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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"# загрузка данных\n",
"df = pd.read_csv(\"data/students_education.csv\")\n",
"# сохранение данных\n",
"df.to_csv(\"lab1.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1205 entries, 0 to 1204\n",
"Data columns (total 11 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Education Level 1205 non-null object\n",
" 1 Institution Type 1205 non-null object\n",
" 2 Gender 1205 non-null object\n",
" 3 Age 1205 non-null int64 \n",
" 4 Device 1204 non-null object\n",
" 5 IT Student 1205 non-null object\n",
" 6 Location 1205 non-null object\n",
" 7 Financial Condition 1201 non-null object\n",
" 8 Internet Type 1204 non-null object\n",
" 9 Network Type 1205 non-null object\n",
" 10 Flexibility Level 1205 non-null object\n",
"dtypes: int64(1), object(10)\n",
"memory usage: 103.7+ KB\n"
]
}
],
"source": [
"# получение сведений о датафрейме\n",
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Education Level object\n",
"Institution Type object\n",
"Gender object\n",
"Age int64\n",
"Device object\n",
"IT Student object\n",
"Location object\n",
"Financial Condition object\n",
"Internet Type object\n",
"Network Type object\n",
"Flexibility Level object\n",
"dtype: object"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 University\n",
"1 University\n",
"2 College\n",
"3 School\n",
"4 School\n",
" ... \n",
"95 University\n",
"96 School\n",
"97 School\n",
"98 University\n",
"99 College\n",
"Name: Education Level, Length: 100, dtype: object\n"
]
}
],
"source": [
"# вывод первых 100 строк из столбца Education Level\n",
"education = df.iloc[0:100, 0]\n",
"print(education)\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Education Level Institution Type Gender Age Device IT Student \\\n",
"54 School Private Male 9 Mobile No \n",
"55 School Private Female 9 Mobile No \n",
"1155 School Private Female 9 Mobile No \n",
"886 School Private Female 9 Mobile No \n",
"916 School Private Female 9 Mobile No \n",
"... ... ... ... ... ... ... \n",
"1157 University Public Male 27 Computer Yes \n",
"714 University Public Female 27 Mobile No \n",
"717 University Public Female 27 Mobile No \n",
"16 University Public Female 27 Computer Yes \n",
"1190 University Private Male 27 Mobile Yes \n",
"\n",
" Location Financial Condition Internet Type Network Type Flexibility Level \n",
"54 Town Poor Mobile Data 4G Low \n",
"55 Town Mid Mobile Data 4G Moderate \n",
"1155 Town Poor Mobile Data 4G Moderate \n",
"886 Town Poor Mobile Data 4G Moderate \n",
"916 Town Mid Mobile Data 4G Moderate \n",
"... ... ... ... ... ... \n",
"1157 Town Rich Wifi 4G High \n",
"714 Town Mid Wifi 4G Low \n",
"717 Town Mid Mobile Data 4G Low \n",
"16 Town Poor Mobile Data 4G Low \n",
"1190 Town Mid Wifi 3G Moderate \n",
"\n",
"[1205 rows x 11 columns]\n"
]
}
],
"source": [
"# сортировка датафрейма по возрасту\n",
"sorted_df = df.sort_values(by=\"Age\")\n",
"print(sorted_df)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Education Level Institution Type Gender Age Device IT Student \\\n",
"0 University Private Male 23 Tab No \n",
"1 University Private Female 23 Mobile No \n",
"2 College Public Female 18 Mobile No \n",
"4 School Private Female 18 Mobile No \n",
"8 College Public Male 18 Mobile No \n",
"... ... ... ... ... ... ... \n",
"1198 College Public Male 18 Mobile Yes \n",
"1199 University Private Male 23 Computer Yes \n",
"1200 College Private Female 18 Mobile No \n",
"1201 College Private Female 18 Mobile No \n",
"1203 College Private Female 18 Mobile No \n",
"\n",
" Location Financial Condition Internet Type Network Type Flexibility Level \n",
"0 Town Mid Wifi 4G Moderate \n",
"1 Town NaN Mobile Data 4G Moderate \n",
"2 Town Mid Wifi 4G Moderate \n",
"4 Town Poor NaN 3G Low \n",
"8 Town Mid Wifi 4G Low \n",
"... ... ... ... ... ... \n",
"1198 Rural Mid Mobile Data 4G Low \n",
"1199 Town Mid Wifi 4G Low \n",
"1200 Town Mid Wifi 4G Low \n",
"1201 Rural Mid Wifi 4G Moderate \n",
"1203 Rural Mid Wifi 4G Low \n",
