Mii_Kislitsa_Egor_Pibd_33/lec1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Работа с NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matrix = \n",
      " [[4 5 0]\n",
      " [9 9 9]] \n",
      "\n",
      "tmatrix = \n",
      " [[4 9]\n",
      " [5 9]\n",
      " [0 9]] \n",
      "\n",
      "vector = \n",
      " [4 5 0 9 9 9] \n",
      "\n",
      "tvector = \n",
      " [[4]\n",
      " [5]\n",
      " [0]\n",
      " [9]\n",
      " [9]\n",
      " [9]] \n",
      "\n",
      "list_matrix = \n",
      " [array([4, 5, 0]), array([9, 9, 9])] \n",
      "\n",
      "matrix as str = \n",
      " [[4 5 0]\n",
      " [9 9 9]] \n",
      "\n",
      "matrix type is <class 'numpy.ndarray'> \n",
      "\n",
      "vector type is <class 'numpy.ndarray'> \n",
      "\n",
      "list_matrix type is <class 'list'> \n",
      "\n",
      "str_matrix type is <class 'str'> \n",
      "\n",
      "formatted_vector = \n",
      " 4; 5; 0; 9; 9; 9 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "matrix = np.array([[4, 5, 0], [9, 9, 9]])\n",
    "print(\"matrix = \\n\", matrix, \"\\n\")\n",
    "\n",
    "\n",
    "tmatrix = matrix.T\n",
    "print(\"tmatrix = \\n\", tmatrix, \"\\n\")\n",
    "\n",
    "\n",
    "vector = np.ravel(matrix)\n",
    "print(\"vector = \\n\", vector, \"\\n\")\n",
    "\n",
    "\n",
    "tvector = np.reshape(vector, (6, 1))\n",
    "print(\"tvector = \\n\", tvector, \"\\n\")\n",
    "\n",
    "list_matrix = list(matrix)\n",
    "print(\"list_matrix = \\n\", list_matrix, \"\\n\")\n",
    "\n",
    "str_matrix = str(matrix)\n",
    "print(\"matrix as str = \\n\", str_matrix, \"\\n\")\n",
    "\n",
    "print(\"matrix type is\", type(matrix), \"\\n\")\n",
    "\n",
    "print(\"vector type is\", type(vector), \"\\n\")\n",
    "\n",
    "print(\"list_matrix type is\", type(list_matrix), \"\\n\")\n",
    "\n",
    "print(\"str_matrix type is\", type(str_matrix), \"\\n\")\n",
    "\n",
    "\n",
    "formatted_vector = \"; \".join(map(str, vector))\n",
    "\n",
    "print(\"formatted_vector = \\n\", formatted_vector, \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Работа с Pandas DataFrame\n",
    "\n",
    "https://pandas.pydata.org/docs/user_guide/10min.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Работа с данными - чтение и запись CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"data/healthcare-dataset-stroke-data.csv\", index_col=\"id\")\n",
    "\n",
    "df.to_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Работа с данными - основные команды"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 5110 entries, 9046 to 44679\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   gender             5110 non-null   object \n",
      " 1   age                5110 non-null   float64\n",
      " 2   hypertension       5110 non-null   int64  \n",
      " 3   heart_disease      5110 non-null   int64  \n",
      " 4   ever_married       5110 non-null   object \n",
      " 5   work_type          5110 non-null   object \n",
      " 6   Residence_type     5110 non-null   object \n",
      " 7   avg_glucose_level  5110 non-null   float64\n",
      " 8   bmi                4909 non-null   float64\n",
      " 9   smoking_status     5110 non-null   object \n",
      " 10  stroke             5110 non-null   int64  \n",
      "dtypes: float64(3), int64(3), object(5)\n",
      "memory usage: 608.1+ KB\n",
      "                    count        mean        std    min     25%     50%  \\\n",
      "age                5110.0   43.226614  22.612647   0.08  25.000  45.000   \n",
      "hypertension       5110.0    0.097456   0.296607   0.00   0.000   0.000   \n",
      "heart_disease      5110.0    0.054012   0.226063   0.00   0.000   0.000   \n",
      "avg_glucose_level  5110.0  106.147677  45.283560  55.12  77.245  91.885   \n",
      "bmi                4909.0   28.893237   7.854067  10.30  23.500  28.100   \n",
      "stroke             5110.0    0.048728   0.215320   0.00   0.000   0.000   \n",
      "\n",
      "                      75%     max  \n",
      "age                 61.00   82.00  \n",
      "hypertension         0.00    1.00  \n",
      "heart_disease        0.00    1.00  \n",
      "avg_glucose_level  114.09  271.74  \n",
      "bmi                 33.10   97.60  \n",
      "stroke               0.00    1.00  \n",
      "       gender   age  hypertension      work_type  avg_glucose_level   bmi  \\\n",
      "id                                                                          \n",
      "9046     Male  67.0             0        Private             228.69  36.6   \n",
      "51676  Female  61.0             0  Self-employed             202.21   NaN   \n",
      "31112    Male  80.0             0        Private             105.92  32.5   \n",
      "60182  Female  49.0             0        Private             171.23  34.4   \n",
