06-DA_Pandas_introduction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Pandas Introduction\n",
    "\n",
    "In the previous chapters, we have learned how to handle Numpy arrays that can be used to efficiently perform numerical calculations. Those arrays are however homogeneous structures i.e. they can only contain one type of data. Also, even if we have a single type of data, the different rows or columns of an array do not have labels, making it difficult to track what they contain. For such cases, we need a structure closer to a table as can be found in Excel, and these structures are implemented by the package Pandas.\n",
    "\n",
    "But why can't we simply use Excel then? While Excel is practical to browse through data, it is very cumbersome to use to combine, re-arrange and thoroughly analyze data: code is hidden and difficult to share, there's no version control, it's difficult to automate tasks, the manual clicking around leads to mistakes etc.\n",
    "\n",
    "In the next chapters, you will learn how to handle tabular data with Pandas, a Python package widely used in the scientific and data science areas. You will learn how to create and import tables, how to combine them, modify them, do statistical analysis on them and finally how to use them to easily create complex visualizations.\n",
    "\n",
    "So that you see where this leads, we start with a short example of how Pandas can be used in a project. We look here at data provided openly by the Swiss National Science Foundation about grants attributed since 1975."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1 Importing data\n",
    "\n",
    "Before anything, we need access to the data that can be found [here](https://opendata.swiss/de/dataset/p3-export-projects-people-and-publications). We can either manually download them and then use the path to read the data or directly use the url. The latter has the advantage that if you have an evolving source of data, these will always be up to date:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# local import\n",
    "projects = pd.read_csv('Data/P3_GrantExport.csv',sep = ';')\n",
    "\n",
    "# import from url\n",
    "#projects = pd.read_csv('http://p3.snf.ch/P3Export/P3_GrantExport.csv',sep = ';')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can have a brief look at the table itself that Jupyter displays in a formated way and limit the view to the 5 first rows using ```head()```:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Project Number</th>\n",
       "      <th>Project Number String</th>\n",
       "      <th>Project Title</th>\n",
       "      <th>Project Title English</th>\n",
       "      <th>Responsible Applicant</th>\n",
       "      <th>Funding Instrument</th>\n",
       "      <th>Funding Instrument Hierarchy</th>\n",
       "      <th>Institution</th>\n",
       "      <th>Institution Country</th>\n",
       "      <th>University</th>\n",
       "      <th>Discipline Number</th>\n",
       "      <th>Discipline Name</th>\n",
       "      <th>Discipline Name Hierarchy</th>\n",
       "      <th>All disciplines</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>End Date</th>\n",
       "      <th>Approved Amount</th>\n",
       "      <th>Keywords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1000-000001</td>\n",
       "      <td>Schlussband (Bd. VI) der Jacob Burckhardt-Biog...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kaegi Werner</td>\n",
       "      <td>Project funding (Div. I-III)</td>\n",
       "      <td>Project funding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unassignable - NA</td>\n",
       "      <td>10302</td>\n",
       "      <td>Swiss history</td>\n",
       "      <td>Humanities and Social Sciences;Theology &amp; reli...</td>\n",
       "      <td>10302</td>\n",
       "      <td>01.10.1975</td>\n",
       "      <td>30.09.1976</td>\n",
       "      <td>11619.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>1000-000004</td>\n",
       "      <td>Batterie de tests à l'usage des enseignants po...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Massarenti Léonard</td>\n",
       "      <td>Project funding (Div. I-III)</td>\n",
       "      <td>Project funding</td>\n",
       "      <td>Faculté de Psychologie et des Sciences de l'Ed...</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>University of Geneva - GE</td>\n",
       "      <td>10104</td>\n",
       "      <td>Educational science and Pedagogy</td>\n",
       "      <td>Humanities and Social Sciences;Psychology, edu...</td>\n",
       "      <td>10104</td>\n",
       "      <td>01.10.1975</td>\n",
       "      <td>30.09.1976</td>\n",
       "      <td>41022.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>1000-000005</td>\n",
       "      <td>Kritische Erstausgabe der 'Evidentiae contra D...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kommission für das Corpus philosophorum medii ...</td>\n",
