{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "99c606f8-037f-4258-81e7-a9f4ac511242",
   "metadata": {},
   "source": [
    "# Introduction to working with DataFrames\n",
    "In basic python, we often use dictionaries containing our measurements as vectors. While these basic structures are handy for collecting data, they are suboptimal for further data processing. For that we introduce [panda DataFrames](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) which are more handy in the next steps. In Python, scientists often call tables \"DataFrames\".  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0cfceb6c-1acc-4632-b084-8b0871a7c50a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b77888b-c9a8-4a67-a4eb-f7df46eda970",
   "metadata": {},
   "source": [
    "## Creating DataFrames from a dictionary of lists\n",
    "Assume we did some image processing and have some results in available in a dictionary that contains lists of numbers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ff80484f-657b-4231-8d8f-cdc26577542b",
   "metadata": {},
   "outputs": [],
   "source": [
    "measurements = {\n",
    "    \"labels\":      [1, 2, 3],\n",
    "    \"area\":       [45, 23, 68],\n",
    "    \"minor_axis\": [2, 4, 4],\n",
    "    \"major_axis\": [3, 4, 5],\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2afa6a9-e15c-4147-bdd4-ec4d4f87fb36",
   "metadata": {},
   "source": [
    "This data structure can be nicely visualized using a DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8bf4e4b5-ef72-4f63-84d2-48cc3a77c297",
   "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>labels</th>\n",
       "      <th>area</th>\n",
       "      <th>minor_axis</th>\n",
       "      <th>major_axis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   labels  area  minor_axis  major_axis\n",
       "0       1    45           2           3\n",
       "1       2    23           4           4\n",
       "2       3    68           4           5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(measurements)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "930c082b-8f16-4711-b3e0-e56a7ec6d272",
   "metadata": {},
   "source": [
    "Using these DataFrames, data modification is straighforward. For example one can append a new column and compute its values from existing columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a34866ff-a2cb-4a7c-a4e8-4544559b634c",
   "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>labels</th>\n",
       "      <th>area</th>\n",
       "      <th>minor_axis</th>\n",
       "      <th>major_axis</th>\n",
       "      <th>aspect_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   labels  area  minor_axis  major_axis  aspect_ratio\n",
       "0       1    45           2           3          1.50\n",
       "1       2    23           4           4          1.00\n",
       "2       3    68           4           5          1.25"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"aspect_ratio\"] = df[\"major_axis\"] / df[\"minor_axis\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "201a2142-22c7-4607-bc2d-f1dfce4c7e26",
   "metadata": {},
   "source": [
    "## Saving data frames\n",
    "We can also save this table for continuing to work with it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fb01d2d9-4d8b-4b6a-b158-9516a581e000",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"../../data/short_table.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0240857d-292f-4ac3-ba87-8878aa941cde",
   "metadata": {},
   "source": [
    "## Creating DataFrames from lists of lists\n",
    "Sometimes, we are confronted to data in form of lists of lists. To make pandas understand that form of data correctly, we also need to provide the headers in the same order as the lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c72a82b1-4da6-468d-afa6-149cb00f7d37",
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>labels</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <td>45</td>\n",
       "      <td>23</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>minor_axis</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>major_axis</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             0   1   2\n",
       "labels       1   2   3\n",
       "area        45  23  68\n",
       "minor_axis   2   4   4\n",
       "major_axis   3   4   5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "header = ['labels', 'area', 'minor_axis', 'major_axis']\n",
    "\n",
    "data = [\n",
    "    [1, 2, 3],\n",
    "    [45, 23, 68],\n",
    "    [2, 4, 4],\n",
    "    [3, 4, 5],\n",
    "]\n",
    "          \n",
    "# convert the data and header arrays in a pandas data frame\n",
    "data_frame = pd.DataFrame(data, header)\n",
    "\n",
    "# show it\n",
    "data_frame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8b1b6b0-027c-4536-8710-e3f87aca1896",
   "metadata": {},
   "source": [
    "As you can see, this tabls is _rotated_. We can bring it in the usual form like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "40669e82-4264-4883-9c4e-8a366b061610",
   "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>labels</th>\n",
       "      <th>area</th>\n",
