{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basics of python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Datatypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = \"a\" # declared variable, string a denoted with \"\" ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n"
     ]
    }
   ],
   "source": [
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 1 + 2 * 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1.2 / 1.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 ** 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iterables "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "mylist = ['a', 'b', 1, 1.2, \"blub\"] # list with square brackets and commas, mixed datatypes allowed "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 1, 1.2, None]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'b'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylist[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python is zero indexed!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylist[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mylist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'blub'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylist[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "mylist.append('a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 1, 1.2, 'blub', 'a']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1|1|a\n",
      "2|4|b\n",
      "3|9|1\n",
      "4|16|1.2\n",
      "5|25|blub\n",
      "6|36|a\n"
     ]
    }
   ],
   "source": [
    "counter = 0\n",
    "for i in mylist:\n",
    "    counter +=1 # counter = counter +1  \n",
    "    print(counter, counter**2, i, sep='|')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dictionary "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "key -> value\n",
    "\n",
    "first_car -> green\n",
    "second_car -> black"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {\n",
    "    'first_car': 'green',\n",
    "    'second_car': 'black'\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'green'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d['first_car']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_color_dict = {\n",
    "    'first_car': 'green',\n",
    "    'second_car': 'black'\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'black'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_color_dict['second_car']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Datascience with python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np  # matrix math\n",
    "import pandas as pd # \"excel like data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([1,2, 3]) # one datatype "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = np.array([2, 2, 2]) # one datatype "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 4, 5])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 6])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a * b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.5, 1. , 1.5])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a / b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.84147098, 0.90929743, 0.14112001])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sin(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = np.array([[1,2,3], [1,2,3]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [1, 2, 3]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3,)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 4, 6],\n",
       "       [2, 4, 6]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a + c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pandas gives dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(\n",
    "        [\n",
    "         {'name': 'Kevin',\n",
    "        'email': 'kevin.jablonka@epfl.ch'\n",
    "         } ,\n",
    "        {'name': 'Berend',\n",
    "        'email': 'berend.smit@epfl.ch'\n",
    "         } \n",
    "        ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>name</th>\n",
       "      <th>email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kevin</td>\n",
       "      <td>kevin.jablonka@epfl.ch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Berend</td>\n",
       "      <td>berend.smit@epfl.ch</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     name                   email\n",
       "0   Kevin  kevin.jablonka@epfl.ch\n",
       "1  Berend     berend.smit@epfl.ch"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>name</th>\n",
       "      <th>email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Kevin</td>\n",
       "      <td>kevin.jablonka@epfl.ch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name                   email\n",
       "count       2                       2\n",
       "unique      2                       2\n",
       "top     Kevin  kevin.jablonka@epfl.ch\n",
