00-csv-files.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a29773c1",
   "metadata": {},
   "source": [
    "<h1>CSV-Files</h1>\n",
    "CSV stands for \"Comma Separated Values\" and means that we store data in a list. List elements are separated using commas. Sometimes, other symbold are used as delimiters, such as semicolons, tabspaces, return, etc. Often, commas separate columns and return separates lines, creating a table rather than a list. Such a list or table is saved as a CSV file. CSV files usually contain strings that are saved using UTF-8, ANSI, or other character sets. CSV files have several strengths:\n",
    "<ul>\n",
    "    <li>In contrast to binary files, the raw text of CSV files (as displayed by text editors) is interpretable by readers (if not compressed)</li>\n",
    "    <li>CSV files can be imported and exported by spreadsheet softwares, like Google Docs, Microsoft Excel, <a href=\"https://www.openoffice.org/\" target=\"_blank\">Open Office Calc</a> and <a href=\"https://de.libreoffice.org/\" target=\"_blank\">Libre Office Calc</a>, by statistical softwares, such as IBM SPSS and <a href=\"https://cran.r-project.org/bin/windows/base/\" target=\"_blank\">R</a>, by database softwares, like <a href=\"https://www.mysql.com/\" target=\"_blank\">MySQL</a>, and by audio softwares, like <a href=\"https://www.audacity.de/\" target=\"_blank\">Audacity</a>, <a href=\"https://puredata.info/\", target=\"_blank\">Pure Data</a> (iemlib library) and <a href=\"https://sonification.highcharts.com/#/app\" target=\"_blank\">Highchart Sonification Studio</a></li>\n",
    "</ul>\n",
    "Some weaknesses of CSV files are:\n",
    "<ul>\n",
    "    <li>CSV files tend to be larger than binary files</li>\n",
    "    <li>CSV files are only semi-<a href=\"https://datatracker.ietf.org/doc/html/rfc4180.html\" target=\"_blank\">standardized</a> for lists and tables, but not for higher order arrays and matrices</li>\n",
    "    <li>The option to use any symbol as separator and arbitrary symbols (usually single quote or double quote) to indicate a string can cause confusion and unexpected text display</li>\n",
    "    <li>Language settings can cause awkward tables, as Germans use commas as decimal separator and, sometimes, points as thousands separator. In English it is the other way round. Language settings can even descroy existing CSV files by overwriting commas with points or the other way round.</li>\n",
    "    <li>Small errors that a human makes when manually editing a csv file can cause unexpected harm</li>\n",
    "    <li>Some softwares allow compressing CSV files when exporting them. Such a compression makes the raw text unreadable and foils the universality of CSV files.</li>\n",
    "</ul>\n",
    "\n",
    "Generally, storing data in files on a hard drive is always a good choice if you want to use it later on, in another software, or in another jupyter notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bfd1d67",
   "metadata": {},
   "source": [
    "<h2>Pandas</h2>\n",
    "Pandas is often used to import, handle, analyze and export data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0e8d1cb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install pandas\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed9278b8",
   "metadata": {},
   "source": [
    "You can easily import a CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a02e8316",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "a_list = pd.read_table(r'C:\\python\\gits\\early-techno\\example-data\\germany\\Captain Hollywood Project - Flying High (Spaceship Mix) (1994).mp3.csv', sep=',',index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccdc096e",
   "metadata": {},
   "source": [
    "and display its content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8ebecc2a",
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>PeakMeterHigh</th>\n",
       "      <th>RMSHigh</th>\n",
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       "      <th>bpm</th>\n",
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       "<p>7679 rows × 21 columns</p>\n",
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      "text/plain": [
       "        PhaseSpace  ChannelCorrelation  PeakMeter        RMS  CrestFactor  \\\n",
