pandas_matplotlib_session_supervisor_version.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas and Matplotlib Session"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This session, we will cover the basics of Pandas and Matplotlib - two packages used to manipulate, analyse and visualise data. The content herein aims to act as an introduction to Pandas/Matplotlib and is by no means exhastive. Refering to the [Pandas](https://pandas.pydata.org/pandas-docs/stable/index.html) and [Matplotlib](https://matplotlib.org/index.html) documentation is an excellent way to extend your knowledge of these two modules and find answers to edge cases.\n",
    "\n",
    "___\n",
    "\n",
    "Let's start by importing the modules required for the session. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this session, you will need the data set named ```pdb_data_no_dups.csv```, which can be downloaded from our [GitHub repository](https://github.com/IC-Computational-Biology-Society/Pandas_Matplotlib_session.git) dedicated to today's workshop. Make sure you save it in the same directory as this Jupyter notebook. \n",
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas is a Python module, underpinned by Numpy, that enables efficient handling of tabular data structures, which are stored as DataFrame objects. These can either be created within a Python script or imported from separate files such as CSVs or Excel spreadsheets. \n",
    "\n",
    "Here, we will be working with a data set on summary protein structure determination informaiton taken from [Kaggle](https://www.kaggle.com/shahir/protein-data-set) to introduce basic Pandas commands.\n",
    "\n",
    "We will start by importing the data set into this Jupyter notebook and assigning it as a DataFrame to the variable ```df```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('pdb_data_no_dups.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The method ```.columns``` returns an array of column names from the DataFrame. Let's apply this to our ```df``` DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 1\n",
    "Convert the array of columns into a list and assign it to a Python variable. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['structureId',\n",
       " 'classification',\n",
       " 'experimentalTechnique',\n",
       " 'macromoleculeType',\n",
       " 'residueCount',\n",
       " 'resolution',\n",
       " 'structureMolecularWeight',\n",
       " 'crystallizationMethod',\n",
       " 'crystallizationTempK',\n",
       " 'densityMatthews',\n",
       " 'densityPercentSol',\n",
       " 'pdbxDetails',\n",
       " 'phValue',\n",
       " 'publicationYear']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Task 1 ###\n",
    "column_list = list(df.columns)\n",
    "\n",
    "column_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "The top five rows of of the Pandas DataFrame can be returned by using the method ```.head()```. Run ```.head()``` on your ```df``` to get a brief insight to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>structureId</th>\n",
       "      <th>classification</th>\n",
       "      <th>experimentalTechnique</th>\n",
       "      <th>macromoleculeType</th>\n",
       "      <th>residueCount</th>\n",
       "      <th>resolution</th>\n",
       "      <th>structureMolecularWeight</th>\n",
       "      <th>crystallizationMethod</th>\n",
       "      <th>crystallizationTempK</th>\n",
       "      <th>densityMatthews</th>\n",
       "      <th>densityPercentSol</th>\n",
       "      <th>pdbxDetails</th>\n",
       "      <th>phValue</th>\n",
       "      <th>publicationYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100D</td>\n",
       "      <td>DNA-RNA HYBRID</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA/RNA Hybrid</td>\n",
       "      <td>20</td>\n",
       "      <td>1.90</td>\n",
       "      <td>6360.30</td>\n",
       "      <td>VAPOR DIFFUSION, HANGING DROP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.78</td>\n",
       "      <td>30.89</td>\n",
       "      <td>pH 7.00, VAPOR DIFFUSION, HANGING DROP</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1994.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101D</td>\n",
       "      <td>DNA</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA</td>\n",
       "      <td>24</td>\n",
       "      <td>2.25</td>\n",
       "      <td>7939.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.00</td>\n",
       "      <td>38.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101M</td>\n",
       "      <td>OXYGEN TRANSPORT</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>154</td>\n",
       "      <td>2.07</td>\n",
       "      <td>18112.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.09</td>\n",
       "      <td>60.20</td>\n",
       "      <td>3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102D</td>\n",
       "      <td>DNA</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA</td>\n",
       "      <td>24</td>\n",
       "      <td>2.20</td>\n",
       "      <td>7637.17</td>\n",
       "      <td>VAPOR DIFFUSION, SITTING DROP</td>\n",
       "      <td>277.0</td>\n",
       "      <td>2.28</td>\n",
       "      <td>46.06</td>\n",
       "      <td>pH 7.00, VAPOR DIFFUSION, SITTING DROP, temper...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1995.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>102L</td>\n",
       "      <td>HYDROLASE(O-GLYCOSYL)</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>165</td>\n",
       "      <td>1.74</td>\n",
       "      <td>18926.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.75</td>\n",
       "      <td>55.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1993.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  structureId         classification experimentalTechnique macromoleculeType  \\\n",
