05_Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas\n",
    "\n",
    "The `numpy` module is excellent for numerical computations, but to handle missing data or arrays with mixed types takes more work. The `pandas` module is currently the most widely used tool for data manipulation, providing high-performance, easy-to-use data structures and advanced data analysis tools.\n",
    "\n",
    "In particular `pandas` features:\n",
    "\n",
    "* A fast and efficient \"DataFrame\" object for data manipulation with integrated indexing;\n",
    "* Tools for reading and writing data between in-memory data structures and different formats (CSV, Excel, SQL, HDF5);\n",
    "* Intelligent data alignment and integrated handling of missing data;\n",
    "* Intelligent label-based slicing, fancy indexing, and subsetting of large data sets;\n",
    "* Aggregating or transforming data with a powerful \"group-by\" engine; \n",
    "* High performance merging and joining of data sets;\n",
    "* Hierarchical axis indexing provides an intuitive way of working with high-dimensional data in a lower-dimensional data structure;\n",
    "* Time series-functionalities;\n",
    "* Highly optimized for performance, with critical code paths written in Cython or C.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Series\n",
    "\n",
    "Series are completely equivalent to 1D array but with axis labels and the possibility to store heterogeneous elements. Of paramount importance are the time-series, used to define time evolutions of a phenomenon. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xs: a    0.0\n",
      "b    0.5\n",
      "c    1.0\n",
      "d    1.5\n",
      "e    2.0\n",
      "f    2.5\n",
      "g    3.0\n",
      "h    3.5\n",
      "i    4.0\n",
      "j    4.5\n",
      "dtype: float64 \n",
      "\n",
      "xs indexes: Index(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j'], dtype='object') \n",
      "\n",
      "xs values: [0.  0.5 1.  1.5 2.  2.5 3.  3.5 4.  4.5] <class 'numpy.ndarray'> \n",
      "\n",
      "2.5 2.5 3.5 \n",
      "\n",
      "d    1.5\n",
      "f    2.5\n",
      "h    3.5\n",
      "dtype: float64 \n",
      "\n",
      "<class 'pandas.core.series.Series'> \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from string import ascii_lowercase as letters\n",
    "\n",
    "# Creating a series, accessing indexes, values and values by their index \n",
    "xs = pd.Series(np.arange(10)*0.5, index=tuple(letters[:10]))\n",
    "print (\"xs:\", xs,'\\n')\n",
    "print (\"xs indexes:\",xs.index,'\\n')\n",
    "# Values of the Series are actually a numpy array\n",
    "print (\"xs values:\", xs.values, type(xs.values),'\\n')\n",
    "print (xs['f'], xs.f, xs.h, '\\n')\n",
    "print (xs[['d', 'f', 'h']], '\\n')\n",
    "print (type(xs[['d', 'f', 'h']]), '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    0.0\n",
      "b    0.5\n",
      "c    1.0\n",
      "dtype: float64 \n",
      "\n",
      "h    3.5\n",
      "i    4.0\n",
      "j    4.5\n",
      "dtype: float64 \n",
      "\n",
      "a    0.0\n",
      "d    1.5\n",
      "g    3.0\n",
      "j    4.5\n",
      "dtype: float64 \n",
      "\n",
      "h    3.5\n",
      "i    4.0\n",
      "j    4.5\n",
      "dtype: float64 \n",
      "\n",
      "a     1.000000\n",
      "b     1.648721\n",
      "c     2.718282\n",
      "d     4.481689\n",
      "e     7.389056\n",
      "f    12.182494\n",
      "g    20.085537\n",
      "h    33.115452\n",
      "i    54.598150\n",
      "j    90.017131\n",
      "dtype: float64 \n",
      "\n",
      "2.25 1.4361406616345072 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Extracting elements and operations: same as numpy array\n",
    "print (xs[:3],'\\n')\n",
    "print (xs[7:], '\\n')\n",
    "print (xs[::3], '\\n')\n",
    "print (xs[xs>3], '\\n')\n",
    "print (np.exp(xs), '\\n')\n",
    "print (np.mean(xs), np.std(xs), '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b         1\n",
       "a       cat\n",
       "c    [2, 3]\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Series can be created from python dictionary too.\n",
    "# Not that the elements can be whatever!\n",
    "d = {'b' : 1, 'a' : 'cat', 'c' : [2,3]}\n",
    "pd.Series(d)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A key difference between Series and ndarray is that operations between Series automatically align the data based on label. Thus, you can write computations without considering whether the Series involved have the same labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a   -1.597030\n",
      "b    1.466260\n",
      "c   -1.022832\n",
      "d    1.592826\n",
      "e    1.672429\n",
      "dtype: float64\n",
      "a         NaN\n",
      "b    2.932520\n",
      "c   -2.045663\n",
      "d    3.185651\n",
      "e         NaN\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "s = pd.Series(np.random.randn(5), index=tuple(letters[:5]))\n",
    "print(s)\n",
    "s = s[1:] + s[:-1]\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time series\n",
    "\n",
    "Time series are very often used to profile the behaviour of a quantity as a function of time. Pandas has a special index for that, `DatetimeIndex`, that can be created e.g. with the function `pd.data_range()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-11-24\n",
      "2021-11-22 12:45:10.000015\n",
      "22/11/2021 12:45:00\n",
      "DatetimeIndex(['2021-11-22 12:45:00', '2021-11-23 12:45:00',\n",
      "               '2021-11-24 12:45:00', '2021-11-25 12:45:00',\n",
      "               '2021-11-26 12:45:00', '2021-11-27 12:45:00',\n",
      "               '2021-11-28 12:45:00'],\n",
      "              dtype='datetime64[ns]', freq='D')\n",
      "DatetimeIndex(['2021-11-22 12:45:00', '2021-11-22 12:45:01',\n",
      "               '2021-11-22 12:45:02', '2021-11-22 12:45:03',\n",
      "               '2021-11-22 12:45:04', '2021-11-22 12:45:05',\n",
      "               '2021-11-22 12:45:06', '2021-11-22 12:45:07',\n",
      "               '2021-11-22 12:45:08', '2021-11-22 12:45:09',\n",
      "               ...\n",
      "               '2021-11-22 13:44:50', '2021-11-22 13:44:51',\n",
      "               '2021-11-22 13:44:52', '2021-11-22 13:44:53',\n",
      "               '2021-11-22 13:44:54', '2021-11-22 13:44:55',\n",
      "               '2021-11-22 13:44:56', '2021-11-22 13:44:57',\n",
      "               '2021-11-22 13:44:58', '2021-11-22 13:44:59'],\n",
      "              dtype='datetime64[ns]', length=3600, freq='S')\n"
     ]
    }
   ],
   "source": [
    "# to define a date, the datetime module is very useful\n",
    "import datetime as dt\n",
    "date = dt.date.today()\n",
    "print(date)\n",
    "\n",
    "date = dt.datetime(2021,11,22,12,45,10,15)\n",
    "print (date)\n",
    "\n",
    "# otherwise, several notations are interpreted too\n",
    "date = 'Nov 22 2021'\n",
    "# or alternatively\n",
    "date = '22/11/2021 12:45:00'\n",
    "print (date)\n",
    "\n",
    "days = pd.date_range(date, periods=7, freq='D')\n",
    "print (days)\n",
    "\n",
    "seconds = pd.date_range(date, periods=3600, freq='s')\n",
    "print (seconds)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To learn more about the frequency strings, please see this [link](http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases)\n",
    "\n",
    "\n",
    "Timestamped data is the most basic type of time series data that associates values with points in time. For pandas objects it means using the points in time.\n",
    "\n",
    "functions like `pd.to_datetime` can be used, for instance, when reading information as string from a dataset.\n",
    "\n",
    "Timestamp is the pandas equivalent of python’s Datetime and is interchangeable with it in most cases. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1604880000000000000\n",
      "<class 'pandas._libs.tslibs.timestamps.Timestamp'>\n",
      "1289520000000000000\n",
      "2010-11-12 00:00:00\n",
      "1289520000000000000\n"
     ]
    }
   ],
   "source": [
    "tstamp = pd.Timestamp(dt.datetime(2020, 11, 9))\n",
    "\n",
    "# internally it counts the nanoseconds from January 1st 19\n",
    "#tstamp = pd.Timestamp(dt.datetime(1970, 1, 1, 0, 0, 0, 1))\n",
    "print(tstamp.value)\n",
    "\n",
    "# when creating a timestamp the format can be explicitly passed\n",
    "ts = pd.to_datetime('2010/11/12', format='%Y/%m/%d')\n",
    "print (type(ts))\n",
    "print (ts.value)\n",
    "ts = pd.to_datetime('12-11-2010 00:00', format='%d-%m-%Y %H:%M')\n",
    "print (ts)\n",
    "print (ts.value)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A standard series can be created and (range of) elements can be used as indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-11-22 12:45:00    11.518286\n",
      "2021-11-23 12:45:00    10.670238\n",
      "2021-11-24 12:45:00     9.700275\n",
      "2021-11-25 12:45:00    10.171782\n",
      "Freq: D, dtype: float64 \n",
      "\n",
      "2021-11-22 12:45:00    11.518286\n",
      "2021-11-23 12:45:00    10.670238\n",
      "Freq: D, dtype: float64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "tseries = pd.Series(np.random.normal(10, 1, len(days)), index=days)\n",
    "# Extracting elements\n",
    "print (tseries[0:4], '\\n')\n",
    "print (tseries['2021-11-22':'2021-11-23'], '\\n') # Note - includes end time\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`pd.to_datetime` can also be used to create a `DatetimeIndex`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1980-02-04', '1980-02-05', '1980-02-06', '1980-02-07'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime([1, 2, 3, 4], unit='D', origin=pd.Timestamp('1980-02-03'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrame\n",
    "\n",
    "A pandas DataFrame is like a simple tabular spreadsheet. For future reference (or for people already familiar with R), a pandas DataFrame is very similar to the R DataFrame.\n",
    "\n",
    "Each column in a DataFrame is a Series object.\n",
    "\n",
    "The element can be whatever, missing data are dealt with too (as NaN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataFrame creation\n",
    "\n",
    "A DataFrame can be created implicitly, with, e.g., a DatatimeIndex object as index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-11-22 12:45:00</th>\n",
       "      <td>0.221715</td>\n",
       "      <td>0.271638</td>\n",
       "      <td>1.073060</td>\n",
       "      <td>1.032407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 13:45:00</th>\n",
       "      <td>1.069119</td>\n",
       "      <td>0.261487</td>\n",
       "      <td>-0.584936</td>\n",
       "      <td>0.923981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 14:45:00</th>\n",
       "      <td>-0.853114</td>\n",
       "      <td>-1.128664</td>\n",
       "      <td>-0.372564</td>\n",
       "      <td>0.434111</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-22 15:45:00</th>\n",
       "      <td>0.193889</td>\n",
       "      <td>0.423385</td>\n",
       "      <td>0.237047</td>\n",
       "      <td>-0.488023</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-22 16:45:00</th>\n",
       "      <td>-0.592327</td>\n",
       "      <td>-0.324345</td>\n",
       "      <td>0.939298</td>\n",
       "      <td>-1.035246</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-22 17:45:00</th>\n",
       "      <td>-0.247706</td>\n",
       "      <td>-0.793702</td>\n",
       "      <td>-0.801049</td>\n",
       "      <td>1.284632</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-22 18:45:00</th>\n",
       "      <td>2.029969</td>\n",
       "      <td>-0.838669</td>\n",
       "      <td>0.785490</td>\n",
       "      <td>1.539324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 19:45:00</th>\n",
       "      <td>-0.867608</td>\n",
       "      <td>1.419248</td>\n",
       "      <td>0.659899</td>\n",
       "      <td>-1.300290</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-22 20:45:00</th>\n",
       "      <td>0.131926</td>\n",
       "      <td>0.487907</td>\n",
       "      <td>1.368617</td>\n",
       "      <td>0.375278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 21:45:00</th>\n",
       "      <td>0.136987</td>\n",
       "      <td>1.449805</td>\n",
       "      <td>-0.121675</td>\n",
       "      <td>-0.437476</td>\n",
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      ],
      "text/plain": [
       "                            A         B         C         D\n",
       "2021-11-22 12:45:00  0.221715  0.271638  1.073060  1.032407\n",
       "2021-11-22 13:45:00  1.069119  0.261487 -0.584936  0.923981\n",
       "2021-11-22 14:45:00 -0.853114 -1.128664 -0.372564  0.434111\n",