       "freq        1                       1"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/Users/kevinmaikjablonka/Dropbox (LSMO)/proj66_18e_rule/20200417-235858_featurized_merged_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['new_feature'] = df['sgl_bd'] + df['tri_bipyr']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(1.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float(1.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 == 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>sgl_bd</th>\n",
       "      <th>bent</th>\n",
       "      <th>T</th>\n",
       "      <th>see_saw_rect</th>\n",
       "      <th>tet</th>\n",
       "      <th>oct</th>\n",
       "      <th>tri_plan</th>\n",
       "      <th>sq_plan</th>\n",
       "      <th>pent_plan</th>\n",
       "      <th>hex_bipyr</th>\n",
       "      <th>...</th>\n",
       "      <th>based_on_smiles</th>\n",
       "      <th>CN</th>\n",
       "      <th>number_ligands</th>\n",
       "      <th>homoleptic</th>\n",
       "      <th>ligands_clean</th>\n",
       "      <th>electroncount_ionic</th>\n",
       "      <th>electroncount_neutral</th>\n",
       "      <th>formal_charges_metal_centers</th>\n",
       "      <th>electron_counts_ionic_from_formal_charge</th>\n",
       "      <th>new_feature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.022136e-03</td>\n",
       "      <td>0.092255</td>\n",
       "      <td>0.345547</td>\n",
       "      <td>0.229039</td>\n",
       "      <td>0.019030</td>\n",
       "      <td>0.074364</td>\n",
       "      <td>0.034016</td>\n",
       "      <td>0.092733</td>\n",
       "      <td>0.047836</td>\n",
       "      <td>0.316051</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>('Cp', 'Cp')</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>0.231137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.136424e-01</td>\n",
       "      <td>0.096205</td>\n",
       "      <td>0.368450</td>\n",
       "      <td>0.235727</td>\n",
       "      <td>0.018147</td>\n",
       "      <td>0.072852</td>\n",
       "      <td>0.035168</td>\n",
       "      <td>0.090308</td>\n",
       "      <td>0.045841</td>\n",
       "      <td>0.327994</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>('F', 'Cp', 'Cp')</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>0.423926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.132352e-02</td>\n",
       "      <td>0.094367</td>\n",
       "      <td>0.364063</td>\n",
       "      <td>0.248527</td>\n",
       "      <td>0.019591</td>\n",
       "      <td>0.077272</td>\n",
       "      <td>0.035736</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.044025</td>\n",
       "      <td>0.376013</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>('Cl', 'Cp', 'Cp')</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>0.266155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.403701e-03</td>\n",
       "      <td>0.094124</td>\n",
       "      <td>0.358381</td>\n",
       "      <td>0.264267</td>\n",
       "      <td>0.019050</td>\n",
       "      <td>0.077548</td>\n",
       "      <td>0.035305</td>\n",
       "      <td>0.096320</td>\n",
       "      <td>0.045428</td>\n",
       "      <td>0.373334</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>('Br', 'Cp', 'Cp')</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>0.259847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.421093e-03</td>\n",
       "      <td>0.093739</td>\n",
       "      <td>0.358767</td>\n",
       "      <td>0.255406</td>\n",
       "      <td>0.019325</td>\n",
       "      <td>0.081393</td>\n",
       "      <td>0.035736</td>\n",
       "      <td>0.100615</td>\n",
       "      <td>0.044202</td>\n",
       "      <td>0.367071</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>('I', 'Cp', 'Cp')</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>0.268151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67745</th>\n",
       "      <td>1.554312e-15</td>\n",
       "      <td>0.200379</td>\n",
       "      <td>0.347704</td>\n",
       "      <td>0.185002</td>\n",
       "      <td>0.101527</td>\n",
       "      <td>0.506645</td>\n",
       "      <td>0.003065</td>\n",
       "      <td>0.242988</td>\n",
       "      <td>0.011906</td>\n",
       "      <td>0.556566</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>('NR3', 'NR3', 'SR2', 'SR2', 'SR2', 'SR2')</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>0.285254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67746</th>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>0.200120</td>\n",
       "      <td>0.493788</td>\n",
       "      <td>0.771903</td>\n",
       "      <td>0.036373</td>\n",
       "      <td>0.854225</td>\n",
       "      <td>0.001838</td>\n",
       "      <td>0.343532</td>\n",
       "      <td>0.009952</td>\n",
       "      <td>0.553505</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>('SR2', 'SR2', 'SR2', 'SR2', 'SR2', 'SR2')</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>0.447939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67747</th>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>0.200164</td>\n",
