missing data instead of `NaN`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       0\n",
       "0    1.0\n",
       "1 -999.0\n",
       "2    2.0\n",
       "3 -999.0\n",
       "4    3.0\n",
       "5    4.0\n",
       "6 -999.0\n",
       "7 -999.0\n",
       "8    7.0"
      ]
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     "execution_count": 99,
     "metadata": {},
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    "data = pd.DataFrame([1., -999., 2., -999., 3., 4., -999, -999, 7.])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
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       "     0\n",
       "0  1.0\n",
       "1  NaN\n",
       "2  2.0\n",
       "3  NaN\n",
       "4  3.0\n",
       "5  4.0\n",
       "6  NaN\n",
       "7  NaN\n",
       "8  7.0"
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   "source": [
    "data.replace(-999, np.nan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Renaming axis indexes\n",
    "Similar to `replace` you can also rename the labels of your axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Edinburgh</th>\n",
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       "      <td>3</td>\n",
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       "      <th>Glasgow</th>\n",
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       "           0  1   2   3\n",
       "Edinburgh  0  1   2   3\n",
       "Glasgow    4  5   6   7\n",
       "Aberdeen   8  9  10  11"
      ]
     },
     "execution_count": 101,
     "metadata": {},
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   "source": [
    "data = pd.DataFrame(np.arange(12).reshape((3, 4)),\n",
    "                    index=['Edinburgh', 'Glasgow', 'Aberdeen'])\n",
    "data"
   ]
  },
  {
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       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           one  two  three  four\n",
       "Edinburgh    0    1      2     3\n",
       "Glasgow      4    5      6     7\n",
       "Aberdeen     8    9     10    11"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a map using a standard Python dictionary\n",
    "mapping = { 0 : \"one\",\n",
    "            1 : \"two\",\n",
    "            2 : \"three\",\n",
    "            3 : \"four\"}\n",
    "\n",
    "# now rename the columns\n",
    "data.rename(columns=mapping)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rows can be renamed in a similar fashion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Detection and Filtering Outliers\n",
    "Filtering or transforming outliers is largely a matter of applying array operations. Consider a DataFrame with some normally distributed data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.026552</td>\n",
       "      <td>-0.007912</td>\n",
       "      <td>0.001678</td>\n",
       "      <td>-0.047619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.010300</td>\n",
       "      <td>0.980964</td>\n",
       "      <td>1.022289</td>\n",
       "      <td>1.008740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-3.490886</td>\n",
       "      <td>-3.952883</td>\n",
       "      <td>-2.824025</td>\n",
       "      <td>-3.410687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.696013</td>\n",
       "      <td>-0.664201</td>\n",
       "      <td>-0.667879</td>\n",
       "      <td>-0.735838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.014608</td>\n",
       "      <td>0.012376</td>\n",
       "      <td>0.018254</td>\n",
       "      <td>-0.078759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.660866</td>\n",
       "      <td>0.642424</td>\n",
       "      <td>0.678173</td>\n",
       "      <td>0.617265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.185923</td>\n",
       "      <td>3.109778</td>\n",
       "      <td>3.361027</td>\n",
       "      <td>3.371655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0            1            2            3\n",
       "count  1000.000000  1000.000000  1000.000000  1000.000000\n",
       "mean     -0.026552    -0.007912     0.001678    -0.047619\n",
       "std       1.010300     0.980964     1.022289     1.008740\n",
       "min      -3.490886    -3.952883    -2.824025    -3.410687\n",
       "25%      -0.696013    -0.664201    -0.667879    -0.735838\n",
       "50%      -0.014608     0.012376     0.018254    -0.078759\n",
       "75%       0.660866     0.642424     0.678173     0.617265\n",
       "max       3.185923     3.109778     3.361027     3.371655"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame(np.random.randn(1000, 4))\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppose you now want to lower all absolute values exceeding 3 from one of the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54    3.361027\n",
       "Name: 2, dtype: float64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = data[2]\n",
    "col[np.abs(col) > 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.025998</td>\n",
       "      <td>-0.007102</td>\n",