       "0        100D         DNA-RNA HYBRID     X-RAY DIFFRACTION    DNA/RNA Hybrid   \n",
       "1        101D                    DNA     X-RAY DIFFRACTION               DNA   \n",
       "2        101M       OXYGEN TRANSPORT     X-RAY DIFFRACTION           Protein   \n",
       "3        102D                    DNA     X-RAY DIFFRACTION               DNA   \n",
       "4        102L  HYDROLASE(O-GLYCOSYL)     X-RAY DIFFRACTION           Protein   \n",
       "\n",
       "   residueCount  resolution  structureMolecularWeight  \\\n",
       "0            20        1.90                   6360.30   \n",
       "1            24        2.25                   7939.35   \n",
       "2           154        2.07                  18112.80   \n",
       "3            24        2.20                   7637.17   \n",
       "4           165        1.74                  18926.61   \n",
       "\n",
       "           crystallizationMethod  crystallizationTempK  densityMatthews  \\\n",
       "0  VAPOR DIFFUSION, HANGING DROP                   NaN             1.78   \n",
       "1                            NaN                   NaN             2.00   \n",
       "2                            NaN                   NaN             3.09   \n",
       "3  VAPOR DIFFUSION, SITTING DROP                 277.0             2.28   \n",
       "4                            NaN                   NaN             2.75   \n",
       "\n",
       "   densityPercentSol                                        pdbxDetails  \\\n",
       "0              30.89             pH 7.00, VAPOR DIFFUSION, HANGING DROP   \n",
       "1              38.45                                                NaN   \n",
       "2              60.20  3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...   \n",
       "3              46.06  pH 7.00, VAPOR DIFFUSION, SITTING DROP, temper...   \n",
       "4              55.28                                                NaN   \n",
       "\n",
       "   phValue  publicationYear  \n",
       "0      7.0           1994.0  \n",
       "1      NaN           1995.0  \n",
       "2      9.0           1999.0  \n",
       "3      7.0           1995.0  \n",
       "4      NaN           1993.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Enter code below\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The method ```.index``` can give you some information on the number of entries in the DataFrame. Apply this method to your DataFrame. Is the result useful?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Enter code below\n",
    "\n",
    "df.index  # Not useful if you want the result as an integer. Therefore, run len() function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2\n",
    "Determine the number of rows (entries) in your ```df``` DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Task 2 ### \n",
    "\n",
    "len(df.index)    # Number of rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "DataFrames are typically structured with a single Python data type assigned to each column. In other words, columns containing numeric data should not include strings (e.g. 1, 2, 3, 'four' is not good practice). What Python function is used to return the data type of the variable's below? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n"
     ]
    }
   ],
   "source": [
    "example_string = 'Hello_world'\n",
    "example_integer = 1234\n",
    "example_float = 1.234\n",
    "\n",
    "### Enter code below\n",
    "\n",
    "# type() returns object type in Python\n",
    "\n",
    "# for i in [example_string, example_integer, example_float]:\n",
    "#     print(type(i))\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "number_one = '1'\n",
    "\n",
    "print(type(number_one))\n",
    "\n",
    "number_1 = 1\n",
    "\n",
    "print(type(number_1))\n",
    "\n",
    "number_int_one = int(number_one)\n",
    "\n",
    "print(type(number_int_one))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas allows you to check the data type contained within a column using the method ```.dtype```. Apply this to your DataFrame. Are the results what you would expect considering your previous insight into the DataFrame?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "structureId                  object\n",
       "classification               object\n",
       "experimentalTechnique        object\n",
       "macromoleculeType            object\n",
       "residueCount                  int64\n",
       "resolution                  float64\n",
       "structureMolecularWeight    float64\n",
       "crystallizationMethod        object\n",
       "crystallizationTempK        float64\n",
       "densityMatthews             float64\n",
       "densityPercentSol           float64\n",
       "pdbxDetails                  object\n",
       "phValue                     float64\n",
       "publicationYear             float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Enter code below\n",
    "\n",
    "df.dtypes      # They should be what you expect. Although, year is an anomaly. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another useful feature of Pandas is the ability to find all the unique values contained within a column. Squre brackets ```[]``` are used to slice a DataFrame into a single column and the method ```.unique()``` returns non-duplicate values. Run the cell below to see what the code returns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['X-RAY DIFFRACTION', 'SOLUTION NMR', 'FIBER DIFFRACTION',\n",
       "       'ELECTRON CRYSTALLOGRAPHY', 'ELECTRON MICROSCOPY',\n",
       "       'NEUTRON DIFFRACTION', 'SOLID-STATE NMR', 'INFRARED SPECTROSCOPY',\n",
       "       'SOLUTION NMR, THEORETICAL MODEL', 'SOLUTION SCATTERING',\n",
       "       'POWDER DIFFRACTION', 'CRYO-ELECTRON MICROSCOPY',\n",