       "2021-11-22 15:45:00  0.193889  0.423385  0.237047 -0.488023\n",
       "2021-11-22 16:45:00 -0.592327 -0.324345  0.939298 -1.035246\n",
       "2021-11-22 17:45:00 -0.247706 -0.793702 -0.801049  1.284632\n",
       "2021-11-22 18:45:00  2.029969 -0.838669  0.785490  1.539324\n",
       "2021-11-22 19:45:00 -0.867608  1.419248  0.659899 -1.300290\n",
       "2021-11-22 20:45:00  0.131926  0.487907  1.368617  0.375278\n",
       "2021-11-22 21:45:00  0.136987  1.449805 -0.121675 -0.437476"
      ]
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     "execution_count": 10,
     "metadata": {},
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    }
   ],
   "source": [
    "entries=10\n",
    "dates=pd.date_range('11/22/2021 12:45:00',freq='h', periods=entries)\n",
    "df = pd.DataFrame(np.random.randn(entries,4), index=dates, columns=['A','B','C','D'])\n",
    "df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or by means of a dictionary:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9</td>\n",
       "      <td>test</td>\n",
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       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>train</td>\n",
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       "     A          B    C   D      E\n",
       "0  1.0 2013-01-02  1.0   7   test\n",
       "1  1.0 2013-01-02  1.0   8  train\n",
       "2  1.0 2013-01-02  1.0   9   test\n",
       "3  1.0 2013-01-02  1.0  10  train"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame(\n",
    "    { 'A' : 1.,\n",
    "      'B' : pd.Timestamp('20130102'),\n",
    "      'C' : pd.Series(1,index=range(4),dtype='float32'),\n",
    "      'D' : np.arange(7,11),\n",
    "      'E' : pd.Categorical([\"test\",\"train\",\"test\",\"train\"]),\n",
    "    }\n",
    "    )\n",
    "df2\n",
    "\n",
    "# check what happens if D and E had different lenghts "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Viewing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "        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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-11-22 12:45:00</th>\n",
       "      <td>0.221715</td>\n",
       "      <td>0.271638</td>\n",
       "      <td>1.073060</td>\n",
       "      <td>1.032407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 13:45:00</th>\n",
       "      <td>1.069119</td>\n",
       "      <td>0.261487</td>\n",
       "      <td>-0.584936</td>\n",
       "      <td>0.923981</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            A         B         C         D\n",
       "2021-11-22 12:45:00  0.221715  0.271638  1.073060  1.032407\n",
       "2021-11-22 13:45:00  1.069119  0.261487 -0.584936  0.923981"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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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></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-11-22 18:45:00</th>\n",
       "      <td>2.029969</td>\n",
       "      <td>-0.838669</td>\n",
       "      <td>0.785490</td>\n",
       "      <td>1.539324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 19:45:00</th>\n",
       "      <td>-0.867608</td>\n",
       "      <td>1.419248</td>\n",
       "      <td>0.659899</td>\n",
       "      <td>-1.300290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 20:45:00</th>\n",
       "      <td>0.131926</td>\n",
       "      <td>0.487907</td>\n",
       "      <td>1.368617</td>\n",
       "      <td>0.375278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 21:45:00</th>\n",
       "      <td>0.136987</td>\n",
       "      <td>1.449805</td>\n",
       "      <td>-0.121675</td>\n",
       "      <td>-0.437476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            A         B         C         D\n",
       "2021-11-22 18:45:00  2.029969 -0.838669  0.785490  1.539324\n",
       "2021-11-22 19:45:00 -0.867608  1.419248  0.659899 -1.300290\n",
       "2021-11-22 20:45:00  0.131926  0.487907  1.368617  0.375278\n",
       "2021-11-22 21:45:00  0.136987  1.449805 -0.121675 -0.437476"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2021-11-22 12:45:00', '2021-11-22 13:45:00',\n",
       "               '2021-11-22 14:45:00', '2021-11-22 15:45:00',\n",
       "               '2021-11-22 16:45:00', '2021-11-22 17:45:00',\n",
       "               '2021-11-22 18:45:00', '2021-11-22 19:45:00',\n",
       "               '2021-11-22 20:45:00', '2021-11-22 21:45:00'],\n",
       "              dtype='datetime64[ns]', freq='H')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['A', 'B', 'C', 'D'], dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.22171452,  0.27163796,  1.07306047,  1.03240698],\n",
       "       [ 1.06911885,  0.26148743, -0.58493631,  0.92398076],\n",
       "       [-0.85311361, -1.12866361, -0.37256433,  0.43411118],\n",
       "       [ 0.19388854,  0.42338455,  0.23704695, -0.48802307],\n",
       "       [-0.59232729, -0.32434472,  0.9392979 , -1.03524623],\n",
       "       [-0.2477059 , -0.79370198, -0.80104872,  1.28463219],\n",
       "       [ 2.02996932, -0.83866888,  0.78549031,  1.53932408],\n",
       "       [-0.86760807,  1.41924757,  0.65989925, -1.3002904 ],\n",
       "       [ 0.1319264 ,  0.48790699,  1.36861733,  0.37527783],\n",
       "       [ 0.13698733,  1.44980504, -0.12167511, -0.43747632]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "        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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.122285</td>\n",
       "      <td>0.122809</td>\n",
       "      <td>0.318319</td>\n",
       "      <td>0.232870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.890064</td>\n",
       "      <td>0.896738</td>\n",
       "      <td>0.755695</td>\n",
       "      <td>0.995459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.867608</td>\n",
       "      <td>-1.128664</td>\n",
       "      <td>-0.801049</td>\n",
       "      <td>-1.300290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.506172</td>\n",
       "      <td>-0.676363</td>\n",
       "      <td>-0.309842</td>\n",
       "      <td>-0.475386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.134457</td>\n",
       "      <td>0.266563</td>\n",
       "      <td>0.448473</td>\n",
       "      <td>0.404695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.214758</td>\n",
       "      <td>0.471776</td>\n",
       "      <td>0.900846</td>\n",
       "      <td>1.005300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.029969</td>\n",
       "      <td>1.449805</td>\n",
       "      <td>1.368617</td>\n",
       "      <td>1.539324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A          B          C          D\n",
       "count  10.000000  10.000000  10.000000  10.000000\n",
       "mean    0.122285   0.122809   0.318319   0.232870\n",
       "std     0.890064   0.896738   0.755695   0.995459\n",
       "min    -0.867608  -1.128664  -0.801049  -1.300290\n",
       "25%    -0.506172  -0.676363  -0.309842  -0.475386\n",
       "50%     0.134457   0.266563   0.448473   0.404695\n",
       "75%     0.214758   0.471776   0.900846   1.005300\n",
       "max     2.029969   1.449805   1.368617   1.539324"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2021-11-22 12:45:00</th>\n",
       "      <th>2021-11-22 13:45:00</th>\n",
       "      <th>2021-11-22 14:45:00</th>\n",
       "      <th>2021-11-22 15:45:00</th>\n",
       "      <th>2021-11-22 16:45:00</th>\n",
       "      <th>2021-11-22 17:45:00</th>\n",
       "      <th>2021-11-22 18:45:00</th>\n",
       "      <th>2021-11-22 19:45:00</th>\n",
       "      <th>2021-11-22 20:45:00</th>\n",
       "      <th>2021-11-22 21:45:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.221715</td>\n",
       "      <td>1.069119</td>\n",
       "      <td>-0.853114</td>\n",
       "      <td>0.193889</td>\n",
       "      <td>-0.592327</td>\n",
       "      <td>-0.247706</td>\n",
       "      <td>2.029969</td>\n",
       "      <td>-0.867608</td>\n",
       "      <td>0.131926</td>\n",
       "      <td>0.136987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.271638</td>\n",
       "      <td>0.261487</td>\n",
       "      <td>-1.128664</td>\n",
       "      <td>0.423385</td>\n",
       "      <td>-0.324345</td>\n",
       "      <td>-0.793702</td>\n",
       "      <td>-0.838669</td>\n",
       "      <td>1.419248</td>\n",
       "      <td>0.487907</td>\n",
       "      <td>1.449805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>1.073060</td>\n",
       "      <td>-0.584936</td>\n",
       "      <td>-0.372564</td>\n",
       "      <td>0.237047</td>\n",
       "      <td>0.939298</td>\n",
       "      <td>-0.801049</td>\n",
       "      <td>0.785490</td>\n",
       "      <td>0.659899</td>\n",
       "      <td>1.368617</td>\n",
       "      <td>-0.121675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>1.032407</td>\n",
       "      <td>0.923981</td>\n",
       "      <td>0.434111</td>\n",
       "      <td>-0.488023</td>\n",
       "      <td>-1.035246</td>\n",
       "      <td>1.284632</td>\n",
       "      <td>1.539324</td>\n",
       "      <td>-1.300290</td>\n",
       "      <td>0.375278</td>\n",
       "      <td>-0.437476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2021-11-22 12:45:00  2021-11-22 13:45:00  2021-11-22 14:45:00  \\\n",
       "A             0.221715             1.069119            -0.853114   \n",
       "B             0.271638             0.261487            -1.128664   \n",
       "C             1.073060            -0.584936            -0.372564   \n",
       "D             1.032407             0.923981             0.434111   \n",
       "\n",
       "   2021-11-22 15:45:00  2021-11-22 16:45:00  2021-11-22 17:45:00  \\\n",
       "A             0.193889            -0.592327            -0.247706   \n",
       "B             0.423385            -0.324345            -0.793702   \n",
       "C             0.237047             0.939298            -0.801049   \n",
       "D            -0.488023            -1.035246             1.284632   \n",
       "\n",
       "   2021-11-22 18:45:00  2021-11-22 19:45:00  2021-11-22 20:45:00  \\\n",
       "A             2.029969            -0.867608             0.131926   \n",
       "B            -0.838669             1.419248             0.487907   \n",
       "C             0.785490             0.659899             1.368617   \n",
       "D             1.539324            -1.300290             0.375278   \n",
       "\n",
       "   2021-11-22 21:45:00  \n",
       "A             0.136987  \n",
       "B             1.449805  \n",
       "C            -0.121675  \n",
       "D            -0.437476  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-11-22 12:45:00</th>\n",
       "      <td>0.221715</td>\n",
       "      <td>0.271638</td>\n",
       "      <td>1.073060</td>\n",
       "      <td>1.032407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 13:45:00</th>\n",
       "      <td>1.069119</td>\n",
       "      <td>0.261487</td>\n",
       "      <td>-0.584936</td>\n",
       "      <td>0.923981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 14:45:00</th>\n",
       "      <td>-0.853114</td>\n",
       "      <td>-1.128664</td>\n",
       "      <td>-0.372564</td>\n",
       "      <td>0.434111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 15:45:00</th>\n",
       "      <td>0.193889</td>\n",
       "      <td>0.423385</td>\n",
       "      <td>0.237047</td>\n",
       "      <td>-0.488023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 16:45:00</th>\n",
       "      <td>-0.592327</td>\n",
       "      <td>-0.324345</td>\n",
       "      <td>0.939298</td>\n",
       "      <td>-1.035246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 17:45:00</th>\n",
       "      <td>-0.247706</td>\n",
       "      <td>-0.793702</td>\n",
       "      <td>-0.801049</td>\n",
       "      <td>1.284632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 18:45:00</th>\n",
       "      <td>2.029969</td>\n",
       "      <td>-0.838669</td>\n",
       "      <td>0.785490</td>\n",
       "      <td>1.539324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 19:45:00</th>\n",
       "      <td>-0.867608</td>\n",
       "      <td>1.419248</td>\n",
       "      <td>0.659899</td>\n",
       "      <td>-1.300290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 20:45:00</th>\n",
       "      <td>0.131926</td>\n",
       "      <td>0.487907</td>\n",
       "      <td>1.368617</td>\n",
       "      <td>0.375278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 21:45:00</th>\n",
       "      <td>0.136987</td>\n",
       "      <td>1.449805</td>\n",
       "      <td>-0.121675</td>\n",
