Day_81_Creating_DataFrames.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d15d4dc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a204b98",
   "metadata": {},
   "source": [
    "### Creating a Series Object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cfb2c9b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.550798\n",
       "1    0.708148\n",
       "2    0.290905\n",
       "3    0.510828\n",
       "4    0.892947\n",
       "5    0.896293\n",
       "Name: random, dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(3)\n",
    "pd.Series(np.random.rand(6), name='random')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "804d7a44",
   "metadata": {},
   "source": [
    "### Creating a DataFrame object from a Series object\n",
    "Use the to_frame() method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "09e76b34",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0\n",
       "0   0.0\n",
       "1   2.0\n",
       "2   4.0\n",
       "3   6.0\n",
       "4   8.0\n",
       "5  10.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(np.linspace(0, 10, num=6)).to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e4a1e48",
   "metadata": {},
   "source": [
    "### Creating a DataFrame from Python Data Structures\n",
    "#### From a dictionary of list-like structures\n",
    "The dictionary values can be lists, NumPy arrays, etc. as long as they have length (generators don't have length so we can't use them here):\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "700ce54d",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>random</th>\n",
       "      <th>text</th>\n",
       "      <th>truth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-04-16</th>\n",
       "      <td>0.550798</td>\n",
       "      <td>hot</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-17</th>\n",
       "      <td>0.708148</td>\n",
       "      <td>warm</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-18</th>\n",
       "      <td>0.290905</td>\n",
       "      <td>cool</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-19</th>\n",
       "      <td>0.510828</td>\n",
       "      <td>cold</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-20</th>\n",
       "      <td>0.892947</td>\n",
       "      <td>hot</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-21</th>\n",
       "      <td>0.896293</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              random  text  truth\n",
       "Date                             \n",
       "2019-04-16  0.550798   hot  False\n",
       "2019-04-17  0.708148  warm  False\n",
       "2019-04-18  0.290905  cool   True\n",
       "2019-04-19  0.510828  cold  False\n",
       "2019-04-20  0.892947   hot  False\n",
       "2019-04-21  0.896293  None   True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(3)\n",
    "pd.DataFrame(\n",
    "    {\n",
    "        \n",
    "        'random': np.random.rand(6),\n",
    "        'text': ['hot', 'warm', 'cool', 'cold', 'hot', None],\n",
    "        'truth': [np.random.choice([True, False]) for _ in range(6)]\n",
    "    },\n",
    "    index = pd.date_range(\n",
    "        end = dt.date(2019, 4, 21),\n",
    "        freq = '1D',\n",
    "        periods = 6,\n",
    "        name = 'Date'\n",
    "    )\n",
    "\n",
    "\n",
    "\n",
    "\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a82600e5",
   "metadata": {},
   "source": [
    "### From a list of Dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5a71a537",
   "metadata": {},
   "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>mag</th>\n",
       "      <th>place</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.2</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.2</td>\n",
       "      <td>Alaska</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.2</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mag       place\n",
       "0  5.2  California\n",
       "1  1.2      Alaska\n",
       "2  0.2  California"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([\n",
    "    {'mag': 5.2, 'place': 'California'},\n",
    "    {'mag': 1.2, 'place': 'Alaska'},\n",
    "    {'mag': 0.2, 'place': 'California'}\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc04be89",
   "metadata": {},
   "source": [
    "### From a list of tuples\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c91c2e56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 0, 0), (1, 1, 1), (2, 4, 8), (3, 9, 27), (4, 16, 64), (5, 25, 125)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_of_tuples = [(n, n**2, n**3) for n in range(6)]\n",
    "list_of_tuples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f4d42926",
   "metadata": {},
   "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>n</th>\n",
       "      <th>n_squared</th>\n",
       "      <th>n_cubed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   n  n_squared  n_cubed\n",
       "0  0          0        0\n",
       "1  1          1        1\n",
       "2  2          4        8\n",
       "3  3          9       27\n",
       "4  4         16       64\n",
       "5  5         25      125"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "    list_of_tuples,\n",
    "    columns = ['n', 'n_squared', 'n_cubed']\n",
    "\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "894e0a41",
   "metadata": {},
   "source": [
    "### From a Numpy Array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a944279c",
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n</th>\n",
       "      <th>n_squared</th>\n",
       "      <th>n_cubed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   n  n_squared  n_cubed\n",
       "0  0          0        0\n",
       "1  1          1        1\n",
       "2  2          4        8\n",
       "3  3          9       27\n",
       "4  4         16       64\n",
       "5  5         25      125"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "    np.array([\n",
    "        [0, 0, 0],\n",
    "        [1, 1, 1],\n",
    "        [2, 4, 8],\n",
    "        [3, 9, 27],\n",
    "        [4, 16, 64],\n",
    "        [5, 25, 125]\n",
    "        \n",
    "    ]), columns=['n', 'n_squared', 'n_cubed']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b75d57a8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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