notebooks/reading_local_files.ipynb

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 "cells": [
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   "source": [
    "# An Introduction to Jupyter Notebooks\n",
    "\n",
    "Jupyter Notebooks are a file format (```*.ipynb```) that you can execute and explain your code in a step-wise format.\n",
    "> Jupyter Notebooks supports not only code execution in Python, but over 40 languages including R, Lua, Rust, and Julia with numerous [kernels](https://docs.jupyter.org/en/latest/projects/kernels.html#kernels-programming-languages).\n",
    "\n",
    "We can write in Markdown to write text with some level of control over your formatting.\n",
    "- [Here's a Link to Basic Markdown](https://www.markdownguide.org/basic-syntax/)\n",
    "- [Here's a link to Markdown's Extended Syntax](https://www.markdownguide.org/extended-syntax/)\n",
    "\n",
    "Topics We Will Cover\n",
    "- Importing different files and filetypes with [```pandas```](https://pandas.pydata.org/docs/index.html)\n",
    "- Basic Statistical Analysis of tabular data with ```pandas``` and ```numpy```\n",
    "- Creating Charts with python packages from the [Matplotlib](https://matplotlib.org/), [Plotly](https://plotly.com/python/), or [HoloViz Ecosystem](https://holoviz.org/background.html#background-why-holoviz)\n",
    "- Evaluate the potential usecases for each visualization package\n",
    "\n",
    "![EvidenceOfLearning](../images/learning.gif)\n",
    "<br>\n",
    "*This is you, enjoying the learning process.*"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 1: Import ```pandas``` into your python program."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# This will import the pandas and numpy packages into your Python program.\n",
    "\n",
    "df_json = pd.read_json('../data/food-waste-pilot/food-waste-pilot.json')\n",
    "df_csv = pd.read_csv('../data/food-waste-pilot/food-waste-pilot.csv')\n",
    "df_xlsx = pd.read_excel('../data/food-waste-pilot/food-waste-pilot.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(152, 3)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_csv.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Collection Date</th>\n",
       "      <th>Food Waste Collected</th>\n",
       "      <th>Estimated Earned Compost Created</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-02-25</td>\n",
       "      <td>250.8</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-03-02</td>\n",
       "      <td>298.8</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-03-21</td>\n",
       "      <td>601.2</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-03-28</td>\n",
       "      <td>857.2</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-03-30</td>\n",
       "      <td>610.8</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "  Collection Date  Food Waste Collected  Estimated Earned Compost Created\n",
       "0      2022-02-25                 250.8                                25\n",
       "1      2022-03-02                 298.8                                30\n",
       "2      2022-03-21                 601.2                                60\n",
       "3      2022-03-28                 857.2                                86\n",
       "4      2022-03-30                 610.8                                61"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_csv.head() # Grabs the top 5 items in your Dataframe by default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>2022-10-12</td>\n",
       "      <td>385.8</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>2022-10-28</td>\n",
       "      <td>713.6</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>2022-10-31</td>\n",
       "      <td>953.4</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>2022-12-14</td>\n",
       "      <td>694.4</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>2023-01-06</td>\n",
       "      <td>968.6</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "    Collection Date  Food Waste Collected  Estimated Earned Compost Created\n",
       "147      2022-10-12                 385.8                                39\n",
       "148      2022-10-28                 713.6                                71\n",
       "149      2022-10-31                 953.4                                95\n",
       "150      2022-12-14                 694.4                                69\n",
       "151      2023-01-06                 968.6                                97"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df_csv.tail() # Grabs the bottom 5 items in your Dataframe by default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Collection Date', 'Food Waste Collected',\n",
       "       'Estimated Earned Compost Created'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_csv.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Collection Date                      object\n",
       "Food Waste Collected                float64\n",
       "Estimated Earned Compost Created      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_csv.dtypes # Returns the data types of your columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Food Waste Collected</th>\n",
       "      <th>Estimated Earned Compost Created</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>152.000000</td>\n",
       "      <td>152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>526.873684</td>\n",