       "      <td>0.369578</td>\n",
       "      <td>0.473603</td>\n",
       "      <td>0.031159</td>\n",
       "      <td>0.753196</td>\n",
       "      <td>0.001077</td>\n",
       "      <td>0.315353</td>\n",
       "      <td>0.005358</td>\n",
       "      <td>0.507564</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>('SR2', 'SR2', 'SR2', 'SR2', 'SR2', 'SR2')</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>0.386436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67748</th>\n",
       "      <td>2.553513e-15</td>\n",
       "      <td>0.200135</td>\n",
       "      <td>0.504980</td>\n",
       "      <td>0.767596</td>\n",
       "      <td>0.037106</td>\n",
       "      <td>0.829385</td>\n",
       "      <td>0.001792</td>\n",
       "      <td>0.336072</td>\n",
       "      <td>0.009551</td>\n",
       "      <td>0.569624</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>('SR2', 'SR2', 'SR2', 'SR2', 'SR2', 'SR2')</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>0.439859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67749</th>\n",
       "      <td>3.330669e-16</td>\n",
       "      <td>0.200151</td>\n",
       "      <td>0.416668</td>\n",
       "      <td>0.532722</td>\n",
       "      <td>0.031306</td>\n",
       "      <td>0.791856</td>\n",
       "      <td>0.001700</td>\n",
       "      <td>0.318466</td>\n",
       "      <td>0.008428</td>\n",
       "      <td>0.478841</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>('SR2', 'SR2', 'SR2', 'SR2', 'SR2', 'SR2')</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>0.399146</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67750 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             sgl_bd      bent         T  see_saw_rect       tet       oct  \\\n",
       "0      4.022136e-03  0.092255  0.345547      0.229039  0.019030  0.074364   \n",
       "1      2.136424e-01  0.096205  0.368450      0.235727  0.018147  0.072852   \n",
       "2      1.132352e-02  0.094367  0.364063      0.248527  0.019591  0.077272   \n",
       "3      3.403701e-03  0.094124  0.358381      0.264267  0.019050  0.077548   \n",
       "4      2.421093e-03  0.093739  0.358767      0.255406  0.019325  0.081393   \n",
       "...             ...       ...       ...           ...       ...       ...   \n",
       "67745  1.554312e-15  0.200379  0.347704      0.185002  0.101527  0.506645   \n",
       "67746  2.220446e-16  0.200120  0.493788      0.771903  0.036373  0.854225   \n",
       "67747  1.110223e-16  0.200164  0.369578      0.473603  0.031159  0.753196   \n",
       "67748  2.553513e-15  0.200135  0.504980      0.767596  0.037106  0.829385   \n",
       "67749  3.330669e-16  0.200151  0.416668      0.532722  0.031306  0.791856   \n",
       "\n",
       "       tri_plan   sq_plan  pent_plan  hex_bipyr  ...  based_on_smiles  CN  \\\n",
       "0      0.034016  0.092733   0.047836   0.316051  ...            False  10   \n",
       "1      0.035168  0.090308   0.045841   0.327994  ...             True  11   \n",
       "2      0.035736  0.095921   0.044025   0.376013  ...             True  11   \n",
       "3      0.035305  0.096320   0.045428   0.373334  ...             True  11   \n",
       "4      0.035736  0.100615   0.044202   0.367071  ...             True  11   \n",
       "...         ...       ...        ...        ...  ...              ...  ..   \n",
       "67745  0.003065  0.242988   0.011906   0.556566  ...            False   6   \n",
       "67746  0.001838  0.343532   0.009952   0.553505  ...            False   6   \n",
       "67747  0.001077  0.315353   0.005358   0.507564  ...            False   6   \n",
       "67748  0.001792  0.336072   0.009551   0.569624  ...            False   6   \n",
       "67749  0.001700  0.318466   0.008428   0.478841  ...            False   6   \n",
       "\n",
       "       number_ligands  homoleptic                               ligands_clean  \\\n",
       "0                   2        True                                ('Cp', 'Cp')   \n",
       "1                   3       False                           ('F', 'Cp', 'Cp')   \n",
       "2                   3       False                          ('Cl', 'Cp', 'Cp')   \n",
       "3                   3       False                          ('Br', 'Cp', 'Cp')   \n",
       "4                   3       False                           ('I', 'Cp', 'Cp')   \n",
       "...               ...         ...                                         ...   \n",
       "67745               6       False  ('NR3', 'NR3', 'SR2', 'SR2', 'SR2', 'SR2')   \n",
       "67746               6        True  ('SR2', 'SR2', 'SR2', 'SR2', 'SR2', 'SR2')   \n",
       "67747               6        True  ('SR2', 'SR2', 'SR2', 'SR2', 'SR2', 'SR2')   \n",