       "      <td>Project funding (Div. I-III)</td>\n",
       "      <td>Project funding</td>\n",
       "      <td>Kommission für das Corpus philosophorum medii ...</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Non-profit organisations (libraries, museums, ...</td>\n",
       "      <td>10101</td>\n",
       "      <td>Philosophy</td>\n",
       "      <td>Humanities and Social Sciences;Linguistics and...</td>\n",
       "      <td>10101</td>\n",
       "      <td>01.03.1976</td>\n",
       "      <td>28.02.1985</td>\n",
       "      <td>79732.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>1000-000006</td>\n",
       "      <td>Katalog der datierten Handschriften in der Sch...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Burckhardt Max</td>\n",
       "      <td>Project funding (Div. I-III)</td>\n",
       "      <td>Project funding</td>\n",
       "      <td>Abteilung Handschriften und Alte Drucke Univer...</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>University of Basel - BS</td>\n",
       "      <td>10302</td>\n",
       "      <td>Swiss history</td>\n",
       "      <td>Humanities and Social Sciences;Theology &amp; reli...</td>\n",
       "      <td>10302</td>\n",
       "      <td>01.10.1975</td>\n",
       "      <td>30.09.1976</td>\n",
       "      <td>52627.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>1000-000007</td>\n",
       "      <td>Wissenschaftliche Mitarbeit am Thesaurus Lingu...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Schweiz. Thesauruskommission</td>\n",
       "      <td>Project funding (Div. I-III)</td>\n",
       "      <td>Project funding</td>\n",
       "      <td>Schweiz. Thesauruskommission</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Non-profit organisations (libraries, museums, ...</td>\n",
       "      <td>10303</td>\n",
       "      <td>Ancient history and Classical studies</td>\n",
       "      <td>Humanities and Social Sciences;Theology &amp; reli...</td>\n",
       "      <td>10303</td>\n",
       "      <td>01.01.1976</td>\n",
       "      <td>30.04.1978</td>\n",
       "      <td>120042.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Project Number Project Number String  \\\n",
       "0               1           1000-000001   \n",
       "1               4           1000-000004   \n",
       "2               5           1000-000005   \n",
       "3               6           1000-000006   \n",
       "4               7           1000-000007   \n",
       "\n",
       "                                       Project Title Project Title English  \\\n",
       "0  Schlussband (Bd. VI) der Jacob Burckhardt-Biog...                   NaN   \n",
       "1  Batterie de tests à l'usage des enseignants po...                   NaN   \n",
       "2  Kritische Erstausgabe der 'Evidentiae contra D...                   NaN   \n",
       "3  Katalog der datierten Handschriften in der Sch...                   NaN   \n",
       "4  Wissenschaftliche Mitarbeit am Thesaurus Lingu...                   NaN   \n",
       "\n",
       "                               Responsible Applicant  \\\n",
       "0                                       Kaegi Werner   \n",
       "1                                 Massarenti Léonard   \n",
       "2  Kommission für das Corpus philosophorum medii ...   \n",
       "3                                     Burckhardt Max   \n",
       "4                       Schweiz. Thesauruskommission   \n",
       "\n",
       "             Funding Instrument Funding Instrument Hierarchy  \\\n",
       "0  Project funding (Div. I-III)              Project funding   \n",
       "1  Project funding (Div. I-III)              Project funding   \n",
       "2  Project funding (Div. I-III)              Project funding   \n",
       "3  Project funding (Div. I-III)              Project funding   \n",
       "4  Project funding (Div. I-III)              Project funding   \n",
       "\n",
       "                                         Institution Institution Country  \\\n",
       "0                                                NaN                 NaN   \n",
       "1  Faculté de Psychologie et des Sciences de l'Ed...         Switzerland   \n",
       "2  Kommission für das Corpus philosophorum medii ...         Switzerland   \n",
       "3  Abteilung Handschriften und Alte Drucke Univer...         Switzerland   \n",
       "4                       Schweiz. Thesauruskommission         Switzerland   \n",
       "\n",
       "                                          University  Discipline Number  \\\n",
       "0                                  Unassignable - NA              10302   \n",
       "1                          University of Geneva - GE              10104   \n",
       "2  Non-profit organisations (libraries, museums, ...              10101   \n",
       "3                           University of Basel - BS              10302   \n",