       "       'ELECTRON DIFFRACTION', 'X-RAY DIFFRACTION, SOLUTION NMR',\n",
       "       'THEORETICAL MODEL, SOLUTION NMR', 'FLUORESCENCE TRANSFER',\n",
       "       'X-RAY DIFFRACTION, EPR', 'SOLUTION NMR, SOLUTION SCATTERING',\n",
       "       'SOLID-STATE NMR, SOLUTION NMR', 'SOLUTION NMR, EPR',\n",
       "       'SOLUTION SCATTERING, SOLUTION NMR',\n",
       "       'SOLUTION NMR, SOLID-STATE NMR',\n",
       "       'SOLID-STATE NMR, ELECTRON MICROSCOPY', 'EPR, X-RAY DIFFRACTION',\n",
       "       'NEUTRON DIFFRACTION, X-RAY DIFFRACTION',\n",
       "       'FIBER DIFFRACTION, SOLID-STATE NMR',\n",
       "       'SOLUTION SCATTERING, ELECTRON MICROSCOPY',\n",
       "       'ELECTRON MICROSCOPY, SOLUTION SCATTERING',\n",
       "       'X-RAY DIFFRACTION, NEUTRON DIFFRACTION',\n",
       "       'ELECTRON MICROSCOPY, SOLUTION SCATTERING, SOLID-STATE NMR',\n",
       "       'ELECTRON MICROSCOPY, SOLID-STATE NMR',\n",
       "       'SOLUTION SCATTERING, X-RAY DIFFRACTION',\n",
       "       'NEUTRON DIFFRACTION, SOLUTION NMR'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['experimentalTechnique'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3 \n",
    "What types of macromolecules are included in your DataFrame?\n",
    "\n",
    "How could you clean this data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['DNA/RNA Hybrid', 'DNA', 'Protein', 'Protein#DNA', nan, 'DNA#RNA',\n",
       "       'RNA', 'DNA#DNA/RNA Hybrid', 'Protein#RNA', 'RNA#DNA/RNA Hybrid',\n",
       "       'Protein#DNA/RNA Hybrid', 'Protein#DNA#RNA',\n",
       "       'Protein#DNA#DNA/RNA Hybrid', 'Protein#RNA#DNA/RNA Hybrid'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Task 3 ###\n",
    "\n",
    "df['macromoleculeType'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "As well as returning unique entries in a column, we can find their frequencies using the method ```.value_counts()```. \n",
    "\n",
    "### Task 4 \n",
    "\n",
    "Apply ```.value_counts()``` to an appropriate column of your choice. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X-RAY DIFFRACTION                                            126432\n",
      "SOLUTION NMR                                                  12268\n",
      "ELECTRON MICROSCOPY                                            2252\n",
      "SOLID-STATE NMR                                                  99\n",
      "ELECTRON CRYSTALLOGRAPHY                                         74\n",
      "NEUTRON DIFFRACTION                                              60\n",
      "FIBER DIFFRACTION                                                40\n",
      "NEUTRON DIFFRACTION, X-RAY DIFFRACTION                           38\n",
      "X-RAY DIFFRACTION, NEUTRON DIFFRACTION                           33\n",
      "SOLUTION SCATTERING                                              32\n",
      "POWDER DIFFRACTION                                               19\n",
      "SOLUTION SCATTERING, SOLUTION NMR                                 9\n",
      "SOLUTION NMR, SOLUTION SCATTERING                                 6\n",
      "X-RAY DIFFRACTION, EPR                                            5\n",
      "SOLID-STATE NMR, ELECTRON MICROSCOPY                              5\n",
      "SOLUTION NMR, THEORETICAL MODEL                                   5\n",
      "INFRARED SPECTROSCOPY                                             4\n",
      "SOLID-STATE NMR, SOLUTION NMR                                     3\n",
      "EPR, X-RAY DIFFRACTION                                            2\n",
      "THEORETICAL MODEL, SOLUTION NMR                                   2\n",
      "ELECTRON MICROSCOPY, SOLUTION SCATTERING, SOLID-STATE NMR         1\n",
      "ELECTRON MICROSCOPY, SOLID-STATE NMR                              1\n",
      "CRYO-ELECTRON MICROSCOPY                                          1\n",
      "SOLUTION NMR, EPR                                                 1\n",
      "FIBER DIFFRACTION, SOLID-STATE NMR                                1\n",
      "ELECTRON DIFFRACTION                                              1\n",
      "ELECTRON MICROSCOPY, SOLUTION SCATTERING                          1\n",
      "SOLUTION SCATTERING, ELECTRON MICROSCOPY                          1\n",
      "X-RAY DIFFRACTION, SOLUTION NMR                                   1\n",
      "FLUORESCENCE TRANSFER                                             1\n",
      "NEUTRON DIFFRACTION, SOLUTION NMR                                 1\n",
      "SOLUTION SCATTERING, X-RAY DIFFRACTION                            1\n",
      "SOLUTION NMR, SOLID-STATE NMR                                     1\n",
      "Name: experimentalTechnique, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "### Task 4 ###\n",
    "\n",
    "counts = df['experimentalTechnique'].value_counts()\n",
    "\n",
    "print(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "Often, we only want to work with numeric data from a DataFrame. Columns containing strings can confound analysis if they are not removed. We can create a new DataFrame that countains only floats and integers using the following code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DataFrame copy made of df, containing only integers and floats.  \n",
    "df_numeric = df.select_dtypes(include = [\"int64\", \"float64\"])\n",
    "\n",
    "# Displaying new DataFrame.\n",
    "df_numeric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Task 5\n",
    "Modify the code above to create a copy of ```df``` that contains only string-type data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Task 5 ###\n",
    "\n",
    "df_text = df.select_dtypes(include = [\"object\"])\n",
    "\n",
