11_joining_data.ipynb

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    "# ICS 434: DATA SCIENCE FUNDAMENTALS\n",
    "\n",
    "## Data Wrangling: Joining, Combining and Reshaping Data \n",
    "\n",
    "---\n",
    "\n",
    "* This chapter focuces on the tools you need to combine, join and rearrange data\n"
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   "source": [
    "# ICS 434: DATA SCIENCE FUNDAMENTALS\n",
    "\n",
    "## Data Wrangling: Joining and Combining Data \n",
    "\n",
    "---"
   ]
  },
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from IPython.display import Image"
   ]
  },
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    "np.random.seed(12345)\n",
    "pd.options.display.max_rows = 20\n",
    "plt.rc('figure', figsize=(10, 6))"
   ]
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    "### Combining and Merging Datasets\n",
    "\n",
    "* You can combine `DataFrames` using different strategies\n",
    " * You can achieve similar effect as `join` in SQL\n",
    " * You can also concatenate `DataFrames` horizontally or vertically\n",
    " \n",
    "* The operation we will cover and use are\n",
    "  - `merge()`: connect rows in `DataFrames` based on keys (similar to database `Join` operation)\n",
    "  - `concat()`: concatenate data (horizontally) or stacks it (vertically) "
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       "  key  data1\n",
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    "df1 = pd.DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'a', 'b'], 'data1': range(7)})\n",
    "df1"
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       "  key  data2\n",
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    "df2 = pd.DataFrame({'key': ['a', 'b', 'd'], 'data2': range(3)})\n",
    "df2"
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    "### Database-Style DataFrame Joins\n",
    "\n",
    "* Merge on a common `keys`\n",
    "\n",
    "* We can merge `df1` and `df2` on the common column name `key`  \n",
    "  * Pandas can infer the overlapping `key`, or you can specify it explicitly using `on=` \n",
    "  * Using `on=` avoids mistakes in large dataset with dozens of columns\n",
    "* Since some labels are duplicated in `df1` while `df2` has a single row for `key` value, this amount to a `many-to-one` join\n"
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       "  key  data1  data2\n",
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    "merged_df = pd.merge(df1, df2)\n",
    "merged_df"
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       "  key  data1  data2\n",
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    "merged_df = pd.merge(df1, df2, on='key')\n",
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       "  key  data1  data2\n",
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    "merged_df.sort_values(\"key\")"
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    "- If the columns are different in both datasets, you can specify them separately using `left_on=` and `right_on=`"
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       "      <td>b</td>\n",
       "      <td>b</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  lkey rkey  data1  data2\n",
       "0    b    b      0      1\n",
       "1    b    b      1      1\n",
       "2    b    b      6      1\n",
       "3    a    a      2      0\n",
       "4    a    a      4      0\n",
       "5    a    a      5      0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.DataFrame({'lkey': ['b', 'b', 'a', 'c', 'a', 'a', 'b'],\n",
    "                    'data1': range(7)})\n",
    "df4 = pd.DataFrame({'rkey': ['a', 'b', 'd'],\n",
    "                    'data2': range(3)})\n",
    "display(df3)\n",
    "display(df4)\n",
    "merged_df = pd.merge(df3, df4, left_on='lkey', right_on='rkey')\n",
    "merged_df[[\"lkey\", \"rkey\", \"data1\", \"data2\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Unmatches Values\n",
    "\n",
    "![](https://www.dropbox.com/s/u0wuwrpk8vga60h/missing_keys.png?dl=1)\n",
    "\n",
    "* What happened to lkey=`c`   and rkey=`d`? Why aren't they showing in the resulting `DataFrame`?\n",
    "  * Unmatched keys are dropped from the merged result\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Merge Types\n",
    "\n",
    "* By default, the merge does an `inner` join\n",
    "\n",
    "* Different merge types in `pandas.merge()`\n",
    "\n",
    "| Option        | Behavior           |\n",
    "| ------------- |:------------- | \n",
    "| `inner`      | Use set of keys present in both frames (intersection) | \n",
    "| `left`      | Use keys found in left frame      | \n",
    "| `right` | Use keys found in right frame      | \n",
    "| `outer` | Use the union of keys found in left and right frames      | "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2</th>\n",
       "      <td>b</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>a</td>\n",
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       "      <th>5</th>\n",
       "      <td>a</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>c</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>d</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data1  data2\n",
       "0   b    0.0    1.0\n",
       "1   b    1.0    1.0\n",
       "2   b    6.0    1.0\n",
       "3   a    2.0    0.0\n",
