15_`Renaming` the index.ipynb

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    "# 15_\"Renaming\" the index\n",
    "\n",
    "After pivoting or aggregating data, Pandas often leaves the appearance that the column name of the index, is the column that you aggregated by. This is how to rename it to something else\n",
    "\n",
    "\n",
    "### Example\n",
    "Suppose you have aggregated some raw data using a pivot table.\n",
    "\n",
    "### Prepare the DataFrame"
   ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>weekday</th>\n",
       "      <th>Friday</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>2017-02-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2017-02-03</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2017-02-04</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "weekday     Friday  Monday  Saturday  Sunday  Thursday  Tuesday  Wednesday\n",
       "Date                                                                      \n",
       "2017-02-01     NaN     NaN       NaN     NaN       NaN      NaN        0.0\n",
       "2017-02-02     NaN     NaN       NaN     NaN       0.0      NaN        NaN\n",
       "2017-02-03     0.0     NaN       NaN     NaN       NaN      NaN        NaN\n",
       "2017-02-04     NaN     NaN       0.0     NaN       NaN      NaN        NaN\n",
       "2017-02-05     NaN     NaN       NaN     0.0       NaN      NaN        NaN"
      ]
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   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load the example daily time series dataset:\n",
    "df = pd.read_csv(\"./data_etc/timeseries_daily.csv\")\n",
    "\n",
    "# Convert the 'Date' column from string to DateTime:\n",
    "df[\"Date\"] = pd.to_datetime(df[\"Date\"], dayfirst=True)\n",
    "\n",
    "# Create a pivot table:\n",
    "df = pd.pivot_table(df, columns=\"weekday\", index=\"Date\", values=\"feature_1\", aggfunc=\"mean\")\n",
    "\n",
    "df.head()"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "### Notice how `weekday` now appears as if it is the column name of the index\n",
    "It can be removed/changed like so:"
   ]
  },
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   "cell_type": "code",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Friday</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
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       "      <th>Date</th>\n",
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       "    <tr>\n",
       "      <th>2017-02-01</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2017-02-04</th>\n",
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       "      <th>2017-02-05</th>\n",
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      "text/plain": [
       "            Friday  Monday  Saturday  Sunday  Thursday  Tuesday  Wednesday\n",
       "Date                                                                      \n",
       "2017-02-01     NaN     NaN       NaN     NaN       NaN      NaN        0.0\n",
       "2017-02-02     NaN     NaN       NaN     NaN       0.0      NaN        NaN\n",
       "2017-02-03     0.0     NaN       NaN     NaN       NaN      NaN        NaN\n",
       "2017-02-04     NaN     NaN       0.0     NaN       NaN      NaN        NaN\n",
       "2017-02-05     NaN     NaN       NaN     0.0       NaN      NaN        NaN"
      ]
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   "source": [
    "df.columns.name = \"\"\n",
    "df.head()"
   ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Index</th>\n",
       "      <th>Friday</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>2017-02-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "Index       Friday  Monday  Saturday  Sunday  Thursday  Tuesday  Wednesday\n",
       "Date                                                                      \n",
       "2017-02-01     NaN     NaN       NaN     NaN       NaN      NaN        0.0\n",
       "2017-02-02     NaN     NaN       NaN     NaN       0.0      NaN        NaN\n",
       "2017-02-03     0.0     NaN       NaN     NaN       NaN      NaN        NaN\n",
       "2017-02-04     NaN     NaN       0.0     NaN       NaN      NaN        NaN\n",
       "2017-02-05     NaN     NaN       NaN     0.0       NaN      NaN        NaN"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.columns.name = \"Index\"\n",
    "df.head()"
   ]
  }
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