       "      <td>-0.437476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            A         B         C         D\n",
       "2021-11-22 12:45:00  0.221715  0.271638  1.073060  1.032407\n",
       "2021-11-22 13:45:00  1.069119  0.261487 -0.584936  0.923981\n",
       "2021-11-22 14:45:00 -0.853114 -1.128664 -0.372564  0.434111\n",
       "2021-11-22 15:45:00  0.193889  0.423385  0.237047 -0.488023\n",
       "2021-11-22 16:45:00 -0.592327 -0.324345  0.939298 -1.035246\n",
       "2021-11-22 17:45:00 -0.247706 -0.793702 -0.801049  1.284632\n",
       "2021-11-22 18:45:00  2.029969 -0.838669  0.785490  1.539324\n",
       "2021-11-22 19:45:00 -0.867608  1.419248  0.659899 -1.300290\n",
       "2021-11-22 20:45:00  0.131926  0.487907  1.368617  0.375278\n",
       "2021-11-22 21:45:00  0.136987  1.449805 -0.121675 -0.437476"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(axis=0,ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-11-22 17:45:00</th>\n",
       "      <td>-0.247706</td>\n",
       "      <td>-0.793702</td>\n",
       "      <td>-0.801049</td>\n",
       "      <td>1.284632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 13:45:00</th>\n",
       "      <td>1.069119</td>\n",
       "      <td>0.261487</td>\n",
       "      <td>-0.584936</td>\n",
       "      <td>0.923981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 14:45:00</th>\n",
       "      <td>-0.853114</td>\n",
       "      <td>-1.128664</td>\n",
       "      <td>-0.372564</td>\n",
       "      <td>0.434111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 21:45:00</th>\n",
       "      <td>0.136987</td>\n",
       "      <td>1.449805</td>\n",
       "      <td>-0.121675</td>\n",
       "      <td>-0.437476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 15:45:00</th>\n",
       "      <td>0.193889</td>\n",
       "      <td>0.423385</td>\n",
       "      <td>0.237047</td>\n",
       "      <td>-0.488023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 19:45:00</th>\n",
       "      <td>-0.867608</td>\n",
       "      <td>1.419248</td>\n",
       "      <td>0.659899</td>\n",
       "      <td>-1.300290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 18:45:00</th>\n",
       "      <td>2.029969</td>\n",
       "      <td>-0.838669</td>\n",
       "      <td>0.785490</td>\n",
       "      <td>1.539324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 16:45:00</th>\n",
       "      <td>-0.592327</td>\n",
       "      <td>-0.324345</td>\n",
       "      <td>0.939298</td>\n",
       "      <td>-1.035246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 12:45:00</th>\n",
       "      <td>0.221715</td>\n",
       "      <td>0.271638</td>\n",
       "      <td>1.073060</td>\n",
       "      <td>1.032407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 20:45:00</th>\n",
       "      <td>0.131926</td>\n",
       "      <td>0.487907</td>\n",
       "      <td>1.368617</td>\n",
       "      <td>0.375278</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            A         B         C         D\n",
       "2021-11-22 17:45:00 -0.247706 -0.793702 -0.801049  1.284632\n",
       "2021-11-22 13:45:00  1.069119  0.261487 -0.584936  0.923981\n",
       "2021-11-22 14:45:00 -0.853114 -1.128664 -0.372564  0.434111\n",
       "2021-11-22 21:45:00  0.136987  1.449805 -0.121675 -0.437476\n",
       "2021-11-22 15:45:00  0.193889  0.423385  0.237047 -0.488023\n",
       "2021-11-22 19:45:00 -0.867608  1.419248  0.659899 -1.300290\n",
       "2021-11-22 18:45:00  2.029969 -0.838669  0.785490  1.539324\n",
       "2021-11-22 16:45:00 -0.592327 -0.324345  0.939298 -1.035246\n",
       "2021-11-22 12:45:00  0.221715  0.271638  1.073060  1.032407\n",
       "2021-11-22 20:45:00  0.131926  0.487907  1.368617  0.375278"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=\"C\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selection\n",
    "\n",
    "### Getting slices\n",
    "\n",
    "The following show how to get part of the DataFrame (i.e. not just the elements)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-11-22 12:45:00    0.221715\n",
      "2021-11-22 13:45:00    1.069119\n",
      "2021-11-22 14:45:00   -0.853114\n",
      "2021-11-22 15:45:00    0.193889\n",
      "2021-11-22 16:45:00   -0.592327\n",
      "2021-11-22 17:45:00   -0.247706\n",
      "2021-11-22 18:45:00    2.029969\n",
      "2021-11-22 19:45:00   -0.867608\n",
      "2021-11-22 20:45:00    0.131926\n",
      "2021-11-22 21:45:00    0.136987\n",
      "Freq: H, Name: A, dtype: float64 \n",
      "\n",
      "2021-11-22 12:45:00    0.221715\n",
      "2021-11-22 13:45:00    1.069119\n",
      "2021-11-22 14:45:00   -0.853114\n",
      "2021-11-22 15:45:00    0.193889\n",
      "2021-11-22 16:45:00   -0.592327\n",
      "2021-11-22 17:45:00   -0.247706\n",
      "2021-11-22 18:45:00    2.029969\n",
      "2021-11-22 19:45:00   -0.867608\n",
      "2021-11-22 20:45:00    0.131926\n",
      "2021-11-22 21:45:00    0.136987\n",
      "Freq: H, Name: A, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "## standard and safe\n",
    "print (df['A'],'\\n')\n",
    "\n",
    "## equivalent but dangerous (imagine blank spaces in the name of the column..)\n",
    "print (df.A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            A         B         C         D\n",
      "2021-11-22 12:45:00  0.221715  0.271638  1.073060  1.032407\n",
      "2021-11-22 13:45:00  1.069119  0.261487 -0.584936  0.923981\n",
      "2021-11-22 14:45:00 -0.853114 -1.128664 -0.372564  0.434111\n",
      "                            A         B         C         D\n",
      "2021-11-22 12:45:00  0.221715  0.271638  1.073060  1.032407\n",
      "2021-11-22 13:45:00  1.069119  0.261487 -0.584936  0.923981\n",
      "2021-11-22 14:45:00 -0.853114 -1.128664 -0.372564  0.434111\n"
     ]
    }
   ],
   "source": [
    "# selecting rows by counting\n",
    "print (df[0:3])\n",
    "\n",
    "# or by index\n",
    "print (df[\"2021-11-22 12:45:00\":\"2021-11-22 14:45:00\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selection by label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.221715\n",
       "B    0.271638\n",
       "C    1.073060\n",
       "D    1.032407\n",
       "Name: 2021-11-22 12:45:00, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting a cross section (part of the DataFrame) using a label\n",
    "df.loc[dates[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <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>2021-11-22 12:45:00</th>\n",
       "      <td>0.221715</td>\n",
       "      <td>0.271638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 13:45:00</th>\n",
       "      <td>1.069119</td>\n",
       "      <td>0.261487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 14:45:00</th>\n",
       "      <td>-0.853114</td>\n",
       "      <td>-1.128664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 15:45:00</th>\n",
       "      <td>0.193889</td>\n",
       "      <td>0.423385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 16:45:00</th>\n",
       "      <td>-0.592327</td>\n",
       "      <td>-0.324345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 17:45:00</th>\n",
       "      <td>-0.247706</td>\n",
       "      <td>-0.793702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 18:45:00</th>\n",
       "      <td>2.029969</td>\n",
       "      <td>-0.838669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 19:45:00</th>\n",
       "      <td>-0.867608</td>\n",
       "      <td>1.419248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 20:45:00</th>\n",
       "      <td>0.131926</td>\n",
       "      <td>0.487907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 21:45:00</th>\n",
       "      <td>0.136987</td>\n",
       "      <td>1.449805</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            A         B\n",
       "2021-11-22 12:45:00  0.221715  0.271638\n",
       "2021-11-22 13:45:00  1.069119  0.261487\n",
       "2021-11-22 14:45:00 -0.853114 -1.128664\n",
       "2021-11-22 15:45:00  0.193889  0.423385\n",
       "2021-11-22 16:45:00 -0.592327 -0.324345\n",
       "2021-11-22 17:45:00 -0.247706 -0.793702\n",
       "2021-11-22 18:45:00  2.029969 -0.838669\n",
       "2021-11-22 19:45:00 -0.867608  1.419248\n",
       "2021-11-22 20:45:00  0.131926  0.487907\n",
       "2021-11-22 21:45:00  0.136987  1.449805"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecting on a multi-axis by label:\n",
    "df.loc[:,['A','B']]\n",
    "#a=df.loc[:,['A','B']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-11-22 16:45:00</th>\n",
       "      <td>-0.592327</td>\n",
       "      <td>-0.324345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 17:45:00</th>\n",
       "      <td>-0.247706</td>\n",
       "      <td>-0.793702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 18:45:00</th>\n",
       "      <td>2.029969</td>\n",
       "      <td>-0.838669</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            A         B\n",
       "2021-11-22 16:45:00 -0.592327 -0.324345\n",
       "2021-11-22 17:45:00 -0.247706 -0.793702\n",
       "2021-11-22 18:45:00  2.029969 -0.838669"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# showing label slicing, both endpoints are included:\n",
    "df.loc['2021-11-22 16:45:00':'2021-11-22 18:45:00',['A','B']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0691188452349765\n",
      "1.0691188452349765\n"
     ]
    }
   ],
   "source": [
    "# getting an individual element\n",
    "print (df.loc[dates[1],'A'])\n",
    "\n",
    "# equivalently\n",
    "print (df.at[dates[1],'A'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting by position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A    0.193889\n",
      "B    0.423385\n",
      "C    0.237047\n",
      "D   -0.488023\n",
      "Name: 2021-11-22 15:45:00, dtype: float64 \n",
      "\n",
      "                            A         B\n",
      "2021-11-22 15:45:00  0.193889  0.423385\n",
      "2021-11-22 16:45:00 -0.592327 -0.324345\n"
     ]
    }
   ],
   "source": [
    "# select via the position of the passed integers:\n",
    "print (df.iloc[3],'\\n')\n",
    "\n",
    "# notation similar to numpy/python\n",
    "print (df.iloc[3:5,0:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>A</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-11-22 13:45:00</th>\n",
       "      <td>1.069119</td>\n",
       "      <td>-0.584936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 14:45:00</th>\n",
       "      <td>-0.853114</td>\n",
       "      <td>-0.372564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 16:45:00</th>\n",
       "      <td>-0.592327</td>\n",
       "      <td>0.939298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            A         C\n",
       "2021-11-22 13:45:00  1.069119 -0.584936\n",
       "2021-11-22 14:45:00 -0.853114 -0.372564\n",
       "2021-11-22 16:45:00 -0.592327  0.939298"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecting raws 1,2 and 4 for columns 0 and 2\n",
    "df.iloc[[1,2,4],[0,2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            A         B         C         D\n",
      "2021-11-22 13:45:00  1.069119  0.261487 -0.584936  0.923981\n",
      "2021-11-22 14:45:00 -0.853114 -1.128664 -0.372564  0.434111 \n",
      "\n",
      "                            B         C\n",
      "2021-11-22 12:45:00  0.271638  1.073060\n",
      "2021-11-22 13:45:00  0.261487 -0.584936\n",
      "2021-11-22 14:45:00 -1.128664 -0.372564\n",
      "2021-11-22 15:45:00  0.423385  0.237047\n",
      "2021-11-22 16:45:00 -0.324345  0.939298\n",
      "2021-11-22 17:45:00 -0.793702 -0.801049\n",
      "2021-11-22 18:45:00 -0.838669  0.785490\n",
      "2021-11-22 19:45:00  1.419248  0.659899\n",
      "2021-11-22 20:45:00  0.487907  1.368617\n",
      "2021-11-22 21:45:00  1.449805 -0.121675\n"
     ]
    }
   ],
   "source": [
    "# slicing rows explicitly\n",
    "print (df.iloc[1:3,:],'\\n')\n",
    "\n",
    "# slicing columns explicitly\n",
    "print (df.iloc[:,1:3])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2614874269927052"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecting an individual element by position\n",
    "df.iloc[1,1]\n",
    "df.iat[1,1]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boolean index\n",
    "\n",
    "Very powerful way of filtering out data with certain features. Notation is very similar to numpy arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
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       "      <th>D</th>\n",
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       "      <th>2021-11-22 12:45:00</th>\n",
       "      <td>0.221715</td>\n",