       "4   a    4.0    0.0\n",
       "5   a    5.0    0.0\n",
       "6   c    3.0    NaN\n",
       "7   d    NaN    2.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df2, on='key', how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "  key  data1\n",
       "0   b      0\n",
       "1   b      1\n",
       "2   a      2\n",
       "3   c      3\n",
       "4   a      4\n",
       "5   b      5"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'b'],\n",
    "                    'data1': range(6)})\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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      "text/plain": [
       "  key  data2\n",
       "0   a      0\n",
       "1   b      1\n",
       "2   a      2\n",
       "3   b      3\n",
       "4   d      4"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'key': ['a', 'b', 'a', 'b', 'd'],\n",
    "                    'data2': range(5)})\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((6, 2), (6, 2))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape,  df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>2.0</td>\n",
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       "      <th>6</th>\n",
       "      <td>c</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>a</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>a</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>b</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>b</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  data1  data2\n",
       "0    b      0    1.0\n",
       "1    b      0    3.0\n",
       "2    b      1    1.0\n",
       "3    b      1    3.0\n",
       "4    a      2    0.0\n",
       "5    a      2    2.0\n",
       "6    c      3    NaN\n",
       "7    a      4    0.0\n",
       "8    a      4    2.0\n",
       "9    b      5    1.0\n",
       "10   b      5    3.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# many-to-many merge\n",
    "\n",
    "pd.merge(df1, df2, on='key', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "      <td>a</td>\n",
       "      <td>4</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data1  data2\n",
       "0   b      0      1\n",
       "1   b      0      3\n",
       "2   b      1      1\n",
       "3   b      1      3\n",
       "4   b      5      1\n",
       "5   b      5      3\n",
       "6   a      2      0\n",
       "7   a      2      2\n",
       "8   a      4      0\n",
       "9   a      4      2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df2, how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
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       "      <td>b</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>0</td>\n",
       "      <td>d</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>b</td>\n",
       "      <td>5</td>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>b</td>\n",
       "      <td>5</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>b</td>\n",
       "      <td>5</td>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>b</td>\n",
       "      <td>5</td>\n",
       "      <td>b</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>b</td>\n",
       "      <td>5</td>\n",
       "      <td>d</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   key_x  data1 key_y  data2\n",
       "0      b      0     a      0\n",
       "1      b      0     b      1\n",
       "2      b      0     a      2\n",
       "3      b      0     b      3\n",
       "4      b      0     d      4\n",
       "..   ...    ...   ...    ...\n",
       "25     b      5     a      0\n",
       "26     b      5     b      1\n",
       "27     b      5     a      2\n",
       "28     b      5     b      3\n",
       "29     b      5     d      4\n",
       "\n",
       "[30 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df2, how='cross').head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "  key1 key2  lval\n",
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       "2  bar  one     3"
      ]
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     "execution_count": 15,
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    "left = pd.DataFrame({'key1': ['foo', 'foo', 'bar'],\n",
    "                     'key2': ['one', 'two', 'one'],\n",
    "                     'lval': [1, 2, 3]})\n",
    "left"
   ]
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   "execution_count": 16,
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    {
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      "text/plain": [
       "  key1 key2  rval\n",
       "0  foo  one     4\n",
       "1  foo  one     5\n",
       "2  bar  one     6\n",
       "3  bar  two     7"
      ]
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     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "right = pd.DataFrame({'key1': ['foo', 'foo', 'bar', 'bar'],\n",
    "                      'key2': ['one', 'one', 'one', 'two'],\n",
    "                      'rval': [4, 5, 6, 7]})\n",
    "right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
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     "slide_type": "slide"
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    {
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
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      ],
      "text/plain": [
       "  key1 key2  lval  rval\n",