       "4  Non-profit organisations (libraries, museums, ...              10303   \n",
       "\n",
       "                         Discipline Name  \\\n",
       "0                          Swiss history   \n",
       "1       Educational science and Pedagogy   \n",
       "2                             Philosophy   \n",
       "3                          Swiss history   \n",
       "4  Ancient history and Classical studies   \n",
       "\n",
       "                           Discipline Name Hierarchy All disciplines  \\\n",
       "0  Humanities and Social Sciences;Theology & reli...           10302   \n",
       "1  Humanities and Social Sciences;Psychology, edu...           10104   \n",
       "2  Humanities and Social Sciences;Linguistics and...           10101   \n",
       "3  Humanities and Social Sciences;Theology & reli...           10302   \n",
       "4  Humanities and Social Sciences;Theology & reli...           10303   \n",
       "\n",
       "   Start Date    End Date Approved Amount Keywords  \n",
       "0  01.10.1975  30.09.1976        11619.00      NaN  \n",
       "1  01.10.1975  30.09.1976        41022.00      NaN  \n",
       "2  01.03.1976  28.02.1985        79732.00      NaN  \n",
       "3  01.10.1975  30.09.1976        52627.00      NaN  \n",
       "4  01.01.1976  30.04.1978       120042.00      NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "projects.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.2 Exploring data\n",
    "\n",
    "Pandas offers a variety of tools to compile information about data, and that compilation can be done very efficiently without the need for loops, conditionals etc.\n",
    "\n",
    "For example we can quickly count how many times each University appear in that table. We just use the ```value_counts()``` method for that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Institution abroad - IACH      13348\n",
       "University of Zurich - ZH       8170\n",
       "University of Geneva - GE       7385\n",
       "ETH Zurich - ETHZ               7278\n",
       "University of Berne - BE        6445\n",
       "University of Basel - BS        5560\n",
       "EPF Lausanne - EPFL             5174\n",
       "University of Lausanne - LA     4944\n",
       "Unassignable - NA               2642\n",
       "University of Fribourg - FR     2535\n",
       "Name: University, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "projects['University'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can very easily plot the resulting information, either using directly Pandas or a more advanced library like Seaborn, plotnine or Altair.\n",
    "\n",
    "Here first with plain Pandas (using Matplotlib under the hood):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x104df7040>"
      ]
     },
     "execution_count": 5,
     "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": [
    "projects['University'].value_counts().head(10).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.3 Handling different data types\n",
    "\n",
    "Unlike Numpy arrays, Pandas can handle a variety of different data types in a dataframe. For example it is very efficient at dealing with dates. We see that our table contains e.g. a ```Start Date```. We can turn this string into an actual date:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "projects['start'] = pd.to_datetime(projects['Start Date'])\n",
    "projects['year'] = projects.start.apply(lambda x: x.year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('1975-01-10 00:00:00')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "projects.loc[0].start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1975.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "projects.loc[0].year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.4 Data wrangling, aggregation and statistics\n",
    "\n",
    "Pandas is very efficient at wrangling and aggregating data, i.e. grouping several elements of a table to calculate statistics on them. For example we first need here to convert the ```Approved Amount``` to a numeric value. Certain rows contain text (e.g. \"not applicable\") and we force the conversion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "projects['Approved Amount'] = pd.to_numeric(projects['Approved Amount'], errors = 'coerce')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we want to extract the type of filed without subfields e.g. \"Humanities\" instead of \"Humanities and Social Sciences;Theology & religion\". For that we can create a custom function and apply it to an entire column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "science_types = ['Humanities', 'Mathematics','Biology']\n",
    "projects['Field'] = projects['Discipline Name Hierarchy'].apply(\n",
    "    lambda el: next((y for y in [x for x in science_types if x in el] if y is not None),None) if not pd.isna(el) else el)\n",
