13-housing-market.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 2. Create 3 differents Series, each of length 100, as follows:\n",
    "\n",
    "    The first a random number from 1 to 4\n",
    "    The second a random number from 1 to 3\n",
    "    The third a random number from 10,000 to 30,000\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "rand1 = pd.Series(np.random.randint(1, high=5, size=100), dtype='l')\n",
    "rand2 = pd.Series(np.random.randint(1, high=4, size=100), dtype='l')\n",
    "rand3 = pd.Series(np.random.randint(10000, high=30001, size=100), dtype='l')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 3. Let's create a DataFrame by joining the Series by column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>22031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>29400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>24407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>15074</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    0  1      2\n",
       "0   2  3  10902\n",
       "1   4  2  24317\n",
       "2   4  1  10510\n",
       "3   3  1  23560\n",
       "4   2  1  16692\n",
       ".. .. ..    ...\n",
       "95  3  3  22031\n",
       "96  2  3  29400\n",
       "97  3  2  24407\n",
       "98  2  1  13443\n",
       "99  2  2  15074\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([rand1, rand2, rand3], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 4. Change the name of the columns to bedrs, bathrs, price_sqr_meter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={0: 'bedrs', 1: 'bathrs', 2: 'price_sqr_meter'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>bedrs</th>\n",
       "      <th>bathrs</th>\n",
       "      <th>price_sqr_meter</th>\n",
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       "      <td>1</td>\n",
       "      <td>13443</td>\n",
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       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>15074</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    bedrs  bathrs  price_sqr_meter\n",
       "0       2       3            10902\n",
       "1       4       2            24317\n",
       "2       4       1            10510\n",
       "3       3       1            23560\n",
       "4       2       1            16692\n",
       "..    ...     ...              ...\n",
       "95      3       3            22031\n",
       "96      2       3            29400\n",
       "97      3       2            24407\n",
       "98      2       1            13443\n",
       "99      2       2            15074\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 5. Create a one column DataFrame with the values of the 3 Series and assign it to 'bigcolumn'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "      <th>99</th>\n",
       "      <td>15074</td>\n",
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       "<p>300 rows × 1 columns</p>\n",
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      "text/plain": [
       "        0\n",
       "0       2\n",
       "1       4\n",
       "2       4\n",
       "3       3\n",
       "4       2\n",
       "..    ...\n",
       "95  22031\n",
       "96  29400\n",
       "97  24407\n",
       "98  13443\n",
       "99  15074\n",
       "\n",
       "[300 rows x 1 columns]"
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     "execution_count": 29,
     "metadata": {},
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   "source": [
    "bigcolumn = pd.concat([rand1, rand2, rand3], axis=0)\n",
    "\n",
    "bigcolumn = bigcolumn.to_frame()\n",
    "\n",
    "bigcolumn\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 6. Oops, it seems it is going only until index 99. Is it true?\n",
    "\n",
    "#### Step 7. Reindex the DataFrame so it goes from 0 to 299"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
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       "      <th>295</th>\n",
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       "<p>300 rows × 1 columns</p>\n",
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       "         0\n",
       "0        2\n",
       "1        4\n",
       "2        4\n",
       "3        3\n",
       "4        2\n",
       "..     ...\n",
       "295  22031\n",
       "296  29400\n",
       "297  24407\n",
       "298  13443\n",
       "299  15074\n",
       "\n",
       "[300 rows x 1 columns]"
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   "source": [
    "bigcolumn.reset_index(drop=True, inplace=True)\n",
    "bigcolumn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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