       "0.0         0.0650            0.997516  -1.935620 -11.557975     9.622355   \n",
       "1.0         0.0775            0.994773  -1.480405 -11.120875     9.640470   \n",
       "2.0         0.0750            0.998545  -2.611259  -7.592365     4.981106   \n",
       "3.0         0.0700            0.995732  -3.420472 -45.194821    41.774349   \n",
       "4.0         0.0150            0.995242 -18.549834 -20.813745     2.263911   \n",
       "...            ...                 ...        ...        ...          ...   \n",
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       "7675.0      0.0325            0.074877 -10.009109 -35.657383    25.648274   \n",
       "7676.0      0.0275            0.407785 -10.747808 -32.037168    21.289361   \n",
       "7677.0      0.0250            0.456575 -12.727383 -49.617160    36.889777   \n",
       "7678.0      0.0225            0.275594 -12.728972 -53.958184    41.229212   \n",
       "\n",
       "        PhaseSpaceLow  ChannelCorrelationLow  PeakMeterLow     RMSLow  \\\n",
       "0.0            0.0625               0.997729     -4.365814 -10.215280   \n",
       "1.0            0.0800               0.995848     -1.183590  -6.533047   \n",
       "2.0            0.0725               0.998998     -2.535162  -3.866641   \n",
       "3.0            0.0725               0.997112     -2.549647 -22.412361   \n",
       "4.0            0.0150               0.992734    -18.673684 -25.383565   \n",
       "...               ...                    ...           ...        ...   \n",
       "7674.0         0.0100               0.449547    -21.360133 -33.341319   \n",
       "7675.0         0.0100               0.593814    -21.714508 -26.497132   \n",
       "7676.0         0.0100               0.736471    -21.789313 -23.177839   \n",
       "7677.0         0.0100               0.757330    -23.245507 -28.644949   \n",
       "7678.0         0.0075               0.712818    -25.783224 -55.283531   \n",
       "\n",
       "        CrestFactorLow  ...  ChannelCorrelationMid  PeakMeterMid     RMSMid  \\\n",
       "0.0           5.849466  ...               0.993727    -10.731547 -14.988900   \n",
       "1.0           5.349457  ...               0.991905     -9.272907 -27.416254   \n",
       "2.0           1.331479  ...               0.989210    -13.529488 -24.457612   \n",
       "3.0          19.862714  ...               0.988271    -21.989021 -54.212844   \n",
       "4.0           6.709881  ...               0.957202    -38.817900 -46.132094   \n",
       "...                ...  ...                    ...           ...        ...   \n",
       "7674.0       11.981186  ...              -0.030037     -9.700914 -23.680416   \n",
       "7675.0        4.782623  ...              -0.041718    -10.877874 -36.747760   \n",
       "7676.0        1.388526  ...               0.279194    -12.458199 -30.875436   \n",
       "7677.0        5.399442  ...               0.432950    -12.968551 -34.124531   \n",
       "7678.0       29.500307  ...               0.237760    -13.296599 -35.434146   \n",
       "\n",
       "        CrestFactorMid  PhaseSpaceHigh  ChannelCorrelationHigh  PeakMeterHigh  \\\n",
       "0.0           4.257353          0.0175                0.999906     -14.451853   \n",
       "1.0          18.143347          0.0050                0.922103     -57.206026   \n",
       "2.0          10.928124          0.0050                0.711311     -61.925656   \n",
       "3.0          32.223823          0.0050                0.605584     -68.467609   \n",
       "4.0           7.314194          0.0050                0.638600     -68.330868   \n",
       "...                ...             ...                     ...            ...   \n",
       "7674.0       13.979502          0.0100                0.240179     -24.839740   \n",
       "7675.0       25.869886          0.0050               -0.181073     -27.273891   \n",
       "7676.0       18.417237          0.0050                0.068897     -27.228361   \n",
       "7677.0       21.155979          0.0050               -0.187916     -28.510941   \n",
       "7678.0       22.137548          0.0050                0.322326     -27.350090   \n",