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       "      <td>1.073060</td>\n",
       "      <td>1.032407</td>\n",
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       "      <th>2021-11-22 13:45:00</th>\n",
       "      <td>1.069119</td>\n",
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       "      <th>2021-11-22 15:45:00</th>\n",
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       "      <td>-0.488023</td>\n",
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       "      <th>2021-11-22 19:45:00</th>\n",
       "      <td>-0.867608</td>\n",
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       "      <td>0.659899</td>\n",
       "      <td>-1.300290</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-22 20:45:00</th>\n",
       "      <td>0.131926</td>\n",
       "      <td>0.487907</td>\n",
       "      <td>1.368617</td>\n",
       "      <td>0.375278</td>\n",
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       "      <th>2021-11-22 21:45:00</th>\n",
       "      <td>0.136987</td>\n",
       "      <td>1.449805</td>\n",
       "      <td>-0.121675</td>\n",
       "      <td>-0.437476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            A         B         C         D\n",
       "2021-11-22 12:45:00  0.221715  0.271638  1.073060  1.032407\n",
       "2021-11-22 13:45:00  1.069119  0.261487 -0.584936  0.923981\n",
       "2021-11-22 15:45:00  0.193889  0.423385  0.237047 -0.488023\n",
       "2021-11-22 19:45:00 -0.867608  1.419248  0.659899 -1.300290\n",
       "2021-11-22 20:45:00  0.131926  0.487907  1.368617  0.375278\n",
       "2021-11-22 21:45:00  0.136987  1.449805 -0.121675 -0.437476"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter by a boolean condition on the values of a single column\n",
    "df[df['B'] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "      <th>D</th>\n",
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       "      <th>2021-11-22 12:45:00</th>\n",
       "      <td>0.221715</td>\n",
       "      <td>0.271638</td>\n",
       "      <td>1.073060</td>\n",
       "      <td>1.032407</td>\n",
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       "      <th>2021-11-22 13:45:00</th>\n",
       "      <td>1.069119</td>\n",
       "      <td>0.261487</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.434111</td>\n",
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       "      <th>2021-11-22 16:45:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.939298</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-22 17:45:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.284632</td>\n",
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       "      <th>2021-11-22 18:45:00</th>\n",
       "      <td>2.029969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.785490</td>\n",
       "      <td>1.539324</td>\n",
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       "      <th>2021-11-22 19:45:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.419248</td>\n",
       "      <td>0.659899</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2021-11-22 20:45:00</th>\n",
       "      <td>0.131926</td>\n",
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       "      <td>1.368617</td>\n",
       "      <td>0.375278</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-22 21:45:00</th>\n",
       "      <td>0.136987</td>\n",
       "      <td>1.449805</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            A         B         C         D\n",
       "2021-11-22 12:45:00  0.221715  0.271638  1.073060  1.032407\n",
       "2021-11-22 13:45:00  1.069119  0.261487       NaN  0.923981\n",
       "2021-11-22 14:45:00       NaN       NaN       NaN  0.434111\n",
       "2021-11-22 15:45:00  0.193889  0.423385  0.237047       NaN\n",
       "2021-11-22 16:45:00       NaN       NaN  0.939298       NaN\n",
       "2021-11-22 17:45:00       NaN       NaN       NaN  1.284632\n",
       "2021-11-22 18:45:00  2.029969       NaN  0.785490  1.539324\n",
       "2021-11-22 19:45:00       NaN  1.419248  0.659899       NaN\n",
       "2021-11-22 20:45:00  0.131926  0.487907  1.368617  0.375278\n",
       "2021-11-22 21:45:00  0.136987  1.449805       NaN       NaN"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting on the basis of boolean conditions applied to the whole DataFrame\n",
    "df[df>0]\n",
    "\n",
    "# a DataFrame with the same shape is returned, with NaN's where condition is not met"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting\n",
    "\n",
    "Combination of selection and setting of values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setting values by label (same as by position)\n",
    "df.at[dates[0],'A'] = 0\n",
    "\n",
    "# setting and assigning a numpy array\n",
    "df.loc[:,'D'] = np.array([5] * len(df))\n",
    "\n",
    "# defining a brend new column\n",
    "df['E'] = np.arange(len(df))*0.5\n",
    "\n",
    "# defining a brend new column by means of a pd.Series: indexes must be the same!\n",
    "df['E prime'] = pd.Series(np.arange(len(df))*2, index=df.index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       "      <th>cosine</th>\n",
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       "      <th>2021-11-22 12:45:00</th>\n",
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       "      <th>2021-11-22 16:45:00</th>\n",
       "      <td>-0.592327</td>\n",
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       "      <th>2021-11-22 17:45:00</th>\n",
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       "      <td>3.5</td>\n",
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       "      <td>0.998135</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-22 20:45:00</th>\n",
       "      <td>0.131926</td>\n",
       "      <td>0.487907</td>\n",
       "      <td>1.368617</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
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      "text/plain": [
       "                            A         B         C  D    E  E prime    cosine\n",
       "2021-11-22 12:45:00  0.000000  0.271638  1.073060  5  0.0        0  1.000000\n",
       "2021-11-22 13:45:00  1.069119  0.261487 -0.584936  5  0.5        2  0.999962\n",
       "2021-11-22 14:45:00 -0.853114 -1.128664 -0.372564  5  1.0        4  0.999848\n",
       "2021-11-22 15:45:00  0.193889  0.423385  0.237047  5  1.5        6  0.999657\n",
       "2021-11-22 16:45:00 -0.592327 -0.324345  0.939298  5  2.0        8  0.999391\n",
       "2021-11-22 17:45:00 -0.247706 -0.793702 -0.801049  5  2.5       10  0.999048\n",
       "2021-11-22 18:45:00  2.029969 -0.838669  0.785490  5  3.0       12  0.998630\n",
       "2021-11-22 19:45:00 -0.867608  1.419248  0.659899  5  3.5       14  0.998135\n",
       "2021-11-22 20:45:00  0.131926  0.487907  1.368617  5  4.0       16  0.997564\n",
       "2021-11-22 21:45:00  0.136987  1.449805 -0.121675  5  4.5       18  0.996917"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def dcos(theta):\n",
    "    theta = theta*(np.pi/180)\n",
    "    return np.cos(theta)\n",
    " \n",
    "df['cosine'] = pd.Series(df[\"E\"].apply(dcos), index=df.index)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.939298</td>\n",
       "      <td>-5</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-8</td>\n",
       "      <td>-0.999391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 17:45:00</th>\n",
       "      <td>-0.247706</td>\n",
       "      <td>-0.793702</td>\n",
       "      <td>-0.801049</td>\n",
       "      <td>-5</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>-10</td>\n",
       "      <td>-0.999048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 18:45:00</th>\n",
       "      <td>-2.029969</td>\n",
       "      <td>-0.838669</td>\n",
       "      <td>-0.785490</td>\n",
       "      <td>-5</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-12</td>\n",
       "      <td>-0.998630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 19:45:00</th>\n",
       "      <td>-0.867608</td>\n",
       "      <td>-1.419248</td>\n",
       "      <td>-0.659899</td>\n",
       "      <td>-5</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>-14</td>\n",
       "      <td>-0.998135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 20:45:00</th>\n",
       "      <td>-0.131926</td>\n",
       "      <td>-0.487907</td>\n",
       "      <td>-1.368617</td>\n",
       "      <td>-5</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-16</td>\n",
       "      <td>-0.997564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 21:45:00</th>\n",
       "      <td>-0.136987</td>\n",
       "      <td>-1.449805</td>\n",
       "      <td>-0.121675</td>\n",
       "      <td>-5</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>-18</td>\n",
       "      <td>-0.996917</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            A         B         C  D    E  E prime    cosine\n",
       "2021-11-22 12:45:00  0.000000 -0.271638 -1.073060 -5  0.0        0 -1.000000\n",
       "2021-11-22 13:45:00 -1.069119 -0.261487 -0.584936 -5 -0.5       -2 -0.999962\n",
       "2021-11-22 14:45:00 -0.853114 -1.128664 -0.372564 -5 -1.0       -4 -0.999848\n",
       "2021-11-22 15:45:00 -0.193889 -0.423385 -0.237047 -5 -1.5       -6 -0.999657\n",
       "2021-11-22 16:45:00 -0.592327 -0.324345 -0.939298 -5 -2.0       -8 -0.999391\n",
       "2021-11-22 17:45:00 -0.247706 -0.793702 -0.801049 -5 -2.5      -10 -0.999048\n",
       "2021-11-22 18:45:00 -2.029969 -0.838669 -0.785490 -5 -3.0      -12 -0.998630\n",
       "2021-11-22 19:45:00 -0.867608 -1.419248 -0.659899 -5 -3.5      -14 -0.998135\n",
       "2021-11-22 20:45:00 -0.131926 -0.487907 -1.368617 -5 -4.0      -16 -0.997564\n",
       "2021-11-22 21:45:00 -0.136987 -1.449805 -0.121675 -5 -4.5      -18 -0.996917"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# another example of global setting\n",
    "df2=df.copy()\n",
    "\n",
    "df2[df2>0] = -df2\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>E prime</th>\n",
       "      <th>cosine</th>\n",
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       "      <th>2021-11-22 12:45:00</th>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-22 13:45:00</th>\n",
       "      <td>1.069119</td>\n",
       "      <td>0.261487</td>\n",
       "      <td>-0.584936</td>\n",
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       "      <td>0.5</td>\n",
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       "      <th>2021-11-22 14:45:00</th>\n",
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       "    <tr>\n",
       "      <th>2021-11-22 15:45:00</th>\n",
       "      <td>0.193889</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-22 16:45:00</th>\n",
       "      <td>-0.592327</td>\n",
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       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-22 17:45:00</th>\n",
       "      <td>-0.247706</td>\n",
       "      <td>-0.793702</td>\n",
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       "      <td>5</td>\n",
       "      <td>2.5</td>\n",
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       "      <th>2021-11-22 18:45:00</th>\n",
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       "      <td>3.0</td>\n",
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       "      <th>2021-11-22 19:45:00</th>\n",
       "      <td>-0.867608</td>\n",
       "      <td>1.419248</td>\n",
       "      <td>0.659899</td>\n",
       "      <td>5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>14</td>\n",
       "      <td>0.998135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 20:45:00</th>\n",
       "      <td>0.131926</td>\n",
       "      <td>0.487907</td>\n",
       "      <td>1.368617</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-22 21:45:00</th>\n",
       "      <td>0.136987</td>\n",
       "      <td>1.449805</td>\n",
       "      <td>-0.121675</td>\n",
       "      <td>5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>18</td>\n",
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       "    </tr>\n",
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      ],
      "text/plain": [
       "                            A         B         C  D    E  E prime    cosine\n",
       "2021-11-22 12:45:00  0.000000  0.271638  1.073060  5  0.0        0  1.000000\n",
       "2021-11-22 13:45:00  1.069119  0.261487 -0.584936  5  0.5        2  0.999962\n",
       "2021-11-22 14:45:00 -0.853114 -1.128664 -0.372564  5  1.0        4  0.999848\n",
       "2021-11-22 15:45:00  0.193889  0.423385  0.237047  5  1.5        6  0.999657\n",
       "2021-11-22 16:45:00 -0.592327 -0.324345  0.939298  5  2.0        8  0.999391\n",