       "67748               6        True  ('SR2', 'SR2', 'SR2', 'SR2', 'SR2', 'SR2')   \n",
       "67749               6        True  ('SR2', 'SR2', 'SR2', 'SR2', 'SR2', 'SR2')   \n",
       "\n",
       "       electroncount_ionic  electroncount_neutral  \\\n",
       "0                     12.0                     12   \n",
       "1                      NaN                     14   \n",
       "2                      NaN                     14   \n",
       "3                      NaN                     14   \n",
       "4                      NaN                     14   \n",
       "...                    ...                    ...   \n",
       "67745                 22.0                     22   \n",
       "67746                 22.0                     22   \n",
       "67747                 22.0                     22   \n",
       "67748                 22.0                     22   \n",
       "67749                 22.0                     22   \n",
       "\n",
       "       formal_charges_metal_centers  electron_counts_ionic_from_formal_charge  \\\n",
       "0                                 3                                        12   \n",
       "1                                 3                                        14   \n",
       "2                                 3                                        14   \n",
       "3                                 3                                        14   \n",
       "4                                 3                                        14   \n",
       "...                             ...                                       ...   \n",
       "67745                             0                                        24   \n",
       "67746                             2                                        22   \n",
       "67747                             2                                        22   \n",
       "67748                             2                                        22   \n",
       "67749                             2                                        22   \n",
       "\n",
       "       new_feature  \n",
       "0         0.231137  \n",
       "1         0.423926  \n",
       "2         0.266155  \n",
       "3         0.259847  \n",
       "4         0.268151  \n",
       "...            ...  \n",
       "67745     0.285254  \n",
       "67746     0.447939  \n",
       "67747     0.386436  \n",
       "67748     0.439859  \n",
       "67749     0.399146  \n",
       "\n",
       "[67750 rows x 66 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>sgl_bd</th>\n",
       "      <th>bent</th>\n",
       "      <th>T</th>\n",
       "      <th>see_saw_rect</th>\n",
       "      <th>tet</th>\n",
       "      <th>oct</th>\n",
       "      <th>tri_plan</th>\n",
       "      <th>sq_plan</th>\n",
       "      <th>pent_plan</th>\n",
       "      <th>hex_bipyr</th>\n",
       "      <th>...</th>\n",
       "      <th>molecule_formal_charge</th>\n",
       "      <th>volume_y</th>\n",
       "      <th>oxidationstate_clean</th>\n",
       "      <th>CN</th>\n",
       "      <th>number_ligands</th>\n",
       "      <th>electroncount_ionic</th>\n",
       "      <th>electroncount_neutral</th>\n",
       "      <th>formal_charges_metal_centers</th>\n",
       "      <th>electron_counts_ionic_from_formal_charge</th>\n",
       "      <th>new_feature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.775000e+04</td>\n",
       "      <td>67750.000000</td>\n",
       "      <td>6.775000e+04</td>\n",
       "      <td>67750.000000</td>\n",
       "      <td>6.663800e+04</td>\n",
       "      <td>67743.000000</td>\n",
       "      <td>6.663800e+04</td>\n",
       "      <td>67743.000000</td>\n",
       "      <td>66638.000000</td>\n",
       "      <td>67750.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>67750.000000</td>\n",
       "      <td>63418.000000</td>\n",
       "      <td>52066.000000</td>\n",
       "      <td>67750.000000</td>\n",
       "      <td>67750.000000</td>\n",
       "      <td>52066.000000</td>\n",
       "      <td>67750.000000</td>\n",
       "      <td>67750.000000</td>\n",
       "      <td>67750.000000</td>\n",
       "      <td>67750.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.230312e-03</td>\n",
       "      <td>0.187261</td>\n",
       "      <td>3.991934e-01</td>\n",
       "      <td>0.709934</td>\n",
       "      <td>6.176000e-02</td>\n",
       "      <td>0.682264</td>\n",
       "      <td>1.774031e-02</td>\n",
       "      <td>0.327322</td>\n",
       "      <td>0.018350</td>\n",
       "      <td>0.478373</td>\n",
       "      <td>...</td>\n",
       "      <td>1.019793</td>\n",
       "      <td>224.205760</td>\n",
       "      <td>1.974398</td>\n",
       "      <td>6.352369</td>\n",
       "      <td>4.915557</td>\n",
       "      <td>18.743652</td>\n",
       "      <td>18.384546</td>\n",
