{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6074dfcc",
   "metadata": {},
   "source": [
    "# Transposing data in pandas (switching columns and rows)\n",
    "\n",
    "Switching the columns and rows in a dataset is called **transposing**, and is one of the simplest forms of reshaping data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "531a3a9b",
   "metadata": {},
   "outputs": [
    {
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       "         date    beef  ofada  cafish\n",
       "0  2017-01-01  1001.2  377.4   899.6\n",
       "1  2017-01-01  1129.0  472.3   916.5\n",
       "2  2017-01-01  1067.7  319.9   919.8"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame([\n",
    "    { 'date': '2017-01-01', 'beef': 1001.2, 'ofada': 377.4, 'cafish': 899.6},\n",
    "    { 'date': '2017-01-01', 'beef': 1129.0, 'ofada': 472.3, 'cafish': 916.5},\n",
    "    { 'date': '2017-01-01', 'beef': 1067.7, 'ofada': 319.9, 'cafish': 919.8}\n",
    "])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01d3ffcc",
   "metadata": {},
   "source": [
    "## Simple transposition\n",
    "\n",
    "To switch columns and rows, you can just use `df.T`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e745432b",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1129.0</td>\n",
       "      <td>1067.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ofada</th>\n",
       "      <td>377.4</td>\n",
       "      <td>472.3</td>\n",
       "      <td>319.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cafish</th>\n",
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      "text/plain": [
       "                 0           1           2\n",
       "date    2017-01-01  2017-01-01  2017-01-01\n",
       "beef        1001.2      1129.0      1067.7\n",
       "ofada        377.4       472.3       319.9\n",
       "cafish       899.6       916.5       919.8"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8ddecad",
   "metadata": {},
   "source": [
    "The problem we can see is the first row should be the column names! You could manually the first row and rename your column headers..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "69b165b8",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2017-01-01</th>\n",
       "      <th>2017-01-01</th>\n",
       "      <th>2017-01-01</th>\n",
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       "      <td>472.3</td>\n",
       "      <td>319.9</td>\n",
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       "    <tr>\n",
       "      <th>cafish</th>\n",
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       "      <td>916.5</td>\n",
       "      <td>919.8</td>\n",
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       "       2017-01-01 2017-01-01 2017-01-01\n",
       "beef       1001.2     1129.0     1067.7\n",
       "ofada       377.4      472.3      319.9\n",
       "cafish      899.6      916.5      919.8"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T.iloc[1:].rename(columns=df.date)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79d1df6a",
   "metadata": {},
   "source": [
    "...but there's another way, too."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68fe2986",
   "metadata": {},
   "source": [
    "## Transpose but fix column issue with `.set_index`\n",
    "\n",
    "With our starting dataset, the date is just the same as any other column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7efc7041",
   "metadata": {},
   "outputs": [
    {
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       "         date    beef  ofada  cafish\n",
       "0  2017-01-01  1001.2  377.4   899.6\n",
       "1  2017-01-01  1129.0  472.3   916.5\n",
       "2  2017-01-01  1067.7  319.9   919.8"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a4253bd",
   "metadata": {},
   "source": [
    "When we transpose, it's the same as any other row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "03f9d0bd",
   "metadata": {},
   "outputs": [
    {
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       "      <th>date</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>2017-01-01</td>\n",
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       "    <tr>\n",
       "      <th>ofada</th>\n",
       "      <td>377.4</td>\n",
       "      <td>472.3</td>\n",
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       "      <th>cafish</th>\n",
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       "                 0           1           2\n",
       "date    2017-01-01  2017-01-01  2017-01-01\n",
       "beef        1001.2      1129.0      1067.7\n",
       "ofada        377.4       472.3       319.9\n",
       "cafish       899.6       916.5       919.8"
      ]
     },
     "execution_count": 70,
     "metadata": {},
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    "df.T"
   ]
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  {
   "cell_type": "markdown",
   "id": "98d8729b",
   "metadata": {},
   "source": [
    "Where do the numbers on the columns come from? When we transpose, the **index of the original dataframe becomes the column headers**. To allow our dates to become column headers, we just need to set it as the index before we transpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "db323f54",