    "df_text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "A quick summary of numeric data within a DataFrame can be obtained using the method ```.describe()```. Apply this to your ```df```. Ask your supervisor how you can transpose the DataFrame to move your summary statistics returned by ```.describe()``` to the column position. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Enter code below\n",
    "\n",
    "### ANSWER ###\n",
    "\n",
    "df_numeric.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A major problem that arises when working with unfamiliar data sets is the insertion of ```NULL```, ```Na``` or ```NaN``` values. Rows with any of these values can be removed using the ```.dropna()``` method. \n",
    "\n",
    "How many entries of your original ```df``` DataFrame are free from missing values for all parameters?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "141401\n",
      "65886\n"
     ]
    }
   ],
   "source": [
    "# Enter code below\n",
    "\n",
    "print(len(df))\n",
    "\n",
    "df_copy = df.dropna()\n",
    "\n",
    "print(len(df_copy.index))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Does the presence of missing values in a DataFrame affect summary statistics? Write some code in the cell below to test your hypothesis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.788685377223861"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Enter code below \n",
    "\n",
    "df['phValue'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.78788376893422"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy['phValue'].mean()      # Remove\n",
    "\n",
    "# Shows NULL values must be removed for tasks like this "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "The final feature Pandas we will look at is boolean operators. The method ```.iloc[]``` will return the entry at a given index. A further argument can be parsed into ```.iloc[]``` to return both the entry at a given index and that entry's parameter value at a given column. Pandas, like Python, uses a starting index of ```0```. Run the code below to see ```.iloc[]``` in action. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "entry_two = df.iloc[1]                   # Returns the entire entry from the second column\n",
    "\n",
    "entry_three_column_four = df.iloc[2, 3]  # Returns the single value in row 3, column 4\n",
    "\n",
    "print(entry_two)\n",
    "print('-------')                         # Arbitrary seperator\n",
    "print(entry_three_column_four)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another useful method is ```.loc[]```. It works in a similar way to ```.iloc[]``` but is used as a logical entry selector. Boolean operators are used with ```.loc[]``` to exclusively select entries based on values in a specified column. Run the code below to get a feel for ```.loc[]```.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select only entries solved by X-ray diffraction and assign to new DataFrame\n",
    "xray_structures = df.loc[df['experimentalTechnique'] == 'X-RAY DIFFRACTION']\n",
    "\n",
    "# Select only entries solved by X-ray diffraciton and are DNA-based\n",
    "dna_xray_structures = df.loc[(df['experimentalTechnique'] == 'X-RAY DIFFRACTION') & (df['classification'] == 'DNA')]\n",
    "\n",
    "# Shows the DataFrame contains only one unique entry in the 'experimentalTechnique' field. \n",
    "xray_structures['experimentalTechnique'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the second variable is defined using the element-wise boolean operator ```&```. The element-wise booleans in Pandas for AND, OR and XOR are ```&```, ```|``` and ```^```, respectively. \n",
    "\n",
    "\n",
    "It is good practice to use ```.loc[]``` to conditionally select entries from DataFrames but can be omitted, giving the same result. \n",
    "\n",
    "\n",
    "### Task 6 \n",
    "\n",
    "What year was the 100th protein structure (```macromoleculeType```) solved solely by ```ELECTRON MICROSCOPY```?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2007.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### Task 6 ###\n",
    "\n",
    "df.loc[(df['experimentalTechnique'] == 'ELECTRON MICROSCOPY') & (df['macromoleculeType'] == 'Protein')].iloc[99, 13]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplotlib introduction\n",
    "\n",
    "This section will introduce some of the basics of Maplotlib. It is by no means an exhaustive overview of the package and you should look through the [Matplotlib documentation](https://matplotlib.org/) when working on your own projects in the future. \n",
    "\n",
    "The terminology of Matplotlib can cause confusion for those unfamiliar with the package. The term ***figure*** refers to the entire plot area of a graph, which i sseparate from the plot area. An ***axis*** is placed onto the Matplotlib *figure* and defines the area onto which your data will be plotted. A Matplotlib *axis*, ambiguously, includes both x- and y-axes. Multiple *axes* can be plotted onto a single figure. \n",
    "\n",
    "Let's start by plotting a histogram of the resolution data in the DataFrame. By doing so, we can get an insight into how the data is distributed. \n",
    "\n",
