02_Add new columns to a DataFrame.ipynb

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   "metadata": {},
   "source": [
    "# Add new columns to a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create an example DataFrame of normally distributed random numbers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.624345</td>\n",
       "      <td>-0.611756</td>\n",
       "      <td>-0.528172</td>\n",
       "      <td>-1.072969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.865408</td>\n",
       "      <td>-2.301539</td>\n",
       "      <td>1.744812</td>\n",
       "      <td>-0.761207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.319039</td>\n",
       "      <td>-0.249370</td>\n",
       "      <td>1.462108</td>\n",
       "      <td>-2.060141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.322417</td>\n",
       "      <td>-0.384054</td>\n",
       "      <td>1.133769</td>\n",
       "      <td>-1.099891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.172428</td>\n",
       "      <td>-0.877858</td>\n",
       "      <td>0.042214</td>\n",
       "      <td>0.582815</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  1.624345 -0.611756 -0.528172 -1.072969\n",
       "1  0.865408 -2.301539  1.744812 -0.761207\n",
       "2  0.319039 -0.249370  1.462108 -2.060141\n",
       "3 -0.322417 -0.384054  1.133769 -1.099891\n",
       "4 -0.172428 -0.877858  0.042214  0.582815"
      ]
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use a random seed to make reproducible data.\n",
    "np.random.seed(1)\n",
    "df = pd.DataFrame(np.random.randn(5,4), columns=list(\"ABCD\"))\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add a new column with 1 value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.624345</td>\n",
       "      <td>-0.611756</td>\n",
       "      <td>-0.528172</td>\n",
       "      <td>-1.072969</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.865408</td>\n",
       "      <td>-2.301539</td>\n",
       "      <td>1.744812</td>\n",
       "      <td>-0.761207</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.319039</td>\n",
       "      <td>-0.249370</td>\n",
       "      <td>1.462108</td>\n",
       "      <td>-2.060141</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.322417</td>\n",
       "      <td>-0.384054</td>\n",
       "      <td>1.133769</td>\n",
       "      <td>-1.099891</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.172428</td>\n",
       "      <td>-0.877858</td>\n",
       "      <td>0.042214</td>\n",
       "      <td>0.582815</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "          A         B         C         D   E\n",
       "0  1.624345 -0.611756 -0.528172 -1.072969 NaN\n",
       "1  0.865408 -2.301539  1.744812 -0.761207 NaN\n",
       "2  0.319039 -0.249370  1.462108 -2.060141 NaN\n",
       "3 -0.322417 -0.384054  1.133769 -1.099891 NaN\n",
       "4 -0.172428 -0.877858  0.042214  0.582815 NaN"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"E\"] = np.nan\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add a series as a new column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>cat_column</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.624345</td>\n",
       "      <td>-0.611756</td>\n",
       "      <td>-0.528172</td>\n",
       "      <td>-1.072969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.865408</td>\n",
       "      <td>-2.301539</td>\n",
       "      <td>1.744812</td>\n",
       "      <td>-0.761207</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.319039</td>\n",
       "      <td>-0.249370</td>\n",
       "      <td>1.462108</td>\n",
       "      <td>-2.060141</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.322417</td>\n",
       "      <td>-0.384054</td>\n",
       "      <td>1.133769</td>\n",
       "      <td>-1.099891</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.172428</td>\n",
       "      <td>-0.877858</td>\n",
       "      <td>0.042214</td>\n",
       "      <td>0.582815</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cat</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D   E cat_column\n",
       "0  1.624345 -0.611756 -0.528172 -1.072969 NaN        cat\n",
       "1  0.865408 -2.301539  1.744812 -0.761207 NaN        cat\n",
       "2  0.319039 -0.249370  1.462108 -2.060141 NaN        dog\n",
       "3 -0.322417 -0.384054  1.133769 -1.099891 NaN        dog\n",
       "4 -0.172428 -0.877858  0.042214  0.582815 NaN        cat"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a categorical series the same length as the DataFrame:\n",
    "categorical_series = np.random.choice([\"cat\", \"dog\"], size=(5,1))\n",
    "df[\"cat_column\"] = categorical_series\n",
    "df"
   ]
  }
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