MII/mai/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": [
    "import numpy as np\n",
    "\n",
    "matrix = np.array([[4, 5, 0], [9, 9, 9]])\n",
    "print(\"matrix = \\n\", matrix, \"\\n\")\n",
    "\n",
    "tmatrix = matrix.T\n",
    "print(\"tmatrix = \\n\", tmatrix, \"\\n\")\n",
    "\n",
    "vector = np.ravel(matrix)\n",
    "print(\"vector = \\n\", vector, \"\\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",
    "formatted_vector = \"; \".join(map(str, vector))\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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"data/titanic.csv\", index_col=\"PassengerId\")\n",
    "\n",
    "df.to_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Работа с данными - основные команды"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 891 entries, 1 to 891\n",
      "Data columns (total 11 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Survived  891 non-null    int64  \n",
      " 1   Pclass    891 non-null    int64  \n",
      " 2   Name      891 non-null    object \n",
      " 3   Sex       891 non-null    object \n",
      " 4   Age       714 non-null    float64\n",
      " 5   SibSp     891 non-null    int64  \n",
      " 6   Parch     891 non-null    int64  \n",
      " 7   Ticket    891 non-null    object \n",
      " 8   Fare      891 non-null    float64\n",
      " 9   Cabin     204 non-null    object \n",
      " 10  Embarked  889 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 83.5+ KB\n",
      "          count       mean        std   min      25%      50%   75%       max\n",
      "Survived  891.0   0.383838   0.486592  0.00   0.0000   0.0000   1.0    1.0000\n",
      "Pclass    891.0   2.308642   0.836071  1.00   2.0000   3.0000   3.0    3.0000\n",
      "Age       714.0  29.699118  14.526497  0.42  20.1250  28.0000  38.0   80.0000\n",
      "SibSp     891.0   0.523008   1.102743  0.00   0.0000   0.0000   1.0    8.0000\n",
      "Parch     891.0   0.381594   0.806057  0.00   0.0000   0.0000   0.0    6.0000\n",
      "Fare      891.0  32.204208  49.693429  0.00   7.9104  14.4542  31.0  512.3292\n",
      "             Survived  Pclass     Sex   Age  SibSp  Parch     Fare Cabin\n",
      "PassengerId                                                             \n",
      "1                   0       3    male  22.0      1      0   7.2500   NaN\n",
      "2                   1       1  female  38.0      1      0  71.2833   C85\n",
      "3                   1       3  female  26.0      0      0   7.9250   NaN\n",
      "4                   1       1  female  35.0      1      0  53.1000  C123\n",
      "5                   0       3    male  35.0      0      0   8.0500   NaN\n",
      "             Survived  Pclass     Sex   Age  SibSp  Parch   Fare Cabin\n",