       "\n",
       "           RMSHigh  CrestFactorHigh         bpm  \n",
       "0.0     -81.438264        66.986410  151.999081  \n",
       "1.0    -101.969236        44.763210  151.999081  \n",
       "2.0     -80.700843        18.775187  151.999081  \n",
       "3.0     -86.229192        17.761583  151.999081  \n",
       "4.0     -90.427522        22.096654  151.999081  \n",
       "...            ...              ...         ...  \n",
       "7674.0  -33.286721         8.446980  151.999081  \n",
       "7675.0  -37.422171        10.148280  151.999081  \n",
       "7676.0  -37.170043         9.941682  151.999081  \n",
       "7677.0  -35.434530         6.923589  151.999081  \n",
       "7678.0  -38.473879        11.123789  151.999081  \n",
       "\n",
       "[7679 rows x 21 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0da5eea1",
   "metadata": {},
   "source": [
    "If the CSV files uses other delimiters, you can indicate them as sep <font color=#a2f>= </font><font color=#ba2121>'delimiter'</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "92406ae5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#b_list = pd.read_table('composers-semicolon.csv',sep=';')#\n",
    "#b_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25017d25",
   "metadata": {},
   "source": [
    "Sometimes, CSV files contain too much data to be displayed. In that case, you can use the head() function to display only the first five rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c5500c49",
   "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>PhaseSpace</th>\n",
       "      <th>ChannelCorrelation</th>\n",
       "      <th>PeakMeter</th>\n",
       "      <th>RMS</th>\n",
       "      <th>CrestFactor</th>\n",
       "      <th>PhaseSpaceLow</th>\n",
       "      <th>ChannelCorrelationLow</th>\n",
       "      <th>PeakMeterLow</th>\n",
       "      <th>RMSLow</th>\n",
       "      <th>CrestFactorLow</th>\n",
       "      <th>...</th>\n",
       "      <th>ChannelCorrelationMid</th>\n",
       "      <th>PeakMeterMid</th>\n",
       "      <th>RMSMid</th>\n",
       "      <th>CrestFactorMid</th>\n",
       "      <th>PhaseSpaceHigh</th>\n",
       "      <th>ChannelCorrelationHigh</th>\n",
       "      <th>PeakMeterHigh</th>\n",
       "      <th>RMSHigh</th>\n",
       "      <th>CrestFactorHigh</th>\n",
       "      <th>bpm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.0650</td>\n",
       "      <td>0.997516</td>\n",
       "      <td>-1.935620</td>\n",
       "      <td>-11.557975</td>\n",
       "      <td>9.622355</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>0.997729</td>\n",
       "      <td>-4.365814</td>\n",
       "      <td>-10.215280</td>\n",
       "      <td>5.849466</td>\n",
       "      <td>...</td>\n",
       "      <td>0.993727</td>\n",
       "      <td>-10.731547</td>\n",
       "      <td>-14.988900</td>\n",
       "      <td>4.257353</td>\n",
       "      <td>0.0175</td>\n",
       "      <td>0.999906</td>\n",
       "      <td>-14.451853</td>\n",
       "      <td>-81.438264</td>\n",
       "      <td>66.986410</td>\n",
       "      <td>151.999081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.0775</td>\n",
       "      <td>0.994773</td>\n",
       "      <td>-1.480405</td>\n",
       "      <td>-11.120875</td>\n",
       "      <td>9.640470</td>\n",
       "      <td>0.0800</td>\n",
       "      <td>0.995848</td>\n",
       "      <td>-1.183590</td>\n",
       "      <td>-6.533047</td>\n",
       "      <td>5.349457</td>\n",
       "      <td>...</td>\n",
       "      <td>0.991905</td>\n",
       "      <td>-9.272907</td>\n",
       "      <td>-27.416254</td>\n",
       "      <td>18.143347</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.922103</td>\n",
       "      <td>-57.206026</td>\n",
       "      <td>-101.969236</td>\n",
       "      <td>44.763210</td>\n",
       "      <td>151.999081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.0750</td>\n",
       "      <td>0.998545</td>\n",
       "      <td>-2.611259</td>\n",
       "      <td>-7.592365</td>\n",
       "      <td>4.981106</td>\n",
       "      <td>0.0725</td>\n",
       "      <td>0.998998</td>\n",
       "      <td>-2.535162</td>\n",
