{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Работа с NumPy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"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": null,
"metadata": {},
"outputs": [],
"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": null,
"metadata": {},
"outputs": [],
"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": null,
"metadata": {},
"outputs": [],
"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": 4,
"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",
"![](\"assets/quantile.png\")
"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "'Pclass'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[9], line 26\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhigh_iqr\u001b[39m(x):\n\u001b[0;32m 23\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m q3(x) \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1.5\u001b[39m \u001b[38;5;241m*\u001b[39m iqr(x)\n\u001b[1;32m---> 26\u001b[0m quantiles \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mAge\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPclass\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39maggregate([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m\"\u001b[39m, q1, q2, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmedian\u001b[39m\u001b[38;5;124m\"\u001b[39m, q3, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m 27\u001b[0m \u001b[38;5;28mprint\u001b[39m(quantiles)\n\u001b[0;32m 29\u001b[0m iqrs \u001b[38;5;241m=\u001b[39m data[[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPclass\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAge\u001b[39m\u001b[38;5;124m\"\u001b[39m]]\u001b[38;5;241m.\u001b[39mgroupby([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPclass\u001b[39m\u001b[38;5;124m\"\u001b[39m])\u001b[38;5;241m.\u001b[39maggregate([low_iqr, iqr, high_iqr])\n",
"File \u001b[1;32mc:\\Users\\1\\Desktop\\улгту\\3 курс\\МИИ\\mai\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:9183\u001b[0m, in \u001b[0;36mDataFrame.groupby\u001b[1;34m(self, by, axis, level, as_index, sort, group_keys, observed, dropna)\u001b[0m\n\u001b[0;32m 9180\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m level \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m by \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 9181\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou have to supply one of \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mby\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m and \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlevel\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m-> 9183\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mDataFrameGroupBy\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 9184\u001b[0m \u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 9185\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mby\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 9186\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 9187\u001b[0m \u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 9188\u001b[0m \u001b[43m \u001b[49m\u001b[43mas_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mas_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 9189\u001b[0m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 9190\u001b[0m \u001b[43m \u001b[49m\u001b[43mgroup_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 9191\u001b[0m \u001b[43m \u001b[49m\u001b[43mobserved\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobserved\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 9192\u001b[0m \u001b[43m \u001b[49m\u001b[43mdropna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdropna\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 9193\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[1;32mc:\\Users\\1\\Desktop\\улгту\\3 курс\\МИИ\\mai\\.venv\\Lib\\site-packages\\pandas\\core\\groupby\\groupby.py:1329\u001b[0m, in \u001b[0;36mGroupBy.__init__\u001b[1;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, observed, dropna)\u001b[0m\n\u001b[0;32m 1326\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdropna \u001b[38;5;241m=\u001b[39m dropna\n\u001b[0;32m 1328\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m grouper \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1329\u001b[0m grouper, exclusions, obj \u001b[38;5;241m=\u001b[39m \u001b[43mget_grouper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1330\u001b[0m \u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1331\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1332\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1333\u001b[0m \u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1334\u001b[0m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1335\u001b[0m \u001b[43m \u001b[49m\u001b[43mobserved\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mobserved\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mno_default\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mobserved\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1336\u001b[0m \u001b[43m \u001b[49m\u001b[43mdropna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdropna\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1337\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1339\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m observed \u001b[38;5;129;01mis\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mno_default:\n\u001b[0;32m 1340\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28many\u001b[39m(ping\u001b[38;5;241m.\u001b[39m_passed_categorical \u001b[38;5;28;01mfor\u001b[39;00m ping \u001b[38;5;129;01min\u001b[39;00m grouper\u001b[38;5;241m.\u001b[39mgroupings):\n",
"File \u001b[1;32mc:\\Users\\1\\Desktop\\улгту\\3 курс\\МИИ\\mai\\.venv\\Lib\\site-packages\\pandas\\core\\groupby\\grouper.py:1043\u001b[0m, in \u001b[0;36mget_grouper\u001b[1;34m(obj, key, axis, level, sort, observed, validate, dropna)\u001b[0m\n\u001b[0;32m 1041\u001b[0m in_axis, level, gpr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m, gpr, \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 1042\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1043\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(gpr)\n\u001b[0;32m 1044\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(gpr, Grouper) \u001b[38;5;129;01mand\u001b[39;00m gpr\u001b[38;5;241m.\u001b[39mkey \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 1045\u001b[0m \u001b[38;5;66;03m# Add key to exclusions\u001b[39;00m\n\u001b[0;32m 1046\u001b[0m exclusions\u001b[38;5;241m.\u001b[39madd(gpr\u001b[38;5;241m.\u001b[39mkey)\n",
"\u001b[1;31mKeyError\u001b[0m: 'Pclass'"
]
}
],
"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": null,
"metadata": {},
"outputs": [],
"source": [
"data.plot.hist(column=[\"Age\"], bins=80)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Визуализация - Точечная диаграмма"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 10,
"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": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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",
"text/plain": [
""
]
},
"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": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"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": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 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": [
""
]
},
"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) # создали новый столбец дэйт, с помощью эплай достучались до всех строк, лямбда выражение принимает одну строку ну и переделали из строки в нормальную дату\n",
"\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)) #ставим интервал по времени в 10 дней\n",
"plot.xaxis.set_major_formatter(md.DateFormatter(\"%d.%m.%Y\")) # определяем формат даты\n",
"plot.tick_params(axis=\"x\", labelrotation=90) # угол поворота подписей нижней оси"
]
}
],
"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
}