       "0  foo  one   1.0   4.0\n",
       "1  foo  one   1.0   5.0\n",
       "2  foo  two   2.0   NaN\n",
       "3  bar  one   3.0   6.0\n",
       "4  bar  two   NaN   7.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge with multiple keys\n",
    "\n",
    "pd.merge(left, right, on=['key1', 'key2'], how='outer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Avoid Column Collision\n",
    "\n",
    "<img src=\"images/tables.png\">\n",
    "\n",
    "* Pandas renames similar columns to avoid ambiguity\n",
    "* When `merge()` creates ambiguity, `pandas` renames identical columns across tables, adding suffix `_x` to the left frame and `_y` to the right frame by default "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Avoid Column Collision\n",
    "\n",
    "<img src=\"images/tables.png\">\n",
    "\n",
    "* When `merge()` creates ambiguity, pandas renames identical columns across tables, adding suffix `_x` to the left frame and `_y` to the right frame by default "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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      "text/plain": [
       "  key1 key2_x  lval key2_y  rval\n",
       "0  foo    one     1    one     4\n",
       "1  foo    one     1    one     5\n",
       "2  foo    two     2    one     4\n",
       "3  foo    two     2    one     5\n",
       "4  bar    one     3    one     6\n",
       "5  bar    one     3    two     7"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, on='key1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   "outputs": [
    {
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      "text/plain": [
       "  key1 key2_left  lval key2_right  rval\n",
       "0  foo       one     1        one     4\n",
       "1  foo       one     1        one     5\n",
       "2  foo       two     2        one     4\n",
       "3  foo       two     2        one     5\n",
       "4  bar       one     3        one     6\n",
       "5  bar       one     3        two     7"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, on='key1', suffixes=('_left', '_right'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Merging on Index\n",
    "\n",
    "* Merging on the index is also very common\n",
    "* Merge on one or more indexes with hierarchical keys operates the same way as a merge on multiple columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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      "text/plain": [
       "  key  value\n",
       "0   a      0\n",
       "1   b      1\n",
       "2   a      2\n",
       "3   a      3\n",
       "4   b      4\n",
       "5   c      5"
      ]
     },
     "execution_count": 20,
     "metadata": {},
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    }
   ],
   "source": [
    "left1 = pd.DataFrame({'key': ['a', 'b', 'a', 'a', 'b', 'c'],\n",
    "                      'value': range(6)})\n",
    "left1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
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   "outputs": [
    {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>3.5</td>\n",
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       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>7.0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   group_val\n",
       "a        3.5\n",
       "b        7.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right1 = pd.DataFrame({'group_val': [3.5, 7]}, index=['a', 'b'])\n",
    "right1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
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   "outputs": [
    {
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       "  key  value  group_val\n",
       "0   a      0        3.5\n",
       "2   a      2        3.5\n",
       "3   a      3        3.5\n",
       "1   b      1        7.0\n",
       "4   b      4        7.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "pd.merge(left1, right1, left_on='key', right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
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    }
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    {
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      "text/plain": [
       "  key  value  group_val\n",
       "0   a      0        3.5\n",
       "2   a      2        3.5\n",
       "3   a      3        3.5\n",
       "1   b      1        7.0\n",
       "4   b      4        7.0\n",
       "5   c      5        NaN"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left1, right1, left_on='key', right_index=True, how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>Nevada</td>\n",
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       "      <td>4.0</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "     key1  key2  data\n",
       "0    Ohio  2000   0.0\n",
       "1    Ohio  2001   1.0\n",
       "2    Ohio  2002   2.0\n",
       "3  Nevada  2001   3.0\n",
       "4  Nevada  2002   4.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left_hierarchial = pd.DataFrame({'key1': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada'],\n",
    "                                 'key2': [2000, 2001, 2002, 2001, 2002],\n",
    "                                 'data': np.arange(5.)})\n",
    "left_hierarchial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "             event1  event2\n",
       "Nevada 2001       0       1\n",