    "                                            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we group the data by discipline and year, and calculate the mean of each group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "aggregated = projects.groupby(['Institution Country', 'year','Field'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we can use Seaborn to plot the data by \"Field\" using just keywords to indicate what the axes and colours should mean (following some principles of the grammar of graphics):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": [
    "sns.lineplot(data = aggregated, x = 'year', y='Approved Amount', hue='Field');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that here, axis labelling, colorouring, legend, interval of confidence have been done automatically based on the content of the dataframe.\n",
    "\n",
    "We see a drastic augmentation around 2010: let's have a closer look. We can here again group data by year and funding type and calculate the total funding:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped = projects.groupby(['year','Funding Instrument Hierarchy']).agg(\n",
    "    total_sum=pd.NamedAgg(column='Approved Amount', aggfunc='sum')).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>year</th>\n",
       "      <th>Funding Instrument Hierarchy</th>\n",
       "      <th>total_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1975.0</td>\n",
       "      <td>Project funding</td>\n",
       "      <td>32124534.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1975.0</td>\n",
       "      <td>Science communication</td>\n",
       "      <td>44600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1976.0</td>\n",
       "      <td>Programmes;National Research Programmes (NRPs)</td>\n",
       "      <td>268812.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1976.0</td>\n",
       "      <td>Project funding</td>\n",
       "      <td>96620284.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1976.0</td>\n",
       "      <td>Science communication</td>\n",
       "      <td>126939.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>Programmes;r4d (Swiss Programme for Research o...</td>\n",
       "      <td>195910.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>Project funding</td>\n",
       "      <td>193568294.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>Project funding;Project funding (special)</td>\n",
       "      <td>19239681.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>Science communication</td>\n",
       "      <td>3451740.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>2021.0</td>\n",
       "      <td>Science communication</td>\n",
       "      <td>55200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>383 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       year                       Funding Instrument Hierarchy    total_sum\n",
       "0    1975.0                                    Project funding   32124534.0\n",
       "1    1975.0                              Science communication      44600.0\n",
       "2    1976.0     Programmes;National Research Programmes (NRPs)     268812.0\n",
       "3    1976.0                                    Project funding   96620284.0\n",
       "4    1976.0                              Science communication     126939.0\n",
       "..      ...                                                ...          ...\n",
       "378  2020.0  Programmes;r4d (Swiss Programme for Research o...     195910.0\n",
       "379  2020.0                                    Project funding  193568294.0\n",
       "380  2020.0          Project funding;Project funding (special)   19239681.0\n",
       "381  2020.0                              Science communication    3451740.0\n",
       "382  2021.0                              Science communication      55200.0\n",
       "\n",
       "[383 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, for each year we keep only the 5 largest funding types to be able to plot them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_sorted = grouped.groupby('year',as_index=False).apply(lambda x: (x.groupby('Funding Instrument Hierarchy')\n",
    "                                      .sum()\n",
    "                                      .sort_values('total_sum', ascending=False))\n",
    "                                     .head(5)).reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we only keep year in the 2000's:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "instruments_by_year = group_sorted[(group_sorted.year > 2005) & (group_sorted.year < 2012)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x105e35670>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.barplot(data=instruments_by_year,\n",
    "            x='year', y='total_sum', hue='Funding Instrument Hierarchy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the main change, is the sudden increase in funding for national research programs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}