       "      <td>52.611842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>197.838075</td>\n",
       "      <td>19.787631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>398.050000</td>\n",
       "      <td>39.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>531.500000</td>\n",
       "      <td>53.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>658.900000</td>\n",
       "      <td>66.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1065.800000</td>\n",
       "      <td>107.000000</td>\n",
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      "text/plain": [
       "       Food Waste Collected  Estimated Earned Compost Created\n",
       "count            152.000000                        152.000000\n",
       "mean             526.873684                         52.611842\n",
       "std              197.838075                         19.787631\n",
       "min                0.000000                          0.000000\n",
       "25%              398.050000                         39.750000\n",
       "50%              531.500000                         53.000000\n",
       "75%              658.900000                         66.000000\n",
       "max             1065.800000                        107.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_csv.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 152 entries, 0 to 151\n",
      "Data columns (total 3 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   Collection Date                   152 non-null    object \n",
      " 1   Food Waste Collected              152 non-null    float64\n",
      " 2   Estimated Earned Compost Created  152 non-null    int64  \n",
      "dtypes: float64(1), int64(1), object(1)\n",
      "memory usage: 3.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df_csv.info() # Returns index, column names, a count of Non-Null values, and data types."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are multiple methods to do type conversion using pandas as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Oh no, we can see that our Collection Date is not the data type that we want, we need to convert it to a date value.\n",
    "\n",
    "df_csv['Collection Date'] = pd.to_datetime(df_csv['Collection Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 152 entries, 0 to 151\n",
      "Data columns (total 3 columns):\n",
      " #   Column                            Non-Null Count  Dtype         \n",
      "---  ------                            --------------  -----         \n",
      " 0   Collection Date                   152 non-null    datetime64[ns]\n",
      " 1   Food Waste Collected              152 non-null    float64       \n",
      " 2   Estimated Earned Compost Created  152 non-null    int64         \n",
      "dtypes: datetime64[ns](1), float64(1), int64(1)\n",
      "memory usage: 3.7 KB\n"
     ]
    }
   ],
   "source": [
    "df_csv.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# An alternative way to do this date conversion:\n",
    "\n",
    "df_csv['Collection Date'] = df_csv['Collection Date'].apply(pd.to_datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# astype() is more generic method to convert data types\n",
    "\n",
    "df_csv['Collection Date'] = df_csv['Collection Date'].astype('datetime64[ns]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Collection Date                     datetime64[ns]\n",
       "Food Waste Collected                       float64\n",
       "Estimated Earned Compost Created             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_csv.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now that we have converted our Collection Date column to a datetime data type, we can use the dt.day_name() method to create a new column that contains the day of the week.\n",
    "\n",
    "df_csv['Day of Week'] = df_csv['Collection Date'].dt.day_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Collection Date    2022-08-10 00:00:00\n",
       "Name: 20, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What if we want to know the date that we collected the most food waste?\n",
    "\n",
    "df_csv.loc[\n",
    "    df_csv['Food Waste Collected'].idxmax(),\n",
    "    ['Collection Date']\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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       "      <th>20</th>\n",
       "      <td>2022-08-10</td>\n",
       "      <td>1065.8</td>\n",
       "      <td>107</td>\n",
       "      <td>Wednesday</td>\n",
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       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2022-12-27</td>\n",
       "      <td>987.4</td>\n",
       "      <td>99</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2022-09-12</td>\n",
       "      <td>977.8</td>\n",
       "      <td>98</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>2023-01-06</td>\n",
       "      <td>968.6</td>\n",
       "      <td>97</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>2022-10-31</td>\n",
       "      <td>953.4</td>\n",
       "      <td>95</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-03-28</td>\n",
       "      <td>857.2</td>\n",
       "      <td>86</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>2022-11-28</td>\n",
       "      <td>844.4</td>\n",
       "      <td>84</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2022-12-05</td>\n",