       "       2000       2       3\n",
       "Ohio   2000       4       5\n",
       "       2000       6       7\n",
       "       2001       8       9\n",
       "       2002      10      11"
      ]
     },
     "execution_count": 25,
     "metadata": {},
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   "source": [
    "right_hierarchial = pd.DataFrame(np.arange(12).reshape((6, 2)),\n",
    "                                 index=[['Nevada', 'Nevada', 'Ohio', 'Ohio','Ohio', 'Ohio'],\n",
    "                                        [2001, 2000, 2000, 2000, 2001, 2002]],\n",
    "                                 columns=['event1', 'event2'])\n",
    "right_hierarchial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "     key1  key2  data  event1  event2\n",
       "0    Ohio  2000   0.0       4       5\n",
       "0    Ohio  2000   0.0       6       7\n",
       "1    Ohio  2001   1.0       8       9\n",
       "2    Ohio  2002   2.0      10      11\n",
       "3  Nevada  2001   3.0       0       1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left_hierarchial, right_hierarchial, left_on=['key1', 'key2'], right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "      <th>4</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     key1  key2  data  event1  event2\n",
       "0    Ohio  2000   0.0     4.0     5.0\n",
       "0    Ohio  2000   0.0     6.0     7.0\n",
       "1    Ohio  2001   1.0     8.0     9.0\n",
       "2    Ohio  2002   2.0    10.0    11.0\n",
       "3  Nevada  2001   3.0     0.0     1.0\n",
       "4  Nevada  2002   4.0     NaN     NaN\n",
       "4  Nevada  2000   NaN     2.0     3.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left_hierarchial, right_hierarchial, left_on=['key1', 'key2'],\n",
    "         right_index=True, how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "  </thead>\n",
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       "      <th>a</th>\n",
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      "text/plain": [
       "   Ohio  Nevada\n",
       "a   1.0     2.0\n",
       "c   3.0     4.0\n",
       "e   5.0     6.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
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   "source": [
    "left2 = pd.DataFrame([[1., 2.], [3., 4.], [5., 6.]],\n",
    "                     index=['a', 'c', 'e'],\n",
    "                     columns=['Ohio', 'Nevada'])\n",
    "left2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
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       "\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>Missouri</th>\n",
       "      <th>Alabama</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Missouri  Alabama\n",
       "b       7.0      8.0\n",
       "c       9.0     10.0\n",
       "d      11.0     12.0\n",
       "e      13.0     14.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right2 = pd.DataFrame([[7., 8.], [9., 10.], [11., 12.], [13, 14]],\n",
    "                      index=['b', 'c', 'd', 'e'],\n",
    "                      columns=['Missouri', 'Alabama'])\n",
    "right2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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",
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       "    }\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>Ohio</th>\n",
       "      <th>Nevada</th>\n",
       "      <th>Missouri</th>\n",
       "      <th>Alabama</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ohio  Nevada  Missouri  Alabama\n",
       "a   1.0     2.0       NaN      NaN\n",
       "b   NaN     NaN       7.0      8.0\n",
       "c   3.0     4.0       9.0     10.0\n",
       "d   NaN     NaN      11.0     12.0\n",
       "e   5.0     6.0      13.0     14.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left2, right2, how='outer', left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Concatenating Along an Axis\n",
    "\n",
    "- Also known as binding or stacking\n",
    "- By default, concatenation \n",
    "  - operates on `axis=0`\n",
    "  - uses `join=outer`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(12).reshape((3, 4))\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11],\n",
       "       [ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate([arr, arr])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7,  4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11,  8,  9, 10, 11]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate([arr, arr], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    0\n",
      "b    1\n",
      "dtype: int64\n",
      "**********\n",
      "c    2\n",
      "d    3\n",
      "e    4\n",
      "dtype: int64\n",
      "**********\n",
      "f    5\n",
      "g    6\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "s1 = pd.Series([0, 1], index=['a', 'b'])\n",
    "s2 = pd.Series([2, 3, 4], index=['c', 'd', 'e'])\n",
    "s3 = pd.Series([5, 6], index=['f', 'g'])\n",
    "\n",
    "print(s1)\n",
    "\n",
    "print(\"*\" * 10)\n",
    "print(s2)\n",
    "\n",
    "print(\"*\" * 10)\n",
    "print(s3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "b    1\n",
       "c    2\n",
       "d    3\n",
       "e    4\n",
       "f    5\n",
       "g    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([s1, s2, s3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2\n",
       "a  0.0  NaN  NaN\n",