       "2021-11-22 17:45:00 -0.247706 -0.793702 -0.801049  5  2.5       10  0.999048\n",
       "2021-11-22 18:45:00  2.029969 -0.838669  0.785490  5  3.0       12  0.998630\n",
       "2021-11-22 19:45:00 -0.867608  1.419248  0.659899  5  3.5       14  0.998135\n",
       "2021-11-22 20:45:00  0.131926  0.487907  1.368617  5  4.0       16  0.997564\n",
       "2021-11-22 21:45:00  0.136987  1.449805 -0.121675  5  4.5       18  0.996917"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping\n",
    "\n",
    "N.B.: dropping doesn't act permanently on the DataFrame, i.e. to get that do :\n",
    "```python\n",
    "df = df.drop(....)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
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       "      <th>cosine</th>\n",
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       "      <th>2021-11-22 12:45:00</th>\n",
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       "      <th>2021-11-22 14:45:00</th>\n",
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       "      <th>2021-11-22 16:45:00</th>\n",
       "      <td>-0.592327</td>\n",
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       "      <th>2021-11-22 17:45:00</th>\n",
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       "      <td>5</td>\n",
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       "      <td>0.999048</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-22 18:45:00</th>\n",
       "      <td>2.029969</td>\n",
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       "      <th>2021-11-22 19:45:00</th>\n",
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       "    <tr>\n",
       "      <th>2021-11-22 20:45:00</th>\n",
       "      <td>0.131926</td>\n",
       "      <td>0.487907</td>\n",
       "      <td>1.368617</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.997564</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-22 21:45:00</th>\n",
       "      <td>0.136987</td>\n",
       "      <td>1.449805</td>\n",
       "      <td>-0.121675</td>\n",
       "      <td>5</td>\n",
       "      <td>4.5</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                            A         B         C  D    E    cosine\n",
       "2021-11-22 12:45:00  0.000000  0.271638  1.073060  5  0.0  1.000000\n",
       "2021-11-22 13:45:00  1.069119  0.261487 -0.584936  5  0.5  0.999962\n",
       "2021-11-22 14:45:00 -0.853114 -1.128664 -0.372564  5  1.0  0.999848\n",
       "2021-11-22 15:45:00  0.193889  0.423385  0.237047  5  1.5  0.999657\n",
       "2021-11-22 16:45:00 -0.592327 -0.324345  0.939298  5  2.0  0.999391\n",
       "2021-11-22 17:45:00 -0.247706 -0.793702 -0.801049  5  2.5  0.999048\n",
       "2021-11-22 18:45:00  2.029969 -0.838669  0.785490  5  3.0  0.998630\n",
       "2021-11-22 19:45:00 -0.867608  1.419248  0.659899  5  3.5  0.998135\n",
       "2021-11-22 20:45:00  0.131926  0.487907  1.368617  5  4.0  0.997564\n",
       "2021-11-22 21:45:00  0.136987  1.449805 -0.121675  5  4.5  0.996917"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping by column\n",
    "df.drop(['E prime'], axis=1)\n",
    "\n",
    "#which is equivalent to\n",
    "new_df=df.drop(columns=['E prime'])\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                            A         B         C  D    E  E prime    cosine\n",
       "2021-11-22 12:45:00  0.000000  0.271638  1.073060  5  0.0        0  1.000000\n",
       "2021-11-22 17:45:00 -0.247706 -0.793702 -0.801049  5  2.5       10  0.999048\n",
       "2021-11-22 18:45:00  2.029969 -0.838669  0.785490  5  3.0       12  0.998630\n",
       "2021-11-22 19:45:00 -0.867608  1.419248  0.659899  5  3.5       14  0.998135\n",
       "2021-11-22 20:45:00  0.131926  0.487907  1.368617  5  4.0       16  0.997564\n",
       "2021-11-22 21:45:00  0.136987  1.449805 -0.121675  5  4.5       18  0.996917"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping by raws\n",
    "# safe and always working\n",
    "df.drop(df.index[[1,2,3,4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>E prime</th>\n",
       "      <th>cosine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2021-11-22 12:45:00</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.271638</td>\n",
       "      <td>1.073060</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
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       "      <th>2021-11-22 13:45:00</th>\n",
       "      <td>1.069119</td>\n",
       "      <td>0.261487</td>\n",
       "      <td>-0.584936</td>\n",
       "      <td>5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
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       "      <th>2021-11-22 14:45:00</th>\n",
       "      <td>-0.853114</td>\n",
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       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-22 15:45:00</th>\n",
       "      <td>0.193889</td>\n",
       "      <td>0.423385</td>\n",
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       "      <td>5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-22 16:45:00</th>\n",
       "      <td>-0.592327</td>\n",
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       "      <td>0.939298</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.999391</td>\n",
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       "      <th>2021-11-22 17:45:00</th>\n",
       "      <td>-0.247706</td>\n",
       "      <td>-0.793702</td>\n",
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       "      <td>5</td>\n",
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       "      <th>2021-11-22 18:45:00</th>\n",
       "      <td>2.029969</td>\n",
       "      <td>-0.838669</td>\n",
       "      <td>0.785490</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12</td>\n",
       "      <td>0.998630</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-22 19:45:00</th>\n",
       "      <td>-0.867608</td>\n",
       "      <td>1.419248</td>\n",
       "      <td>0.659899</td>\n",
       "      <td>5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>14</td>\n",
       "      <td>0.998135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 20:45:00</th>\n",
       "      <td>0.131926</td>\n",
       "      <td>0.487907</td>\n",
       "      <td>1.368617</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16</td>\n",
       "      <td>0.997564</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-22 21:45:00</th>\n",
       "      <td>0.136987</td>\n",
       "      <td>1.449805</td>\n",
       "      <td>-0.121675</td>\n",
       "      <td>5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>18</td>\n",
       "      <td>0.996917</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            A         B         C  D    E  E prime    cosine\n",
       "2021-11-22 12:45:00  0.000000  0.271638  1.073060  5  0.0        0  1.000000\n",
       "2021-11-22 13:45:00  1.069119  0.261487 -0.584936  5  0.5        2  0.999962\n",
       "2021-11-22 14:45:00 -0.853114 -1.128664 -0.372564  5  1.0        4  0.999848\n",
       "2021-11-22 15:45:00  0.193889  0.423385  0.237047  5  1.5        6  0.999657\n",
       "2021-11-22 16:45:00 -0.592327 -0.324345  0.939298  5  2.0        8  0.999391\n",
       "2021-11-22 17:45:00 -0.247706 -0.793702 -0.801049  5  2.5       10  0.999048\n",
       "2021-11-22 18:45:00  2.029969 -0.838669  0.785490  5  3.0       12  0.998630\n",
       "2021-11-22 19:45:00 -0.867608  1.419248  0.659899  5  3.5       14  0.998135\n",
       "2021-11-22 20:45:00  0.131926  0.487907  1.368617  5  4.0       16  0.997564\n",
       "2021-11-22 21:45:00  0.136987  1.449805 -0.121675  5  4.5       18  0.996917"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>E prime</th>\n",
       "      <th>cosine</th>\n",
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       "      <th>2021-11-22 12:45:00</th>\n",
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       "      <th>2021-11-22 13:45:00</th>\n",
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       "    <tr>\n",
       "      <th>2021-11-22 16:45:00</th>\n",
       "      <td>-0.592327</td>\n",
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       "      <td>0.939298</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.999391</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-22 17:45:00</th>\n",
       "      <td>-0.247706</td>\n",
       "      <td>-0.793702</td>\n",
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       "      <td>5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.999048</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-22 18:45:00</th>\n",
       "      <td>2.029969</td>\n",
       "      <td>-0.838669</td>\n",
       "      <td>0.785490</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>0.998630</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-22 19:45:00</th>\n",
       "      <td>-0.867608</td>\n",
       "      <td>1.419248</td>\n",
       "      <td>0.659899</td>\n",
       "      <td>5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>14</td>\n",
       "      <td>0.998135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 21:45:00</th>\n",
       "      <td>0.136987</td>\n",
       "      <td>1.449805</td>\n",
       "      <td>-0.121675</td>\n",
       "      <td>5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>18</td>\n",
       "      <td>0.996917</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            A         B         C  D    E  E prime    cosine\n",
       "2021-11-22 12:45:00  0.000000  0.271638  1.073060  5  0.0        0  1.000000\n",
       "2021-11-22 13:45:00  1.069119  0.261487 -0.584936  5  0.5        2  0.999962\n",
       "2021-11-22 14:45:00 -0.853114 -1.128664 -0.372564  5  1.0        4  0.999848\n",
       "2021-11-22 15:45:00  0.193889  0.423385  0.237047  5  1.5        6  0.999657\n",
       "2021-11-22 16:45:00 -0.592327 -0.324345  0.939298  5  2.0        8  0.999391\n",
       "2021-11-22 17:45:00 -0.247706 -0.793702 -0.801049  5  2.5       10  0.999048\n",
       "2021-11-22 18:45:00  2.029969 -0.838669  0.785490  5  3.0       12  0.998630\n",
       "2021-11-22 19:45:00 -0.867608  1.419248  0.659899  5  3.5       14  0.998135\n",
       "2021-11-22 21:45:00  0.136987  1.449805 -0.121675  5  4.5       18  0.996917"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# something like df.drop('index_name') \n",
    "# would work but the type of index must be specificed, \n",
    "# in particular with DatetimeIndex\n",
    "df.drop(pd.to_datetime(\"2021-11-22 20:45:00\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing data\n",
    "\n",
    "pandas primarily uses the value np.nan to represent missing data. It is by default not included in computations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2021-11-22 12:45:00</th>\n",
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       "    <tr>\n",
       "      <th>2021-11-22 15:45:00</th>\n",
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       "      <td>0.999657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 16:45:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.939298</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.999391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 17:45:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.999048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 18:45:00</th>\n",
       "      <td>2.029969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.785490</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.998630</td>\n",
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       "    <tr>\n",
       "      <th>2021-11-22 19:45:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.419248</td>\n",
       "      <td>0.659899</td>\n",
       "      <td>5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.998135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 20:45:00</th>\n",
       "      <td>0.131926</td>\n",
       "      <td>0.487907</td>\n",
       "      <td>1.368617</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.997564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 21:45:00</th>\n",
       "      <td>0.136987</td>\n",
       "      <td>1.449805</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.996917</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            A         B         C  D    E  E prime    cosine\n",
       "2021-11-22 12:45:00       NaN  0.271638  1.073060  5  NaN      NaN  1.000000\n",
       "2021-11-22 13:45:00  1.069119  0.261487       NaN  5  0.5      2.0  0.999962\n",
       "2021-11-22 14:45:00       NaN       NaN       NaN  5  1.0      4.0  0.999848\n",
       "2021-11-22 15:45:00  0.193889  0.423385  0.237047  5  1.5      6.0  0.999657\n",