       "      <th>minor_axis</th>\n",
       "      <th>major_axis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   labels  area  minor_axis  major_axis\n",
       "0       1    45           2           3\n",
       "1       2    23           4           4\n",
       "2       3    68           4           5"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rotate/flip it\n",
    "data_frame = data_frame.transpose()\n",
    "\n",
    "# show it\n",
    "data_frame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccf08662-fccf-4dc1-91c2-3365fa85a96b",
   "metadata": {},
   "source": [
    "## Loading data frames\n",
    "Tables can also be read from CSV files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aa7c74db-68ab-4004-aa5e-01ba1ad88c79",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>area</th>\n",
       "      <th>mean_intensity</th>\n",
       "      <th>minor_axis_length</th>\n",
       "      <th>major_axis_length</th>\n",
       "      <th>eccentricity</th>\n",
       "      <th>extent</th>\n",
       "      <th>feret_diameter_max</th>\n",
       "      <th>equivalent_diameter_area</th>\n",
       "      <th>bbox-0</th>\n",
       "      <th>bbox-1</th>\n",
       "      <th>bbox-2</th>\n",
       "      <th>bbox-3</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>422</td>\n",
       "      <td>192.379147</td>\n",
       "      <td>16.488550</td>\n",
       "      <td>34.566789</td>\n",
       "      <td>0.878900</td>\n",
       "      <td>0.586111</td>\n",
       "      <td>35.227830</td>\n",
       "      <td>23.179885</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>30</td>\n",
       "      <td>35</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>182</td>\n",
       "      <td>180.131868</td>\n",
       "      <td>11.736074</td>\n",
       "      <td>20.802697</td>\n",
       "      <td>0.825665</td>\n",
       "      <td>0.787879</td>\n",
       "      <td>21.377558</td>\n",
       "      <td>15.222667</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>11</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>661</td>\n",
       "      <td>205.216339</td>\n",
       "      <td>28.409502</td>\n",
       "      <td>30.208433</td>\n",
       "      <td>0.339934</td>\n",
       "      <td>0.874339</td>\n",
       "      <td>32.756679</td>\n",
       "      <td>29.010538</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "      <td>28</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>437</td>\n",
       "      <td>216.585812</td>\n",
       "      <td>23.143996</td>\n",
       "      <td>24.606130</td>\n",
       "      <td>0.339576</td>\n",
       "      <td>0.826087</td>\n",
       "      <td>26.925824</td>\n",
       "      <td>23.588253</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>23</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>476</td>\n",
       "      <td>212.302521</td>\n",
       "      <td>19.852882</td>\n",
       "      <td>31.075106</td>\n",
       "      <td>0.769317</td>\n",
       "      <td>0.863884</td>\n",
       "      <td>31.384710</td>\n",
       "      <td>24.618327</td>\n",
       "      <td>0</td>\n",
       "      <td>237</td>\n",
       "      <td>29</td>\n",
       "      <td>256</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>56</td>\n",
       "      <td>211</td>\n",
       "      <td>185.061611</td>\n",
       "      <td>14.522762</td>\n",
       "      <td>18.489138</td>\n",
       "      <td>0.618893</td>\n",
       "      <td>0.781481</td>\n",
       "      <td>18.973666</td>\n",
       "      <td>16.390654</td>\n",
       "      <td>232</td>\n",
       "      <td>39</td>\n",
       "      <td>250</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>57</td>\n",
       "      <td>78</td>\n",
       "      <td>185.230769</td>\n",
       "      <td>6.028638</td>\n",
       "      <td>17.579799</td>\n",
       "      <td>0.939361</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>18.027756</td>\n",
       "      <td>9.965575</td>\n",
       "      <td>248</td>\n",
       "      <td>170</td>\n",
       "      <td>254</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>58</td>\n",
       "      <td>86</td>\n",
       "      <td>183.720930</td>\n",
       "      <td>5.426871</td>\n",
       "      <td>21.261427</td>\n",
       "      <td>0.966876</td>\n",
       "      <td>0.781818</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>10.464158</td>\n",
       "      <td>249</td>\n",
       "      <td>117</td>\n",
       "      <td>254</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>59</td>\n",
       "      <td>51</td>\n",
       "      <td>190.431373</td>\n",
       "      <td>5.032414</td>\n",
       "      <td>13.742079</td>\n",
       "      <td>0.930534</td>\n",
       "      <td>0.728571</td>\n",
       "      <td>14.035669</td>\n",
       "      <td>8.058239</td>\n",
       "      <td>249</td>\n",
       "      <td>228</td>\n",
       "      <td>254</td>\n",
       "      <td>242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>60</td>\n",
       "      <td>46</td>\n",
       "      <td>175.304348</td>\n",
       "      <td>3.803982</td>\n",
       "      <td>15.948714</td>\n",