       "b  1.0  NaN  NaN\n",
       "c  NaN  2.0  NaN\n",
       "d  NaN  3.0  NaN\n",
       "e  NaN  4.0  NaN\n",
       "f  NaN  NaN  5.0\n",
       "g  NaN  NaN  6.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([s1, s2, s3], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    0\n",
      "b    1\n",
      "dtype: int64\n",
      "**********\n",
      "f    5\n",
      "g    6\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "b    1\n",
       "f    5\n",
       "g    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(s1)\n",
    "print(\"*\" * 10)\n",
    "print(s3)\n",
    "\n",
    "s4 = pd.concat([s1, s3])\n",
    "s4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     0  1\n",
      "a  0.0  0\n",
      "b  1.0  1\n",
      "f  NaN  5\n",
      "g  NaN  6\n",
      "**********\n",
      "   0  1\n",
      "a  0  0\n",
      "b  1  1\n"
     ]
    }
   ],
   "source": [
    "print(pd.concat([s1, s4], axis=1))\n",
    "print(\"*\" * 10)\n",
    "print(pd.concat([s1, s4], axis=1, join='inner'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Creating Indexes by Concatenation\n",
    "\n",
    "* Concatenation is useful for creating a hierarchical index of the data\n",
    "  * Use `keys=` pamareter of the same size as the array objects to concatenate\n",
    "  * If `axis=1`, `keys` become the column (header) names when concatenating `Series`\n",
    "  * If `axis=1`, `keys` are the level names when concatenating `DataFrames`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "b    1\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "c    2\n",
       "d    3\n",
       "e    4\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "f    5\n",
       "g    6\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(s1)\n",
    "display(s2)\n",
    "display(s3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "patient_1  a    0\n",
       "           b    1\n",
       "patient_2  c    2\n",
       "           d    3\n",
       "           e    4\n",
       "patient_3  f    5\n",
       "           g    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.concat([s1, s2, s3], keys=['patient_1', 'patient_2', 'patient_3'])\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "      <th>g</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>patient_1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient_2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient_3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             a    b    c    d    e    f    g\n",
       "patient_1  0.0  1.0  NaN  NaN  NaN  NaN  NaN\n",
       "patient_2  NaN  NaN  2.0  3.0  4.0  NaN  NaN\n",
       "patient_3  NaN  NaN  NaN  NaN  NaN  5.0  6.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>COL_1</th>\n",
       "      <th>COL_2</th>\n",
       "      <th>COL_3</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>a</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
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      "text/plain": [
       "   COL_1  COL_2  COL_3\n",
       "a    0.0    NaN    NaN\n",
       "b    1.0    NaN    NaN\n",
       "c    NaN    2.0    NaN\n",
       "d    NaN    3.0    NaN\n",
       "e    NaN    4.0    NaN\n",
       "f    NaN    NaN    5.0\n",
       "g    NaN    NaN    6.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([s1, s2, s3], axis=1, keys=['COL_1', 'COL_2', 'COL_3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "      <th>one</th>\n",
       "      <th>two</th>\n",
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       "   one  two\n",
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       "c    4    5"
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       "   three  four\n",
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       "c      7     8"
      ]
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   ],
   "source": [
    "df1 = pd.DataFrame(np.arange(6).reshape(3, 2), index=['a', 'b', 'c'], columns=['one', 'two'])\n",
    "df2 = pd.DataFrame(5 + np.arange(4).reshape(2, 2), index=['a', 'c'], columns=['three', 'four'])\n",
    "\n",
    "display(df1)\n",
    "display(df2)"
   ]
  },
  {
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   "execution_count": 44,
   "metadata": {},
   "outputs": [
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       "      <td>6.0</td>\n",
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       "  level1     level2     \n",
       "     one two  three four\n",
       "a      0   1    5.0  6.0\n",
       "b      2   3    NaN  NaN\n",
       "c      4   5    7.0  8.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1, df2], axis=1, keys=['level1', 'level2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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       "      <td>6.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>c</th>\n",
       "      <td>4</td>\n",
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      "text/plain": [
       "  level1     level2     \n",
       "     one two  three four\n",
       "a      0   1    5.0  6.0\n",
       "b      2   3    NaN  NaN\n",
       "c      4   5    7.0  8.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
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    }
   ],
   "source": [
    "pd.concat({'level1': df1, 'level2': df2}, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>-0.555730</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.965781</td>\n",