       "2021-11-22 16:45:00       NaN       NaN  0.939298  5  2.0      8.0  0.999391\n",
       "2021-11-22 17:45:00       NaN       NaN       NaN  5  2.5     10.0  0.999048\n",
       "2021-11-22 18:45:00  2.029969       NaN  0.785490  5  3.0     12.0  0.998630\n",
       "2021-11-22 19:45:00       NaN  1.419248  0.659899  5  3.5     14.0  0.998135\n",
       "2021-11-22 20:45:00  0.131926  0.487907  1.368617  5  4.0     16.0  0.997564\n",
       "2021-11-22 21:45:00  0.136987  1.449805       NaN  5  4.5     18.0  0.996917"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wNan = df[df>0]\n",
    "df_wNan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>E prime</th>\n",
       "      <th>cosine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-11-22 15:45:00</th>\n",
       "      <td>0.193889</td>\n",
       "      <td>0.423385</td>\n",
       "      <td>0.237047</td>\n",
       "      <td>5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.999657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 20:45:00</th>\n",
       "      <td>0.131926</td>\n",
       "      <td>0.487907</td>\n",
       "      <td>1.368617</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.997564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            A         B         C  D    E  E prime    cosine\n",
       "2021-11-22 15:45:00  0.193889  0.423385  0.237047  5  1.5      6.0  0.999657\n",
       "2021-11-22 20:45:00  0.131926  0.487907  1.368617  5  4.0     16.0  0.997564"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dropping raws with at least a Nan\n",
    "df_wNan.dropna(how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>E</th>\n",
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       "      <td>False</td>\n",
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       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 20:45:00</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 21:45:00</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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      ],
      "text/plain": [
       "                         A      B      C      D      E  E prime  cosine\n",
       "2021-11-22 12:45:00   True  False  False  False   True     True   False\n",
       "2021-11-22 13:45:00  False  False   True  False  False    False   False\n",
       "2021-11-22 14:45:00   True   True   True  False  False    False   False\n",
       "2021-11-22 15:45:00  False  False  False  False  False    False   False\n",
       "2021-11-22 16:45:00   True   True  False  False  False    False   False\n",
       "2021-11-22 17:45:00   True   True   True  False  False    False   False\n",
       "2021-11-22 18:45:00  False   True  False  False  False    False   False\n",
       "2021-11-22 19:45:00   True  False  False  False  False    False   False\n",
       "2021-11-22 20:45:00  False  False  False  False  False    False   False\n",
       "2021-11-22 21:45:00  False  False   True  False  False    False   False"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting a mask\n",
    "df_wNan.isna()\n",
    "#df_wNan.notna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
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       "      <th>E prime</th>\n",
       "      <th>cosine</th>\n",
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       "      <th>2021-11-22 12:45:00</th>\n",
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       "      <td>0.193889</td>\n",
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       "      <td>8.0</td>\n",
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       "      <th>2021-11-22 17:45:00</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>3.0</td>\n",
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       "      <th>2021-11-22 19:45:00</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.419248</td>\n",
       "      <td>0.659899</td>\n",
       "      <td>5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.998135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 20:45:00</th>\n",
       "      <td>0.131926</td>\n",
       "      <td>0.487907</td>\n",
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       "      <td>4.0</td>\n",
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      "text/plain": [
       "                            A         B         C  D    E  E prime    cosine\n",
       "2021-11-22 12:45:00  0.000000  0.271638  1.073060  5  0.0      0.0  1.000000\n",
       "2021-11-22 13:45:00  1.069119  0.261487  0.000000  5  0.5      2.0  0.999962\n",
       "2021-11-22 14:45:00  0.000000  0.000000  0.000000  5  1.0      4.0  0.999848\n",
       "2021-11-22 15:45:00  0.193889  0.423385  0.237047  5  1.5      6.0  0.999657\n",
       "2021-11-22 16:45:00  0.000000  0.000000  0.939298  5  2.0      8.0  0.999391\n",
       "2021-11-22 17:45:00  0.000000  0.000000  0.000000  5  2.5     10.0  0.999048\n",
       "2021-11-22 18:45:00  2.029969  0.000000  0.785490  5  3.0     12.0  0.998630\n",
       "2021-11-22 19:45:00  0.000000  1.419248  0.659899  5  3.5     14.0  0.998135\n",
       "2021-11-22 20:45:00  0.131926  0.487907  1.368617  5  4.0     16.0  0.997564\n",
       "2021-11-22 21:45:00  0.136987  1.449805  0.000000  5  4.5     18.0  0.996917"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filling missing data\n",
    "df_wNan.fillna(value=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fill gaps forward or backward by propagating non-NA values forward or backward:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "                            A         B         C  D    E  E prime    cosine\n",
       "2021-11-22 12:45:00       NaN  0.271638  1.073060  5  NaN      NaN  1.000000\n",
       "2021-11-22 13:45:00  1.069119  0.261487  1.073060  5  0.5      2.0  0.999962\n",
       "2021-11-22 14:45:00  1.069119  0.261487  1.073060  5  1.0      4.0  0.999848\n",
       "2021-11-22 15:45:00  0.193889  0.423385  0.237047  5  1.5      6.0  0.999657\n",
       "2021-11-22 16:45:00  0.193889  0.423385  0.939298  5  2.0      8.0  0.999391\n",
       "2021-11-22 17:45:00  0.193889  0.423385  0.939298  5  2.5     10.0  0.999048\n",
       "2021-11-22 18:45:00  2.029969  0.423385  0.785490  5  3.0     12.0  0.998630\n",
       "2021-11-22 19:45:00  2.029969  1.419248  0.659899  5  3.5     14.0  0.998135\n",
       "2021-11-22 20:45:00  0.131926  0.487907  1.368617  5  4.0     16.0  0.997564\n",
       "2021-11-22 21:45:00  0.136987  1.449805  1.368617  5  4.5     18.0  0.996917"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wNan.fillna(method='pad')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Operations\n",
    "\n",
    "Here comes the most relevant advantage of DataFrame. Operations on columns are extremly fast, almost as fast as the actual operation between elements in a raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A          0.100114\n",
      "B          0.122809\n",
      "C          0.318319\n",
      "D          5.000000\n",
      "E          2.250000\n",
      "E prime    9.000000\n",
      "cosine     0.998915\n",
      "dtype: float64 \n",
      "\n",
      "2021-11-22 12:45:00    1.049243\n",
      "2021-11-22 13:45:00    1.320805\n",
      "2021-11-22 14:45:00    1.235072\n",
      "2021-11-22 15:45:00    2.050568\n",
      "2021-11-22 16:45:00    2.288860\n",
      "2021-11-22 17:45:00    2.379513\n",
      "2021-11-22 18:45:00    3.282203\n",
      "2021-11-22 19:45:00    3.529953\n",
      "2021-11-22 20:45:00    3.998002\n",
      "2021-11-22 21:45:00    4.280291\n",
      "Freq: H, dtype: float64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Some statistics (mean() just as an example)\n",
    "# raws\n",
    "print (df.mean(axis=0),'\\n')\n",
    "# columns\n",
    "print (df.mean(axis=1),'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>E prime</th>\n",
       "      <th>cosine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-11-22 12:45:00</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.271638</td>\n",
       "      <td>1.073060</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 13:45:00</th>\n",
       "      <td>1.069119</td>\n",
       "      <td>0.533125</td>\n",
       "      <td>0.488124</td>\n",
       "      <td>10</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1.999962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 14:45:00</th>\n",
       "      <td>0.216005</td>\n",
       "      <td>-0.595538</td>\n",
       "      <td>0.115560</td>\n",
       "      <td>15</td>\n",
       "      <td>1.5</td>\n",
       "      <td>6</td>\n",
       "      <td>2.999810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 15:45:00</th>\n",
       "      <td>0.409894</td>\n",
       "      <td>-0.172154</td>\n",
       "      <td>0.352607</td>\n",
       "      <td>20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12</td>\n",
       "      <td>3.999467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 16:45:00</th>\n",
       "      <td>-0.182434</td>\n",
       "      <td>-0.496498</td>\n",
       "      <td>1.291905</td>\n",
       "      <td>25</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20</td>\n",
       "      <td>4.998858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 17:45:00</th>\n",
       "      <td>-0.430139</td>\n",
       "      <td>-1.290200</td>\n",
       "      <td>0.490856</td>\n",
       "      <td>30</td>\n",
       "      <td>7.5</td>\n",
       "      <td>30</td>\n",
       "      <td>5.997906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 18:45:00</th>\n",
       "      <td>1.599830</td>\n",
       "      <td>-2.128869</td>\n",
       "      <td>1.276346</td>\n",
       "      <td>35</td>\n",
       "      <td>10.5</td>\n",
       "      <td>42</td>\n",
       "      <td>6.996536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 19:45:00</th>\n",
       "      <td>0.732222</td>\n",
       "      <td>-0.709622</td>\n",
       "      <td>1.936246</td>\n",
       "      <td>40</td>\n",
       "      <td>14.0</td>\n",
       "      <td>56</td>\n",
       "      <td>7.994670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 20:45:00</th>\n",
       "      <td>0.864148</td>\n",
       "      <td>-0.221715</td>\n",
       "      <td>3.304863</td>\n",
       "      <td>45</td>\n",
       "      <td>18.0</td>\n",
       "      <td>72</td>\n",
       "      <td>8.992234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 21:45:00</th>\n",
       "      <td>1.001136</td>\n",
       "      <td>1.228090</td>\n",
       "      <td>3.183188</td>\n",
       "      <td>50</td>\n",
       "      <td>22.5</td>\n",
       "      <td>90</td>\n",
       "      <td>9.989152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            A         B         C   D     E  E prime    cosine\n",
       "2021-11-22 12:45:00  0.000000  0.271638  1.073060   5   0.0        0  1.000000\n",
       "2021-11-22 13:45:00  1.069119  0.533125  0.488124  10   0.5        2  1.999962\n",
       "2021-11-22 14:45:00  0.216005 -0.595538  0.115560  15   1.5        6  2.999810\n",
       "2021-11-22 15:45:00  0.409894 -0.172154  0.352607  20   3.0       12  3.999467\n",
       "2021-11-22 16:45:00 -0.182434 -0.496498  1.291905  25   5.0       20  4.998858\n",
       "2021-11-22 17:45:00 -0.430139 -1.290200  0.490856  30   7.5       30  5.997906\n",
       "2021-11-22 18:45:00  1.599830 -2.128869  1.276346  35  10.5       42  6.996536\n",
       "2021-11-22 19:45:00  0.732222 -0.709622  1.936246  40  14.0       56  7.994670\n",
       "2021-11-22 20:45:00  0.864148 -0.221715  3.304863  45  18.0       72  8.992234\n",
       "2021-11-22 21:45:00  1.001136  1.228090  3.183188  50  22.5       90  9.989152"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# global operations on columns\n",
    "df.apply(np.cumsum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>E prime</th>\n",
       "      <th>cosine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-11-22 12:45:00</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.271638</td>\n",
       "      <td>1.073060</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 13:45:00</th>\n",
       "      <td>1.069119</td>\n",
       "      <td>0.261487</td>\n",
       "      <td>-0.584936</td>\n",
       "      <td>5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.999962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 14:45:00</th>\n",
       "      <td>-0.853114</td>\n",
       "      <td>-1.128664</td>\n",
       "      <td>-0.372564</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.999848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 15:45:00</th>\n",
       "      <td>0.193889</td>\n",
       "      <td>0.423385</td>\n",
       "      <td>0.237047</td>\n",
       "      <td>5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>6</td>\n",