       "      <td>0.971139</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>15.033296</td>\n",
       "      <td>7.653040</td>\n",
       "      <td>250</td>\n",
       "      <td>67</td>\n",
       "      <td>254</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  area  mean_intensity  minor_axis_length  major_axis_length  \\\n",
       "0            0   422      192.379147          16.488550          34.566789   \n",
       "1            1   182      180.131868          11.736074          20.802697   \n",
       "2            2   661      205.216339          28.409502          30.208433   \n",
       "3            3   437      216.585812          23.143996          24.606130   \n",
       "4            4   476      212.302521          19.852882          31.075106   \n",
       "..         ...   ...             ...                ...                ...   \n",
       "56          56   211      185.061611          14.522762          18.489138   \n",
       "57          57    78      185.230769           6.028638          17.579799   \n",
       "58          58    86      183.720930           5.426871          21.261427   \n",
       "59          59    51      190.431373           5.032414          13.742079   \n",
       "60          60    46      175.304348           3.803982          15.948714   \n",
       "\n",
       "    eccentricity    extent  feret_diameter_max  equivalent_diameter_area  \\\n",
       "0       0.878900  0.586111           35.227830                 23.179885   \n",
       "1       0.825665  0.787879           21.377558                 15.222667   \n",
       "2       0.339934  0.874339           32.756679                 29.010538   \n",
       "3       0.339576  0.826087           26.925824                 23.588253   \n",
       "4       0.769317  0.863884           31.384710                 24.618327   \n",
       "..           ...       ...                 ...                       ...   \n",
       "56      0.618893  0.781481           18.973666                 16.390654   \n",
       "57      0.939361  0.722222           18.027756                  9.965575   \n",
       "58      0.966876  0.781818           22.000000                 10.464158   \n",
       "59      0.930534  0.728571           14.035669                  8.058239   \n",
       "60      0.971139  0.766667           15.033296                  7.653040   \n",
       "\n",
       "    bbox-0  bbox-1  bbox-2  bbox-3  \n",
       "0        0      11      30      35  \n",
       "1        0      53      11      74  \n",
       "2        0      95      28     122  \n",
       "3        0     144      23     167  \n",
       "4        0     237      29     256  \n",
       "..     ...     ...     ...     ...  \n",
       "56     232      39     250      54  \n",
       "57     248     170     254     188  \n",
       "58     249     117     254     139  \n",
       "59     249     228     254     242  \n",
       "60     250      67     254      82  \n",
       "\n",
       "[61 rows x 13 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_csv = pd.read_csv('../../data/blobs_statistics.csv')\n",
    "df_csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01732b57-35d9-4b25-9c1b-d322487d2757",
   "metadata": {},
   "source": [
    "Typically, we don't need all the information in these tables and thus, it makes sense to reduce the table. For that, we print out the column names first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cc7d6cbe-6487-49a6-84b2-e837f7070f25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'area', 'mean_intensity', 'minor_axis_length',\n",
       "       'major_axis_length', 'eccentricity', 'extent', 'feret_diameter_max',\n",
       "       'equivalent_diameter_area', 'bbox-0', 'bbox-1', 'bbox-2', 'bbox-3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_csv.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff187a52-9fc0-4f6f-b143-f872dfe620c2",
   "metadata": {},
   "source": [
    "## Selecting columns\n",
    "We can then copy&paste the colum names we're interested in and create a new data frame. This is recommended especially when tables are overwhelmingly large."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b1f03533-e9d0-4880-af3f-c9766df56f29",
   "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>area</th>\n",
       "      <th>mean_intensity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>422</td>\n",
       "      <td>192.379147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>182</td>\n",
       "      <td>180.131868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>661</td>\n",
       "      <td>205.216339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>437</td>\n",
       "      <td>216.585812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>476</td>\n",
       "      <td>212.302521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>211</td>\n",
       "      <td>185.061611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>78</td>\n",
       "      <td>185.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>86</td>\n",
       "      <td>183.720930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>51</td>\n",