       "      <td>-3.866641</td>\n",
       "      <td>1.331479</td>\n",
       "      <td>...</td>\n",
       "      <td>0.989210</td>\n",
       "      <td>-13.529488</td>\n",
       "      <td>-24.457612</td>\n",
       "      <td>10.928124</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.711311</td>\n",
       "      <td>-61.925656</td>\n",
       "      <td>-80.700843</td>\n",
       "      <td>18.775187</td>\n",
       "      <td>151.999081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.0700</td>\n",
       "      <td>0.995732</td>\n",
       "      <td>-3.420472</td>\n",
       "      <td>-45.194821</td>\n",
       "      <td>41.774349</td>\n",
       "      <td>0.0725</td>\n",
       "      <td>0.997112</td>\n",
       "      <td>-2.549647</td>\n",
       "      <td>-22.412361</td>\n",
       "      <td>19.862714</td>\n",
       "      <td>...</td>\n",
       "      <td>0.988271</td>\n",
       "      <td>-21.989021</td>\n",
       "      <td>-54.212844</td>\n",
       "      <td>32.223823</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.605584</td>\n",
       "      <td>-68.467609</td>\n",
       "      <td>-86.229192</td>\n",
       "      <td>17.761583</td>\n",
       "      <td>151.999081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.0150</td>\n",
       "      <td>0.995242</td>\n",
       "      <td>-18.549834</td>\n",
       "      <td>-20.813745</td>\n",
       "      <td>2.263911</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>0.992734</td>\n",
       "      <td>-18.673684</td>\n",
       "      <td>-25.383565</td>\n",
       "      <td>6.709881</td>\n",
       "      <td>...</td>\n",
       "      <td>0.957202</td>\n",
       "      <td>-38.817900</td>\n",
       "      <td>-46.132094</td>\n",
       "      <td>7.314194</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.638600</td>\n",
       "      <td>-68.330868</td>\n",
       "      <td>-90.427522</td>\n",
       "      <td>22.096654</td>\n",
       "      <td>151.999081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PhaseSpace  ChannelCorrelation  PeakMeter        RMS  CrestFactor  \\\n",
       "0.0      0.0650            0.997516  -1.935620 -11.557975     9.622355   \n",
       "1.0      0.0775            0.994773  -1.480405 -11.120875     9.640470   \n",
       "2.0      0.0750            0.998545  -2.611259  -7.592365     4.981106   \n",
       "3.0      0.0700            0.995732  -3.420472 -45.194821    41.774349   \n",
       "4.0      0.0150            0.995242 -18.549834 -20.813745     2.263911   \n",
       "\n",
       "     PhaseSpaceLow  ChannelCorrelationLow  PeakMeterLow     RMSLow  \\\n",
       "0.0         0.0625               0.997729     -4.365814 -10.215280   \n",
       "1.0         0.0800               0.995848     -1.183590  -6.533047   \n",
       "2.0         0.0725               0.998998     -2.535162  -3.866641   \n",
       "3.0         0.0725               0.997112     -2.549647 -22.412361   \n",
       "4.0         0.0150               0.992734    -18.673684 -25.383565   \n",
       "\n",
       "     CrestFactorLow  ...  ChannelCorrelationMid  PeakMeterMid     RMSMid  \\\n",
       "0.0        5.849466  ...               0.993727    -10.731547 -14.988900   \n",
       "1.0        5.349457  ...               0.991905     -9.272907 -27.416254   \n",
       "2.0        1.331479  ...               0.989210    -13.529488 -24.457612   \n",
       "3.0       19.862714  ...               0.988271    -21.989021 -54.212844   \n",
       "4.0        6.709881  ...               0.957202    -38.817900 -46.132094   \n",
       "\n",
       "     CrestFactorMid  PhaseSpaceHigh  ChannelCorrelationHigh  PeakMeterHigh  \\\n",
       "0.0        4.257353          0.0175                0.999906     -14.451853   \n",
       "1.0       18.143347          0.0050                0.922103     -57.206026   \n",
       "2.0       10.928124          0.0050                0.711311     -61.925656   \n",
       "3.0       32.223823          0.0050                0.605584     -68.467609   \n",
       "4.0        7.314194          0.0050                0.638600     -68.330868   \n",
       "\n",
       "        RMSHigh  CrestFactorHigh         bpm  \n",
