Lost_Voices_CSV_Viewer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d11L2LyyBEG3"
   },
   "source": [
    "# Pandas for Lost Voices CSV\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cDL2dspQBEG4"
   },
   "source": [
    "## Import the Tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "BEm7Z7MjBEG5"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PnNWQx5MBEG8"
   },
   "source": [
    "## Import the CSV Created by Lost Voices\n",
    "\n",
    "- CSV files can be uploaded from your computer.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pon93RXdlPaF"
   },
   "source": [
    "## Name the Important File as a DataSet\n",
    "- Give the imported file a simple name to be used in the subsequent analysis and visualization.  For instance `Data_Name =`\n",
    "- Pandas will read the csv, but be sure to check the path and file name: `pd.read_csv('/content/Stras_Brumel_Exact_V5_P4.csv')`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "wQvVRtvKBEG9"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv ('https://raw.githubusercontent.com/RichardFreedman/LostVoicesCadenceViewer/main/LV_CadenceData.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-mRdcOJSBEG_"
   },
   "source": [
    "## Inspect It \n",
    "- The `.head(n)` allows you to check the columns and contents.  `n` determines the number of rows in the preview."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 394
    },
    "id": "-qQLaAW_BEHA",
    "outputId": "2003011f-02da-4734-aaab-3afb5f179455"
   },
   "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>phrase_number</th>\n",
       "      <th>composition_number</th>\n",
       "      <th>cadence_role_cantz</th>\n",
       "      <th>cadence_alter</th>\n",
       "      <th>cadence_final_tone</th>\n",
       "      <th>cadence_role_tenz</th>\n",
       "      <th>cadence_kind</th>\n",
       "      <th>cadence_final_tone_before</th>\n",
       "      <th>cadence_final_tone_after</th>\n",
       "      <th>cadence_kind_before</th>\n",
       "      <th>cadence_kind_after</th>\n",
       "      <th>final_cadence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>DC0625.8: Dont nous serons plus contentz qu'eulx.</td>\n",
       "      <td>DC0625: Si je n'avois de fermeté</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>A</td>\n",
       "      <td>None</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>None</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>DC0625.6: Ami perfaict plus que les dieux,</td>\n",
       "      <td>DC0625: Si je n'avois de fermeté</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>D</td>\n",
       "      <td>G</td>\n",
       "      <td>Plagal</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>DC0625.5: Mais ne fault point que doubte on face,</td>\n",
       "      <td>DC0625: Si je n'avois de fermeté</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>None</td>\n",
       "      <td>Plagal</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>DC0625.2: Non plus que j'ay de bonne grace,</td>\n",
       "      <td>DC0625: Si je n'avois de fermeté</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>C</td>\n",
       "      <td>D</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>Plagal</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>DC0625.1: Si je n'avois de fermeté,</td>\n",
       "      <td>DC0625: Si je n'avois de fermeté</td>\n",
       "      <td>T</td>\n",
       "      <td>Displaced, Inverted, Evaded</td>\n",
       "      <td>C</td>\n",
       "      <td>Ct</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>None</td>\n",
       "      <td>G</td>\n",
       "      <td>None</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       phrase_number  \\\n",
       "0  DC0625.8: Dont nous serons plus contentz qu'eulx.   \n",
       "1         DC0625.6: Ami perfaict plus que les dieux,   \n",
       "2  DC0625.5: Mais ne fault point que doubte on face,   \n",
       "3        DC0625.2: Non plus que j'ay de bonne grace,   \n",
       "4                DC0625.1: Si je n'avois de fermeté,   \n",
       "\n",
       "                 composition_number cadence_role_cantz  \\\n",
       "0  DC0625: Si je n'avois de fermeté                  S   \n",
       "1  DC0625: Si je n'avois de fermeté                  S   \n",
       "2  DC0625: Si je n'avois de fermeté               None   \n",
       "3  DC0625: Si je n'avois de fermeté                  S   \n",
       "4  DC0625: Si je n'avois de fermeté                  T   \n",