"\n",
"[720 rows x 11 columns]\n"
]
}
],
"source": [
"# вывод студентов, которым 18 лет и больше\n",
"Age = df[df['Age'] >= 18]\n",
"print(Age)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Education Level</th>\n",
" <th>Institution Type</th>\n",
" <th>Gender</th>\n",
" <th>Age</th>\n",
" <th>Device</th>\n",
" <th>IT Student</th>\n",
" <th>Financial Condition</th>\n",
" <th>Internet Type</th>\n",
" <th>Network Type</th>\n",
" <th>Flexibility Level</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>University</td>\n",
" <td>Private</td>\n",
" <td>Male</td>\n",
" <td>23</td>\n",
" <td>Tab</td>\n",
" <td>No</td>\n",
" <td>Mid</td>\n",
" <td>Wifi</td>\n",
" <td>4G</td>\n",
" <td>Moderate</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>University</td>\n",
" <td>Private</td>\n",
" <td>Female</td>\n",
" <td>23</td>\n",
" <td>Mobile</td>\n",
" <td>No</td>\n",
" <td>NaN</td>\n",
" <td>Mobile Data</td>\n",
" <td>4G</td>\n",
" <td>Moderate</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>College</td>\n",
" <td>Public</td>\n",
" <td>Female</td>\n",
" <td>18</td>\n",
" <td>Mobile</td>\n",
" <td>No</td>\n",
" <td>Mid</td>\n",
" <td>Wifi</td>\n",
" <td>4G</td>\n",
" <td>Moderate</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>School</td>\n",
" <td>Private</td>\n",
" <td>Female</td>\n",
" <td>11</td>\n",
" <td>Mobile</td>\n",
" <td>No</td>\n",
" <td>NaN</td>\n",
" <td>Mobile Data</td>\n",
" <td>4G</td>\n",
" <td>Moderate</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>School</td>\n",
" <td>Private</td>\n",
" <td>Female</td>\n",
" <td>18</td>\n",
" <td>Mobile</td>\n",
" <td>No</td>\n",
" <td>Poor</td>\n",
" <td>NaN</td>\n",
" <td>3G</td>\n",
" <td>Low</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Education Level Institution Type Gender Age Device IT Student \\\n",
"0 University Private Male 23 Tab No \n",
"1 University Private Female 23 Mobile No \n",
"2 College Public Female 18 Mobile No \n",
"3 School Private Female 11 Mobile No \n",
"4 School Private Female 18 Mobile No \n",
"\n",
" Financial Condition Internet Type Network Type Flexibility Level \n",
"0 Mid Wifi 4G Moderate \n",
"1 NaN Mobile Data 4G Moderate \n",
"2 Mid Wifi 4G Moderate \n",
"3 NaN Mobile Data 4G Moderate \n",
"4 Poor NaN 3G Low "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.drop(['Location'], axis=1).head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Education Level</th>\n",
" <th>Institution Type</th>\n",
" <th>Gender</th>\n",
" <th>Age</th>\n",
" <th>Device</th>\n",
" <th>IT Student</th>\n",
" <th>Location</th>\n",
" <th>Financial Condition</th>\n",
" <th>Internet Type</th>\n",
" <th>Network Type</th>\n",
" <th>Flexibility Level</th>\n",
" <th>age_group</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>University</td>\n",
" <td>Private</td>\n",
" <td>Male</td>\n",
" <td>23</td>\n",
" <td>Tab</td>\n",
" <td>No</td>\n",
" <td>Town</td>\n",
" <td>Mid</td>\n",
" <td>Wifi</td>\n",
" <td>4G</td>\n",
" <td>Moderate</td>\n",
" <td>average</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>University</td>\n",
" <td>Private</td>\n",
" <td>Female</td>\n",
" <td>23</td>\n",
" <td>Mobile</td>\n",
" <td>No</td>\n",
" <td>Town</td>\n",
" <td>NaN</td>\n",
" <td>Mobile Data</td>\n",
" <td>4G</td>\n",
" <td>Moderate</td>\n",
" <td>average</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>College</td>\n",
" <td>Public</td>\n",
" <td>Female</td>\n",
" <td>18</td>\n",
" <td>Mobile</td>\n",
" <td>No</td>\n",
" <td>Town</td>\n",
" <td>Mid</td>\n",
" <td>Wifi</td>\n",
" <td>4G</td>\n",
" <td>Moderate</td>\n",
" <td>average</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>School</td>\n",
" <td>Private</td>\n",
" <td>Female</td>\n",
" <td>11</td>\n",
" <td>Mobile</td>\n",
" <td>No</td>\n",
" <td>Town</td>\n",
" <td>NaN</td>\n",
" <td>Mobile Data</td>\n",
" <td>4G</td>\n",
" <td>Moderate</td>\n",
" <td>young</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>School</td>\n",
" <td>Private</td>\n",
" <td>Female</td>\n",
" <td>18</td>\n",