      "1665   Female  79.0             1  Self-employed             174.12  24.0   \n",
      "\n",
      "        smoking_status  stroke  \n",
      "id                              \n",
      "9046   formerly smoked       1  \n",
      "51676     never smoked       1  \n",
      "31112     never smoked       1  \n",
      "60182           smokes       1  \n",
      "1665      never smoked       1  \n",
      "       gender   age  hypertension      work_type  avg_glucose_level   bmi  \\\n",
      "id                                                                          \n",
      "18234  Female  80.0             1        Private              83.75   NaN   \n",
      "44873  Female  81.0             0  Self-employed             125.20  40.0   \n",
      "19723  Female  35.0             0  Self-employed              82.99  30.6   \n",
      "37544    Male  51.0             0        Private             166.29  25.6   \n",
      "44679  Female  44.0             0       Govt_job              85.28  26.2   \n",
      "\n",
      "        smoking_status  stroke  \n",
      "id                              \n",
      "18234     never smoked       0  \n",
      "44873     never smoked       0  \n",
      "19723     never smoked       0  \n",
      "37544  formerly smoked       0  \n",
      "44679          Unknown       0  \n",
      "       gender   age  hypertension work_type  avg_glucose_level   bmi  \\\n",
      "id                                                                     \n",
      "47350  Female  0.08             0  children             139.67  14.1   \n",
      "29955    Male  0.08             0  children              70.33  16.9   \n",
      "22877    Male  0.16             0  children             114.71  17.4   \n",
      "41500    Male  0.16             0  children              69.79  13.0   \n",
      "8247     Male  0.16             0  children             109.52  13.9   \n",
      "\n",
      "      smoking_status  stroke  \n",
      "id                            \n",
      "47350        Unknown       0  \n",
      "29955        Unknown       0  \n",
      "22877        Unknown       0  \n",
      "41500        Unknown       0  \n",
      "8247         Unknown       0  \n",
      "       gender   age  hypertension      work_type  avg_glucose_level   bmi  \\\n",
      "id                                                                          \n",
      "38829  Female  82.0             0        Private              59.32  33.2   \n",
      "25510    Male  82.0             0  Self-employed             111.81  19.8   \n",
      "27705  Female  82.0             0  Self-employed              88.60  32.5   \n",
      "40163  Female  82.0             1        Private             222.52   NaN   \n",
      "64778    Male  82.0             0        Private             208.30  32.5   \n",
      "\n",
      "        smoking_status  stroke  \n",
      "id                              \n",
      "38829     never smoked       1  \n",
      "25510  formerly smoked       0  \n",
      "27705          Unknown       0  \n",
      "40163  formerly smoked       0  \n",
      "64778          Unknown       1  \n"
     ]
    }
   ],
   "source": [
    "df.info()\n",
    "\n",
    "print(df.describe().transpose())\n",
    "\n",
    "clear_df = df.drop([\"heart_disease\", \"ever_married\", \"Residence_type\"], axis=1)\n",
    "print(clear_df.head())\n",
    "print(clear_df.tail())\n",
    "\n",
    "sorted_df = clear_df.sort_values(by=\"age\")\n",
    "print(sorted_df.head())\n",
    "print(sorted_df.tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Работа с данными - работа с элементами"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id\n",
      "9046     36.6\n",
      "51676     NaN\n",
      "31112    32.5\n",
      "60182    34.4\n",
      "1665     24.0\n",
      "         ... \n",
      "18234     NaN\n",
      "44873    40.0\n",
      "19723    30.6\n",
      "37544    25.6\n",
      "44679    26.2\n",
      "Name: bmi, Length: 5110, dtype: float64\n",
      "gender                          Male\n",
      "age                             67.0\n",
      "hypertension                       0\n",
      "heart_disease                      1\n",
      "ever_married                     Yes\n",
      "work_type                    Private\n",
      "Residence_type                 Urban\n",
      "avg_glucose_level             228.69\n",
      "bmi                             36.6\n",
      "smoking_status       formerly smoked\n",
      "stroke                             1\n",
      "Name: 9046, dtype: object\n",
      "Male\n",
      "       gender   bmi\n",
      "id                 \n",
      "9046     Male  36.6\n",
      "51676  Female   NaN\n",
      "31112    Male  32.5\n",
      "60182  Female  34.4\n",
      "1665   Female  24.0\n",
      "56669    Male  29.0\n",
      "53882    Male  27.4\n",
      "       gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