       "      <td>0.999657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 16:45:00</th>\n",
       "      <td>-0.592327</td>\n",
       "      <td>-0.324345</td>\n",
       "      <td>0.939298</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.999391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 17:45:00</th>\n",
       "      <td>-0.247706</td>\n",
       "      <td>-0.793702</td>\n",
       "      <td>-0.801049</td>\n",
       "      <td>5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.999048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 18:45:00</th>\n",
       "      <td>2.029969</td>\n",
       "      <td>-0.838669</td>\n",
       "      <td>0.785490</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12</td>\n",
       "      <td>0.998630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 19:45:00</th>\n",
       "      <td>-0.867608</td>\n",
       "      <td>1.419248</td>\n",
       "      <td>0.659899</td>\n",
       "      <td>5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>14</td>\n",
       "      <td>0.998135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 20:45:00</th>\n",
       "      <td>0.131926</td>\n",
       "      <td>0.487907</td>\n",
       "      <td>1.368617</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16</td>\n",
       "      <td>0.997564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22 21:45:00</th>\n",
       "      <td>0.136987</td>\n",
       "      <td>1.449805</td>\n",
       "      <td>-0.121675</td>\n",
       "      <td>5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>18</td>\n",
       "      <td>0.996917</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            A         B         C  D    E  E prime    cosine\n",
       "2021-11-22 12:45:00  0.000000  0.271638  1.073060  5  0.0        0  1.000000\n",
       "2021-11-22 13:45:00  1.069119  0.261487 -0.584936  5  0.5        2  0.999962\n",
       "2021-11-22 14:45:00 -0.853114 -1.128664 -0.372564  5  1.0        4  0.999848\n",
       "2021-11-22 15:45:00  0.193889  0.423385  0.237047  5  1.5        6  0.999657\n",
       "2021-11-22 16:45:00 -0.592327 -0.324345  0.939298  5  2.0        8  0.999391\n",
       "2021-11-22 17:45:00 -0.247706 -0.793702 -0.801049  5  2.5       10  0.999048\n",
       "2021-11-22 18:45:00  2.029969 -0.838669  0.785490  5  3.0       12  0.998630\n",
       "2021-11-22 19:45:00 -0.867608  1.419248  0.659899  5  3.5       14  0.998135\n",
       "2021-11-22 20:45:00  0.131926  0.487907  1.368617  5  4.0       16  0.997564\n",
       "2021-11-22 21:45:00  0.136987  1.449805 -0.121675  5  4.5       18  0.996917"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A           2.897577\n",
       "B           2.578469\n",
       "C           2.169666\n",
       "D           0.000000\n",
       "E           4.500000\n",
       "E prime    18.000000\n",
       "cosine      0.003083\n",
       "dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(lambda x: x.max() - x.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2021-11-22 12:45:00    0.271638\n",
       "2021-11-22 13:45:00    1.330606\n",
       "2021-11-22 14:45:00   -1.981777\n",
       "2021-11-22 15:45:00    0.617273\n",
       "2021-11-22 16:45:00   -0.916672\n",
       "2021-11-22 17:45:00   -1.041408\n",
       "2021-11-22 18:45:00    1.191300\n",
       "2021-11-22 19:45:00    0.551640\n",
       "2021-11-22 20:45:00    0.619833\n",
       "2021-11-22 21:45:00    1.586792\n",
       "Freq: H, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# syntax is as usual similar to that of numpy arrays\n",
    "df['A']+df['B']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's play it hard and load (in memory) a (relatively) large dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-11-24 14:31:37--  https://www.dropbox.com/s/xvjzaxzz3ysphme/data_000637.txt\n",
      "Resolving www.dropbox.com (www.dropbox.com)... 162.125.4.18, 2620:100:601c:18::a27d:612\n",
      "Connecting to www.dropbox.com (www.dropbox.com)|162.125.4.18|:443... connected.\n",
      "HTTP request sent, awaiting response... 301 Moved Permanently\n",
      "Location: /s/raw/xvjzaxzz3ysphme/data_000637.txt [following]\n",
      "--2021-11-24 14:31:38--  https://www.dropbox.com/s/raw/xvjzaxzz3ysphme/data_000637.txt\n",
      "Reusing existing connection to www.dropbox.com:443.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://uc14337bc2aa4315991223affed1.dl.dropboxusercontent.com/cd/0/inline/BanD_CEKwxMLvFTkHhZgnMcugzKCZB_68OGD6o9kU8iFWYl27LPx3F4QlrJsH8JuPF82ZUmC55VyW_lB5g7F1Il_-tT0RuhghyxTGLcvGdcpfu8M1VTnwlNeo-PJ6i2DUfczJS23W2ZwzcsBPeqfQHTX/file# [following]\n",
      "--2021-11-24 14:31:38--  https://uc14337bc2aa4315991223affed1.dl.dropboxusercontent.com/cd/0/inline/BanD_CEKwxMLvFTkHhZgnMcugzKCZB_68OGD6o9kU8iFWYl27LPx3F4QlrJsH8JuPF82ZUmC55VyW_lB5g7F1Il_-tT0RuhghyxTGLcvGdcpfu8M1VTnwlNeo-PJ6i2DUfczJS23W2ZwzcsBPeqfQHTX/file\n",
      "Resolving uc14337bc2aa4315991223affed1.dl.dropboxusercontent.com (uc14337bc2aa4315991223affed1.dl.dropboxusercontent.com)... 162.125.4.15, 2620:100:601c:15::a27d:60f\n",
      "Connecting to uc14337bc2aa4315991223affed1.dl.dropboxusercontent.com (uc14337bc2aa4315991223affed1.dl.dropboxusercontent.com)|162.125.4.15|:443... connected.\n",
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      "Saving to: ‘data_000637’\n",
      "\n",
      "637                 100%[===================>]  31.64M  9.20MB/s    in 3.4s    \n",
      "\n",
      "2021-11-24 14:31:43 (9.20 MB/s) - ‘data_000637’ saved [33179236/33179236]\n",
      "\n"
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       "      <th></th>\n",
       "      <th>HEAD</th>\n",
       "      <th>FPGA</th>\n",
       "      <th>TDC_CHANNEL</th>\n",
       "      <th>ORBIT_CNT</th>\n",
       "      <th>BX_COUNTER</th>\n",
       "      <th>TDC_MEAS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>123</td>\n",
       "      <td>3869200167</td>\n",
       "      <td>2374</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>124</td>\n",
       "      <td>3869200167</td>\n",
       "      <td>2374</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>3869200167</td>\n",
       "      <td>2553</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>3869200167</td>\n",
       "      <td>2558</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>3869200167</td>\n",
       "      <td>2760</td>\n",
       "      <td>25</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1310715</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>3869211171</td>\n",
       "      <td>762</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1310716</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3869211171</td>\n",
       "      <td>763</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1310717</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>3869211171</td>\n",
       "      <td>764</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1310718</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>139</td>\n",
       "      <td>3869211171</td>\n",
       "      <td>769</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1310719</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>3869211171</td>\n",
       "      <td>762</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1310720 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         HEAD  FPGA  TDC_CHANNEL   ORBIT_CNT  BX_COUNTER  TDC_MEAS\n",
       "0           1     0          123  3869200167        2374        26\n",
       "1           1     0          124  3869200167        2374        27\n",
       "2           1     0           63  3869200167        2553        28\n",
       "3           1     0           64  3869200167        2558        19\n",
       "4           1     0           64  3869200167        2760        25\n",
       "...       ...   ...          ...         ...         ...       ...\n",
       "1310715     1     0           62  3869211171         762        14\n",
       "1310716     1     1            4  3869211171         763        11\n",
       "1310717     1     0           64  3869211171         764         0\n",
       "1310718     1     0          139  3869211171         769         0\n",
       "1310719     1     0           61  3869211171         762        18\n",
       "\n",
       "[1310720 rows x 6 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# WARNING! link in past notebook was wrong!, (if needed) get the right file from:\n",
    "!wget -O data_000637 https://www.dropbox.com/s/xvjzaxzz3ysphme/data_000637.txt -P ../data/\n",
    "\n",
    "file_name=\"../data/data_000637.txt\"\n",
    "data=pd.read_csv(file_name)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now do some operations among (elements of) columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the one-liner killing it all\n",
    "data['timens']=data['TDC_MEAS']*25/30+data['BX_COUNTER']*25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          59371.666667\n",
       "1          59372.500000\n",
       "2          63848.333333\n",
       "3          63965.833333\n",
       "4          69020.833333\n",
       "               ...     \n",
       "1310715    19061.666667\n",
       "1310716    19084.166667\n",
       "1310717    19100.000000\n",
       "1310718    19225.000000\n",
       "1310719    19065.000000\n",
       "Name: timens, Length: 1310720, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['timens']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the old slooow way\n",
    "def conversion(data):\n",
    "    result=[]\n",
    "    for i in range(len(data)): \n",
    "        result.append(data.loc[data.index[i],'TDC_MEAS']*25/30.+data.loc[data.index[i],'BX_COUNTER']*25)\n",
    "    return result\n",
    "\n",
    "data['timens']=conversion(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge\n",
    "\n",
    "pandas provides various facilities for easily combining together Series, DataFrame, and Panel objects with various kinds of set logic for the indexes and relational algebra functionality in the case of join / merge-type operations.\n",
    "\n",
    "### Concat\n",
    "\n",
    "concatenation (adding rows) is straightforward\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.345730</td>\n",
       "      <td>0.268793</td>\n",
       "      <td>0.594239</td>\n",
       "      <td>-0.804834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.340742</td>\n",
       "      <td>0.805053</td>\n",
       "      <td>-0.126656</td>\n",
       "      <td>0.903780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.429040</td>\n",
       "      <td>0.688033</td>\n",
       "      <td>1.962774</td>\n",
       "      <td>2.531949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.882486</td>\n",
       "      <td>0.244168</td>\n",
       "      <td>1.835459</td>\n",
       "      <td>-0.783870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.229848</td>\n",
       "      <td>-0.830981</td>\n",
       "      <td>0.664160</td>\n",
       "      <td>-0.243984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.218332</td>\n",
       "      <td>0.477782</td>\n",
       "      <td>-0.411237</td>\n",
       "      <td>-0.739213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.204853</td>\n",
       "      <td>-0.722165</td>\n",
       "      <td>1.227503</td>\n",
       "      <td>-0.290115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.074629</td>\n",
       "      <td>0.124110</td>\n",
       "      <td>-2.258097</td>\n",
       "      <td>1.235884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.593596</td>\n",
       "      <td>0.195949</td>\n",
       "      <td>0.248771</td>\n",
       "      <td>-0.450632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.891701</td>\n",
       "      <td>0.288881</td>\n",
       "      <td>0.639323</td>\n",
       "      <td>-0.527991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0 -1.345730  0.268793  0.594239 -0.804834\n",
       "1 -0.340742  0.805053 -0.126656  0.903780\n",
       "2  0.429040  0.688033  1.962774  2.531949\n",
       "3  0.882486  0.244168  1.835459 -0.783870\n",
       "4  0.229848 -0.830981  0.664160 -0.243984\n",
       "5  0.218332  0.477782 -0.411237 -0.739213\n",
       "6  0.204853 -0.722165  1.227503 -0.290115\n",
       "7  0.074629  0.124110 -2.258097  1.235884\n",
       "8  0.593596  0.195949  0.248771 -0.450632\n",
       "9  0.891701  0.288881  0.639323 -0.527991"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdf = pd.DataFrame(np.random.randn(10, 4))\n",
    "rdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[          0         1         2         3\n",
       " 0 -1.345730  0.268793  0.594239 -0.804834\n",
       " 1 -0.340742  0.805053 -0.126656  0.903780\n",