      "PassengerId                                                           \n",
      "887                 0       2    male  27.0      0      0  13.00   NaN\n",
      "888                 1       1  female  19.0      0      0  30.00   B42\n",
      "889                 0       3  female   NaN      1      2  23.45   NaN\n",
      "890                 1       1    male  26.0      0      0  30.00  C148\n",
      "891                 0       3    male  32.0      0      0   7.75   NaN\n",
      "             Survived  Pclass     Sex   Age  SibSp  Parch     Fare Cabin\n",
      "PassengerId                                                             \n",
      "804                 1       3    male  0.42      0      1   8.5167   NaN\n",
      "756                 1       2    male  0.67      1      1  14.5000   NaN\n",
      "470                 1       3  female  0.75      2      1  19.2583   NaN\n",
      "645                 1       3  female  0.75      2      1  19.2583   NaN\n",
      "79                  1       2    male  0.83      0      2  29.0000   NaN\n",
      "             Survived  Pclass     Sex  Age  SibSp  Parch     Fare Cabin\n",
      "PassengerId                                                            \n",
      "860                 0       3    male  NaN      0      0   7.2292   NaN\n",
      "864                 0       3  female  NaN      8      2  69.5500   NaN\n",
      "869                 0       3    male  NaN      0      0   9.5000   NaN\n",
      "879                 0       3    male  NaN      0      0   7.8958   NaN\n",
      "889                 0       3  female  NaN      1      2  23.4500   NaN\n"
     ]
    }
   ],
   "source": [
    "df.info()\n",
    "\n",
    "print(df.describe().transpose())\n",
    "\n",
    "cleared_df = df.drop([\"Name\", \"Ticket\", \"Embarked\"], axis=1)\n",
    "print(cleared_df.head())\n",
    "print(cleared_df.tail())\n",
    "\n",
    "sorted_df = cleared_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": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PassengerId\n",
      "1      22.0\n",
      "2      38.0\n",
      "3      26.0\n",
      "4      35.0\n",
      "5      35.0\n",
      "       ... \n",
      "887    27.0\n",
      "888    19.0\n",
      "889     NaN\n",
      "890    26.0\n",
      "891    32.0\n",
      "Name: Age, Length: 891, dtype: float64\n",
      "Survived                    0\n",
      "Pclass                      2\n",
      "Name        Kantor, Mr. Sinai\n",
      "Sex                      male\n",
      "Age                      34.0\n",
      "SibSp                       1\n",
      "Parch                       0\n",
      "Ticket                 244367\n",
      "Fare                     26.0\n",
      "Cabin                     NaN\n",
      "Embarked                    S\n",