       "0.0  -81.438264        66.986410  151.999081  \n",
       "1.0 -101.969236        44.763210  151.999081  \n",
       "2.0  -80.700843        18.775187  151.999081  \n",
       "3.0  -86.229192        17.761583  151.999081  \n",
       "4.0  -90.427522        22.096654  151.999081  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_list.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a78f175",
   "metadata": {},
   "source": [
    "Sometimes, you need a quick check of the type of data and the consistency of a CSV table. You can do that via info(). It tells you,\n",
    "<ul>\n",
    "    <li>whether your data is of the Series or DataFrame class</li>\n",
    "    <li>how many rows you've got</li>\n",
    "    <li>how many columns you've got</li>\n",
    "    <li>the names of all columns</li>\n",
    "    <li>the data types contained in all columns</li>\n",
    "    <li>the memory occupied by the table</li>\n",
    "    <li>the table's data type (Series or DataFrame)</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ecd067a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Float64Index: 7679 entries, 0.0 to 7678.0\n",
      "Data columns (total 21 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   PhaseSpace              7679 non-null   float64\n",
      " 1   ChannelCorrelation      7679 non-null   float64\n",
      " 2   PeakMeter               7679 non-null   float64\n",
      " 3   RMS                     7679 non-null   float64\n",
      " 4   CrestFactor             7679 non-null   float64\n",
      " 5   PhaseSpaceLow           7679 non-null   float64\n",
      " 6   ChannelCorrelationLow   7679 non-null   float64\n",
      " 7   PeakMeterLow            7679 non-null   float64\n",
      " 8   RMSLow                  7679 non-null   float64\n",
      " 9   CrestFactorLow          7679 non-null   float64\n",
      " 10  PhaseSpaceMid           7679 non-null   float64\n",
      " 11  ChannelCorrelationMid   7679 non-null   float64\n",
      " 12  PeakMeterMid            7679 non-null   float64\n",
      " 13  RMSMid                  7679 non-null   float64\n",
      " 14  CrestFactorMid          7679 non-null   float64\n",
      " 15  PhaseSpaceHigh          7679 non-null   float64\n",
      " 16  ChannelCorrelationHigh  7679 non-null   float64\n",
      " 17  PeakMeterHigh           7679 non-null   float64\n",
      " 18  RMSHigh                 7679 non-null   float64\n",
      " 19  CrestFactorHigh         7679 non-null   float64\n",
      " 20  bpm                     7679 non-null   float64\n",
      "dtypes: float64(21)\n",
      "memory usage: 1.3 MB\n"
     ]
    }
   ],
   "source": [
    "a_list.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbe6375f",
   "metadata": {},
   "source": [
    "To display all column names, you can loop through the DataFrame or via list() command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "52dcff01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PhaseSpace\n",
      "ChannelCorrelation\n",
      "PeakMeter\n",
      "RMS\n",
      "CrestFactor\n",
      "PhaseSpaceLow\n",
      "ChannelCorrelationLow\n",
      "PeakMeterLow\n",
      "RMSLow\n",
      "CrestFactorLow\n",
      "PhaseSpaceMid\n",
      "ChannelCorrelationMid\n",
      "PeakMeterMid\n",
      "RMSMid\n",
      "CrestFactorMid\n",
      "PhaseSpaceHigh\n",
      "ChannelCorrelationHigh\n",
      "PeakMeterHigh\n",
      "RMSHigh\n",
      "CrestFactorHigh\n",
      "bpm\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['PhaseSpace',\n",
       " 'ChannelCorrelation',\n",
       " 'PeakMeter',\n",
       " 'RMS',\n",
       " 'CrestFactor',\n",
       " 'PhaseSpaceLow',\n",
       " 'ChannelCorrelationLow',\n",
       " 'PeakMeterLow',\n",
       " 'RMSLow',\n",
       " 'CrestFactorLow',\n",
       " 'PhaseSpaceMid',\n",
       " 'ChannelCorrelationMid',\n",
       " 'PeakMeterMid',\n",
       " 'RMSMid',\n",
       " 'CrestFactorMid',\n",
       " 'PhaseSpaceHigh',\n",
       " 'ChannelCorrelationHigh',\n",
       " 'PeakMeterHigh',\n",
       " 'RMSHigh',\n",
       " 'CrestFactorHigh',\n",
       " 'bpm']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in a_list.columns:\n",
    "    print(col)\n",