       "\n",
       "                 cadence_alter cadence_final_tone cadence_role_tenz  \\\n",
       "0                          NaN                  G                 T   \n",
       "1                          NaN                  A                 T   \n",
       "2                          NaN                  D              None   \n",
       "3                          NaN                  G                 T   \n",
       "4  Displaced, Inverted, Evaded                  C                Ct   \n",
       "\n",
       "  cadence_kind cadence_final_tone_before cadence_final_tone_after  \\\n",
       "0    Authentic                         A                     None   \n",
       "1    Authentic                         D                        G   \n",
       "2       Plagal                         G                        A   \n",
       "3    Authentic                         C                        D   \n",
       "4    Authentic                      None                        G   \n",
       "\n",
       "  cadence_kind_before cadence_kind_after final_cadence  \n",
       "0           Authentic               None             G  \n",
       "1              Plagal          Authentic             G  \n",
       "2           Authentic          Authentic             G  \n",
       "3           Authentic             Plagal             G  \n",
       "4                None          Authentic             G  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KDt5y57gBEHC"
   },
   "source": [
    "## Get Basic Information\n",
    "- Another overview with `.info()` provides the total dimensions (rows + columns), plus the column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iESZ3TglBEHD",
    "outputId": "cde0cd05-7cb0-4ead-9dac-3c2b32802e4d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1195 entries, 0 to 1194\n",
      "Data columns (total 12 columns):\n",
      "phrase_number                1195 non-null object\n",
      "composition_number           1195 non-null object\n",
      "cadence_role_cantz           1168 non-null object\n",
      "cadence_alter                753 non-null object\n",
      "cadence_final_tone           1195 non-null object\n",
      "cadence_role_tenz            1168 non-null object\n",
      "cadence_kind                 1195 non-null object\n",
      "cadence_final_tone_before    1195 non-null object\n",
      "cadence_final_tone_after     1195 non-null object\n",
      "cadence_kind_before          1195 non-null object\n",
      "cadence_kind_after           1195 non-null object\n",
      "final_cadence                1195 non-null object\n",
      "dtypes: object(12)\n",
      "memory usage: 112.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bC0kUzHmBEHF"
   },
   "source": [
    "## Get \"Counts\" for Values of a Particular Column\n",
    "- For any column in the data_set, `['Column_Name']` in the code below, like `data_set_name['Column_Name'].value_counts()`\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uT1trXxBBEHF",
    "outputId": "d9174041-a9ac-42b8-e7c4-89f40d3fd8da"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Authentic    882\n",
       "Plagal       158\n",
       "Phrygian      98\n",
       "CadInCad      37\n",
       "CAD NDLT      20\n",
       "Name: cadence_kind, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cadence_kind'].value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NbfXhQMvBEHI"
   },
   "source": [
    "## Plot Values as Histogram\n",
    "\n",
    "Now using the matlib plotly resource, we can visualize in various formats\n",
    "In the code below:\n",
    "- `(fisgsize=(16,6))` determines the size of the image (w,h)\n",
    "- `data=name` = name given the imported file above\n",
    "- `x='column_name'` is the column to use for the X axis\n",
    "- `hue='column_name` is the column to use for the colors (and key)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 316
    },
    "id": "3NZneB-WBEHI",
    "outputId": "9ba203a5-090d-4756-9e1f-d191c1022266"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(24,6))\n",
    "sns.countplot(x='cadence_final_tone',data=df, hue='cadence_kind');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Xi9FheeDBEHK"
   },
   "source": [
    "## Get Data for an Individual Value with a Column\n",
    "- For example:  a particular Piece within `Piece_Title`\n",
    "- By default Pandas will display just the first and last five rows of the result.\n",