      "Name: 100, dtype: object\n",
      "Kantor, Mr. Sinai\n",
      "              Age                                    Name\n",
      "PassengerId                                              \n",
      "100          34.0                       Kantor, Mr. Sinai\n",
      "101          28.0                 Petranec, Miss. Matilda\n",
      "102           NaN        Petroff, Mr. Pastcho (\"Pentcho\")\n",
      "103          21.0               White, Mr. Richard Frasar\n",
      "104          33.0              Johansson, Mr. Gustaf Joel\n",
      "...           ...                                     ...\n",
      "196          58.0                    Lurette, Miss. Elise\n",
      "197           NaN                     Mernagh, Mr. Robert\n",
      "198          42.0        Olsen, Mr. Karl Siegwart Andreas\n",
      "199           NaN        Madigan, Miss. Margaret \"Maggie\"\n",
      "200          24.0  Yrois, Miss. Henriette (\"Mrs Harbeck\")\n",
      "\n",
      "[101 rows x 2 columns]\n",
      "             Survived  Pclass  \\\n",
      "PassengerId                     \n",
      "1                   0       3   \n",
      "2                   1       1   \n",
      "3                   1       3   \n",
      "\n",
      "                                                          Name     Sex   Age  \\\n",
      "PassengerId                                                                    \n",
      "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
      "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
      "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
      "\n",
      "             SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
      "PassengerId                                                          \n",
      "1                1      0         A/5 21171   7.2500   NaN        S  \n",
      "2                1      0          PC 17599  71.2833   C85        C  \n",
      "3                0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
      "Survived                          0\n",
      "Pclass                            3\n",
      "Name        Braund, Mr. Owen Harris\n",
      "Sex                            male\n",
      "Age                            22.0\n",
      "SibSp                             1\n",
      "Parch                             0\n",
      "Ticket                    A/5 21171\n",
      "Fare                           7.25\n",
      "Cabin                           NaN\n",
      "Embarked                          S\n",
      "Name: 1, dtype: object\n",
      "             Survived  Pclass\n",
      "PassengerId                  \n",
      "4                   1       1\n",
      "5                   0       3\n",
      "             Survived  Pclass\n",