       "      <td>1.726273</td>\n",
       "      <td>18.698111</td>\n",
       "      <td>0.435522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.840706e-02</td>\n",
       "      <td>0.123226</td>\n",
       "      <td>1.840283e-01</td>\n",
       "      <td>0.389851</td>\n",
       "      <td>1.813803e-01</td>\n",
       "      <td>0.393073</td>\n",
       "      <td>5.687895e-02</td>\n",
       "      <td>0.231265</td>\n",
       "      <td>0.019986</td>\n",
       "      <td>0.216983</td>\n",
       "      <td>...</td>\n",
       "      <td>1.267220</td>\n",
       "      <td>173.472214</td>\n",
       "      <td>0.635589</td>\n",
       "      <td>2.009347</td>\n",
       "      <td>1.520042</td>\n",
       "      <td>2.188327</td>\n",
       "      <td>2.128086</td>\n",
       "      <td>1.202532</td>\n",
       "      <td>2.412398</td>\n",
       "      <td>0.188874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000095</td>\n",
       "      <td>9.069719e-14</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.519396e-47</td>\n",
       "      <td>0.000188</td>\n",
       "      <td>2.983762e-14</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>51.157789</td>\n",
       "      <td>-4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.661338e-16</td>\n",
       "      <td>0.140345</td>\n",
       "      <td>3.354978e-01</td>\n",
       "      <td>0.374318</td>\n",
       "      <td>1.453297e-02</td>\n",
       "      <td>0.150791</td>\n",
       "      <td>1.995151e-03</td>\n",
       "      <td>0.124281</td>\n",
       "      <td>0.009718</td>\n",
       "      <td>0.439798</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>112.656762</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.441341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.931390e-04</td>\n",
       "      <td>0.198908</td>\n",
       "      <td>4.190666e-01</td>\n",
       "      <td>0.955060</td>\n",
       "      <td>2.396363e-02</td>\n",
       "      <td>0.938318</td>\n",
       "      <td>2.092845e-03</td>\n",
       "      <td>0.369719</td>\n",
       "      <td>0.012636</td>\n",
       "      <td>0.507540</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>118.415462</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.497549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.062573e-03</td>\n",
       "      <td>0.200074</td>\n",
       "      <td>5.002765e-01</td>\n",
       "      <td>0.983490</td>\n",
       "      <td>2.555994e-02</td>\n",
       "      <td>0.979954</td>\n",
       "      <td>1.907063e-02</td>\n",
       "      <td>0.380385</td>\n",
       "      <td>0.025201</td>\n",
       "      <td>0.527425</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>310.502123</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.518747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.207001e-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.921674</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1897.442320</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.288540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             sgl_bd          bent             T  see_saw_rect           tet  \\\n",
       "count  6.775000e+04  67750.000000  6.775000e+04  67750.000000  6.663800e+04   \n",
       "mean   8.230312e-03      0.187261  3.991934e-01      0.709934  6.176000e-02   \n",
       "std    2.840706e-02      0.123226  1.840283e-01      0.389851  1.813803e-01   \n",
       "min    0.000000e+00      0.000095  9.069719e-14      0.000000  2.519396e-47   \n",
       "25%    6.661338e-16      0.140345  3.354978e-01      0.374318  1.453297e-02   \n",
       "50%    3.931390e-04      0.198908  4.190666e-01      0.955060  2.396363e-02   \n",
       "75%    5.062573e-03      0.200074  5.002765e-01      0.983490  2.555994e-02   \n",
       "max    5.207001e-01      1.000000  1.000000e+00      1.000000  1.000000e+00   \n",
       "\n",
       "                oct      tri_plan       sq_plan     pent_plan     hex_bipyr  \\\n",
       "count  67743.000000  6.663800e+04  67743.000000  66638.000000  67750.000000   \n",
       "mean       0.682264  1.774031e-02      0.327322      0.018350      0.478373   \n",
       "std        0.393073  5.687895e-02      0.231265      0.019986      0.216983   \n",
       "min        0.000188  2.983762e-14      0.000157      0.000001      0.000000   \n",
       "25%        0.150791  1.995151e-03      0.124281      0.009718      0.439798   \n",
       "50%        0.938318  2.092845e-03      0.369719      0.012636      0.507540   \n",
       "75%        0.979954  1.907063e-02      0.380385      0.025201      0.527425   \n",
       "max        1.000000  1.000000e+00      1.000000      0.921674      1.000000   \n",
       "\n",
       "       ...  molecule_formal_charge      volume_y  oxidationstate_clean  \\\n",
       "count  ...            67750.000000  63418.000000          52066.000000   \n",
