10-Pandas_Make_New_Columns.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 영상링크 : https://youtu.be/CGEhwAd7Dbk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2.052028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.710044</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>-1.174701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.234340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>-1.025556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>-0.091295</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>-2.052226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>-0.233776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>-0.783700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>-1.131910</td>\n",
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      "text/plain": [
       "    A         B\n",
       "0   1  2.052028\n",
       "1   2  1.710044\n",
       "2   3 -1.174701\n",
       "3   4  0.234340\n",
       "4   5 -1.025556\n",
       "5   6 -0.091295\n",
       "6   7 -2.052226\n",
       "7   8 -0.233776\n",
       "8   9 -0.783700\n",
       "9  10 -1.131910"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A': range(1, 11), 'B': np.random.randn(10)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.assign?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>ln_A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1.710044</td>\n",
       "      <td>0.693147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>-1.174701</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.234340</td>\n",
       "      <td>1.386294</td>\n",
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      "text/plain": [
       "   A         B      ln_A\n",
       "0  1  2.052028  0.000000\n",
       "1  2  1.710044  0.693147\n",
       "2  3 -1.174701  1.098612\n",
       "3  4  0.234340  1.386294\n",
       "4  5 -1.025556  1.609438"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.assign(ln_A = lambda x: np.log(x.A)).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.710044</td>\n",
       "      <td>0.693147</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>-1.174701</td>\n",
       "      <td>1.098612</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.234340</td>\n",
       "      <td>1.386294</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
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       "      <td>1.609438</td>\n",
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       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>-0.091295</td>\n",
       "      <td>1.791759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>-2.052226</td>\n",
       "      <td>1.945910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
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       "      <td>-0.233776</td>\n",
       "      <td>2.079442</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>-0.783700</td>\n",
       "      <td>2.197225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>-1.131910</td>\n",
       "      <td>2.302585</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "    A         B      ln_A\n",
       "0   1  2.052028  0.000000\n",
       "1   2  1.710044  0.693147\n",
       "2   3 -1.174701  1.098612\n",
       "3   4  0.234340  1.386294\n",
       "4   5 -1.025556  1.609438\n",
       "5   6 -0.091295  1.791759\n",
       "6   7 -2.052226  1.945910\n",
       "7   8 -0.233776  2.079442\n",
       "8   9 -0.783700  2.197225\n",
       "9  10 -1.131910  2.302585"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ln_A = np.log(df.A)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     bad\n",
       "1     bad\n",
       "2    good\n",
       "3     bad\n",
       "4    good\n",
       "5     bad\n",
       "6    good\n",
       "7     bad\n",
       "8    good\n",
       "9    good\n",
       "Name: B, dtype: category\n",
       "Categories (2, object): [good < bad]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.qcut(df.B, 2, labels=[\"good\", \"bad\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A       10.000000\n",
       "B        2.052028\n",
       "ln_A     2.302585\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.max(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A       1.000000\n",
       "B      -2.052226\n",
       "ln_A    0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.min(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.052028\n",
       "1    1.710044\n",
       "2    1.000000\n",
       "3    1.000000\n",
       "4    1.000000\n",
       "5    1.000000\n",
       "6    1.000000\n",
       "7    1.000000\n",
       "8    1.000000\n",
       "9    1.000000\n",
       "Name: B, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"B\"].clip(lower=1,upper=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.052028\n",
       "1    1.710044\n",
       "2    1.174701\n",
       "3    0.234340\n",
       "4    1.025556\n",
       "5    0.091295\n",
       "6    2.052226\n",
       "7    0.233776\n",
       "8    0.783700\n",
       "9    1.131910\n",
       "Name: B, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"B\"].abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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