    "list(a_list.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2719bcb4",
   "metadata": {},
   "source": [
    "You can display column names using the keys() command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c627297c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['PhaseSpace', 'ChannelCorrelation', 'PeakMeter', 'RMS', 'CrestFactor',\n",
      "       'PhaseSpaceLow', 'ChannelCorrelationLow', 'PeakMeterLow', 'RMSLow',\n",
      "       'CrestFactorLow', 'PhaseSpaceMid', 'ChannelCorrelationMid',\n",
      "       'PeakMeterMid', 'RMSMid', 'CrestFactorMid', 'PhaseSpaceHigh',\n",
      "       'ChannelCorrelationHigh', 'PeakMeterHigh', 'RMSHigh', 'CrestFactorHigh',\n",
      "       'bpm'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(a_list.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e167896",
   "metadata": {},
   "source": [
    "You can quickly calculate statistics like mean:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c904741b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PhaseSpace                  0.069116\n",
       "ChannelCorrelation          0.815819\n",
       "PeakMeter                  -3.227079\n",
       "RMS                       -16.690830\n",
       "CrestFactor                13.463750\n",
       "PhaseSpaceLow               0.039585\n",
       "ChannelCorrelationLow       0.881746\n",
       "PeakMeterLow              -10.672712\n",
       "RMSLow                    -21.530061\n",
       "CrestFactorLow             10.857348\n",
       "PhaseSpaceMid               0.038590\n",
       "ChannelCorrelationMid       0.685153\n",
       "PeakMeterMid               -9.840876\n",
       "RMSMid                    -25.053595\n",
       "CrestFactorMid             15.212719\n",
       "PhaseSpaceHigh              0.026268\n",
       "ChannelCorrelationHigh      0.755081\n",
       "PeakMeterHigh             -15.285736\n",
       "RMSHigh                   -33.783105\n",
       "CrestFactorHigh            18.497369\n",
       "bpm                       151.999081\n",
       "dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_list.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "69e3aba6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PhaseSpace                1.525595e-02\n",
       "ChannelCorrelation        2.737943e-01\n",
       "PeakMeter                 2.389379e+00\n",
       "RMS                       9.873347e+00\n",
       "CrestFactor               9.403868e+00\n",
       "PhaseSpaceLow             2.238997e-02\n",
       "ChannelCorrelationLow     3.196356e-01\n",
       "PeakMeterLow              8.411034e+00\n",
       "RMSLow                    1.292388e+01\n",
       "CrestFactorLow            9.360614e+00\n",
       "PhaseSpaceMid             1.923020e-02\n",
       "ChannelCorrelationMid     3.109339e-01\n",
       "PeakMeterMid              5.993462e+00\n",
       "RMSMid                    1.150186e+01\n",
       "CrestFactorMid            1.029301e+01\n",
       "PhaseSpaceHigh            1.106454e-02\n",
       "ChannelCorrelationHigh    2.561669e-01\n",
       "PeakMeterHigh             1.240896e+01\n",
       "RMSHigh                   1.667729e+01\n",
       "CrestFactorHigh           1.054504e+01\n",
       "bpm                       2.842356e-14\n",
       "dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_list.std()# standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "04c34c85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PhaseSpace                2.327440e-04\n",
       "ChannelCorrelation        7.496334e-02\n",
       "PeakMeter                 5.709133e+00\n",
       "RMS                       9.748298e+01\n",
       "CrestFactor               8.843274e+01\n",
       "PhaseSpaceLow             5.013105e-04\n",
       "ChannelCorrelationLow     1.021669e-01\n",
       "PeakMeterLow              7.074549e+01\n",
       "RMSLow                    1.670267e+02\n",
       "CrestFactorLow            8.762110e+01\n",
       "PhaseSpaceMid             3.698004e-04\n",
       "ChannelCorrelationMid     9.667987e-02\n",
       "PeakMeterMid              3.592158e+01\n",
       "RMSMid                    1.322928e+02\n",
       "CrestFactorMid            1.059461e+02\n",
       "PhaseSpaceHigh            1.224239e-04\n",
