14.2 Converting Between String and Datetime.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>"
   ]
  },
  {
   "cell_type": "markdown",
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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": [
    "## 14.2 Converting Between String and Datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we import pandas and datetime libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we create a string variable with a date value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2021, 09, 12'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_str = '2021, 09, 12'\n",
    "time_str"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To convert this variable into a datetime object, we use the function **datatime.strptime()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2021, 9, 12, 0, 0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time1 = datetime.strptime(time_str, '%Y, %m, %d')\n",
    "time1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is another example with different date format:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "time_str2 = '2021-08-13'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can convert this variable using the function **datetime.strptime()** and passing the appropriate format to this function like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2021, 8, 13, 0, 0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time2 = datetime.strptime(time_str2, '%Y-%m-%d')\n",
    "time2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For dealing with datetime columns in dataframe, lets read this dataframe from a csv file using the function **pd.read_csv()**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
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       "      <th>apple</th>\n",
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       "      <th>0</th>\n",
       "      <td>30-Apr-21</td>\n",
       "      <td>2410.12</td>\n",
       "      <td>131.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29-Apr-21</td>\n",
       "      <td>2429.89</td>\n",
       "      <td>133.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28-Apr-21</td>\n",
       "      <td>2379.91</td>\n",
       "      <td>133.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27-Apr-21</td>\n",
       "      <td>2307.12</td>\n",
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      "text/plain": [
       "        date   google   apple\n",
       "0  30-Apr-21  2410.12  131.24\n",
       "1  29-Apr-21  2429.89  133.25\n",
       "2  28-Apr-21  2379.91  133.35\n",
       "3  27-Apr-21  2307.12  134.16\n",
       "4  26-Apr-21  2326.74  134.49"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/ex8.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can display the second column which contains date data like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    30-Apr-21\n",
       "1    29-Apr-21\n",
       "2    28-Apr-21\n",
       "3    27-Apr-21\n",
       "4    26-Apr-21\n",
       "5    23-Apr-21\n",
       "6    22-Apr-21\n",
       "7    21-Apr-21\n",
       "8    20-Apr-21\n",
       "Name: date, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To display the data type of this column we use the attribute **dtype**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hide_input": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['date'].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The convert the 'date' column to datetime we use the pandas function **pd.to_datetime()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "df['date'] = pd.to_datetime(df['date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets display again the 'date' column and the head of this data frame like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2021-04-30\n",
       "1   2021-04-29\n",
       "2   2021-04-28\n",
       "3   2021-04-27\n",
       "4   2021-04-26\n",
       "5   2021-04-23\n",
       "6   2021-04-22\n",
       "7   2021-04-21\n",
       "8   2021-04-20\n",
       "Name: date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>google</th>\n",
       "      <th>apple</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>2410.12</td>\n",
       "      <td>131.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-04-29</td>\n",
       "      <td>2429.89</td>\n",
       "      <td>133.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-04-28</td>\n",
       "      <td>2379.91</td>\n",
       "      <td>133.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-04-27</td>\n",
       "      <td>2307.12</td>\n",
       "      <td>134.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-04-26</td>\n",
       "      <td>2326.74</td>\n",
       "      <td>134.49</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "        date   google   apple\n",
       "0 2021-04-30  2410.12  131.24\n",
       "1 2021-04-29  2429.89  133.25\n",
       "2 2021-04-28  2379.91  133.35\n",
       "3 2021-04-27  2307.12  134.16\n",
       "4 2021-04-26  2326.74  134.49"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To display the data type of all columns in a dataframe we use the attribute **dtypes**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date      datetime64[ns]\n",
       "google           float64\n",
       "apple            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "<h1 align=\"center\">Thank You</h1> \n",
    "\n",
    "***"
   ]
  }
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