      "id                                                                             \n",
      "9046     Male  67.0             0              1          Yes        Private   \n",
      "51676  Female  61.0             0              0          Yes  Self-employed   \n",
      "31112    Male  80.0             0              1          Yes        Private   \n",
      "\n",
      "      Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
      "id                                                                      \n",
      "9046           Urban             228.69  36.6  formerly smoked       1  \n",
      "51676          Rural             202.21   NaN     never smoked       1  \n",
      "31112          Rural             105.92  32.5     never smoked       1  \n",
      "gender                          Male\n",
      "age                             67.0\n",
      "hypertension                       0\n",
      "heart_disease                      1\n",
      "ever_married                     Yes\n",
      "work_type                    Private\n",
      "Residence_type                 Urban\n",
      "avg_glucose_level             228.69\n",
      "bmi                             36.6\n",
      "smoking_status       formerly smoked\n",
      "stroke                             1\n",
      "Name: 9046, dtype: object\n",
      "       gender   age\n",
      "id                 \n",
      "60182  Female  49.0\n",
      "1665   Female  79.0\n",
      "       gender   age\n",
      "id                 \n",
      "60182  Female  49.0\n",
      "1665   Female  79.0\n"
     ]
    }
   ],
   "source": [
    "print(df[\"bmi\"])\n",
    "\n",
    "print(df.loc[9046])\n",
    "\n",
    "print(df.loc[9046, \"gender\"])\n",
    "\n",
    "print(df.loc[9046:53882, [\"gender\", \"bmi\"]])\n",
    "\n",
    "print(df[0:3])\n",
    "\n",
    "print(df.iloc[0])\n",
    "\n",
    "print(df.iloc[3:5, 0:2])\n",
    "\n",
    "print(df.iloc[[3, 4], [0, 1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Работа с данными - отбор и группировка"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Male' 'Female' 'Other']\n",
      "Male count = 2115\n",
      "Female count = 2994\n",
      "Other count = 1\n",
      "Total count =  5110\n",
      "     ever_married  avg_glucose_level  Count\n",
      "0              No              55.12      1\n",
      "1              No              55.25      1\n",
      "2              No              55.34      1\n",
      "3              No              55.35      1\n",
      "4              No              55.39      1\n",
      "...           ...                ...    ...\n",
      "4445          Yes             263.56      1\n",
      "4446          Yes             267.60      1\n",
      "4447          Yes             267.61      1\n",
      "4448          Yes             267.76      1\n",
      "4449          Yes             271.74      1\n",
      "\n",
      "[4450 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "s_values = df[\"gender\"].unique()\n",
    "print(s_values)\n",
    "\n",
    "s_total = 0\n",
    "for s_value in s_values:\n",
    "    count = df[df[\"gender\"] == s_value].shape[0]\n",
    "    s_total += count\n",
    "    print(s_value, \"count =\", count)\n",
    "print(\"Total count = \", s_total)\n",
    "\n",
    "print(df.groupby([\"ever_married\", \"avg_glucose_level\"]).size().reset_index(name=\"Count\"))  # type: ignore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Визуализация - Исходные данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       gender   age   bmi\n",
      "id                       \n",
      "9046     Male  67.0  36.6\n",
      "31112    Male  80.0  32.5\n",
      "60182  Female  49.0  34.4\n",
      "1665   Female  79.0  24.0\n",
      "56669    Male  81.0  29.0\n",
      "...       ...   ...   ...\n",
      "14180  Female  13.0  18.6\n",
      "44873  Female  81.0  40.0\n",
      "19723  Female  35.0  30.6\n",
      "37544    Male  51.0  25.6\n",
      "44679  Female  44.0  26.2\n",
      "\n",
      "[4909 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "data = df[[\"gender\", \"age\", \"bmi\"]].copy()\n",
    "data.dropna(subset=[\"bmi\"], inplace=True)\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Визуализация - Сводка пяти чисел\n",
    "\n",
    "<img src=\"assets/quantile.png\" width=\"400\" style=\"background-color: white\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          age                               \n",
      "          min    q1    q2 median    q3   max\n",
      "gender                                      \n",
      "Female   0.08  26.0  44.0   44.0  60.0  82.0\n",
      "Male     0.08  21.0  45.0   45.0  60.5  82.0\n",
      "Other   26.00  26.0  26.0   26.0  26.0  26.0\n",
      "           age               \n",
      "       low_iqr   iqr high_iqr\n",
      "gender                       \n",
