python-data-5-pandas.ipynb

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    "s[3]"
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   "source": [
    "s[\"c\"]"
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   "cell_type": "code",
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    {
     "data": {
      "text/plain": [
       "b    1\n",
       "d    3\n",
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   "source": [
    "s[[1,3]]"
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       "a    0\n",
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   "source": [
    "s[s<2]"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A subtle difference when indexing in pandas is that unlike in normal Python, slicing here is inclusive at the end-point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    1\n",
       "c    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
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   ],
   "source": [
    "s[\"b\":\"c\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the above also apply to DataFrames:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>Edinburgh</th>\n",
       "      <th>Glasgow</th>\n",
       "      <th>Aberdeen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   Edinburgh  Glasgow  Aberdeen\n",
       "a          0        1         2\n",
       "b          3        4         5\n",
       "c          6        7         8"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data = np.reshape(np.arange(9), (3,3))\n",
    "df = pd.DataFrame(data, index=[\"a\", \"b\", \"c\"],\n",
    "                  columns=[\"Edinburgh\", \"Glasgow\", \"Aberdeen\"])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Edinburgh</th>\n",
       "      <th>Glasgow</th>\n",
       "      <th>Aberdeen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   Edinburgh  Glasgow  Aberdeen\n",
       "a          0        1         2\n",
       "b          3        4         5"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    4\n",
       "c    7\n",
       "Name: Glasgow, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Glasgow\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### loc and iloc\n",
    "For DataFrame label-indexing on the rows, you can use `loc` for labels and `iloc` for integer-indexing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Edinburgh    3\n",
       "Glasgow      4\n",
       "Aberdeen     5\n",
       "Name: b, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"b\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Glasgow     4\n",
       "Aberdeen    5\n",
       "Name: b, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"b\", [\"Glasgow\", \"Aberdeen\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try `iloc`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Edinburgh    3\n",
       "Glasgow      4\n",
       "Aberdeen     5\n",
       "Name: b, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Glasgow     4\n",
       "Aberdeen    5\n",
       "Name: b, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1, [1,2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Edinburgh</th>\n",
       "      <th>Glasgow</th>\n",
       "      <th>Aberdeen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>b</th>\n",
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      "text/plain": [
       "   Edinburgh  Glasgow  Aberdeen\n",
       "a          0        1         2\n",
       "b          3        4         5"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.iloc[:2]"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summary of indexing:\n",
    "\n",
    "| Type | Notes |\n",
    "| -- | -- |\n",
    "| df\\[val\\]              | Select single column or sequency of columns from a DataFrame |\n",
    "| df.loc\\[val\\]          | Select single row or subset of rows from a DataFrame by label |\n",
    "| df.loc\\[:, val\\]       | Select single column or subset of columns by label |\n",
    "| df.loc\\[val1, val2\\]   | Select both rows and columns by label |\n",
    "| df.iloc\\[idx\\]         | Select single row or subset of rows from DataFrame by integer position |\n",
    "| df.iloc\\[:, idx\\]      | Select single column or subset of columns by integer position |\n",
    "| df.iloc\\[idx1, idx2\\]  | Select both rows and columns by integer position |\n",
    "| reindex method         | Select either rows or columns by labels |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 1\n",
    "A dataset of random numbers is created below. Index the 47th column and the 22nd row. You should get the number **4621**.\n",
    "\n",
    "*Note: Remember that Python uses 0-based indexing*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.reshape(np.arange(10000), (100,100)))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 2\n",
    "Using the same DataFrame from the previous exercise, obtain all rows starting from row 85 to 97."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Arithmetic <a name=\"arithmetic\"></a>\n",
    "When you are performing arithmetic operations between two objects, if any index pairs are not the same, the respective index in the result will be the union of the index pair. Let's have a look"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    NaN\n",
       "b    1.0\n",
       "c    3.0\n",
       "d    5.0\n",
       "e    NaN\n",
       "k    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series(np.arange(5), index=[\"a\", \"b\", \"c\", \"d\", \"e\"])\n",
    "s2 = pd.Series(np.arange(4), index=[\"b\", \"c\", \"d\", \"k\"])\n",
    "s1 + s2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The internal data alignment introduces missing values in the label locations that don't overlap. It is similar for DataFrames:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
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       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   a  b   c   d\n",
       "0  0  1   2   3\n",
       "1  4  5   6   7\n",
       "2  8  9  10  11"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame(np.arange(12).reshape((3,4)),\n",
    "                  columns=list(\"abcd\"))\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
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       "      <th>f</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "    c   d   e   f\n",
       "0   0   1   2   3\n",
       "1   4   5   6   7\n",
       "2   8   9  10  11\n",
       "3  12  13  14  15"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame(np.arange(16).reshape((4,4)),\n",
    "                  columns=list(\"cdef\"))\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a   b     c     d   e   f\n",
       "0 NaN NaN   2.0   4.0 NaN NaN\n",
       "1 NaN NaN  10.0  12.0 NaN NaN\n",
       "2 NaN NaN  18.0  20.0 NaN NaN\n",
       "3 NaN NaN   NaN   NaN NaN NaN"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# adding the two\n",
    "df1+df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how where we don't have matching values from `df1` and `df2` the output of the addition operation is `NaN` since there are no two numbers to add.\n",
    "\n",
    "Well, we can \"fix\" that by filling in the `NaN` values. This effectively tells pandas where there are no two values to add, assume that the missing value is just zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
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       "\n",
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       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
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       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
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      ],
      "text/plain": [
       "     a    b     c     d     e     f\n",
       "0  0.0  1.0   2.0   4.0   2.0   3.0\n",
       "1  4.0  5.0  10.0  12.0   6.0   7.0\n",
       "2  8.0  9.0  18.0  20.0  10.0  11.0\n",
       "3  NaN  NaN  12.0  13.0  14.0  15.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.add(df2, fill_value=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another important point here is although the normal arithmetic operations work here, there also exist dedicated methods like `DataFrame.add()` which achieve the same functionality + a bit extra.\n",
    "\n",
    "Here's a list of all arithmetic operations within pandas:\n",
    "\n",
    "| Operator | Method | Description |\n",
    "| -- | -- | -- |\n",
    "| + | add, radd | Addition |\n",
    "| - | sub, rsub | Subtraction |\n",
    "| / | div, rdiv | Division |\n",
    "| // | floordiv, rfloordiv | Floor division |\n",
    "| * | mul, rmul | Multiplication |\n",
    "| ** | pow, rpow | Exponentiation |\n",
    "\n",
    "Notice how some of the methods have `r` in front of them? That stands for reversed and effectively reverses the operands. For example\n",
    "\n",
    "```python\n",
    "df1.div(df2)\n",
    "```\n",
    "would be the same as\n",
    "```python\n",
    "df2/df1\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 3\n",
    "Create a (3,3) DataFrame and square all elements in it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Broadcasting\n",
    "Similar to numpy, in pandas you can also broadcast data structures. Let's consider a simple example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
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