       "      <td>190.431373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>46</td>\n",
       "      <td>175.304348</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    area  mean_intensity\n",
       "0    422      192.379147\n",
       "1    182      180.131868\n",
       "2    661      205.216339\n",
       "3    437      216.585812\n",
       "4    476      212.302521\n",
       "..   ...             ...\n",
       "56   211      185.061611\n",
       "57    78      185.230769\n",
       "58    86      183.720930\n",
       "59    51      190.431373\n",
       "60    46      175.304348\n",
       "\n",
       "[61 rows x 2 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_analysis = df_csv[['area', 'mean_intensity']]\n",
    "df_analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fe65e75-8003-4175-96c6-21cb26eb5d31",
   "metadata": {},
   "source": [
    "## Selecting rows\n",
    "In case we want to focus our further analysis on cells that have a certain minimum area. We can do this by selecting rows. The process is also sometimes call masking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a4eadd9b-e287-4ca8-b1ff-d1278c24151c",
   "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>area</th>\n",
       "      <th>mean_intensity</th>\n",
       "      <th>total_intensity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>422</td>\n",
       "      <td>192.379147</td>\n",
       "      <td>81184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>182</td>\n",
       "      <td>180.131868</td>\n",
       "      <td>32784.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>661</td>\n",
       "      <td>205.216339</td>\n",
       "      <td>135648.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>437</td>\n",
       "      <td>216.585812</td>\n",
       "      <td>94648.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>476</td>\n",
       "      <td>212.302521</td>\n",
       "      <td>101056.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>277</td>\n",
       "      <td>206.469314</td>\n",
       "      <td>57192.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>259</td>\n",
       "      <td>178.007722</td>\n",
       "      <td>46104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>219</td>\n",
       "      <td>191.598174</td>\n",
       "      <td>41960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>67</td>\n",
       "      <td>167.522388</td>\n",
       "      <td>11224.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>486</td>\n",
       "      <td>190.946502</td>\n",
       "      <td>92800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>630</td>\n",
       "      <td>173.600000</td>\n",
       "      <td>109368.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>221</td>\n",
       "      <td>197.936652</td>\n",
       "      <td>43744.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>78</td>\n",
       "      <td>173.128205</td>\n",
       "      <td>13504.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>449</td>\n",
       "      <td>208.766147</td>\n",
       "      <td>93736.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>516</td>\n",
       "      <td>194.403101</td>\n",
       "      <td>100312.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>390</td>\n",
       "      <td>180.779487</td>\n",
       "      <td>70504.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>419</td>\n",
       "      <td>196.849642</td>\n",
       "      <td>82480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>267</td>\n",
       "      <td>200.958801</td>\n",
       "      <td>53656.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>353</td>\n",
       "      <td>189.779037</td>\n",
       "      <td>66992.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>151</td>\n",
       "      <td>186.225166</td>\n",
       "      <td>28120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>400</td>\n",
       "      <td>187.960000</td>\n",
       "      <td>75184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>426</td>\n",
       "      <td>201.577465</td>\n",
       "      <td>85872.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>246</td>\n",
       "      <td>182.113821</td>\n",
       "      <td>44800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>503</td>\n",
       "      <td>198.648111</td>\n",
       "      <td>99920.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>278</td>\n",
       "      <td>190.187050</td>\n",
       "      <td>52872.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>681</td>\n",
       "      <td>198.308370</td>\n",
       "      <td>135048.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>176</td>\n",
       "      <td>195.272727</td>\n",
       "      <td>34368.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>358</td>\n",
       "      <td>197.787709</td>\n",
       "      <td>70808.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>544</td>\n",
       "      <td>198.455882</td>\n",
       "      <td>107960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>597</td>\n",
       "      <td>190.954774</td>\n",
       "      <td>114000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>181</td>\n",
