11.3 Concatenating Along An Axis.ipynb

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    "![title](img/cover4.png)"
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   "source": [
    "<center><em>Copyright! This material is protected, please do not copy or distribute.    by:Taher Assaf</em></center>"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "<h1 align=\"center\">Udemy course : Python Bootcamp for Data Science 2021 Numpy Pandas & Seaborn</h1> \n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 11.3 Concatenating Along An Axis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we import numpy and pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we create **two** series using the function **pd.Series()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1 = pd.Series([1,2,3], index = ['a', 'b', 'c'])\n",
    "ser1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    4\n",
       "e    5\n",
       "f    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser2 = pd.Series([4,5,6], index = ['d', 'e','f'])\n",
    "ser2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can concatenate these two series **along the rwo axis** using the function **concat()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hide_input": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    4\n",
       "e    5\n",
       "f    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "pd.concat([ser1,ser2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also concatenate these two series along the column axis by adding the argument **(axis = 1)**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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       "     0    1\n",
       "a  1.0  NaN\n",
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    "pd.concat([ser1,ser2], axis = 1)"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets create another series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    7\n",
       "b    8\n",
       "g    9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser3 = pd.Series([7,8,9], index = ['a', 'b','g'])\n",
    "ser3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Concatenating series with common index **along the rows** will include duplicate index values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hide_input": false
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   "outputs": [
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     "data": {
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       "a    1\n",
       "b    2\n",
       "c    3\n",
       "a    7\n",
       "b    8\n",
       "g    9\n",
       "dtype: int64"
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "pd.concat([ser1,ser3])"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Concatenating two series with different index along the column axis, will create missing values:"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
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       "     0    1\n",
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    "pd.concat([ser1,ser3], axis = 1)"
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   "source": [
    "To include only the common index values when concatenating along columns, we add the argument **(join  = 'inner')**:"
   ]
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   "cell_type": "code",
   "execution_count": 9,
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       "   0  1\n",
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    "pd.concat([ser1,ser3], axis = 1, join = 'inner')"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For dataframes, lets first create these two dataframes, using the function **pd.DataFrame()**:"
   ]
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  {
   "cell_type": "code",
   "execution_count": 10,
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       "      <td>Georgia</td>\n",
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       "       states  population\n",
       "0  California          40\n",
       "1     Georgia          10\n",
       "2     Florida          21\n",
       "3     Arizona           7"
      ]
     },
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     "metadata": {},
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   "source": [
    "frame1 = pd.DataFrame({'states': ['California', 'Georgia', 'Florida', 'Arizona'],\n",
    "                       'population': [40, 10, 21,7]})\n",
    "frame1"
   ]
  },
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       "      <td>Colorado</td>\n",
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      "text/plain": [
       "     states  population\n",
       "0    Hawaii         1.5\n",
       "1  Colorado        10.4\n",
       "2   Indiana         5.7\n",
       "3    Alaska         0.7"
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    "frame2 = pd.DataFrame({'states': ['Hawaii', 'Colorado', 'Indiana', 'Alaska', ],\n",
    "                       'population': [1.5, 10.4, 5.7, 0.7]})\n",
    "frame2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can concatenate these two dataframes using the function **concat()**, by default concatenating is done along the rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
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       "       states  population\n",
       "0  California        40.0\n",
       "1     Georgia        10.0\n",
       "2     Florida        21.0\n",
       "3     Arizona         7.0\n",
       "0      Hawaii         1.5\n",
       "1    Colorado        10.4\n",
       "2     Indiana         5.7\n",
       "3      Alaska         0.7"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([frame1,frame2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When concatenating along row, we create new index using the argument **ignore_index = True**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
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       "      <th>2</th>\n",
       "      <td>Florida</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>0.7</td>\n",
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       "       states  population\n",
       "0  California        40.0\n",
       "1     Georgia        10.0\n",
       "2     Florida        21.0\n",
       "3     Arizona         7.0\n",
       "4      Hawaii         1.5\n",
       "5    Colorado        10.4\n",
       "6     Indiana         5.7\n",
       "7      Alaska         0.7"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([frame1,frame2], ignore_index= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets now create these two dataframes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>states</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>California</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Florida</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       states  population\n",
       "0  California          40\n",
       "1     Georgia          10\n",
       "2     Florida          21\n",
       "3     Arizona           7"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3 = pd.DataFrame({'states': ['California', 'Georgia', 'Florida', 'Arizona'],\n",
    "                       'population': [40, 10, 21,7]})\n",
    "frame3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>water</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   water  area\n",
       "0     23   113\n",
       "1     54   104\n",
       "2     12    36\n",
       "3     45    65"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame4 = pd.DataFrame({'water': [23, 54, 12, 45 ], 'area': [113, 104, 36, 65]})\n",
    "frame4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can concatenate these two dataframe along the **column axis** by using the argument **(axis = 1)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>states</th>\n",
       "      <th>population</th>\n",
       "      <th>water</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>California</td>\n",
       "      <td>40</td>\n",
       "      <td>23</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>10</td>\n",
       "      <td>54</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Florida</td>\n",
       "      <td>21</td>\n",
       "      <td>12</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>7</td>\n",
       "      <td>45</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       states  population  water  area\n",
       "0  California          40     23   113\n",
       "1     Georgia          10     54   104\n",
       "2     Florida          21     12    36\n",
       "3     Arizona           7     45    65"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([frame3,frame4], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "<h1 align=\"center\">Thank You</h1> \n",
    "\n",
    "***"
   ]
  }
 ],
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