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       "          a         b         c         d\n",
       "0 -0.204708  0.478943 -0.519439 -0.555730\n",
       "1  1.965781  1.393406  0.092908  0.281746\n",
       "2  0.769023  1.246435  1.007189 -1.296221"
      ]
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    },
    {
     "data": {
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       "      <th>1</th>\n",
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       "          b         d         a\n",
       "0  0.274992  0.228913  1.352917\n",
       "1  0.886429 -2.001637 -0.371843"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "df1 = pd.DataFrame(np.random.randn(3, 4), columns=['a', 'b', 'c', 'd'])\n",
    "df2 = pd.DataFrame(np.random.randn(2, 3), columns=['b', 'd', 'a'])\n",
    "\n",
    "display(df1)\n",
    "display(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
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   },
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.371843</td>\n",
       "      <td>0.886429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.001637</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c         d\n",
       "0 -0.204708  0.478943 -0.519439 -0.555730\n",
       "1  1.965781  1.393406  0.092908  0.281746\n",
       "2  0.769023  1.246435  1.007189 -1.296221\n",
       "3  1.352917  0.274992       NaN  0.228913\n",
       "4 -0.371843  0.886429       NaN -2.001637"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1, df2], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.965781</td>\n",
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       "      <td>0.092908</td>\n",
       "      <td>0.281746</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.769023</td>\n",
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       "      <td>-1.296221</td>\n",
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       "      <th>0</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.228913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.371843</td>\n",
       "      <td>0.886429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.001637</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c         d\n",
       "0 -0.204708  0.478943 -0.519439 -0.555730\n",
       "1  1.965781  1.393406  0.092908  0.281746\n",
       "2  0.769023  1.246435  1.007189 -1.296221\n",
       "0  1.352917  0.274992       NaN  0.228913\n",
       "1 -0.371843  0.886429       NaN -2.001637"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1, df2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- See the `Table 8.3` in *Python for Data Analysis* for other useful `concat()` arguments.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Combining Data with Overlap\n",
    "\n",
    "- `combine_first` is a useful function for combining data using a method that is similar to numpy's `where`\n",
    "  - Test some condition, if true take first value, else take second value\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "f    NaN\n",
       "e    2.5\n",
       "d    NaN\n",
       "c    3.5\n",
       "b    4.5\n",
       "a    NaN\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "c    2.0\n",
       "d    3.0\n",
       "e    4.0\n",
       "f    NaN\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = pd.Series([np.nan, 2.5, np.nan, 3.5, 4.5, np.nan],\n",
    "              index=['f', 'e', 'd', 'c', 'b', 'a'])\n",
    "b = pd.Series(np.arange(len(a), dtype=np.float64),\n",
    "              index=['a', 'b', 'c', 'd', 'e', 'f'])\n",
    "b[-1] = np.nan\n",
    "\n",
    "display(a)\n",
    "display(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 2.5, 2. , 3.5, 4.5, nan])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(pd.isna(a), b, a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "#### Using `numpy.where` doesn't check whether the index labels are aligned or not, and doesn't even require the objects to be the same length. So, if you want to line up values by index, use the Series `combine_first` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    4.5\n",
       "c    3.5\n",
       "d    3.0\n",
       "e    2.5\n",
       "f    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "a.combine_first(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
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   "source": [
    "#### With DataFrames, `combine_first` does the same thing *column by column*, so you can think of it as \"patching\" missing data in the calling object with data from object you pass.\n",
    "\n",
    "#### The output of `combine_first` with DataFrame objects will have the *union* of all the column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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       "     a    b     c\n",
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   "source": [
    "df1 = pd.DataFrame({'a': [1., np.nan, 5., np.nan], \n",
    "                    'b': [np.nan, 2., np.nan, 6.],\n",
    "                    'c': range(2, 18, 4)})\n",
    "df2 = pd.DataFrame({'a': [5., 4., np.nan, 3., 7.], \n",
    "                    'b': [np.nan, 3., 4., 6., 8.]})\n",
    "\n",
    "display(df1)\n",
    "display(df2)\n",
    "df1.combine_first(df2)"
   ]
  }
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