data-analysis/012-parse-date-with-read_csv/parse-date-column-with-read_csv.ipynb

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   "source": [
    "# Tricks to parse date columns with Pandas read_csv()\n",
    "\n",
    "This is a notebook for the medium article [4 tricks you should know to parse date columns with Pandas read_csv()](https://towardsdatascience.com/4-tricks-you-should-know-to-parse-date-columns-with-pandas-read-csv-27355bb2ad0e)\n",
    "\n",
    "Please check out article for instructions\n",
    "\n",
    "**License**: [BSD 2-Clause](https://opensource.org/licenses/BSD-2-Clause)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Reading date columns from a CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3 entries, 0 to 2\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   date     3 non-null      object\n",
      " 1   product  3 non-null      object\n",
      " 2   price    3 non-null      int64 \n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 200.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "# Date columns will be represented as an object by default\n",
    "df = pd.read_csv('data/data_1.csv')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3 entries, 0 to 2\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count  Dtype         \n",
      "---  ------   --------------  -----         \n",
      " 0   date     3 non-null      datetime64[ns]\n",
      " 1   product  3 non-null      object        \n",
      " 2   price    3 non-null      int64         \n",
      "dtypes: datetime64[ns](1), int64(1), object(1)\n",
      "memory usage: 200.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('data/data_1.csv', parse_dates=['date'])\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>product</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>A</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1998-01-03</td>\n",
       "      <td>C</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date product  price\n",
       "0 2019-01-01       A     10\n",
       "1 2020-01-02       B     20\n",
       "2 1998-01-03       C     30"
      ]
     },
     "execution_count": 6,
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Day first input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>product</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>A</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1998-03-01</td>\n",
       "      <td>C</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "        date product  price\n",
       "0 2019-01-01       A     10\n",
       "1 2020-02-01       B     20\n",
       "2 1998-03-01       C     30"
      ]
     },
     "execution_count": 7,
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   ],
   "source": [
    "pd.read_csv('data/data_1.csv', \n",
    "            parse_dates=['date'], \n",
    "            dayfirst=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Combining multiple columns to a datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 3 columns):\n",
      " #   Column          Non-Null Count  Dtype         \n",
      "---  ------          --------------  -----         \n",
      " 0   year_month_day  4 non-null      datetime64[ns]\n",
      " 1   product         4 non-null      object        \n",
      " 2   price           4 non-null      int64         \n",
      "dtypes: datetime64[ns](1), int64(1), object(1)\n",
      "memory usage: 224.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('data/data_2.csv',\n",
    "                 parse_dates=[['year', 'month', 'day']])\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count  Dtype         \n",
      "---  ------   --------------  -----         \n",
      " 0   date     4 non-null      datetime64[ns]\n",
      " 1   product  4 non-null      object        \n",
      " 2   price    4 non-null      int64         \n",
      "dtypes: datetime64[ns](1), int64(1), object(1)\n",
      "memory usage: 224.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "# custom column name\n",
    "df = pd.read_csv('data/data_2.csv',\n",
    "                 parse_dates={ 'date': ['year', 'month', 'day'] })\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Customizing date parser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3 entries, 0 to 2\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count  Dtype         \n",
      "---  ------   --------------  -----         \n",
      " 0   date     3 non-null      datetime64[ns]\n",
      " 1   product  3 non-null      object        \n",
      " 2   price    3 non-null      int64         \n",
      "dtypes: datetime64[ns](1), int64(1), object(1)\n",
      "memory usage: 200.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "# write your own parser to support a different date format, \n",
    "# for example, YYYY DD MM HH:MM:SS\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "custom_date_parser = lambda x: datetime.strptime(x, \"%Y-%d-%m %H:%M:%S\")\n",
    "\n",
    "df = pd.read_csv('data/data_3.csv',\n",
    "                 parse_dates=['date'],\n",
    "                date_parser=custom_date_parser)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-10-06 20:30:00</td>\n",
       "      <td>A</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-07 19:45:30</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-12-10 04:05:01</td>\n",
       "      <td>C</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                 date product  price\n",
       "0 2016-10-06 20:30:00       A     10\n",
       "1 2016-01-07 19:45:30       B     20\n",
       "2 2013-12-10 04:05:01       C     20"
      ]
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     "execution_count": 11,
     "metadata": {},
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    "df"
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