tutorial_combine_data.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Combine data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For combining data we can use the methods `concat`, `merge` and `join`. Sometimes all three methods can be applyed to get the same end result, it depends on your data and your preference. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.concat?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.merge?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame.join?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concat \n",
    "\n",
    "with concat we can combine data. This is espaccially handy in combining arrays. By default the concat works with `axis=0`, e.g the row concationation. It glues the rows of one dataframe or array to another"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "          0         1         2         3         4\n",
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   "source": [
    "df_01 = pd.DataFrame(np.random.randn(3, 5))\n",
    "df_01"
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    "df_02 = pd.DataFrame(np.random.randn(3,5))\n",
    "df_02"
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    "df_03 = pd.concat([df_01, df_02])\n",
    "df_03"
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  {
   "cell_type": "markdown",
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   "source": [
    "if you pass `axis=1` it will glue the dataframes in the collumn direction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
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       "          0         1         2         3         4         0         1  \\\n",
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    "df_03 = pd.concat([df_01, df_02], axis=1)\n",
    "df_03"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In case of unequal shape it will fill the gaps with NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = pd.DataFrame({'key': ['A', 'B', 'C'], 'value': [1, 2, 3]}).set_index('key')\n",
    "B = pd.DataFrame({'key': ['A', 'B', 'X', 'Y'], 'value': [3, 4, 5, 7]}).set_index('key')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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       "     value\n",
       "key       \n",
       "A        1\n",
       "B        2\n",
       "C        3"
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    "A"
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   "execution_count": 8,
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    {
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       "     value\n",
       "key       \n",
       "A        3\n",
       "B        4\n",
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       "Y        7"
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    "B"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also perform a SQL-style join using the .merge() function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.merge?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "  key  left_value\n",
       "0   A           1\n",
       "1   B           2\n",
       "2   C           3"
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   "source": [
    "left = pd.DataFrame({'key': ['A', 'B', 'C'], 'left_value': [1, 2, 3]})\n",
    "left "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>right_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D</td>\n",
       "      <td>51,3</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "  key right_value\n",
       "0   A           3\n",
       "1   B           4\n",
       "2   D        51,3"
      ]
     },
     "execution_count": 11,
     "metadata": {},
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   ],
   "source": [
    "right = pd.DataFrame({'key': ['A', 'B', 'D'], 'right_value': [3, 4, '51,3']})\n",
    "right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <th>right_value</th>\n",
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       "  </thead>\n",
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      "text/plain": [
       "  key  left_value right_value\n",
       "0   A           1           3\n",
       "1   B           2           4"
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     "execution_count": 12,
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    "pd.merge(left, right, how='inner', left_on=['key'], right_on=['key'])"
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   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "  key  left_value right_value\n",
       "0   A         1.0           3\n",
       "1   B         2.0           4\n",
       "2   C         3.0         NaN\n",
       "3   D         NaN        51,3"
      ]
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     "execution_count": 13,
     "metadata": {},
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   "source": [
    "pd.merge(left, right, how='outer', left_on=['key'], right_on=['key'])"
   ]
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  {
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   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "  key  left_value right_value\n",
       "0   A         1.0           3\n",
       "1   B         2.0           4\n",
       "2   D         NaN        51,3"
      ]
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    "pd.merge(left, right, how='right', left_on=['key'], right_on=['key'])"
   ]
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  {
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   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "  key  left_value right_value\n",
       "0   A           1           3\n",
       "1   B           2           4\n",
       "2   C           3         NaN"
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     "execution_count": 15,
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    "pd.merge(left, right, how='left', left_on=['key'], right_on=['key'])"
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   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  key  left_value other_key\n",
       "0   A           1         X\n",
       "1   B           2         Y\n",
       "2   C           3         Z"
      ]
     },
     "execution_count": 16,
     "metadata": {},
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   "source": [
    "left = pd.DataFrame({'key': ['A', 'B', 'C'], 'left_value': [1, 2, 3], 'other_key': ['X','Y','Z']})\n",
    "left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "  key right_value some_key\n",
       "0   A           3        W\n",
       "1   B        53,2        Y\n",
       "2   D           5        Z"
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    "right = pd.DataFrame({'key': ['A', 'B', 'D'], 'right_value': [3,'53,2', 5], 'some_key': ['W','Y', 'Z']})\n",
    "right"
   ]
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  {
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   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "  key  left_value other_key right_value some_key\n",
       "0   B           2         Y        53,2        Y"
      ]