    "- To show show more (or less), add: `pd.set_option(\"max_rows\", None)`.  Note that in this case `None` means \"no maximium, or ALL.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WsVa47qaBEHN"
   },
   "source": [
    "## Sort Data According to Selected Columns\n",
    "- We can also put data in order (ascending/descending) by one more more columns.\n",
    "- Giving this sorted version a new name will allow us to export or display it in this state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 445
    },
    "id": "sF0k_XybBEHN",
    "outputId": "a78cb035-175c-47df-de38-e9b590a4223b",
    "scrolled": true
   },
   "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>phrase_number</th>\n",
       "      <th>composition_number</th>\n",
       "      <th>cadence_role_cantz</th>\n",
       "      <th>cadence_alter</th>\n",
       "      <th>cadence_final_tone</th>\n",
       "      <th>cadence_role_tenz</th>\n",
       "      <th>cadence_kind</th>\n",
       "      <th>cadence_final_tone_before</th>\n",
       "      <th>cadence_final_tone_after</th>\n",
       "      <th>cadence_kind_before</th>\n",
       "      <th>cadence_kind_after</th>\n",
       "      <th>final_cadence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>DC0625.6: Ami perfaict plus que les dieux,</td>\n",
       "      <td>DC0625: Si je n'avois de fermeté</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>D</td>\n",
       "      <td>G</td>\n",
       "      <td>Plagal</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>DC0615.6: Ne me chault d'acquerir leurs graces,</td>\n",
       "      <td>DC0615: N'ayez plus peur, mary jaloux</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>F</td>\n",
       "      <td>C</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>DC0604.7: Mais s'advenu m'estoit par ignorance,</td>\n",
       "      <td>DC0604: Où est ce temps, dictes, mademoiselle</td>\n",
       "      <td>S</td>\n",
       "      <td>Evaded</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>D</td>\n",
       "      <td>G</td>\n",
       "      <td>Plagal</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>DC0603.8: Dont ja commence à m’ennuyer ma vie,</td>\n",
       "      <td>DC0603: Or à ce jour le verd may se termine</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>DC0603.3: Las est à moy tout mon verd si se fine,</td>\n",
       "      <td>DC0603: Or à ce jour le verd may se termine</td>\n",
       "      <td>T</td>\n",
       "      <td>Displaced, Inverted, Evaded</td>\n",
       "      <td>A</td>\n",
       "      <td>Ct</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>Plagal</td>\n",
       "      <td>Plagal</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        phrase_number  \\\n",
       "1          DC0625.6: Ami perfaict plus que les dieux,   \n",
       "40    DC0615.6: Ne me chault d'acquerir leurs graces,   \n",
       "87    DC0604.7: Mais s'advenu m'estoit par ignorance,   \n",
       "93     DC0603.8: Dont ja commence à m’ennuyer ma vie,   \n",
       "97  DC0603.3: Las est à moy tout mon verd si se fine,   \n",
       "\n",
       "                               composition_number cadence_role_cantz  \\\n",
       "1                DC0625: Si je n'avois de fermeté                  S   \n",
       "40          DC0615: N'ayez plus peur, mary jaloux                  S   \n",
       "87  DC0604: Où est ce temps, dictes, mademoiselle                  S   \n",
       "93    DC0603: Or à ce jour le verd may se termine                  S   \n",
       "97    DC0603: Or à ce jour le verd may se termine                  T   \n",
       "\n",
       "                  cadence_alter cadence_final_tone cadence_role_tenz  \\\n",
       "1                           NaN                  A                 T   \n",
       "40                          NaN                  A                 T   \n",
       "87                       Evaded                  A                 T   \n",
       "93                          NaN                  A                 T   \n",
       "97  Displaced, Inverted, Evaded                  A                Ct   \n",
       "\n",
       "   cadence_kind cadence_final_tone_before cadence_final_tone_after  \\\n",
       "1     Authentic                         D                        G   \n",
       "40    Authentic                         F                        C   \n",
       "87    Authentic                         D                        G   \n",