    "Run the code below. What does each line do? Speak with your supervisor for clarification. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD4CAYAAADsKpHdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAR60lEQVR4nO3df4hd5Z3H8fenUVux7WrrrIQk7EgbdkkLje0QXSxLV6lGXTYWuqKwNVukKTSCZQvbWBbsDwX9o3VbsEK6hsbdbqPUFkNNmwYrFGHVjJqqMXWdtRETUjM1UStlldjv/nGfbC/ZmcxkZnLvxPt+wWHO/Z7nPPd50Mznnh/3TKoKSZLe1u8BSJLmBwNBkgQYCJKkxkCQJAEGgiSpOanfA5ipM888s4aHh/s9DEk6oTz66KO/raqhibadsIEwPDzM6Ohov4chSSeUJM9Pts1TRpIkwECQJDUGgiQJMBAkSY2BIEkCDARJUmMgSJIAA0GS1BgIkiTgBP6msjS87r4562v3zZfNWV/SicojBEkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqTEQJEmAgSBJagwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpmTIQkrwjySNJfplkZ5KvtPrZSR5OMpbkriSntPrb2+uxtn24q6/rW/2ZJBd31Ve22liSdcdhnpKkKUznCOF14IKq+hCwHFiZ5DzgFuDWqno/cBC4prW/BjjY6re2diRZBlwJfABYCXw7yYIkC4DbgEuAZcBVra0kqYemDITqeK29PLktBVwA/KDVNwKXt/VV7TVt+4VJ0uqbqur1qvo1MAasaMtYVT1XVW8Am1pbSVIPTesaQvskvwPYD2wD/ht4uaoOtSZ7gEVtfRHwAkDb/grw3u76EftMVp9oHGuSjCYZHR8fn87QJUnTNK1AqKo3q2o5sJjOJ/q/OJ6DOso41lfVSFWNDA0N9WMIkvSWdUx3GVXVy8ADwF8Cpyc5/DeZFwN72/peYAlA2/4nwEvd9SP2mawuSeqh6dxlNJTk9LZ+KvBxYBedYPhka7YauLetb26vadt/XlXV6le2u5DOBpYCjwDbgaXtrqVT6Fx43jwHc5MkHYOTpm7CQmBjuxvobcDdVfXjJE8Dm5LcCDwO3NHa3wH8W5Ix4ACdX/BU1c4kdwNPA4eAtVX1JkCSa4GtwAJgQ1XtnLMZSpKmZcpAqKongHMmqD9H53rCkfX/Af5ukr5uAm6aoL4F2DKN8UqSjhO/qSxJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJMBAkSY2BIEkCDARJUmMgSJIAA0GS1BgIkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCZhGICRZkuSBJE8n2Znkulb/cpK9SXa05dKufa5PMpbkmSQXd9VXttpYknVd9bOTPNzqdyU5Za4nKkk6uukcIRwCvlBVy4DzgLVJlrVtt1bV8rZsAWjbrgQ+AKwEvp1kQZIFwG3AJcAy4Kqufm5pfb0fOAhcM0fzkyRN05SBUFX7quqxtv47YBew6Ci7rAI2VdXrVfVrYAxY0Zaxqnquqt4ANgGrkgS4APhB238jcPkM5yNJmqFjuoaQZBg4B3i4la5N8kSSDUnOaLVFwAtdu+1ptcnq7wVerqpDR9Qnev81SUaTjI6Pjx/L0CVJU5h2ICR5J3AP8PmqehW4HXgfsBzYB3z9eAywW1Wtr6qRqhoZGho63m8nSQPlpOk0SnIynTD4XlX9EKCqXuza/h3gx+3lXmBJ1+6LW41J6i8Bpyc5qR0ldLeXJPXIdO4yCnAHsKuqvtFVX9jV7BPAU219M3BlkrcnORtYCjwCbAeWtjuKTqFz4XlzVRXwAPDJtv9q4N7ZTUuSdKymc4RwPvAp4MkkO1rtS3TuEloOFLAb+CxAVe1McjfwNJ07lNZW1ZsASa4FtgILgA1VtbP190VgU5IbgcfpBJAkqYemDISqehDIBJu2HGWfm4CbJqhvmWi/qnqOzl1IkqQ+8ZvKkiTAQJAkNQaCJAkwECRJzbS+hyC91Q2vu2/O+tp982Vz1pfUSx4hSJIAA0GS1BgIkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVJjIEiSgGn8xbQkS4A7gbOAAtZX1TeTvAe4CxgGdgNXVNXBJAG+CVwK/B74h6p6rPW1Gvjn1vWNVbWx1T8CfBc4FdgCXFdVNUdz1Dwyl3+ZTNLcms4RwiHgC1W1DDgPWJtkGbAOuL+qlgL3t9cAlwBL27IGuB2gBcgNwLnACuCGJGe0fW4HPtO138rZT02SdCymDISq2nf4E35V/Q7YBSwCVgEbW7ONwOVtfRVwZ3U8BJyeZCFwMbCtqg5U1UFgG7CybXt3VT3Ujgru7OpLktQjx3QNIckwcA7wMHBWVe1rm35D55QSdMLiha7d9rTa0ep7JqhP9P5rkowmGR0fHz+WoUuSpjDtQEjyTuAe4PNV9Wr3tvbJ/rif86+q9VU1UlUjQ0NDx/vtJGmgTCsQkpxMJwy+V1U/bOUX2+ke2s/9rb4XWNK1++JWO1p98QR1SVIPTRkI7a6hO4BdVfWNrk2bgdVtfTVwb1f96nScB7zSTi1tBS5Kcka7mHwRsLVtezXJee29ru7qS5LUI1PedgqcD3wKeDLJjlb7EnAzcHeSa4DngSvati10bjkdo3Pb6acBqupAkq8B21u7r1bVgbb+Of542+lP2iJJ6qEpA6GqHgQyyeYLJ2hfwNpJ+toAbJigPgp8cKqxSJKOH7+pLEkCDARJUmMgSJIAA0GS1BgIkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ10/l7CJKOwfC6++asr903XzZnfUlT8QhBkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElqDARJEjCNQEiyIcn+JE911b6cZG+SHW25tGvb9UnGkjyT5OKu+spWG0uyrqt+dpKHW/2uJKfM5QQlSdMznSOE7wIrJ6jfWlXL27IFIMky4ErgA22fbydZkGQBcBtwCbAMuKq1Bbil9fV+4CBwzWwmJEmamSkDoap+ARyYZn+rgE1V9XpV/RoYA1a0ZayqnquqN4BNwKokAS4AftD23whcfmxTkCTNhdlcQ7g2yRPtlNIZrbYIeKGrzZ5Wm6z+XuDlqjp0RF2S1GMzDYTbgfcBy4F9wNfnakBHk2RNktEko+Pj4714S0kaGDMKhKp6sarerKo/AN+hc0oIYC+wpKvp4labrP4ScHqSk46oT/a+66tqpKpGhoaGZjJ0SdIkZhQISRZ2vfwEcPgOpM3AlUnenuRsYCnwCLAdWNruKDqFzoXnzVVVwAPAJ9v+q4F7ZzImSdLsTPn46yTfBz4GnJlkD3AD8LEky4ECdgOfBaiqnUnuBp4GDgFrq+rN1s+1wFZgAbChqna2t/gisCnJjcDjwB1zNTlJ0vRNGQhVddUE5Ul/aVfVTcBNE9S3AFsmqD/HH085SZL6xG8qS5IAA0GS1BgIkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVIz5R/IkdQ/w+vum7O+dt982Zz1pbcmjxAkSYCBIElqDARJEmAgSJIaA0GSBBgIkqRmykBIsiHJ/iRPddXek2RbkmfbzzNaPUm+lWQsyRNJPty1z+rW/tkkq7vqH0nyZNvnW0ky15OUJE1tOkcI3wVWHlFbB9xfVUuB+9trgEuApW1ZA9wOnQABbgDOBVYANxwOkdbmM137HflekqQemDIQquoXwIEjyquAjW19I3B5V/3O6ngIOD3JQuBiYFtVHaiqg8A2YGXb9u6qeqiqCrizqy9JUg/N9BrCWVW1r63/BjirrS8CXuhqt6fVjlbfM0F9QknWJBlNMjo+Pj7DoUuSJjLri8rtk33NwVim817rq2qkqkaGhoZ68ZaSNDBmGggvttM9tJ/7W30vsKSr3eJWO1p98QR1SVKPzTQQNgOH7xRaDdzbVb+63W10HvBKO7W0FbgoyRntYvJFwNa27dUk57W7i67u6kuS1ENTPu00yfeBjwFnJtlD526hm4G7k1wDPA9c0ZpvAS4FxoDfA58GqKoDSb4GbG/tvlpVhy9Uf47OnUynAj9piySpx6YMhKq6apJNF07QtoC1k/SzAdgwQX0U+OBU45AkHV9+U1mSBBgIkqTGQJAkAQaCJKkxECRJgIEgSWqmvO1UGl53X7+HIKkHPEKQJAEGgiSpMRAkSYCBIElqDARJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkxECRJgM8ykgbGXD6TavfNl81ZX5o/PEKQJAEGgiSpMRAkScAsAyHJ7iRPJtmRZLTV3pNkW5Jn288zWj1JvpVkLMkTST7c1c/q1v7ZJKtnNyVJ0kzMxRHCX1fV8qoaaa/XAfdX1VLg/vYa4BJgaVvWALdDJ0CAG4BzgRXADYdDRJLUO8fjlNEqYGNb3whc3lW/szoeAk5PshC4GNhWVQeq6iCwDVh5HMYlSTqK2QZCAT9L8miSNa12VlXta+u/Ac5q64uAF7r23dNqk9X/nyRrkowmGR0fH5/l0CVJ3Wb7PYSPVtXeJH8KbEvyq+6NVVVJapbv0d3femA9wMjIyJz1K0ma5RFCVe1tP/cDP6JzDeDFdiqI9nN/a74XWNK1++JWm6wuSeqhGQdCktOSvOvwOnAR8BSwGTh8p9Bq4N62vhm4ut1tdB7wSju1tBW4KMkZ7WLyRa0mSeqh2ZwyOgv4UZLD/fxHVf00yXbg7iTXAM8DV7T2W4BLgTHg98CnAarqQJKvAdtbu69W1YFZjEuSNAMzDoSqeg740AT1l4ALJ6gXsHaSvjYAG2Y6FknS7PlNZUkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqTEQJEmAgSBJagwESRIw+6edShpAw+vum7O+dt982Zz1pdkxEN6i5vIfrKTB4CkjSRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElq/GKapL7yW8/zh0cIkiTAQJAkNZ4ykvSW4emn2Zk3RwhJViZ5JslYknX9Ho8kDZp5cYSQZAFwG/BxYA+wPcnmqnq6vyOTNKjm6xODj+eRy7wIBGAFMFZVzwEk2QSsAgYqEObr/4CSBsN8CYRFwAtdr/cA5x7ZKMkaYE17+VqSZ2b4fmcCv53hvie6QZ47DPb8B3nu8BaZf26Z0W7dc/+zyRrNl0CYlqpaD6yfbT9JRqtqZA6GdMIZ5LnDYM9/kOcOgz3/6c59vlxU3gss6Xq9uNUkST0yXwJhO7A0ydlJTgGuBDb3eUySNFDmxSmjqjqU5FpgK7AA2FBVO4/jW876tNMJbJDnDoM9/0GeOwz2/Kc191TV8R6IJOkEMF9OGUmS+sxAkCQBAxYIg/x4jCQbkuxP8lS/x9JrSZYkeSDJ00l2Jrmu32PqpSTvSPJIkl+2+X+l32PqtSQLkjye5Mf9HkuvJdmd5MkkO5KMHrXtoFxDaI/H+C+6Ho8BXDUoj8dI8lfAa8CdVfXBfo+nl5IsBBZW1WNJ3gU8Clw+QP/tA5xWVa8lORl4ELiuqh7q89B6Jsk/AiPAu6vqb/o9nl5KshsYqaopv5Q3SEcI//d4jKp6Azj8eIyBUFW/AA70exz9UFX7quqxtv47YBedb8cPhOp4rb08uS2D8UkQSLIYuAz4136PZb4bpECY6PEYA/NLQR1JhoFzgIf7PJSeaqdMdgD7gW1VNUjz/xfgn4A/9Hkc/VLAz5I82h7/M6lBCgQNuCTvBO4BPl9Vr/Z7PL1UVW9W1XI6TwFYkWQgThsm+Rtgf1U92u+x9NFHq+rDwCXA2nb6eEKDFAg+HmOAtXPn9wDfq6of9ns8/VJVLwMPACv7PJReOR/423YefRNwQZJ/7++Qequq9raf+4Ef0Tl9PqFBCgQfjzGg2kXVO4BdVfWNfo+n15IMJTm9rZ9K58aKX/V1UD1SVddX1eKqGqbzb/7nVfX3fR5WzyQ5rd1IQZLTgIuASe80HJhAqKpDwOHHY+wC7j7Oj8eYV5J8H/hP4M+T7ElyTb/H1EPnA5+i8+lwR1su7fegemgh8ECSJ+h8MNpWVQN3++WAOgt4MMkvgUeA+6rqp5M1HpjbTiVJRzcwRwiSpKMzECRJgIEgSWoMBEkSYCBIkhoDQZIEGAiSpOZ/AUs+TCZ2m/9lAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialising figure as a 1x1 grid of axes\n",
    "resolution_fig, ax_res_hist = plt.subplots(1, 1) \n",
    "\n",
    " # Define number, size and range of bins\n",