       "      <td>184.883978</td>\n",
       "      <td>33464.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>629</td>\n",
       "      <td>193.666137</td>\n",
       "      <td>121816.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>596</td>\n",
       "      <td>210.067114</td>\n",
       "      <td>125200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>263</td>\n",
       "      <td>190.022814</td>\n",
       "      <td>49976.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>899</td>\n",
       "      <td>198.291435</td>\n",
       "      <td>178264.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>476</td>\n",
       "      <td>204.924370</td>\n",
       "      <td>97544.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>233</td>\n",
       "      <td>193.167382</td>\n",
       "      <td>45008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>164</td>\n",
       "      <td>184.634146</td>\n",
       "      <td>30280.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>394</td>\n",
       "      <td>181.401015</td>\n",
       "      <td>71472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>411</td>\n",
       "      <td>200.253041</td>\n",
       "      <td>82304.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>235</td>\n",
       "      <td>189.140426</td>\n",
       "      <td>44448.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>375</td>\n",
       "      <td>195.498667</td>\n",
       "      <td>73312.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>654</td>\n",
       "      <td>199.706422</td>\n",
       "      <td>130608.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>376</td>\n",
       "      <td>208.638298</td>\n",
       "      <td>78448.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>579</td>\n",
       "      <td>200.649396</td>\n",
       "      <td>116176.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>64</td>\n",
       "      <td>190.250000</td>\n",
       "      <td>12176.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>161</td>\n",
       "      <td>183.950311</td>\n",
       "      <td>29616.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>457</td>\n",
       "      <td>168.210066</td>\n",
       "      <td>76872.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>625</td>\n",
       "      <td>217.894400</td>\n",
       "      <td>136184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>535</td>\n",
       "      <td>189.936449</td>\n",
       "      <td>101616.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>205</td>\n",
       "      <td>199.180488</td>\n",
       "      <td>40832.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>562</td>\n",
       "      <td>215.928826</td>\n",
       "      <td>121352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>845</td>\n",
       "      <td>198.295858</td>\n",
       "      <td>167560.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>280</td>\n",
       "      <td>189.800000</td>\n",
       "      <td>53144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>211</td>\n",
       "      <td>185.061611</td>\n",
       "      <td>39048.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>78</td>\n",
       "      <td>185.230769</td>\n",
       "      <td>14448.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>86</td>\n",
       "      <td>183.720930</td>\n",
       "      <td>15800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>51</td>\n",
       "      <td>190.431373</td>\n",
       "      <td>9712.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    area  mean_intensity  total_intensity\n",
       "0    422      192.379147          81184.0\n",
       "1    182      180.131868          32784.0\n",
       "2    661      205.216339         135648.0\n",
       "3    437      216.585812          94648.0\n",
       "4    476      212.302521         101056.0\n",
       "5    277      206.469314          57192.0\n",
       "6    259      178.007722          46104.0\n",
       "7    219      191.598174          41960.0\n",
       "8     67      167.522388          11224.0\n",
       "10   486      190.946502          92800.0\n",
       "11   630      173.600000         109368.0\n",
       "12   221      197.936652          43744.0\n",
       "13    78      173.128205          13504.0\n",
       "14   449      208.766147          93736.0\n",
       "15   516      194.403101         100312.0\n",
       "16   390      180.779487          70504.0\n",
       "17   419      196.849642          82480.0\n",
       "18   267      200.958801          53656.0\n",
       "19   353      189.779037          66992.0\n",
       "20   151      186.225166          28120.0\n",
       "21   400      187.960000          75184.0\n",
       "22   426      201.577465          85872.0\n",
       "23   246      182.113821          44800.0\n",
       "24   503      198.648111          99920.0\n",
       "25   278      190.187050          52872.0\n",
       "26   681      198.308370         135048.0\n",
       "27   176      195.272727          34368.0\n",
       "28   358      197.787709          70808.0\n",
       "29   544      198.455882         107960.0\n",