      "PassengerId                  \n",
      "4                   1       1\n",
      "5                   0       3\n"
     ]
    }
   ],
   "source": [
    "print(df[\"Age\"])\n",
    "\n",
    "print(df.loc[100])\n",
    "\n",
    "print(df.loc[100, \"Name\"])\n",
    "\n",
    "print(df.loc[100:200, [\"Age\", \"Name\"]])\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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['male' 'female']\n",
      "male count = 577\n",
      "female count = 314\n",
      "Total count =  891\n",
      "   Pclass  Survived  Count\n",
      "0       1         0     80\n",
      "1       1         1    136\n",
      "2       2         0     97\n",
      "3       2         1     87\n",
      "4       3         0    372\n",
      "5       3         1    119\n"
     ]
    }
   ],
   "source": [
    "s_values = df[\"Sex\"].unique()\n",
    "print(s_values)\n",
    "\n",
    "s_total = 0\n",
    "for s_value in s_values:\n",
    "    count = df[df[\"Sex\"] == 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([\"Pclass\", \"Survived\"]).size().reset_index(name=\"Count\")) # type: ignore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Визуализация - Исходные данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             Pclass  Survived   Age\n",
      "PassengerId                        \n",
      "1                 3         0  22.0\n",
      "2                 1         1  38.0\n",
      "3                 3         1  26.0\n",
      "4                 1         1  35.0\n",
      "5                 3         0  35.0\n",
      "...             ...       ...   ...\n",
      "886               3         0  39.0\n",
      "887               2         0  27.0\n",
      "888               1         1  19.0\n",
      "890               1         1  26.0\n",
      "891               3         0  32.0\n",
      "\n",
      "[714 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "data = df[[\"Pclass\", \"Survived\", \"Age\"]].copy()\n",
    "data.dropna(subset=[\"Age\"], 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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Age                               \n",
      "         min    q1    q2 median    q3   max\n",
      "Pclass                                     \n",
      "1       0.92  27.0  37.0   37.0  49.0  80.0\n",
      "2       0.67  23.0  29.0   29.0  36.0  70.0\n",
      "3       0.42  18.0  24.0   24.0  32.0  74.0\n",
      "           Age               \n",
      "       low_iqr   iqr high_iqr\n",
      "Pclass                       \n",
      "1          0.0  22.0     82.0\n",
      "2          3.5  13.0     55.5\n",
      "3          0.0  14.0     53.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Age'}, xlabel='Pclass'>"
      ]
     },
