7.5 Deleting Rows and Columns.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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   "source": [
    "***\n",
    "<h1 align=\"center\">Udemy course : Python Bootcamp for Data Science 2021 Numpy Pandas & Seaborn</h1> \n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.5 Deleting Rows and Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we import numpy and pandas libraries:"
   ]
  },
  {
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   "execution_count": 1,
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   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "here we create a series with using pandas function **pd.Series()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    11\n",
       "b    22\n",
       "c    33\n",
       "d    44\n",
       "e    55\n",
       "f    66\n",
       "g    77\n",
       "h    88\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "series1 = pd.Series([11,22,33,44,55,66,77,88], index = list('abcdefgh'))\n",
    "series1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To delete a single entry in series, we use the function **drop()** with the index of that entry:"
   ]
  },
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   "execution_count": 3,
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       "a    11\n",
       "b    22\n",
       "c    33\n",
       "d    44\n",
       "f    66\n",
       "g    77\n",
       "h    88\n",
       "dtype: int64"
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     "execution_count": 3,
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   "source": [
    "series1.drop('e')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To delete multiple entries, we enclose them in square brackets [ ] as a list:"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    11\n",
       "c    33\n",
       "d    44\n",
       "e    55\n",
       "f    66\n",
       "h    88\n",
       "dtype: int64"
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     "execution_count": 4,
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   "source": [
    "series1.drop(['b','g'])"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the original series in not changes:"
   ]
  },
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   "execution_count": 5,
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    {
     "data": {
      "text/plain": [
       "a    11\n",
       "b    22\n",
       "c    33\n",
       "d    44\n",
       "e    55\n",
       "f    66\n",
       "g    77\n",
       "h    88\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "series1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets now create a dataframe using the function **pd.DataFrame()**:"
   ]
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    "dt1 = pd.DataFrame(np.arange(24).reshape((6,4)),\n",
    "                   index = list('abcdef'),\n",
    "                  columns= ['red', 'green', 'white', 'black'])\n",
    "dt1"
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   "source": [
    "To delete specific rows, we use the same function which is **drop()**"
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   "source": [
    "To delete columns, we add the argument **axis = 1**:"
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    "dt1.drop('green', axis = 1)"
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    "Notice here also that the original dataframe is not changed"
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    "dt1"
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   "source": [
    "To make the deletion reflected in the original dataframe we use the argument  **inplace = True**"
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   "cell_type": "code",
   "execution_count": 10,
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   "source": [
    "dt1.drop('red', axis = 1, inplace =True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets display the dataframe again after the deletion"
   ]
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   "cell_type": "code",
   "execution_count": 11,
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    "dt1"
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   "source": [
    "***\n",
    "\n",
    "<h1 align=\"center\">Thank You</h1> \n",
    "\n",
    "***"
   ]
  }
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