       "mean   ...                1.019793    224.205760              1.974398   \n",
       "std    ...                1.267220    173.472214              0.635589   \n",
       "min    ...               -8.000000     51.157789             -4.000000   \n",
       "25%    ...                0.000000    112.656762              2.000000   \n",
       "50%    ...                2.000000    118.415462              2.000000   \n",
       "75%    ...                2.000000    310.502123              2.000000   \n",
       "max    ...                6.000000   1897.442320              6.000000   \n",
       "\n",
       "                 CN  number_ligands  electroncount_ionic  \\\n",
       "count  67750.000000    67750.000000         52066.000000   \n",
       "mean       6.352369        4.915557            18.743652   \n",
       "std        2.009347        1.520042             2.188327   \n",
       "min        2.000000        2.000000             6.000000   \n",
       "25%        6.000000        4.000000            17.000000   \n",
       "50%        6.000000        6.000000            19.000000   \n",
       "75%        6.000000        6.000000            20.000000   \n",
       "max       20.000000        6.000000            28.000000   \n",
       "\n",
       "       electroncount_neutral  formal_charges_metal_centers  \\\n",
       "count           67750.000000                  67750.000000   \n",
       "mean               18.384546                      1.726273   \n",
       "std                 2.128086                      1.202532   \n",
       "min                 6.000000                     -8.000000   \n",
       "25%                17.000000                      2.000000   \n",
       "50%                18.000000                      2.000000   \n",
       "75%                20.000000                      2.000000   \n",
       "max                28.000000                      8.000000   \n",
       "\n",
       "       electron_counts_ionic_from_formal_charge   new_feature  \n",
       "count                              67750.000000  67750.000000  \n",
       "mean                                  18.698111      0.435522  \n",
       "std                                    2.412398      0.188874  \n",
       "min                                    6.000000      0.000000  \n",
       "25%                                   17.000000      0.441341  \n",
       "50%                                   18.000000      0.497549  \n",
       "75%                                   20.000000      0.518747  \n",
       "max                                   28.000000      1.288540  \n",
       "\n",
       "[8 rows x 42 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### boolean indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "& and operator\n",
    "| or operator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        SAXTOB\n",
       "1        OGANIW\n",
       "2        OGANOC\n",
       "3        OGANES\n",
       "4        OGANAO\n",
       "          ...  \n",
       "67740    XEKZIX\n",
       "67741    WONKOZ\n",
       "67742    PODWEK\n",
       "67743    TORNUK\n",
       "67744    YIVJES\n",
       "Name: refcode, Length: 21283, dtype: object"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['bent'] > 0.5) | (df['oct'] < 0.5)]['refcode']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18726050251841003"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['bent'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## if statements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "mylist = [1,2,3,4,5,6,7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 smaller or equal than 2\n",
      "2 smaller or equal than 2\n",
      "3 not greater than 4\n",
      "4 not greater than 4\n",
      "5|5\n",
      "6|6\n",
      "7|7\n"
     ]
    }
   ],
   "source": [
    "counter = 0\n",
    "for listitem in mylist:\n",
    "    counter +=1\n",
    "    if listitem > 4:\n",
    "        print(counter, listitem, sep='|')\n",
    "    elif listitem <= 2: \n",
    "        print(counter, 'smaller or equal than 2')\n",
    "    else: \n",
    "        print(counter, 'not greater than 4')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12246c390>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.plotting.boxplot(df, ['bent', 'sgl_bd'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: unrecognized arguments: # magic comment\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline # magic comment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['bent'], df['electroncount_ionic'])\n",
    "plt.xlabel('bent')\n",
    "plt.ylabel('electroncount')\n",
    "plt.title('title')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 2 artists>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(['b', 'a'], [1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:che602] *",
   "language": "python",
   "name": "conda-env-che602-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}