     "execution_count": 7,
     "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[[\"Pclass\", \"Age\"]].groupby([\"Pclass\"]).aggregate([\"min\", q1, q2, \"median\", q3, \"max\"])\n",
    "print(quantiles)\n",
    "\n",
    "iqrs = data[[\"Pclass\", \"Age\"]].groupby([\"Pclass\"]).aggregate([low_iqr, iqr, high_iqr])\n",
    "print(iqrs)\n",
    "\n",
    "data.boxplot(column=\"Age\", by=\"Pclass\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Визуализация - Гистограмма"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 8,
     "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Pclass', ylabel='Age'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsGAVolatWuX1vjfeeMO6GAAAgIrCKkTddNNNGjlypH766Sdn27Fjx3Trrbdq9OjRpVYcAABAeWX9SdSSJUvUvn17bd++XcuXL1fr1q2Vl5en9PT0Ui4RAACg/LEKUZ06dVJ6erpat26tdu3a6fbbb9eIESO0evVqNWrUqLRrBAAAKHesTyzfuXOn0tLS1LBhQwUFBWnHjh0qKCgozdoAAADKLasQ9fLLLyslJUU33HCDtm7dqvXr12vTpk1KTEzUunXrSrtGAACAcscqRE2dOlVLly7V9OnTVaVKFbVu3Vrr16/XHXfcoeuuu66USwQAACh/gmwe9O233yomJsalLTg4WJMmTdItt9xSKoUBAACUZ1afRMXExCgnJ0dvv/22xowZoxMnTkiSNm7cqISEhFItEAAAoDyy+iRqy5Yt6tGjhyIjI7V3714NHjxYNWvW1IcffqisrCzNmzevtOsEAAAoV6w+iRoxYoQGDRqkjIwMValSxdl+880364svvii14gAAAMorq0+i0tLS9Oabb7q1N2jQQIcOHbrsogAAAMo7q0+iQkNDlZeX59a+c+dO1apV67KLAgAAKO+sQtRtt92m559/3vm38xwOh7KysjRq1CjdeeedpVogAABAeWQVoiZPnqxTp06pdu3aOn36tLp166amTZsqPDxcEyZMKO0aAQAAyh2rc6IiIyO1YsUKrVmzRlu2bNGpU6d09dVXq3v37qVdHwAAQLnk0ydR69at00cffeS83aVLF1WrVk2vvfaa+vXrp4ceekhnzpwp9SIBAADKG59C1PPPP69t27Y5b3/77bcaPHiwbrjhBo0ePVp///vfNXHixFIvEgAAoLzxKUSlp6e7/Mpu/vz56tChg9566y09/vjjmjZtmhYuXFjqRQIAAJQ3PoWo7Oxs1alTx3k7NTVVvXr1ct5u37699u/fX3rVAQAAlFM+hag6deooMzNTknT27Flt3LhRHTt2dN5/8uRJBQcHl26FAAAA5ZBPIermm2/W6NGj9eWXX2rMmDEKCwtT165dnfdv2bJFTZs2LfUiAQAAyhufLnHwwgsv6I477lC3bt0UHh6uuXPnKiQkxHn/7Nmz1bNnz1IvEgAAoLxxGGOMrw/Kzc1VeHi4AgMDXdpPnDih8PBwl2D1W5WXl6fIyEjl5uYqIiLC3+UAAIAS8OX92/pim57UrFnTZjgAAIAKx+rPvgAAAPzWEaIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIkDRo0SH369PF3GQAAoAIJ8ncB8N2eo6f0deZx7Tx8Ul98d0y5P57V9S3raNLdbZx9ZqzM0Nrdx9S1WS09en2C1zZJen7ZNn2155i6JNTS2FtaXfK5950oUOPoaoqPqeZsT91xROnf56hdXA11bVar2HZvdXgbe8H6LK3LPK7OTWN0d3JssX298VSHtzG6T1qlfdkFio+uphVPXOdsf2juBn2zP1vtG9XUzAHJkqSOE1bo8MmzqhcRqq+e6uHse8XYf+j0OaOwIIe2v3izs71o//cGd9S+EwV69N00nT5nFB4coK0v9HL2TXrun8o+fU41qwZp4/gbne03/yVVu47lq3ntcE37fTvtO1Ggx+dvUvbpc6pdLVjzH+7knNfNU1IvWceFuru+vFI/5P6o2KiqSh31O49zeX1AsvN1HLkw3W2M5Bc+1bH8n9zqGPJumrPmj4Zd63GOix/trH0nCjTp438r83iBrqwXocWPdva4Xd64L7nYsT2N2zi6mt79aq/Hff2JBen6eu9xpTSJcR5H/d9cpy0HctW2YZTefbCjx/2gX4c45+tRWGjc9rGi26rovD3t097qKNp2S2I953McyD7tcQxP+6m3Y9yXNcFTbcWN4Ym3Oooei8YY57/X7zle4mPflzXI2+vva7sn3uoojXXMF0XraBBV1fk8WcfzPdbni7Ksu6LV4TDGGL88czkyaNAg5eTkaOnSpaU2Zl5eniIjI5Wbm6uIiIhSGTOn4KwefX+jvtp93Gufodc10YzVe0o03hM9EjT5s11u7bPuS1b3VnXcnvuxD9L1RcZRZ9u1zWrpyZuaa8Cs9cou+MnZXiMsWK//vp0e+etGl/bw0ECdOnPe/fkGJGvuv/a5jf1ItyYaMHu9zhX+sosGBTiU2CBCG/fnuvSd3i9JkWHBbmPvO56vPq+udakjqmqwmtUO14Z92S5jNIwM0V/TfnAbo1N8lL7KzHFr96Rm1QCdOF3o1h4dFqDjBe7tnkRVlXJOu7fH16yizBM/lmgMb2LCAnSshHXUCgvU0QL37VUa4qOqKDOnZHNp17C6Nn5/skzqeKBTnGZ9lVWivr1a1dbH24+UqG9oUIDOnHN/ne9p10Afph9w26eH/a6px2OxpIICHBrctbFeT80sUf8/90jQ/3h4vie6J2jyypLVMfS6eM1Y7f58CwZ31DVNo13aPt16UA+9t9Gt77R72mrxph9cjn1vggKkxAZR2rg/x9l2bbNaevLG5how28Ma1L+dHnn/4jUoQKd/KtT5wqLjOvQ/d1+lPy/61m27TL4rUU8s3uLWvmxIZ7VqEOlSn6e1pkZYsP7fXVfpkfc2uY1xVf0Ibfq+ZOuYLzzV4U2NsGAtG9JFsdFhJRrb2/tAadTti7Kuw5f37woXoq677jpdddVVCgwM1Ny5cxUSEqIXX3xRv//97zV06FAtXrxYderU0fTp09WrVy+dP39eDz30kD7//HMdOnRIcXFxevTRRzVs2DDnmBeHqMLCQr3yyit68803dejQITVv3lzPPPOM7rrrrhLXWRYh6r5Z60u02JSGvS/3dnvutbuO6XyR3SXQ4ZDDIZfFwVagw+E29vkS7pqBDoc6J8Ro3gMd3O5Lev7TEi0mvjwfgOJdvH40Hr3ca9/LOfZKcw3yRVCAQ7teutmlraRrjTfFrWO+8LWOGmHB2jSuZ4n6ensfKI26fVHWdfjy/l0hz4maO3euYmJitH79ev3pT3/SI488orvvvludOnXSxo0b1bNnTw0YMEAFBQUqLCxUw4YNtWjRIm3fvl3jxo3TU089pYULF3odf+LEiZo3b55mzpypbdu2acSIEbr33nuVmprq9TFnzpxRXl6ey09p2nP01K8WoCTpxY+2uz33xQvdeWNKbfHyNLYvj/0i46gyj+W7tKfuOFLixYQABZSe11b98mnW88u2Fdv3co690lyDfHGu0GhR2n7nbV/WGm+8rWO+sKkju+AnfVmC95bi3gcut25flJc6LqiQIapNmzYaO3asmjVrpjFjxqhKlSqKiYnR4MGD1axZM40bN07Hjx/Xli1bFBwcrOeee07JycmKj49X//799Yc//MFriDpz5oxeeuklzZ49WzfeeKOaNGmiQYMG6d5779Ubb7zhtaaJEycqMjLS+RMbW/zvzX2170RBqY53KWt2/XJQ/drPbWvvcdeDJ/37HP8UAvzGFX1T/mrPMT9WUnbW7v5lXqW51ly8jvnCto6NWdmX7HOp94HLqdsX5aWOCyrkieWJiYnOfwcGBio6OlpXXXWVs61OnZ/P5zly5OfzF1599VXNnj1bWVlZOn36tM6ePau2bdt6HHvXrl0qKCjQDTfc4NJ+9uxZJSUlea1pzJgxevzxx5238/LySjVINapZst9Zl5YuCb+ccPhrP7etxtGuJxa2bRjln0KA37iiJyx3ahKj7w6d8mM1ZaNz0xjnv0tzrbl4HfOFbR3t4mpcss+l3gcup25flJc6LqiQn0QFB7ueOOZwOFzaHA6HpJ/PbZo/f77+/Oc/64EHHtCnn36q9PR0/eEPf9DZs2c9jn3q1M8H+/Lly5Wenu782b59uxYvXuy1ptDQUEVERLj8lKYmtcJ1reU3KWwU/ebMhecO/M/rekGgw6GgAMfFD7XiaWxfHntts1pu387o1qK2apTwJENfng9A8Yp+S2/cbVcW2/dyjr3SXIN8ERTgcPmWni9rjTfe1jFf2NRRIyy4RN/SK+594HLr9kV5qeOCChmifLF27Vp16tRJjz76qJKSkpSQkKDdu3d77d+qVSuFhoYqKytLCQkJLj+l/Ss6X03vl6ROF33r5WJ/ur5piccb2bO5x/ZZ9yV7fO7OCTEubZ0TYrRsSGe3g7ZGWLAWDO7o1l491PMHn7PuS/Y49oLBHd0WyKAAh9rFRrr1nd7P86eEy4Z0casjqmqw2jdy/Z9X54QY3dfR8/bt0vTS/0u7oFa1QJ/aPYkO83xYNo2uWuIxvPGljjrhZfdBtS9zSY4t3f+QFDW4S+MS9+3dus6lO/1HaJDnbdg3uYHHfdrbsVhSQQGOUjn2fanD2/MtGNzRrc3TmiJJ0/q2dTv2vQkKkNrFRrm0+b4GBSjwok0TFODQtL5tPW4Xb+3LhnTWxTytNTXCgjXrvmSPYyQ1LPk65gtPdXhz4dt5JeXtfaA06vZFealDqqDfzmvbtq2mTJnibGvcuLGGDx+u4cOHO9scDoeWLFmirKwsPfPMM1q4cKHi4+P17rvvatq0aYqPj1d6erok92/njR07VjNnztTkyZPVpUsX5ebmau3atYqIiNDAgQNLVGdZfDvvgsxj+fp6z3FlHD6p1TuOKve0+3WiXlu1S19mHHW5dounNunnk8jX7DpaoutEZR7L197j+W7X5fgy46g2ZmW7XXvEU7u3OryNvShtv9buPuZyfRVvfb3xVIe3MW6YvFqZx/PdrhP18Ltp2rDvhMv1dzq99JkO5p1xu05Uq7H/UIGH6zMV7f/+QynaezxfQ95NU4GH60S1e+6fOuHhOlG3TP1CO4+cUvPa4Zre/2rtPZ6vJ+Zv0on/XCdqwSOdnfPqPSX1knVcqLvbK59rf85pt+tEFZ3LGwPbO1/HUYs2u43R4YVPdeQ/14kqWsef3v/GWXPR6yUVneP/DemivcfzNfmT77T7WL7bdaKKbpc3B7YvdmxP4zaOrqb3/7XP474+ctFmrdtzzOUaSAPe/pfSv89xu05U0f2gf8dGztdDkts+VnRbFZ23p33aWx1F225rW9/5HIdyf/Q4hqf91Nsx7sua4Km24sbwxFsdRY9FSc5/p+09UeJj35c1yNvr72u7J97qKI11zBdF62hYI8z5PN9nF3iszxdlWXd5qKPSX+LAlxDVq1cvPfzww1qyZIkcDof69eunyMhIffzxx15DlDFG06ZN0+uvv649e/YoKipK7dq101NPPaVrr/1lISxOWYYoAABQNip1iKooCFEAAFQ8lf46UQAAAP5GiAIAALBAiAIAALBAiAIAALBAiAIAALBAiAIAALBAiAIAALBAiAIAALBAiAIAALBQdn9h9DfuwoXg8/Ly/FwJAAAoqQvv2yX5gy6EqDJy8uRJSVJsbPF/rBIAAJQ/J0+eVGRkZLF9+Nt5ZaSwsFAHDhxQ9erV5XA4Lnu8vLw8xcbGav/+/ZX2b/Exx4qvss9PYo6VQWWfn8QcL4cxRidPnlT9+vUVEFD8WU98ElVGAgIC1LBhw1IfNyIiotIeEBcwx4qvss9PYo6VQWWfn8QcbV3qE6gLOLEcAADAAiEKAADAAiGqgggNDdX48eMVGhrq71LKDHOs+Cr7/CTmWBlU9vlJzPHXwonlAAAAFvgkCgAAwAIhCgAAwAIhCgAAwAIhCgAAwAIhqoJ49dVX1bhxY1WpUkXXXHON1q9f7++SrH3xxRe69dZbVb9+fTkcDi1dutTlfmOMxo0bp3r16qlq1arq0aOHMjIy/FOshYkTJ6p9+/aqXr26ateurT59+mjHjh0ufX788UcNGTJE0dHRCg8P15133qnDhw/7qWLfvf7660pMTHRe5C4lJUUff/yx8/6KPr+Lvfzyy3I4HBo+fLizraLP8dlnn5XD4XD5admypfP+ij6/C3744Qfde++9io6OVtWqVXXVVVcpLS3NeX9FXm8aN27stg0dDoeGDBkiqXJsw/Pnz+uZZ55RfHy8qlatqqZNm+qFF15w+bt2ft2GBuXe/PnzTUhIiJk9e7bZtm2bGTx4sImKijKHDx/2d2lW/vGPf5inn37afPjhh0aSWbJkicv9L7/8somMjDRLly41mzdvNrfddpuJj483p0+f9k/BPrrxxhvNO++8Y7Zu3WrS09PNzTffbOLi4sypU6ecfR5++GETGxtrVq5cadLS0kzHjh1Np06d/Fi1b5YtW2aWL19udu7caXbs2GGeeuopExwcbLZu3WqMqfjzK2r9+vWmcePGJjEx0QwbNszZXtHnOH78eHPllVeagwcPOn+OHj3qvL+iz88YY06cOGEaNWpkBg0aZL7++muzZ88e889//tPs2rXL2acirzdHjhxx2X4rVqwwksyqVauMMZVjG06YMMFER0ebjz76yGRmZppFixaZ8PBwM3XqVGcff25DQlQF0KFDBzNkyBDn7fPnz5v69eubiRMn+rGq0nFxiCosLDR169Y1kyZNcrbl5OSY0NBQ88EHH/ihwst35MgRI8mkpqYaY36eT3BwsFm0aJGzz7///W8jyaxbt85fZV62GjVqmLfffrtSze/kyZOmWbNmZsWKFaZbt27OEFUZ5jh+/HjTpk0bj/dVhvkZY8yoUaNMly5dvN5f2dabYcOGmaZNm5rCwsJKsw179+5t7r//fpe2O+64w/Tv398Y4/9tyK/zyrmzZ8/qm2++UY8ePZxtAQEB6tGjh9atW+fHyspGZmamDh065DLfyMhIXXPNNRV2vrm5uZKkmjVrSpK++eYb/fTTTy5zbNmypeLi4irkHM+fP6/58+crPz9fKSkplWp+Q4YMUe/evV3mIlWebZiRkaH69eurSZMm6t+/v7KysiRVnvktW7ZMycnJuvvuu1W7dm0lJSXprbfect5fmdabs2fP6r333tP9998vh8NRabZhp06dtHLlSu3cuVOStHnzZq1Zs0a9evWS5P9tyB8gLueOHTum8+fPq06dOi7tderU0XfffeenqsrOoUOHJMnjfC/cV5EUFhZq+PDh6ty5s1q3bi3p5zmGhIQoKirKpW9Fm+O3336rlJQU/fjjjwoPD9eSJUvUqlUrpaenV4r5zZ8/Xxs3btSGDRvc7qsM2/Caa67RnDlz1KJFCx08eFDPPfecunbtqq1bt1aK+UnSnj179Prrr+vxxx/XU089pQ0bNuixxx5TSEiIBg4cWKnWm6VLlyonJ0eDBg2SVDn2UUkaPXq08vLy1LJlSwUGBur8+fOaMGGC+vfvL8n/7xmEKKAMDRkyRFu3btWaNWv8XUqpa9GihdLT05Wbm6vFixdr4MCBSk1N9XdZpWL//v0aNmyYVqxYoSpVqvi7nDJx4X/ykpSYmKhrrrlGjRo10sKFC1W1alU/VlZ6CgsLlZycrJdeekmSlJSUpK1bt2rmzJkaOHCgn6srXbNmzVKvXr1Uv359f5dSqhYuXKj3339ff/3rX3XllVcqPT1dw4cPV/369cvFNuTXeeVcTEyMAgMD3b5RcfjwYdWtW9dPVZWdC3OqDPMdOnSoPvroI61atUoNGzZ0ttetW1dnz55VTk6OS/+KNseQkBAlJCTo6quv1sSJE9WmTRtNnTq1Uszvm2++0ZEjR9SuXTsFBQUpKChIqampmjZtmoKCglSnTp0KP8eLRUVFqXnz5tq1a1el2IaSVK9ePbVq1cql7YorrnD+2rKyrDf79u3TZ599pgcffNDZVlm24ciRIzV69Gj993//t6666ioNGDBAI0aM0MSJEyX5fxsSosq5kJAQXX311Vq5cqWzrbCwUCtXrlRKSoofKysb8fHxqlu3rst88/Ly9PXXX1eY+RpjNHToUC1ZskSff/654uPjXe6/+uqrFRwc7DLHHTt2KCsrq8LM0ZPCwkKdOXOmUsyve/fu+vbbb5Wenu78SU5OVv/+/Z3/ruhzvNipU6e0e/du1atXr1JsQ0nq3Lmz2+VFdu7cqUaNGkmqHOuNJL3zzjuqXbu2evfu7WyrLNuwoKBAAQGuUSUwMFCFhYWSysE2LPNT13HZ5s+fb0JDQ82cOXPM9u3bzUMPPWSioqLMoUOH/F2alZMnT5pNmzaZTZs2GUnmf//3f82mTZvMvn37jDE/f101KirK/O1vfzNbtmwx//Vf/1VhvnJsjDGPPPKIiYyMNKtXr3b5+nFBQYGzz8MPP2zi4uLM559/btLS0kxKSopJSUnxY9W+GT16tElNTTWZmZlmy5YtZvTo0cbhcJhPP/3UGFPx5+dJ0W/nGVPx5/jEE0+Y1atXm8zMTLN27VrTo0cPExMTY44cOWKMqfjzM+bny1MEBQWZCRMmmIyMDPP++++bsLAw89577zn7VPT15vz58yYuLs6MGjXK7b7KsA0HDhxoGjRo4LzEwYcffmhiYmLMk08+6ezjz21IiKogpk+fbuLi4kxISIjp0KGD+de//uXvkqytWrXKSHL7GThwoDHm56+sPvPMM6ZOnTomNDTUdO/e3ezYscO/RfvA09wkmXfeecfZ5/Tp0+bRRx81NWrUMGFhYeb22283Bw8e9F/RPrr//vtNo0aNTEhIiKlVq5bp3r27M0AZU/Hn58nFIaqiz7Fv376mXr16JiQkxDRo0MD07dvX5fpJFX1+F/z97383rVu3NqGhoaZly5bmzTffdLm/oq83//znP40kjzVXhm2Yl5dnhg0bZuLi4kyVKlVMkyZNzNNPP23OnDnj7OPPbegwpshlPwEAAFAinBMFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAEWsW7dOgYGBLn/MFQA84c++AEARDz74oMLDwzVr1izt2LFD9evX93dJAMopPokCgP84deqUFixYoEceeUS9e/fWnDlzXO5ftmyZmjVrpipVquj666/X3Llz5XA4lJOT4+yzZs0ade3aVVWrVlVsbKwee+wx5efn/7oTAfCrIEQBwH8sXLhQLVu2VIsWLXTvvfdq9uzZuvBhfWZmpu666y716dNHmzdv1h//+Ec9/fTTLo/fvXu3brrpJt15553asmWLFixYoDVr1mjo0KH+mA6AMsav8wDgPzp37qx77rlHw4YN07lz51SvXj0tWrRI1113nUaPHq3ly5fr22+/dfYfO3asJkyYoOzsbEVFRenBBx9UYGCg3njjDWefNWvWqFu3bsrPz1eVKlX8MS0AZYRPogBA0o4dO7R+/Xr169dPkhQUFKS+fftq1qxZzvvbt2/v8pgOHTq43N68ebPmzJmj8PBw58+NN96owsJCZWZm/joTAfCrCfJ3AQBQHsyaNUvnzp1zOZHcGKPQ0FDNmDGjRGOcOnVKf/zjH/XYY4+53RcXF1dqtQIoHwhRAH7zzp07p3nz5mny5Mnq2bOny319+vTRBx98oBYtWugf//iHy30bNmxwud2uXTtt375dCQkJZV4zAP/jnCgAv3lLly5V3759deTIEUVGRrrcN2rUKH3++edauHChWrRooREjRuiBBx5Qenq6nnjiCX3//ffKyclRZGSktmzZoo4dO+r+++/Xgw8+qGrVqmn79u1asWJFiT/NAlBxcE4UgN+8WbNmqUePHm4BSpLuvPNOpaWl6eTJk1q8eLE+/PBDJSYm6vXXX3d+Oy80NFSSlJiYqNTUVO3cuVNdu3ZVUlKSxo0bx7WmgEqKT6IAwNKECRM0c+ZM7d+/39+lAPADzokCgBJ67bXX1L59e0VHR2vt2rWaNGkS14ACfsMIUQBQQhkZGXrxxRd14sQJxcXF6YknntCYMWP8XRYAP+HXeQAAABY4sRwAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMDC/wfGGnH4UTTIJwAAAABJRU5ErkJggg==",
      "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=\"Sex\")\n",
    "\n",
    "df.plot.scatter(x=\"Pclass\", y=\"Age\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Визуализация - Столбчатая диаграмма"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1ac1cd1c6e0>"
      ]
     },
     "execution_count": 10,
     "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": [
    "plot = data.groupby([\"Pclass\", \"Survived\"]).size().unstack().plot.bar(color=[\"pink\", \"green\"])\n",
    "plot.legend([\"Not survived\", \"Survived\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Визуализация - Временные ряды"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}