       "ChannelCorrelationHigh    6.562146e-02\n",
       "PeakMeterHigh             1.539822e+02\n",
       "RMSHigh                   2.781321e+02\n",
       "CrestFactorHigh           1.111979e+02\n",
       "bpm                       8.078988e-28\n",
       "dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_list.var()# variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "118de5d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PhaseSpace                  0.012500\n",
       "ChannelCorrelation         -0.586098\n",
       "PeakMeter                 -18.896247\n",
       "RMS                       -78.917217\n",
       "CrestFactor                 0.000000\n",
       "PhaseSpaceLow               0.005000\n",
       "ChannelCorrelationLow      -0.941066\n",
       "PeakMeterLow              -45.183068\n",
       "RMSLow                    -85.416365\n",
       "CrestFactorLow              0.000000\n",
       "PhaseSpaceMid               0.005000\n",
       "ChannelCorrelationMid      -0.884904\n",
       "PeakMeterMid              -39.196883\n",
       "RMSMid                   -106.853104\n",
       "CrestFactorMid              0.000000\n",
       "PhaseSpaceHigh              0.005000\n",
       "ChannelCorrelationHigh     -0.403535\n",
       "PeakMeterHigh             -73.131897\n",
       "RMSHigh                  -128.771698\n",
       "CrestFactorHigh             0.000000\n",
       "bpm                       151.999081\n",
       "dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_list.min()# lowest value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f6cb3387",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PhaseSpace                  0.100000\n",
       "ChannelCorrelation          0.999962\n",
       "PeakMeter                   0.000000\n",
       "RMS                        -0.928246\n",
       "CrestFactor                75.180477\n",
       "PhaseSpaceLow               0.102500\n",
       "ChannelCorrelationLow       0.999999\n",
       "PeakMeterLow                0.400228\n",
       "RMSLow                     -0.288642\n",
       "CrestFactorLow             75.405465\n",
       "PhaseSpaceMid               0.110000\n",
       "ChannelCorrelationMid       0.999996\n",
       "PeakMeterMid                1.539704\n",
       "RMSMid                     -0.896232\n",
       "CrestFactorMid             98.806342\n",
       "PhaseSpaceHigh              0.067500\n",
       "ChannelCorrelationHigh      1.000000\n",
       "PeakMeterHigh              -1.909766\n",
       "RMSHigh                    -5.582387\n",
       "CrestFactorHigh           109.191002\n",
       "bpm                       151.999081\n",
       "dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_list.max()# highest value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4ed17777",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a_list.boxplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3214f8af",
   "metadata": {},
   "source": [
    "You can convert DataFrames to numpy arrays using"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30d7ca1b",
   "metadata": {},
   "source": [
    "The sklearn library, which contains a large number of standard machine learning functions, accepts either DataFrames or numpy arrays. The following example is a random forest classifier that reads one CSV file that contains a five-dimensional feature vector (f1 to f5) and the respective class for each item (line) of a dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1bf29fc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy: 1.000\n",
      "Test Accuracy: 0.906\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "drumtypes = pd.read_csv('mfccs.csv')\n",
    "y = drumtypes['class']#these are the class labels that need to be predicted by the clasifier\n",
    "X = drumtypes.drop(['class'], axis = 1)#\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=9)\n",
    "rf = RandomForestClassifier()#load an instance of the RF classifier\n",
    "rf.fit(X_train, y_train)\n",
    "print(f'Train Accuracy: {rf.score(X_train, y_train):.3f}')\n",
    "print(f'Test Accuracy: {rf.score(X_test, y_test):.3f}')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}