    "res_bins = np.arange(0, 5, 0.3)                   \n",
    "\n",
    "# Plot histogram of non-NaN resolution data using Pandas\n",
    "ax_res_hist.hist(df['resolution'].dropna(),        \n",
    "                 bins=res_bins\n",
    "                 )\n",
    "\n",
    "plt.show()         # Displays all axes on figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 7\n",
    "\n",
    "Plot a histogram for a numerical parameter from ```df``` of your chioce. Inspect the [Maplotlib documentation](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.hist.html?highlight=hist#matplotlib.axes.Axes.hist) for this form of histogram and add additional arguments into your code to format the graph's appearance. \n",
    "\n",
    "Axes labels can also be added using the method ```.set_nlabel```, replacing ```n``` with either ```x``` or ```y```. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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": [
    "### Task 7 ###\n",
    "\n",
    "# Initialising figure as a 1x1 grid of axes\n",
    "resolution_fig, ax_res_hist = plt.subplots(1, 1) \n",
    "\n",
    " # Define number, size and range of bins\n",
    "res_bins = np.arange(0, 5, 0.3)                   \n",
    "\n",
    "# Plot histogram of non-NaN resolution data using Pandas\n",
    "ax_res_hist.hist(df['resolution'].dropna(),        \n",
    "                 bins=res_bins\n",
    "                 )\n",
    "\n",
    "ax_res_hist.set_xlabel('Resulution (Angstrom)')\n",
    "ax_res_hist.set_ylabel('Frequency')\n",
    "\n",
    "plt.show()         # Displays all axes on figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "Scatter plots can be a good way to identify trends or groups within data. \n",
    "\n",
    "Run the code below. Is it valid to say the resolution of solved macromolecules has been worsening over time? What reasons are there for the trends observed? Discuss with your supervisor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAk00lEQVR4nO3de3Bc5Znn8e+jtoRlxyBEjAeEhYnjcQrW2MYKNutUlkCBCUyCNtziQMY1YeM/NrOV23hjb6gBslA44x2GbG1qpshtnQohXGOcgQ1huVR2SDCRY8AYcLiMjVEAezAGAsKW5Wf/6NMgtc7b6tM6fTv6fapc6j7dfc77quWnTz/nfZ/X3B0REcmulno3QEREqkuBXkQk4xToRUQyToFeRCTjFOhFRDJuUi0P9sEPftBnzZpVy0OKiDS9zZs3/5u7T6/09TUN9LNmzaKvr6+WhxQRaXpmtnM8r1fqRkQk4xToRUQyToFeRCTjFOhFRDJOgV5EJOPGHHVjZnOBW4Zt+hDwt8CPo+2zgB3Axe7+evpNFJEkZq2+e9S2HWvPq0NLpFGMeUbv7tvdfYG7LwAWAe8APwdWA/e7+xzg/ui+iNRRXJAvtV0mhqSpmzOB5919J3A+sD7avh7oTbFdIiKSkqSB/rPAzdHtGe7+cnT7FWBG3AvMbKWZ9ZlZ3549eypspoiIVKrsQG9mbcCngduKH/P86iWxK5i4+43u3uPuPdOnVzyDV0REKpTkjP6TwO/d/dXo/qtmdgxA9HN32o0TEZHxSxLol/N+2gZgI7Aiur0CuCutRolIZUKjazTqZmKzctaMNbOpwIvAh9z9jWjbUcCtQDewk/zwyr2l9tPT0+MqaiYikoyZbXb3nkpfX1b1Snd/GziqaNtr5EfhiIhIA9PMWBGRjFOgFxHJOAV6EZGMU6AXEck4BXoRkYxToBcRyTgFehGRjFOgFxHJOAV6EZGMU6AXEck4BXoRkYxToBcRyTgFehGRjFOgFxHJOAV6EZGMU6AXEck4BXoRkYxToBcRyTgFehGRjFOgFxHJuLICvZl1mNntZvaMmT1tZqeZWaeZ3Wdmz0Y/j6x2Y0VEJLlyz+i/A/zS3T8CzAeeBlYD97v7HOD+6L6IiDSYMQO9mR0BfBz4AYC7H3D3fcD5wProaeuB3uo0UURExqOcM/oTgD3Aj8xsi5l938ymAjPc/eXoOa8AM+JebGYrzazPzPr27NmTTqtFRKRs5QT6ScApwD+6+0LgbYrSNO7ugMe92N1vdPced++ZPn36eNsrIiIJlRPoXwJecvdN0f3byQf+V83sGIDo5+7qNFFERMZjzEDv7q8Au8xsbrTpTOApYCOwItq2ArirKi0UEZFxmVTm8/4LcJOZtQEvAH9F/kPiVjO7HNgJXFydJoqIyHiUFejd/TGgJ+ahM1NtjYiIpE4zY0VEMk6BXkQk4xToRUQyToFeRCTjFOhFRDJOgV5EJOMU6EVEMk6BXkQk4xToRUQyToFeRCTjFOhFRDJOgV5EJOMU6EVEMk6BXkQk4xToRUQyToFeRCTjFOhFRDJOgV5EJOMU6EVEMq6sNWPNbAfwFjAEHHT3HjPrBG4BZgE7gIvd/fXqNFNEpDIbtvSz7t7t/HHfAMd2tLNq2Vx6F3bVu1k1leSM/hPuvsDdC4uErwbud/c5wP3RfRGRhrFhSz9r7txK/74BHOjfN8CaO7eyYUt/vZtWU+NJ3ZwPrI9urwd6x90aEZEUrbt3OwODQyO2DQwOse7e7XVqUX2UG+gd+JWZbTazldG2Ge7+cnT7FWBG3AvNbKWZ9ZlZ3549e8bZXBGR8v1x30Ci7VlVbqD/mLufAnwS+JKZfXz4g+7u5D8MRnH3G929x917pk+fPr7WiogkcGxHe6LtWVVWoHf3/ujnbuDnwKnAq2Z2DED0c3e1GikiUolVy+bS3pobsa29NceqZXPr1KL6GDPQm9lUM5tWuA2cDTwJbARWRE9bAdxVrUaKiFSid2EX131mHl0d7RjQ1dHOdZ+ZN+FG3ZQzvHIG8HMzKzz/p+7+SzP7HXCrmV0O7AQurl4zRUQq07uwa8IF9mJjBnp3fwGYH7P9NeDMajRKRETSo5mxIiIZp0AvIpJxCvQiIhmnQC8iknEK9CIiGadALyKScQr0IiIZp0AvIpJxCvQiIhmnQC8iknEK9CIiGadALyKScQr0IiIZp0AvIpJx5dSjFxFpWpd+77c8/Pze9+4vnd3JTV88rY4tqj2d0YtIZhUHeYCHn9/Lpd/7bZ1aVB8K9CKSWcVBfqztWaVALyKScQr0IiIZV3agN7OcmW0xs3+O7p9gZpvM7Dkzu8XM2qrXTBGR5FoDES60PauSdPfLwNPD7n8b+Ad3/zDwOnB5mg0TERmvg4eSbS/YsKWfpWsf4ITVd7N07QNs2NKffuNqqKxAb2bHAecB34/uG3AGcHv0lPVAbxXaJyJSsUmBCBfaDvkgv+bOrfTvG8CB/n0DrLlza1MH+3LP6G8A/itQ+Bw8Ctjn7gej+y8BXek2TURkfAYDZ+6h7QDr7t3OwODQiG0Dg0Osu3d7ii2rrTEDvZn9BbDb3TdXcgAzW2lmfWbWt2fPnkp2ISJSM3/cN5BoezMo54x+KfBpM9sB/Ix8yuY7QIeZFWbWHgfEfq9x9xvdvcfde6ZPn55Ck0VEqufYjvZE25vBmIHe3de4+3HuPgv4LPCAu18KPAhcGD1tBXBX1VopIlIjq5bNpb01N2Jbe2uOVcvm1qlF4zeeQUbfAL5mZs+Rz9n/IJ0miYjUT+/CLq77zDy6OtoxoKujnes+M4/ehc17GTJRUTN3fwh4KLr9AnBq+k0SEUnHnKOn8uzut2O3l9K7sKupA3sxVa8UkUzYsKWfdfdu54/7Bji2o51Vy+Zy39dO56zrHxoR7OccPZX7vnZ6/RpaBwr0ItL0CmPfC8MiC2PfgQkX1ONMsInAIpJFWRz7niad0YtIU4lL0WRx7HuaFOhFpGmEUjQdU1p5/Z3BUc9v5rHvaVLqRkSaRihF407mxr6nSWf0ItI0QqmYNwYG+YdLFoxK6fQu7Kpozdi49FCp4ZaLr72PV9868N79GdPa2PTNs7hiw1Zu3rSLIXdyZixfPJNreucl7PX4mbvX7GA9PT3e19dXs+OJSLYsXfsA/THBvqujnYdXnzFqe9yasVA62BenhyD/7SA0aao4yBdMzhnvDo2Or5ct6U4c7M1ss7v3JHrRMErdiEjTSFqeoJI1Y5OO4IkL8kBskAe4edOu4LGrRakbEam6pKmQkMJr0tgXEJtaqfYInqEaZlEKFOhFpKpKTWaqNNinUZ7gig1b+ckjL753f8idnzzyIlPbcrx9YGjU89MawZMzS2U/SSh1IyJVVc/JTEtndwa3h1Io7xwYSpQemjEtfrnsybn4gL588cxQc6tGgV5Eqqqek5lu+uJpo4J94UJsKIXikKh65aZvnjUq2M+Y1sYz157LZUu63zuDz5lVdCE2DUrdiEhVHdvRHjtSplaTmUKja3JmscE+Z5Y4PbTpm2fFbr+md15dAnsxndGLSFU16kIeoRRKPVIr1aYzehGpqrRHyqSlcKbdCBOaqk0TpkREGpwmTImISEkK9CIiGadALyKScWNejDWzycCvgcOi59/u7lea2QnAz4CjgM3A5909vuiDiNTdyVf+kjf3vz9x6fDDcjxx9TmplSdoRqG+Z+13MubFWDMzYKq7/8nMWoF/Ab4MfA24091/Zmb/BDzu7v9Yal+6GCtSH8VBvmByzrCWlrIrNWZJqErlBYu6uGNzf0P9Tqp+Mdbz/hTdbY3+OXAGcHu0fT3QW2kjRKS64oI85CssTtS1VkOlGW7etCtzv5OyxtGbWY58eubDwHeB54F97n4wespLQOxHnZmtBFYCdHd3j7e9IlIDhfIEWUhhhPoQKsEQKo3QzOvPlhXo3X0IWGBmHcDPgY+UewB3vxG4EfKpmwraKCI1dmxHe+pVJ+uhVB9CpRlCpRGaef3ZRKNu3H0f8CBwGtBhZoUPiuOA/nSbJiJpOfywXOz2yTkLlieoZ9XJtJTqQ6g0w/LFMxuyZMN4lDPqZjow6O77zKwdOAv4NvmAfyH5kTcrgLuq2VCRLEszRRK3ryeuPifxqJuv3vJY7P4rSWHUYu3UuH6UqpxZqjRDz/GdsdtD/Wj0FFc5o25OJn+xNUf+G8Ct7v4tM/sQ+SDfCWwBLnP3/aX2pVE3IqMlXaO0VvtacPWv2DcwOGp7R3srj115dtn7KV7goyDNkr2hfk9ubeH1d0b3IbTGbCmhfiyd3cmjO15ncNjSga05Y92F81ML9rUYdfOEuy9095Pd/d+5+7ei7S+4+6nu/mF3v2isIC8i8dJMkaS5r9BCSEkXSAot8JHm2qmhfruTWhom1N6Hn987IsgDDA45V/9iW+JjVIuqV4rUUNL0QlJp7mtfzJlwqe0Q37/QKJY0104N9e+NgUEuXdI9It1ywaLKliJM2t64bxL1ohIIIjVSSC/07xvAeX8EyBHtrbHPr2SUR+g1tdhXqH8tgW8Aaa6dGmrTEe2t3LG5/70gPeTOHZv72bAl+diReqz1mhYFepEaCaUXzKC1KBq2tlhF6YU0F/lIuq9Q/w6bFB9m0lzgY9WyuaM+UFosn2aqJJV1xYatzF5zD7NW383sNfdwxYatwfa2BdaG7Qh8gNeDAr1IjYTSC6+/MwjFsaLCk8fehV2J1jtNc1+h/r07eKjqa6f27dzLoaLMyiEPp09KpbIKF12HfwsoXISN68ffXTg/9oP6qk+fVGl3UqeFR0RqZOnaBxJN0KlkZEg9hfpXi37MXnNPohx6qTaF9pUz4/nrzo19TbWHV4531I0uxorUyKplc2OHABanFgrGuoBaz7HbcccO9W/VsrlVb2upIJ9rMYaGne7nxkiLVXLxOOli4rWm1I1IjYRSIV0VXEANXfis5CJjUqFjA7H9A6re1tCFUoMRQZ7oft/OvYn31cwXY3VGL1JDoTO/0JlwSKnx8tU+syx17IdXnzHq+EvXPlD1ti5fPDN2MlPoHPzmTbuC1whC+0rz4nGtKdCL1FmpqfghpcbLVztNkvTYaY7tDykE7eLyBHEBG95Pw8S1N7SvUqUOmr4EQpp0MVYkHaELnx3trew/eKiqi2YkPXaLwdsHRl+HOHJKK1v+tvxSCpUodWH17y+en6hcRD0XKql6CQQRaTyhMe6Vjhuv5rHfiQnyALU4xwylW5Yvnpm4XEQzL1Si1I1IEwqle9KsOJnWsUPx/I2oYFrStEeSSpg9x3fGpm96ju/kpkBa54/7Blh87X28+tb7S2DPmNbG7rfil8RuhoVKFOhFmlTchd11926PTaukvWhGkmMb8cH+iPbWxIubFFeQHD6ZKS7YX7UxvrDYVRu3BRceAUYE+cL90JibFmPUZC1orIVKlLoRyZA0SyCkdewpbfGLnpglr7aZtBJmXJnlwvZVy+bSWlS+oDVnwW8goe2HTWpp+IVKdEYv0uAqGelRjxEgSVM6+94ZDFbCTLqea8WVMItfVsFu3h08lFqFzGpRoBdpYKHURt/OvSNGehSnPOoVZJKmk97efzD2rDtU0TNULiI0menIKa2x9W6OnNLKunu3M1iUcym+X45Qhcye4zsbJtgrdSPSwJp5pEdBqXRS0sVNSo2iiXPlp06KTc9c+amTUrtYOjA41PDvhQK9SANLmsJopJEeBaWqYCZd3OSa3nksnd05YtvS2Z3BUTe9C7u45KMzR1ScvOSjM+ld2JXaxdL9Bw/Fbm+k90KpG5EGFhoZEkphNNJIj+FC6aRQ/0otbvL7F98Yse33L77Bhi39wUlOtzy6a0Ra5ZZHd9FzfGfiInNJdUxpnHr0YwZ6M5sJ/BiYQf5SxY3u/h0z6wRuAWYBO4CL3f316jVVZOIJBaMLFnVxy+92jVqQutRIj3pO0z/r+od4dvfb792fc/RU7vva6fmql7c/XnY/So3S6du5d9T4+n9+/OXYPPxVG7fx2JVnj3rNBYu6gmUTkqph0YExlZO6OQh83d1PBJYAXzKzE4HVwP3uPge4P7ovIikKpT16ju9MNGKkntUui4M8wLO73+as6x8CYKhoYe3i+8OFxr337xuIXSyk1PDKDVv6Yy+ipiV07HoY84ze3V8GXo5uv2VmTwNdwPnA6dHT1gMPAd+oSitFJrC4tMfStQ/EnqmGKkLWs9plcZAfvv2qjdsoznAf4v2JTsXfQEIpq0qEfidZlChHb2azgIXAJmBG9CEA8Ar51E7ca1YCKwG6u7srbqiIvC9pRchaVJCsRKkz7rhhpWkFeah/32up7FE3ZvYB4A7gK+7+5vDHPF8CM/YdcPcb3b3H3XumT58+rsaKSF7oYmVa2xtBtc+2q933RlqopKxAb2at5IP8Te5+Z7T5VTM7Jnr8GGB3dZooIsWSljqoZ2mEUJBpIT9xqV5Cv5NJLfEBelKLxT6/eLhnQSMtVDJmoDczA34APO3u1w97aCOwIrq9Argr/eaJSJzehV0cd+TkEduOO3JyMN9eaix7tZWqHXPlp06KfWxKa/IpPsXh2WK2FeTM6F3YxSndR4zYfkr3EaOWHiwYOuSxv8ObvnhaorH99TDmwiNm9jHg/wFb4b3rJv+NfJ7+VqAb2El+eGV4IUa08IhIWi793m95+PnR/92Wzu7kpi+eVocWhS381q+CZQgG9h/k3RKjbJJozdmoYZqDJfZ92ZLu2KGUh01qiZ0EFVooJbQgSSMtPFLOqJt/IfzBeGalBxaRysUF+VLb6yl0LulOakEeGBXUSwV5CFe8DM10dY+fi1DPEU3l0sxYkQYXF1zqLcniH28ERtaEttdK0hE8+wYGR0zu6t83MGqy13ChMf/1oEAv0sBKLcwRkiQIl3P84g+Zvp17Ey3+Mbm1hYHB0WfJoe0FxeUI2ltz7D84FLvIR60k/dbQKFTUTKSBhdICoWuVM6a1xc4QvWJD6Q+HOKHZtKEl+H66KX57KBWy/+AhJufis8KTcxZ74bOeQb6Z6YxepIGFJvUcPJS/8Do8J790diePvBBfbqqQj4470w99A0g6c/RQIIcdCs6HHJ659lxOWH33iJE5Rn47jF5O8Ou3Pp7qpKmJQoFepIGVqu4YN7pm1uq7Y/czPL0y/P6mF14bUaJg+PMqmTkal8MOramaM+OKDVtjS/ZcsWFrbBqomYJ8ewVDRKulcVoiIqNUe6JTqA7NzZt2VVRmNy6HHcjOsHzxzGC6J7S9q4Fn8ha7YNFx9W7Ce3RGL6moZwncLCu1BmxcyiUtQ+6pldkdPJQfsx6XHgqVBA6lgapdQz5NDz6zp95NeM+YE6bSpAlT2VSLCSMy0hUbtsYGySmtLbxTYiRLudKsEgmwY+15sdtDqSaIH3Vz3Wfy6ZziD4CvBBYgL6V9jFE/aQj1O6mqT5gSGUszTBjJmtBkn4GDh2ID5CH32NEvOYO4EYLLF89MbQGOjsBC32MJ/U09vPqMUX9XlQT6ya25qgf6RqEcvYxbo5bAzbLQ2bY7scMSQ0MchzyfVhm+puplS7pTrdMyfVpbavtK828qtC5tFumMXsYt6bqfUl19O/fyyhvv4sArb7xL3869wVRMzoxreudVtQBX6IJvJTqmtKY2Uzj0d5uWnFlwCcVa0xm9jFs9S+DKaHETpkLfAJppuCLAn94d5Cu3PDZiElclaRvI/93mikoSF98fD8NLLqFYSzqjl3ErNTJEqqMrpbPRSvPnSaVVliHNlHrfzr2jShKHShRX4mBgV2l+wymXAr2kIm5dU6meVcvmjiqoNVZZ3jgDg0M1GRobqo0z5+ipdQl8QLCUQxYp0Is0qTQKbO0/eGhE6mM8qZCkfrrpRQ6fXL8VpporaTU+ytGLNKE1dz5R1f1//dbHuGxJd1WPccjDi4M3k6Rp/TlHT61OQ0rQGb1Ig4tLrZQa/11cWyZUa6aUIc+XHP7XPX8aVTitERc3qafQ79aADxelpuo16kaBXqSBVVKPPmfGoWGjaYrvJzn2oztGVsMsvi9h7a0tdQnqcZS6EWlgSUsFAwwWnWIW3y/X1b/YVtWFNtIbyNiY0ihFkRYFepEGVs/ZxXELeqepkcr4Zt2Yv2kz+6GZ7TazJ4dt6zSz+8zs2ejnkdVtpsjEFJpd3NXRHlu6oJnK+E6UOjONoJyP1P8NnFO0bTVwv7vPAe6P7otIykrNOr6mdx7PX3cuO9aex/PXncs1vfP4xEemp3bstM63p7blYrerREbtjPleuvuvgeLL7OcD66Pb64HedJslIpCfiBZXpCw0oSmtGuiXLekmrfPttw/EX1OYdZQCfa1U+qE9w91fjm6/AswIPdHMVppZn5n17dnTOIX4RbIorZx+qAxymkLr20r6xv3tzPMrlwQvxbv7je7e4+4906en97VSZCIoDK8cXsRrzZ1b2bClP/b5aaVDalHsrNkKqjWzSgP9q2Z2DED0c3d6TRKRglKLusQJ5fSTKlzklfHZsKWfpWsf4ITVd7N07QPBD+hqqzTQbwRWRLdXAHel0xyRbEv6H7/Uoi5x++pd2MUFi7pGjMa5YFHyAmVprj87ka267fER38ZW3fZ4XYL9mDNjzexm4HTgg2b2EnAlsBa41cwuB3YCF1ezkSJZMNYs17gKkqHFMY5ob43dV9/OvdyxuX9EPfo7NtfnLFLiJ69dtXFbzSu9jhno3X154KEzU26LSKaF0jBXbdzG/oOHYj8ADg7Fj1h5Y2Bw1IWxgcGh92q+F29P6qebJk4J31qrRyE31boRqZFQGibuP34hD//qWwdiXxO6jJnWBc4U19+QBqA5yCI1knREjBZXl7TojH4CSGsZt6yoxYpKcUKrQn3gsEmxdWXSXLx6alsuOHEpTntri0oUZIjO6DPuig1bYxeLvmJD6VK3WZV0XHrqilMiDuedfEywzEFa/0HdPfYYS2d3xj7/gkXHpXRkaQQK9BVolLGx5QjNcLx5066m6kdako5LT/vYcaMwHnxmT+yQyN6FXVx/yYLYfSVdpeidwUOxpRR2vBb/jaGSUgpLZ3eOCigKMI1BqZuESg2Ra8TFsUMX54bcm6ofaSk1Lv3S7/121GpKN33xtIqOE5ceCh27f98ANz+6a8S3rpsf3UXP8fmz7eJFv1tzxgt7ki+oHbeA+1cD68NWcn3gud1/GlUfR8mfxmBew2nIPT093tfXV7PjVcPStQ/E5k27Otp5ePUZdWhRabPX3JNoJEaj9iMtofevLWcciFlUY6xgH/fhcFFP94gPUSjMTvVEee+pbTk6prSlkqfvaG/lL+YfM+pazYPP7An+Pad1fSCkFsdoVDvWnpfo+Wa22d17Kj2evlklVOqMsBElneHYqP1IS6hEQFyQB0quj1oc5AvP/8YdT8Smh5Je3Hz7wFBq78f0aW2x12pC4/RrUVnyrYH4oaOSPqVuEgqNhGjU2tqF0TXlnsnVux+hETGVjBwK7eu6e54aEYgPn5wbc1JR3L5CHwL7D6aXsJiScLRMyPAFqocLjdOvxQLgb+4ff7+kPErdlBD3nxuI/VpeqBEeCi61GNKX5Nhj9aPabQo9d9Vtj4+4YNnaYpx6wpGxgeeyJd1c0zsv2L+vxOSfZ0xrCwa3kBsuWcDXb3ucoWHtyrXYiPsiSdQ6daNAH1B80RXeD4QQX5ck9JoLFnVxx+b+REE16QdDJce+re/FRBcfS7Up6YdJ3O/wqo3bEk8Pv+GSBbEBPWeQ1jrWScegi4xFgb5MlZwhJ3lNqYuubw0cGPG18/DDcjxx9TnB1+TMYi+IdnW084mPTB+Vkug5vjMYIPt27o1NYSQ9dmhCzGVLutn0wmsjvurPOXoqX/rEnNiAekM0/C/usY721tjAHdouMlEo0Jdhw5b+2BmG6y6cD4TPtuPSAusumh8b7GetvjtRmw4/LJdaznFKawvvxAThUsH5J4+oCJVIs6h1oG/Ki7FX/2LbiCAPMDjkfPPnWznkxI4Nv2rjtqqWDE3zwlJckAeCozZuUpAXkRIaPtDHpVvi6oJA/CLEhVmPoVRBFlIIuiQoIqU0dKAfa6GGco01Fjl0IVFEJAsaesJUqC5JaDXL0PaxxobHFbkSEcmKhg70oTNxJ38hdbjWFuPSJd3BKoClxH2YiIhkRUMH+tCZeFdHO+sumj+iEt+6i+ZzTe+82Ap9vQu7tKq9iExY48rRm9k5wHeAHPB9d1+bSqsiq5bNjR1PXhgyGTdaJrR9+eKZsUMQ05oMM2NaGweGPHihWESkXio+ozezHPBd4JPAicByMzsxrYZBPmiHztCTuqZ3Hpct6R5R8/uyJd1c+x/nxaZ7brhkAZNzI78FTM4ZO9aex4xpbSO2z5jWxqZvnsWVnzqJ1qLXtOYsuLjD0tmdsSmopKa25Ua1SUSkoOIJU2Z2GnCVuy+L7q8BcPfrQq9p1BIIadahSVqUK+756+7dHjvLtaO9lbf2HxxVc+Xvo0lfi6+9b0QdlxnT2jjrpD+L/SazdHZnbP2YpNtvuGTBqFIKBaFJZKHyBKFSwXOOnhpblKtWE8U0IU3S1jQzY83sQuAcd/9P0f3PA4vd/a+LnrcSWAnQ3d29aOfOnZW2dcKopM5OKUk+ZJJ+KBWOHVq0I1SvPTRLOVTioVSbQqUZrrvnqVEffGvOPTH2+aEPskLhtNDx42ZQpzVL2sj2HIms9y+kkgVtGj7QD9eoZ/SNqF4LWNdKLb5FJX1+mouon3zlL2PrIcV9MOxYex4nrL57RNAz4F/Xnhf8ICv1wRT37SNU/C20vXDGGWpv0u2h/iU9RtJvnKXalPTYlfwO01q1rJ6BPjOpG5FGltYHU1ZOHtJc8jGpev0O6xnoJwF/AM4E+oHfAZ9z922h1yjQi4gkV7eiZu5+0Mz+GriX/PDKH5YK8iIiUh/jGkfv7vcA96TUFhERqYKGnhkrIiLjp0AvIpJxCvQiIhlX06UEzWwPkPaMqQ8C/5byPpuB+j3xTNS+q99wvLtPr3RHNQ301WBmfeMZdtSs1O+JZ6L2Xf0eP6VuREQyToFeRCTjshDob6x3A+pE/Z54Jmrf1e9xavocvYiIlJaFM3oRESlBgV5EJOMaMtCb2Q/NbLeZPTls23wz+62ZbTWzX5jZ4dH2S83ssWH/DpnZguixRdHznzOz/2nW2CuEJ+x3q5mtj7Y/XSgTHT12jpltj/q9uh59SSJhv9vM7EfR9sfN7PRhr2m293ummT1oZk+Z2TYz+3K0vdPM7jOzZ6OfR0bbLerXc2b2hJmdMmxfK6LnP2tmK+rVp3JU0O+PRH8L+83sb4r21TR/6xX0+9Lofd5qZr8xs/nD9pWs3+7ecP+AjwOnAE8O2/Y74D9Et78A/PeY180Dnh92/1FgCfl1Dv4P8Ml69y2tfgOfA34W3Z4C7ABmka8k+jzwIaANeBw4sd59S7HfXwJ+FN0+GtgMtDTp+30McEp0exr5st8nAn8HrI62rwa+Hd0+N+qXRf3cFG3vBF6Ifh4Z3T6y3v1Lsd9HAx8FrgX+Zth+mupvvYJ+//vC+0h+be7C+5243w15Ru/uvwaKl4v5c+DX0e37gAtiXroc+BmAmR0DHO7uj3j+t/NjoLcqDU5Jwn47MDVaF6AdOAC8CZwKPOfuL7j7AfK/j/Or3fbxSNjvE4EHotftBvYBPU36fr/s7r+Pbr8FPA10kX+/1kdPW8/7/Tgf+LHnPQJ0RP1eBtzn7nvd/XXyv69zateTZJL22913u/vvgMGiXTXV33oF/f5N9H4CPAIcF91O3O+GDPQB23i/MxcBM2Oecwlwc3S7C3hp2GMvRduaTajftwNvAy8DLwL/w933ku/jrmGvz1q/Hwc+bWaTzOwEYFH0WFO/32Y2C1gIbAJmuPvL0UOvADOi26H3tmnf8zL7HTKR+n05+W9zUEG/mynQfwH4z2a2mfzXngPDHzSzxcA77v5k3IubWKjfpwJDwLHACcDXzexD9WliVYT6/UPyf9h9wA3Ab8j/HpqWmX0AuAP4iru/Ofyx6NtJJsdAq9/l9dvMPkE+0H+j0mOOa+GRWnL3Z4CzAczsz4Hzip7yWd4/m4f88obHDbt/XLStqZTo9+eAX7r7ILDbzB4Gesh/0g//tpOpfrv7QeCrheeZ2W/I5zpfpwnfbzNrJf+f/iZ3vzPa/KqZHePuL0epmd3R9n7i39t+4PSi7Q9Vs93jlbDfIaHfR8NK2m8zOxn4PvnrTa9FmxP3u2nO6M3s6OhnC3AF8E/DHmsBLibKz0M+Hwa8aWZLotEXfwncVdNGp6BEv18Ezogem0r+4twz5C9izjGzE8ysjfwH4MZat3u8Qv02sylRfzGzs4CD7v5UM77fUTt/ADzt7tcPe2gjUBg5s4L3+7ER+Mto9M0S4I2o3/cCZ5vZkdGIjbOjbQ2pgn6HNNXfetJ+m1k3cCfweXf/w7DnJ+93va9EB65O30w+9zxI/mv65cCXyZ+5/QFYSzSrN3r+6cAjMfvpAZ4kf4X6fw1/TSP+S9Jv4APAbeRz2U8Bq4bt59zo+c8D36x3v1Lu9yxgO/kLWf+XfPnWZn2/P0b+a/oTwGPRv3OBo4D7gWejPnZGzzfgu1H/tgI9w/b1BeC56N9f1btvKff7z6K/izfJX3x/ifyF96b6W6+g398n/0218Ny+YftK1G+VQBARybimSd2IiEhlFOhFRDJOgV5EJOMU6EVEMk6BXkQk4xToRUQyToFeRCTj/j+A8jESETDz3gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialise figure with 1x1 grid of axes\n",
    "res_year_fig, res_year_ax = plt.subplots(1, 1)   \n",
    "\n",
    "''' Remove only NaN/NULL values from the resolution and publicationYear columns and\n",
    "return a DataFrame with only these two columns, saving computer memory '''\n",
    "\n",
    "year_resolution_df = df.dropna(subset=['resolution', 'publicationYear'])[['resolution', 'publicationYear']]\n",
    "\n",
    "res_year_ax.scatter(year_resolution_df['publicationYear'],   # Plot x-axis data\n",
    "                    year_resolution_df['resolution']         # Plot y-axis data\n",
    "                   )\n",
    "\n",
    "plt.show()   # Display figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### ANSWER ### \n",
    "\n",
    "# Minimum resolution decreases with time due to improvements in X-ray diffraction beams, detectors and image processing \n",
    "# Maximum resolution increases with time due to the use of election cryomicroscopy, partciularly for larger macromolecules and viral particles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Task 7\n",
    "\n",
    "Repurpose the code above to plot *resolution* as a function of *molecular weight*. \n",
    "\n",
    "Refer to the [Matplotlib documentation](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.scatter.html?highlight=scatter#matplotlib.axes.Axes.scatter) for scatter plots and add some formatting to change the plot's appearance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Task 7 ###\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "The final plot we will look at is the bar plot, specifically a horizontal bar plot. Run the code below. Discuss the code with your supervisor if anything is unlcear. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setting the style of figure\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "# Initialise figure with 1x1 grid of axes\n",
    "fig, ax = plt.subplots(1, 1)   \n",
    "\n",
    "ax.barh(y=df['experimentalTechnique'].value_counts().index,   # Labels added to y-axis\n",
    "        width=df['experimentalTechnique'].value_counts(),     # Frequency added to x-axis\n",
    "        log=True         # The difference between frequencies spans several orders of magnitude. Using a semi-log scale makes the data easier to interpret here\n",
    "       )                 # Comment out 'log=True' to highlight its usefulness here. \n",
    "\n",
    "plt.show()   # Display figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Task 8\n",
    "\n",
    "What is the most commonly solved type of macromolecule? Plot a bargraph to get your answer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Task 8 ###\n",
    "\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "ax.barh(y=df['macromoleculeType'].value_counts().index, \n",
    "        width=df['macromoleculeType'].value_counts(),\n",
    "        log=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## End of session"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}