       "30   597      190.954774         114000.0\n",
       "31   181      184.883978          33464.0\n",
       "32   629      193.666137         121816.0\n",
       "33   596      210.067114         125200.0\n",
       "35   263      190.022814          49976.0\n",
       "36   899      198.291435         178264.0\n",
       "37   476      204.924370          97544.0\n",
       "38   233      193.167382          45008.0\n",
       "39   164      184.634146          30280.0\n",
       "40   394      181.401015          71472.0\n",
       "41   411      200.253041          82304.0\n",
       "42   235      189.140426          44448.0\n",
       "43   375      195.498667          73312.0\n",
       "44   654      199.706422         130608.0\n",
       "45   376      208.638298          78448.0\n",
       "46   579      200.649396         116176.0\n",
       "47    64      190.250000          12176.0\n",
       "48   161      183.950311          29616.0\n",
       "49   457      168.210066          76872.0\n",
       "50   625      217.894400         136184.0\n",
       "51   535      189.936449         101616.0\n",
       "52   205      199.180488          40832.0\n",
       "53   562      215.928826         121352.0\n",
       "54   845      198.295858         167560.0\n",
       "55   280      189.800000          53144.0\n",
       "56   211      185.061611          39048.0\n",
       "57    78      185.230769          14448.0\n",
       "58    86      183.720930          15800.0\n",
       "59    51      190.431373           9712.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_data  = df_analysis[ df_analysis[\"area\"] > 50]\n",
    "selected_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64eb1086-ebc8-4905-afc2-ed0dc01620b9",
   "metadata": {},
   "source": [
    "## Adding new columns\n",
    "You can then access columns and add new columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "402892eb-b1ea-4f11-b272-9c44207f7991",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rober\\AppData\\Local\\Temp\\ipykernel_25216\\206920941.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_analysis['total_intensity'] = df_analysis['area'] * df_analysis['mean_intensity']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>mean_intensity</th>\n",
       "      <th>total_intensity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>422</td>\n",
       "      <td>192.379147</td>\n",
       "      <td>81184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>182</td>\n",
       "      <td>180.131868</td>\n",
       "      <td>32784.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>661</td>\n",
       "      <td>205.216339</td>\n",
       "      <td>135648.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>437</td>\n",
       "      <td>216.585812</td>\n",
       "      <td>94648.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>476</td>\n",
       "      <td>212.302521</td>\n",
       "      <td>101056.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>211</td>\n",
       "      <td>185.061611</td>\n",
       "      <td>39048.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>78</td>\n",
       "      <td>185.230769</td>\n",
       "      <td>14448.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>86</td>\n",
       "      <td>183.720930</td>\n",
       "      <td>15800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>51</td>\n",
       "      <td>190.431373</td>\n",
       "      <td>9712.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>46</td>\n",
       "      <td>175.304348</td>\n",
       "      <td>8064.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    area  mean_intensity  total_intensity\n",
       "0    422      192.379147          81184.0\n",
       "1    182      180.131868          32784.0\n",
       "2    661      205.216339         135648.0\n",
       "3    437      216.585812          94648.0\n",
       "4    476      212.302521         101056.0\n",
       "..   ...             ...              ...\n",
       "56   211      185.061611          39048.0\n",
       "57    78      185.230769          14448.0\n",
       "58    86      183.720930          15800.0\n",
       "59    51      190.431373           9712.0\n",
       "60    46      175.304348           8064.0\n",
       "\n",
       "[61 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_analysis['total_intensity'] = df_analysis['area'] * df_analysis['mean_intensity']\n",
    "df_analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9db24255-2290-4e83-ac74-93d780378175",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "From the loaded CSV file, create a table that only contains these columns:\n",
    "* `minor_axis_length`\n",
    "* `major_axis_length`\n",
    "* `aspect_ratio`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87f226cd-721b-43e3-a31a-faed5e8a6733",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_shape = pd.read_csv('../../data/blobs_statistics.csv')\n",
    "df_shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0254fc9-b321-4a4a-be35-7d17216bb517",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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