      "Female     0.0  34.0   111.00\n",
      "Male       0.0  39.5   119.75\n",
      "Other     26.0   0.0    26.00\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'age'}, xlabel='gender'>"
      ]
     },
     "execution_count": 28,
     "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": [
    "def q1(x):\n",
    "    return x.quantile(0.25)\n",
    "\n",
    "\n",
    "# median = quantile(0.5)\n",
    "def q2(x):\n",
    "    return x.quantile(0.5)\n",
    "\n",
    "\n",
    "def q3(x):\n",
    "    return x.quantile(0.75)\n",
    "\n",
    "\n",
    "def iqr(x):\n",
    "    return q3(x) - q1(x)\n",
    "\n",
    "\n",
    "def low_iqr(x):\n",
    "    return max(0, q1(x) - 1.5 * iqr(x))\n",
    "\n",
    "\n",
    "def high_iqr(x):\n",
    "    return q3(x) + 1.5 * iqr(x)\n",
    "\n",
    "\n",
    "quantiles = data[[\"gender\", \"age\"]].groupby([\"gender\"]).aggregate([\"min\", q1, q2, \"median\", q3, \"max\"])\n",
    "print(quantiles)\n",
    "\n",
    "iqrs = data[[\"gender\", \"age\"]].groupby([\"gender\"]).aggregate([low_iqr, iqr, high_iqr])\n",
    "print(iqrs)\n",
    "\n",
    "data.boxplot(column=\"age\", by=\"gender\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Визуализация - Гистограмма"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 29,
     "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": [
    "data.plot.hist(column=[\"age\"], bins=80)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Визуализация - Точечная диаграмма"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='smoking_status', ylabel='age'>"
      ]
     },
     "execution_count": 41,
     "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"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x=\"age\", y=\"gender\")\n",
    "\n",
    "df.plot.scatter(x=\"smoking_status\", y=\"age\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Визуализация - Столбчатая диаграмма"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plot = (\n",
    "    df.groupby(\n",
    "        [\"Residence_type\", \"ever_married\"]\n",
    "    )  \n",
    "    .size()\n",
    "    .unstack()  # Преобразование таблицы для корректной визуализации\n",
    "    .plot.bar(\n",
    "        color=[\"pink\", \"green\"], figsize=(10, 6)\n",
    "    )\n",
    ")\n",
    "\n",
    "\n",
    "plot.legend([\"Never married\", \"Ever married\"], title=\"Marital Status\")\n",
    "plot.set_title(\"Распределение по типу проживания и статусу брака\", fontsize=16)\n",
    "plot.set_xlabel(\"Тип проживания\", fontsize=12)\n",
    "plot.set_ylabel(\"Количество\", fontsize=12)\n",
    "\n",
    "# Показать диаграмму\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Визуализация - Временные ряды"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 243 entries, 0 to 242\n",
      "Data columns (total 6 columns):\n",
      " #   Column       Non-Null Count  Dtype         \n",
      "---  ------       --------------  -----         \n",
      " 0   my_date      243 non-null    object        \n",
      " 1   my_value     243 non-null    float64       \n",
      " 2   bullet       2 non-null      object        \n",
      " 3   bulletClass  2 non-null      object        \n",
      " 4   label        2 non-null      object        \n",
      " 5   date         243 non-null    datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(1), object(4)\n",
      "memory usage: 11.5+ KB\n",
      "        my_date  my_value bullet bulletClass label       date\n",
      "0    28.03.2023   76.5662    NaN         NaN   NaN 2023-03-28\n",
      "1    31.03.2023   77.0863    NaN         NaN   NaN 2023-03-31\n",
      "2    01.04.2023   77.3233    NaN         NaN   NaN 2023-04-01\n",
      "3    04.04.2023   77.9510    NaN         NaN   NaN 2023-04-04\n",
      "4    05.04.2023   79.3563    NaN         NaN   NaN 2023-04-05\n",
      "..          ...       ...    ...         ...   ...        ...\n",
      "238  20.03.2024   92.2243    NaN         NaN   NaN 2024-03-20\n",
      "239  21.03.2024   92.6861    NaN         NaN   NaN 2024-03-21\n",
      "240  22.03.2024   91.9499    NaN         NaN   NaN 2024-03-22\n",
      "241  23.03.2024   92.6118    NaN         NaN   NaN 2024-03-23\n",
      "242  26.03.2024   92.7761    NaN         NaN   NaN 2024-03-26\n",
      "\n",
      "[243 rows x 6 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "import matplotlib.dates as md\n",
    "\n",
    "ts = pd.read_csv(\"data/dollar.csv\")\n",
    "ts[\"date\"] = ts.apply(lambda row: datetime.strptime(row[\"my_date\"], \"%d.%m.%Y\"), axis=1)\n",
    "ts.info()\n",
    "\n",
    "print(ts)\n",
    "\n",
    "plot = ts.plot.line(x=\"date\", y=\"my_value\")\n",
    "plot.xaxis.set_major_locator(md.DayLocator(interval=10))\n",
    "plot.xaxis.set_major_formatter(md.DateFormatter(\"%d.%m.%Y\"))\n",
    "plot.tick_params(axis=\"x\", labelrotation=90)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}