       "      <td>834.4</td>\n",
       "      <td>83</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>815.4</td>\n",
       "      <td>82</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>2022-07-18</td>\n",
       "      <td>807.8</td>\n",
       "      <td>81</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Collection Date  Food Waste Collected  Estimated Earned Compost Created  \\\n",
       "20       2022-08-10                1065.8                               107   \n",
       "124      2022-12-27                 987.4                                99   \n",
       "48       2022-09-12                 977.8                                98   \n",
       "151      2023-01-06                 968.6                                97   \n",
       "149      2022-10-31                 953.4                                95   \n",
       "3        2022-03-28                 857.2                                86   \n",
       "123      2022-11-28                 844.4                                84   \n",
       "57       2022-12-05                 834.4                                83   \n",
       "91       2022-12-30                 815.4                                82   \n",
       "137      2022-07-18                 807.8                                81   \n",
       "\n",
       "    Day of Week  \n",
       "20    Wednesday  \n",
       "124     Tuesday  \n",
       "48       Monday  \n",
       "151      Friday  \n",
       "149      Monday  \n",
       "3        Monday  \n",
       "123      Monday  \n",
       "57       Monday  \n",
       "91       Friday  \n",
       "137      Monday  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If you wanted to see our top 10 collection dates, you could do this:\n",
    "\n",
    "df_csv.nlargest(10,'Food Waste Collected')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Collection Date</th>\n",
       "      <th>Food Waste Collected</th>\n",
       "      <th>Estimated Earned Compost Created</th>\n",
       "      <th>Day of Week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2022-11-11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2023-01-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>2022-09-05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>2022-02-09</td>\n",
       "      <td>59.0</td>\n",
       "      <td>6</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2022-02-16</td>\n",
       "      <td>102.8</td>\n",
       "      <td>10</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2022-02-21</td>\n",
       "      <td>183.8</td>\n",
       "      <td>18</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2022-02-11</td>\n",
       "      <td>197.0</td>\n",
       "      <td>20</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2022-04-15</td>\n",
       "      <td>200.8</td>\n",
       "      <td>20</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>2022-02-18</td>\n",
       "      <td>202.8</td>\n",
       "      <td>20</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2022-12-10</td>\n",
       "      <td>205.8</td>\n",
       "      <td>21</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Collection Date  Food Waste Collected  Estimated Earned Compost Created  \\\n",
       "53       2022-11-11                   0.0                                 0   \n",
       "95       2023-01-16                   0.0                                 0   \n",
       "114      2022-09-05                   0.0                                 0   \n",
       "97       2022-02-09                  59.0                                 6   \n",
       "36       2022-02-16                 102.8                                10   \n",
       "63       2022-02-21                 183.8                                18   \n",
       "61       2022-02-11                 197.0                                20   \n",
       "39       2022-04-15                 200.8                                20   \n",
       "62       2022-02-18                 202.8                                20   \n",
       "29       2022-12-10                 205.8                                21   \n",
       "\n",
       "    Day of Week  \n",
       "53       Friday  \n",
       "95       Monday  \n",
       "114      Monday  \n",
       "97    Wednesday  \n",
       "36    Wednesday  \n",
       "63       Monday  \n",
       "61       Friday  \n",
       "39       Friday  \n",
       "62       Friday  \n",
       "29     Saturday  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_csv.nsmallest(10,'Food Waste Collected')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_csv.plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## You have to make sure that `pandas` parses your dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_csv_parsed_dates = pd.read_csv('../data/food-waste-pilot/food-waste-pilot.csv', parse_dates=True, index_col=\"Collection Date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Collection Date'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_csv_parsed_dates.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cary_dev",
   "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": "3.11.3"
  },
  "vscode": {
   "interpreter": {
    "hash": "71fb11914f94fc2c90e6061170f2222c8b50268100b040249549820f4859e21e"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}