   "metadata": {},
   "outputs": [
    {
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       "              beef  ofada  cafish\n",
       "date                             \n",
       "2017-01-01  1001.2  377.4   899.6\n",
       "2017-01-01  1129.0  472.3   916.5\n",
       "2017-01-01  1067.7  319.9   919.8"
      ]
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     "metadata": {},
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   ],
   "source": [
    "df.set_index('date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "fcc11918",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>ofada</th>\n",
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       "      <td>472.3</td>\n",
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       "date    2017-01-01  2017-01-01  2017-01-01\n",
       "beef        1001.2      1129.0      1067.7\n",
       "ofada        377.4       472.3       319.9\n",
       "cafish       899.6       916.5       919.8"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('date').T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7d2bc46",
   "metadata": {},
   "source": [
    "But the opposite happened, too, which is why our foods are bold: **the column headers became the index of our rows**. To turn them into normal rows, we'll use `reset_index()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "7e4fc8b3",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ofada</td>\n",
       "      <td>377.4</td>\n",
       "      <td>472.3</td>\n",
       "      <td>319.9</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cafish</td>\n",
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      "text/plain": [
       "date   index  2017-01-01  2017-01-01  2017-01-01\n",
       "0       beef      1001.2      1129.0      1067.7\n",
       "1      ofada       377.4       472.3       319.9\n",
       "2     cafish       899.6       916.5       919.8"
      ]
     },
     "execution_count": 75,
     "metadata": {},
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    }
   ],
   "source": [
    "df.set_index('date').T.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbe8c8f4",
   "metadata": {},
   "source": [
    "But they have a ridiculous name now! There's no solution to this that makes me happy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "e540cebd",
   "metadata": {},
   "outputs": [
    {
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       "        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>date</th>\n",
       "      <th>food</th>\n",
       "      <th>2017-01-01</th>\n",
       "      <th>2017-01-01</th>\n",
       "      <th>2017-01-01</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>beef</td>\n",
       "      <td>1001.2</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>1067.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ofada</td>\n",
       "      <td>377.4</td>\n",
       "      <td>472.3</td>\n",
       "      <td>319.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cafish</td>\n",
       "      <td>899.6</td>\n",
       "      <td>916.5</td>\n",
       "      <td>919.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "date    food  2017-01-01  2017-01-01  2017-01-01\n",
       "0       beef      1001.2      1129.0      1067.7\n",
       "1      ofada       377.4       472.3       319.9\n",
       "2     cafish       899.6       916.5       919.8"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('date').T.reset_index().rename(columns={'index': 'food'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09b153a4",
   "metadata": {},
   "source": [
    "If we want to get rid of the index label saying `date` when it's just a side effect of the transposition, we can use `rename_axis`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "bca71350",
   "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>food</th>\n",
       "      <th>2017-01-01</th>\n",
       "      <th>2017-01-01</th>\n",
       "      <th>2017-01-01</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>beef</td>\n",
       "      <td>1001.2</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>1067.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ofada</td>\n",
       "      <td>377.4</td>\n",
       "      <td>472.3</td>\n",
       "      <td>319.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cafish</td>\n",
       "      <td>899.6</td>\n",
       "      <td>916.5</td>\n",
       "      <td>919.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     food  2017-01-01  2017-01-01  2017-01-01\n",
       "0    beef      1001.2      1129.0      1067.7\n",
       "1   ofada       377.4       472.3       319.9\n",
       "2  cafish       899.6       916.5       919.8"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('date').T.reset_index().rename(columns={'index': 'food'}).rename_axis(None, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "502234c1",
   "metadata": {},
   "source": [
    "Sigh. This is so much more complicated than the original answer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "254fef84",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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