       " 2  0.429040  0.688033  1.962774  2.531949,\n",
       "           0         1         2         3\n",
       " 3  0.882486  0.244168  1.835459 -0.783870\n",
       " 4  0.229848 -0.830981  0.664160 -0.243984\n",
       " 5  0.218332  0.477782 -0.411237 -0.739213\n",
       " 6  0.204853 -0.722165  1.227503 -0.290115,\n",
       "           0         1         2         3\n",
       " 7  0.074629  0.124110 -2.258097  1.235884\n",
       " 8  0.593596  0.195949  0.248771 -0.450632\n",
       " 9  0.891701  0.288881  0.639323 -0.527991]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# divide it into pieaces raw-wise\n",
    "pieces = [rdf[:3], rdf[3:7], rdf[7:]]\n",
    "pieces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>-1.345730</td>\n",
       "      <td>0.268793</td>\n",
       "      <td>0.594239</td>\n",
       "      <td>-0.804834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.340742</td>\n",
       "      <td>0.805053</td>\n",
       "      <td>-0.126656</td>\n",
       "      <td>0.903780</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.429040</td>\n",
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       "      <td>1.962774</td>\n",
       "      <td>2.531949</td>\n",
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       "      <td>-0.830981</td>\n",
       "      <td>0.664160</td>\n",
       "      <td>-0.243984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.218332</td>\n",
       "      <td>0.477782</td>\n",
       "      <td>-0.411237</td>\n",
       "      <td>-0.739213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.204853</td>\n",
       "      <td>-0.722165</td>\n",
       "      <td>1.227503</td>\n",
       "      <td>-0.290115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.074629</td>\n",
       "      <td>0.124110</td>\n",
       "      <td>-2.258097</td>\n",
       "      <td>1.235884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.593596</td>\n",
       "      <td>0.195949</td>\n",
       "      <td>0.248771</td>\n",
       "      <td>-0.450632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.891701</td>\n",
       "      <td>0.288881</td>\n",
       "      <td>0.639323</td>\n",
       "      <td>-0.527991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0 -1.345730  0.268793  0.594239 -0.804834\n",
       "1 -0.340742  0.805053 -0.126656  0.903780\n",
       "2  0.429040  0.688033  1.962774  2.531949\n",
       "3  0.882486  0.244168  1.835459 -0.783870\n",
       "4  0.229848 -0.830981  0.664160 -0.243984\n",
       "5  0.218332  0.477782 -0.411237 -0.739213\n",
       "6  0.204853 -0.722165  1.227503 -0.290115\n",
       "7  0.074629  0.124110 -2.258097  1.235884\n",
       "8  0.593596  0.195949  0.248771 -0.450632\n",
       "9  0.891701  0.288881  0.639323 -0.527991"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# put it back together\n",
    "#pd.concat(pieces)\n",
    "\n",
    "# indexes can be ignored\n",
    "pd.concat(pieces, ignore_index=True)\n",
    "\n",
    "# in case of dimension mismatch, Nan are added where needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.903780</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.429040</td>\n",
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       "      <td>1.962774</td>\n",
       "      <td>2.531949</td>\n",
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       "      <td>1.835459</td>\n",
       "      <td>-0.783870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.229848</td>\n",
       "      <td>-0.830981</td>\n",
       "      <td>0.664160</td>\n",
       "      <td>-0.243984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.218332</td>\n",
       "      <td>0.477782</td>\n",
       "      <td>-0.411237</td>\n",
       "      <td>-0.739213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>-0.722165</td>\n",
       "      <td>1.227503</td>\n",
       "      <td>-0.290115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.074629</td>\n",
       "      <td>0.124110</td>\n",
       "      <td>-2.258097</td>\n",
       "      <td>1.235884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.593596</td>\n",
       "      <td>0.195949</td>\n",
       "      <td>0.248771</td>\n",
       "      <td>-0.450632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.891701</td>\n",
       "      <td>0.288881</td>\n",
       "      <td>0.639323</td>\n",
       "      <td>-0.527991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0 -1.345730  0.268793  0.594239 -0.804834\n",
       "1 -0.340742  0.805053 -0.126656  0.903780\n",
       "2  0.429040  0.688033  1.962774  2.531949\n",
       "3  0.882486  0.244168  1.835459 -0.783870\n",
       "4  0.229848 -0.830981  0.664160 -0.243984\n",
       "5  0.218332  0.477782 -0.411237 -0.739213\n",
       "6  0.204853 -0.722165  1.227503 -0.290115\n",
       "7  0.074629  0.124110 -2.258097  1.235884\n",
       "8  0.593596  0.195949  0.248771 -0.450632\n",
       "9  0.891701  0.288881  0.639323 -0.527991"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# appending a single row (as a Series)\n",
    "s = rdf.iloc[3]\n",
    "rdf.append(s, ignore_index=True)\n",
    "rdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge/Join\n",
    "\n",
    "SQL like operations on table can be performed on DataFrames. This is all rather sophisticated, refer to the [doc](https://pandas.pydata.org/pandas-docs/stable/merging.html#merging) for more info/examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   key  lval\n",
      "0  foo     1\n",
      "1  bar     2\n",
      "   key  rval\n",
      "0  foo     4\n",
      "1  bar     5\n"
     ]
    },
    {
     "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>key</th>\n",
       "      <th>lval</th>\n",
       "      <th>rval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval  rval\n",
       "0  foo     1     4\n",
       "1  bar     2     5"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left = pd.DataFrame({'key': ['foo', 'bar'], 'lval': [1, 2]})\n",
    "print(left)\n",
    "right = pd.DataFrame({'key': ['foo', 'bar'], 'rval': [4, 5]})\n",
    "print(right)\n",
    "pd.merge(left,right,on=\"key\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouping\n",
    "\n",
    "By “group by” we are referring to a process involving one or more of the following steps:\n",
    "\n",
    "* Splitting the data into groups based on some criteria\n",
    "* Applying a function to each group independently\n",
    "* Combining the results into a data structure\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>1.054397</td>\n",
       "      <td>0.638133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>1.498719</td>\n",
       "      <td>-0.489717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>-1.531167</td>\n",
       "      <td>-0.096963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>-2.108453</td>\n",
       "      <td>-0.141963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.834559</td>\n",
       "      <td>-0.632537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.733942</td>\n",
       "      <td>1.162110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>0.235223</td>\n",
       "      <td>-1.317697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>foo</td>\n",
       "      <td>three</td>\n",
       "      <td>2.626053</td>\n",
       "      <td>-0.249658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A      B         C         D\n",
       "0  foo    one  1.054397  0.638133\n",
       "1  bar    one  1.498719 -0.489717\n",
       "2  foo    two -1.531167 -0.096963\n",
       "3  bar  three -2.108453 -0.141963\n",
       "4  foo    two -0.834559 -0.632537\n",
       "5  bar    two -0.733942  1.162110\n",
       "6  foo    one  0.235223 -1.317697\n",
       "7  foo  three  2.626053 -0.249658"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',\n",
    "                          'foo', 'bar', 'foo', 'foo'],\n",
    "                    'B' : ['one', 'one', 'two', 'three',\n",
    "                           'two', 'two', 'one', 'three'],\n",
    "                    'C' : np.random.randn(8),\n",
    "                    'D' : np.random.randn(8)})\n",
    "gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bar</th>\n",
       "      <td>-1.343676</td>\n",
       "      <td>0.530431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <td>1.549947</td>\n",
       "      <td>-1.658721</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            C         D\n",
       "A                      \n",
       "bar -1.343676  0.530431\n",
       "foo  1.549947 -1.658721"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grouping and then applying the sum() \n",
    "# function to the resulting groups (effective only where number are there).\n",
    "gdf.groupby('A').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi-indexing\n",
    "\n",
    "\n",
    "Hierarchical / Multi-level indexing allows sophisticated data analysis on higher dimensional data. In essence, it enables you to store and manipulate data with an arbitrary number of dimensions in lower dimensional data structures like Series (1d) and DataFrame (2d)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultiIndex([('bar', 'one'),\n",
      "            ('bar', 'two'),\n",
      "            ('baz', 'one'),\n",
      "            ('baz', 'two'),\n",
      "            ('foo', 'one'),\n",
      "            ('foo', 'two'),\n",
      "            ('qux', 'one'),\n",
      "            ('qux', 'two')],\n",
      "           names=['first', 'second']) \n",
      "\n",
      "first  second\n",
      "bar    one       0.187672\n",
      "       two       1.044432\n",
      "baz    one       0.313609\n",
      "       two       0.347875\n",
      "foo    one       1.186574\n",
      "       two       0.535544\n",
      "qux    one       0.514099\n",
      "       two       0.971161\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "tuples = list(zip(['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],\n",
    "          ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']))\n",
    "multi_index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])\n",
    "print (multi_index,'\\n')\n",
    "\n",
    "s = pd.Series(np.random.randn(8), index=multi_index)\n",
    "print (s)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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></th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>1.498719</td>\n",
       "      <td>-0.489717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>-2.108453</td>\n",
       "      <td>-0.141963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.733942</td>\n",
       "      <td>1.162110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>1.289620</td>\n",
       "      <td>-0.679563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2.626053</td>\n",
       "      <td>-0.249658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-2.365726</td>\n",
       "      <td>-0.729500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  C         D\n",
       "A   B                        \n",
       "bar one    1.498719 -0.489717\n",
       "    three -2.108453 -0.141963\n",
       "    two   -0.733942  1.162110\n",
       "foo one    1.289620 -0.679563\n",
       "    three  2.626053 -0.249658\n",
       "    two   -2.365726 -0.729500"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# it enables further features of the groupby method,\n",
    "# e.g. when group-by by multiple columns\n",
    "gdf.groupby(['A','B']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    B       \n",
       "bar  one    C    1.498719\n",
       "            D   -0.489717\n",
       "     three  C   -2.108453\n",
       "            D   -0.141963\n",
       "     two    C   -0.733942\n",
       "            D    1.162110\n",
       "foo  one    C    1.289620\n",
       "            D   -0.679563\n",
       "     three  C    2.626053\n",
       "            D   -0.249658\n",
       "     two    C   -2.365726\n",
       "            D   -0.729500\n",
       "dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# stack() method “compresses” a level in the DataFrame’s columns\n",
    "gdf.groupby(['A','B']).sum().stack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting\n",
    "\n",
    "Just a preview, more on the next lab class!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))\n",
    "ts.cumsum().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f0d4bc72160>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "pdf=pd.DataFrame(np.random.randn(1000, 4), index=ts.index,columns=['A', 'B', 'C', 'D'])\n",
    "pdf = pdf.cumsum()\n",
    "plt.figure(); pdf.plot(); plt.legend(loc='best')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}