" <td>Mobile</td>\n",
" <td>No</td>\n",
" <td>Town</td>\n",
" <td>Poor</td>\n",
" <td>NaN</td>\n",
" <td>3G</td>\n",
" <td>Low</td>\n",
" <td>average</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1200</th>\n",
" <td>College</td>\n",
" <td>Private</td>\n",
" <td>Female</td>\n",
" <td>18</td>\n",
" <td>Mobile</td>\n",
" <td>No</td>\n",
" <td>Town</td>\n",
" <td>Mid</td>\n",
" <td>Wifi</td>\n",
" <td>4G</td>\n",
" <td>Low</td>\n",
" <td>average</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1201</th>\n",
" <td>College</td>\n",
" <td>Private</td>\n",
" <td>Female</td>\n",
" <td>18</td>\n",
" <td>Mobile</td>\n",
" <td>No</td>\n",
" <td>Rural</td>\n",
" <td>Mid</td>\n",
" <td>Wifi</td>\n",
" <td>4G</td>\n",
" <td>Moderate</td>\n",
" <td>average</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1202</th>\n",
" <td>School</td>\n",
" <td>Private</td>\n",
" <td>Male</td>\n",
" <td>11</td>\n",
" <td>Mobile</td>\n",
" <td>No</td>\n",
" <td>Town</td>\n",
" <td>Mid</td>\n",
" <td>Mobile Data</td>\n",
" <td>3G</td>\n",
" <td>Moderate</td>\n",
" <td>young</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1203</th>\n",
" <td>College</td>\n",
" <td>Private</td>\n",
" <td>Female</td>\n",
" <td>18</td>\n",
" <td>Mobile</td>\n",
" <td>No</td>\n",
" <td>Rural</td>\n",
" <td>Mid</td>\n",
" <td>Wifi</td>\n",
" <td>4G</td>\n",
" <td>Low</td>\n",
" <td>average</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1204</th>\n",
" <td>School</td>\n",
" <td>Private</td>\n",
" <td>Female</td>\n",
" <td>11</td>\n",
" <td>Mobile</td>\n",
" <td>No</td>\n",
" <td>Town</td>\n",
" <td>Poor</td>\n",
" <td>Mobile Data</td>\n",
" <td>3G</td>\n",
" <td>Moderate</td>\n",
" <td>young</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1205 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
" Education Level Institution Type Gender Age Device IT Student \\\n",
"0 University Private Male 23 Tab No \n",
"1 University Private Female 23 Mobile No \n",
"2 College Public Female 18 Mobile No \n",
"3 School Private Female 11 Mobile No \n",
"4 School Private Female 18 Mobile No \n",
"... ... ... ... ... ... ... \n",
"1200 College Private Female 18 Mobile No \n",
"1201 College Private Female 18 Mobile No \n",
"1202 School Private Male 11 Mobile No \n",
"1203 College Private Female 18 Mobile No \n",
"1204 School Private Female 11 Mobile No \n",
"\n",
" Location Financial Condition Internet Type Network Type \\\n",
"0 Town Mid Wifi 4G \n",
"1 Town NaN Mobile Data 4G \n",
"2 Town Mid Wifi 4G \n",
"3 Town NaN Mobile Data 4G \n",
"4 Town Poor NaN 3G \n",
"... ... ... ... ... \n",
"1200 Town Mid Wifi 4G \n",
"1201 Rural Mid Wifi 4G \n",
"1202 Town Mid Mobile Data 3G \n",
"1203 Rural Mid Wifi 4G \n",
"1204 Town Poor Mobile Data 3G \n",
"\n",
" Flexibility Level age_group \n",
"0 Moderate average \n",
"1 Moderate average \n",
"2 Moderate average \n",
"3 Moderate young \n",
"4 Low average \n",
"... ... ... \n",
"1200 Low average \n",
"1201 Moderate average \n",
"1202 Moderate young \n",
"1203 Low average \n",
"1204 Moderate young \n",
"\n",
"[1205 rows x 12 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# добавление нового столбца Возрастная группа\n",
"def age_group(value):\n",
" if value < 18:\n",
" return \"young\"\n",
" else:\n",
" return \"average\"\n",
"\n",
"df['age_group'] = df['Age'].map(age_group)\n",
"display(df)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Education Level Institution Type Gender Age Device IT Student \\\n",
"0 University Private Male 23 Tab No \n",
"2 College Public Female 18 Mobile No \n",
"5 School Private Male 11 Mobile No \n",
"8 College Public Male 18 Mobile No \n",
"9 School Private Male 11 Mobile No \n",
"... ... ... ... ... ... ... \n",
"1200 College Private Female 18 Mobile No \n",
"1201 College Private Female 18 Mobile No \n",
"1202 School Private Male 11 Mobile No \n",
"1203 College Private Female 18 Mobile No \n",
"1204 School Private Female 11 Mobile No \n",
"\n",
" Location Financial Condition Internet Type Network Type \\\n",