       "93    Authentic                         D                        A   \n",
       "97    Authentic                         A                        A   \n",
       "\n",
       "   cadence_kind_before cadence_kind_after final_cadence  \n",
       "1               Plagal          Authentic             G  \n",
       "40           Authentic          Authentic             F  \n",
       "87              Plagal          Authentic             G  \n",
       "93           Authentic          Authentic             D  \n",
       "97              Plagal             Plagal             D  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(['cadence_final_tone','cadence_kind'])\n",
    "Sorted = df.sort_values(['cadence_final_tone','cadence_kind'])\n",
    "Sorted.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iE5X72bHx0NY"
   },
   "source": [
    "## Seaborn Plots\n",
    "- An extension of the MatPlot library, Seaborn provides various views of the data using three 'dimensions' of information for position and color.\n",
    "- See samples: https://seaborn.pydata.org/examples/index.html\n",
    "- This is a 'swarmplot', `data=Heth` determines the data to use, `y=\"Pattern_Matched\"` determines y axis, `x=\"First_Note_Measure_Number\"` determines x axis, and `hue=\"Part\"` determines color and key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "vhGAvOrJBEHV",
    "outputId": "bd4524dd-abad-4203-e71b-91c20de17eaa"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1296: UserWarning: 81.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1296: UserWarning: 18.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1296: UserWarning: 26.5% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, '')]"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a categorical scatterplot to show each observation\n",
    "# The problem here is that there are too many data points to display properly with swarmplot for Du Chemin Cadences\n",
    "plt.figure(figsize=(16,4))\n",
    "ax = sns.swarmplot(data=df, y=\"cadence_final_tone\", x=\"cadence_kind\", hue=\"cadence_role_tenz\");\n",
    "ax.set(ylabel=\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "_0uDCqJXAiVl",
    "outputId": "1e11a432-f43a-4009-fd80-3242cce59294"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Strip Plot manages the Du Chemin Cadence Data OK\n",
    "# X plots the Last Cadence of the Piece\n",
    "\n",
    "plt.figure(figsize=(24,4))\n",
    "p = sns.stripplot(data=df,\n",
    "                  x='final_cadence',\n",
    "                  y='cadence_final_tone',\n",
    "                  hue='cadence_kind',\n",
    "                  jitter=.25,\n",
    "                  dodge=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 389
    },
    "id": "8tdRdyEvTInk",
    "outputId": "b87766c9-02ae-4bc0-b07c-f9fa5dfc8131"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,6))\n",
    "ax = sns.stripplot(x=\"final_cadence\", y=\"cadence_final_tone\", hue=\"cadence_kind\",\n",
    "                   data=df, palette=\"Set2\", dodge=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xApGzF5VBU9y"
   },
   "source": [
    "# Grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 796
    },
    "id": "-XzWjf95BEHc",
    "outputId": "671d461b-6f68-4887-c404-c7242dca7cd1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>phrase_number</th>\n",
       "      <th>composition_number</th>\n",
       "      <th>cadence_role_cantz</th>\n",
       "      <th>cadence_alter</th>\n",
       "      <th>cadence_final_tone</th>\n",
       "      <th>cadence_role_tenz</th>\n",
       "      <th>cadence_kind</th>\n",
       "      <th>cadence_final_tone_before</th>\n",
       "      <th>cadence_final_tone_after</th>\n",
       "      <th>cadence_kind_before</th>\n",
       "      <th>cadence_kind_after</th>\n",
       "      <th>final_cadence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DC0625.8: Dont nous serons plus contentz qu'eulx.</td>\n",
       "      <td>DC0625: Si je n'avois de fermeté</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>A</td>\n",
       "      <td>None</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>None</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DC0625.6: Ami perfaict plus que les dieux,</td>\n",
       "      <td>DC0625: Si je n'avois de fermeté</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>D</td>\n",
       "      <td>G</td>\n",
       "      <td>Plagal</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DC0625.5: Mais ne fault point que doubte on face,</td>\n",
       "      <td>DC0625: Si je n'avois de fermeté</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>None</td>\n",
       "      <td>Plagal</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DC0625.2: Non plus que j'ay de bonne grace,</td>\n",
       "      <td>DC0625: Si je n'avois de fermeté</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>C</td>\n",
       "      <td>D</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>Plagal</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DC0625.1: Si je n'avois de fermeté,</td>\n",
       "      <td>DC0625: Si je n'avois de fermeté</td>\n",
       "      <td>T</td>\n",
       "      <td>Displaced, Inverted, Evaded</td>\n",
       "      <td>C</td>\n",
       "      <td>Ct</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>None</td>\n",
       "      <td>G</td>\n",
       "      <td>None</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>DC0207.3: L’on en veit oncques de si belle,</td>\n",
       "      <td>DC0207: Joye et santé, ma demoiselle</td>\n",
       "      <td>S</td>\n",
       "      <td>Evaded</td>\n",
       "      <td>D</td>\n",
       "      <td>T</td>\n",
       "      <td>Phrygian</td>\n",
       "      <td>G</td>\n",
       "      <td>G</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>B-flat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>DC0118.9: Vous les ferez tous vifz crever de r...</td>\n",
       "      <td>DC0118: Vrais amateurs du plaisir de Vénus</td>\n",
       "      <td>S</td>\n",
       "      <td>None</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>E</td>\n",
       "      <td>A</td>\n",
       "      <td>Phrygian</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083</th>\n",
       "      <td>DC0118.8: Besongnez donc et de jour et de nuict.</td>\n",
       "      <td>DC0118: Vrais amateurs du plaisir de Vénus</td>\n",
       "      <td>Ct</td>\n",
       "      <td>Displaced</td>\n",
       "      <td>E</td>\n",
       "      <td>B</td>\n",
       "      <td>Phrygian</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097</th>\n",
       "      <td>DC0116.8: As tu osé luy faire un tel oultrage?</td>\n",
       "      <td>DC0116: Maistre Ambrelin, confesseur de nonettes</td>\n",
       "      <td>B</td>\n",
       "      <td>Displaced, Inverted</td>\n",
       "      <td>E</td>\n",
       "      <td>Ct</td>\n",
       "      <td>Phrygian</td>\n",
       "      <td>E</td>\n",
       "      <td>C</td>\n",
       "      <td>Plagal</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1109</th>\n",
       "      <td>DC0114.9: Elle me paist ceste seconde Heleine</td>\n",
       "      <td>DC0114: Trop justement je forme une complainte</td>\n",
       "      <td>S</td>\n",
       "      <td>Displaced, Evaded</td>\n",
       "      <td>None</td>\n",
       "      <td>Ct</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>177 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          phrase_number  ... final_cadence\n",
       "0     DC0625.8: Dont nous serons plus contentz qu'eulx.  ...             G\n",
       "1            DC0625.6: Ami perfaict plus que les dieux,  ...             G\n",
       "2     DC0625.5: Mais ne fault point que doubte on face,  ...             G\n",
       "3           DC0625.2: Non plus que j'ay de bonne grace,  ...             G\n",
       "4                   DC0625.1: Si je n'avois de fermeté,  ...             G\n",
       "...                                                 ...  ...           ...\n",
       "996         DC0207.3: L’on en veit oncques de si belle,  ...        B-flat\n",
       "1082  DC0118.9: Vous les ferez tous vifz crever de r...  ...             A\n",
       "1083   DC0118.8: Besongnez donc et de jour et de nuict.  ...             A\n",
       "1097     DC0116.8: As tu osé luy faire un tel oultrage?  ...             A\n",
       "1109      DC0114.9: Elle me paist ceste seconde Heleine  ...             A\n",
       "\n",
       "[177 rows x 12 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = df.groupby(['cadence_final_tone', 'final_cadence'])\n",
    "#grouped.describe()\n",
    "gb = df.groupby(\"final_cadence\")\n",
    "gb.count() # or,\n",
    "grouped.head()\n",
    "#pd.DataFrame(grouped)\n",
    "#DF_output = pd.DataFrame(grouped)\n",
    "#DF_output.to_csv(\"grouped_test.csv\")"
   ]
  }
 ],
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