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     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "pd.merge(left, right, how='inner', left_on=['key', 'other_key'], right_on=['key', 'some_key' ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
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   "outputs": [
    {
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       "      <th>2</th>\n",
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      "text/plain": [
       "  key  left_value other_key right_value some_key\n",
       "0   A           1         X           3        W\n",
       "1   B           2         Y        53,2        Y\n",
       "2   C           3         Z         NaN      NaN"
      ]
     },
     "execution_count": 19,
     "metadata": {},
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   ],
   "source": [
    "df_03 = pd.merge(left, right, how='left', left_on=['key'], right_on=['key'])\n",
    "df_03\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Join\n",
    "Pandas DataFrame has als a join function for merging by index. However overlapping columns cannot exist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
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       "  key  left_value other_key\n",
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    "left"
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   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "  key right_value some_key\n",
       "0   A           3        W\n",
       "1   B        53,2        Y\n",
       "2   D           5        Z"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>left_value</th>\n",
       "      <th>other_key</th>\n",
       "      <th>right_value</th>\n",
       "      <th>some_key</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1.0</td>\n",
       "      <td>X</td>\n",
       "      <td>3</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>53,2</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>Z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     left_value other_key right_value some_key\n",
       "key                                           \n",
       "A           1.0         X           3        W\n",
       "B           2.0         Y        53,2        Y\n",
       "C           3.0         Z         NaN      NaN\n",
       "D           NaN       NaN           5        Z"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left.set_index('key').join(right.set_index('key'), how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>right_value</th>\n",
       "      <th>some_key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>53,2</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>Z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name right_value some_key\n",
       "0    A           3        W\n",
       "1    B        53,2        Y\n",
       "2    D           5        Z"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right = right.rename(columns = {'key': 'name'})\n",
    "right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "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>key</th>\n",
       "      <th>left_value</th>\n",
       "      <th>other_key</th>\n",
       "      <th>name</th>\n",
       "      <th>right_value</th>\n",
       "      <th>some_key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>X</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>Y</td>\n",
       "      <td>B</td>\n",
       "      <td>53,2</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>Z</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>Z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  left_value other_key name right_value some_key\n",
       "0   A           1         X    A           3        W\n",
       "1   B           2         Y    B        53,2        Y\n",
       "2   C           3         Z    D           5        Z"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_04 = left.join(right, how='outer')\n",
    "df_04"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the `on=` argument you can match indexes with keys. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "      <th>value</th>\n",
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       "  </thead>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>c</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left1 = pd.DataFrame({'key': ['a','b','a','a','b','c'], 'value': range(6)})\n",
    "left1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group_val</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   group_val\n",
       "a        3.5\n",
       "b        7.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right1 = pd.DataFrame({'group_val': [3.5,7]}, index = ['a','b'])\n",
    "right1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>key</th>\n",
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       "      <th>group_val</th>\n",
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       "  </thead>\n",
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       "      <td>3.5</td>\n",
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       "      <th>1</th>\n",
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       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>4</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>c</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  value  group_val\n",
       "0   a      0        3.5\n",
       "1   b      1        7.0\n",
       "2   a      2        3.5\n",
       "3   a      3        3.5\n",
       "4   b      4        7.0\n",
       "5   c      5        NaN"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_05 = left1.join(right1, on='key')\n",
    "df_05"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merging on indexes with `merge` is also possible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
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       "    <tr>\n",
       "      <th>key</th>\n",
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       "      <td>4</td>\n",
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       "      <th>c</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     value\n",
       "key       \n",
       "a        0\n",
       "b        1\n",
       "a        2\n",
       "a        3\n",
       "b        4\n",
       "c        5"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left1 = left1.set_index('key')\n",
    "left1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "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",
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       "      <th>value</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>2</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>4</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   value  group_val\n",
       "a      0        3.5\n",
       "a      2        3.5\n",
       "a      3        3.5\n",
       "b      1        7.0\n",
       "b      4        7.0\n",
       "c      5        NaN"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_06 = pd.merge(left1, right1, how = 'outer', left_index=True, right_index=True)\n",
    "df_06"
   ]
  }
 ],
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