"0 Town Mid Wifi 4G \n",
"2 Town Mid Wifi 4G \n",
"5 Town Poor Mobile Data 3G \n",
"8 Town Mid Wifi 4G \n",
"9 Town Mid Mobile Data 3G \n",
"... ... ... ... ... \n",
"1200 Town Mid Wifi 4G \n",
"1201 Rural Mid Wifi 4G \n",
"1202 Town Mid Mobile Data 3G \n",
"1203 Rural Mid Wifi 4G \n",
"1204 Town Poor Mobile Data 3G \n",
"\n",
" Flexibility Level age_group \n",
"0 Moderate average \n",
"2 Moderate average \n",
"5 Low young \n",
"8 Low average \n",
"9 Moderate young \n",
"... ... ... \n",
"1200 Low average \n",
"1201 Moderate average \n",
"1202 Moderate young \n",
"1203 Low average \n",
"1204 Moderate young \n",
"\n",
"[1199 rows x 12 columns]\n"
]
}
],
"source": [
"# удаление строк с пустыми значениями\n",
"df_cleaned = df.dropna()\n",
"print(df_cleaned)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Education Level Institution Type Gender Age Device IT Student \\\n",
"0 University Private Male 23 Tab No \n",
"1 University Private Female 23 Mobile No \n",
"2 College Public Female 18 Mobile No \n",
"3 School Private Female 11 Mobile No \n",
"4 School Private Female 18 Mobile No \n",
"... ... ... ... ... ... ... \n",
"1200 College Private Female 18 Mobile No \n",
"1201 College Private Female 18 Mobile No \n",
"1202 School Private Male 11 Mobile No \n",
"1203 College Private Female 18 Mobile No \n",
"1204 School Private Female 11 Mobile No \n",
"\n",
" Location Financial Condition Internet Type Network Type \\\n",
"0 Town Mid Wifi 4G \n",
"1 Town Mid Mobile Data 4G \n",
"2 Town Mid Wifi 4G \n",
"3 Town Mid Mobile Data 4G \n",
"4 Town Poor NaN 3G \n",
"... ... ... ... ... \n",
"1200 Town Mid Wifi 4G \n",
"1201 Rural Mid Wifi 4G \n",
"1202 Town Mid Mobile Data 3G \n",
"1203 Rural Mid Wifi 4G \n",
"1204 Town Poor Mobile Data 3G \n",
"\n",
" Flexibility Level age_group \n",
"0 Moderate average \n",
"1 Moderate average \n",
"2 Moderate average \n",
"3 Moderate young \n",
"4 Low average \n",
"... ... ... \n",
"1200 Low average \n",
"1201 Moderate average \n",
"1202 Moderate young \n",
"1203 Low average \n",
"1204 Moderate young \n",
"\n",
"[1205 rows x 12 columns]\n"
]
}
],
"source": [
"# Вычисление моды (наиболее часто встречающегося значения) для пустых значений\n",
"mode_Financial = df['Financial Condition'].mode()[0] \n",
"df.fillna({'Financial Condition':mode_Financial}, inplace=True)\n",
"print(df)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# линейная диаграмма (средний возраст по уровню образования)\n",
"import matplotlib.pyplot as plt\n",
"plt.plot(df[[\"Education Level\", \"Age\"]].groupby(\"Education Level\").mean())\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: ylabel='Frequency'>"
]
},
"execution_count": 12,
"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": [
"# гистограмма\n",
"df.plot.hist(column=[\"Age\"], bins=80)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Age Axes(0.125,0.11;0.775x0.77)\n",
"dtype: object"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# ящик с усами\n",
"df.plot.box(column=\"Age\", by=\"Gender\", figsize=(10, 8))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='Education Level'>"
]
},
"execution_count": 14,
"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": [
"# диаграмма с областями \n",
"data = (df[[ \"Education Level\", \"Age\"]].groupby(['Education Level']).mean())\n",
"data.plot.area()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='Age', ylabel='Education Level'>"
]
},
"execution_count": 15,
"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": [
"# диаграмма рассеяния \n",
"df.plot.scatter(x =\"Age\", y =\"Education Level\")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: ylabel='Age'>"
]
},
"execution_count": 16,
"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": [
"# круговая диаграмма \n",
"data = (df[[ \"Device\", \"Age\"]].groupby(['Device']).count())\n",
"data.plot.pie(x ='Device', y ='Age')"
]
}
],
"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"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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