{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Начало лабораторной работы №1\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Набор данных \"Наблюдения НЛО в США\"."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Загрузка и сохранение данных"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" summary | \n",
" city | \n",
" state | \n",
" date_time | \n",
" shape | \n",
" duration | \n",
" stats | \n",
" report_link | \n",
" text | \n",
" posted | \n",
" city_latitude | \n",
" city_longitude | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Viewed some red lights in the sky appearing to... | \n",
" Visalia | \n",
" CA | \n",
" 2021-12-15T21:45:00 | \n",
" light | \n",
" 2 minutes | \n",
" Occurred : 12/15/2021 21:45 (Entered as : 12/... | \n",
" http://www.nuforc.org/webreports/165/S165881.html | \n",
" Viewed some red lights in the sky appearing to... | \n",
" 2021-12-19T00:00:00 | \n",
" 36.356650 | \n",
" -119.347937 | \n",
"
\n",
" \n",
" 1 | \n",
" Look like 1 or 3 crafts from North traveling s... | \n",
" Cincinnati | \n",
" OH | \n",
" 2021-12-16T09:45:00 | \n",
" triangle | \n",
" 14 seconds | \n",
" Occurred : 12/16/2021 09:45 (Entered as : 12/... | \n",
" http://www.nuforc.org/webreports/165/S165888.html | \n",
" Look like 1 or 3 crafts from North traveling s... | \n",
" 2021-12-19T00:00:00 | \n",
" 39.174503 | \n",
" -84.481363 | \n",
"
\n",
" \n",
" 2 | \n",
" seen dark rectangle moving slowly thru the sky... | \n",
" Tecopa | \n",
" CA | \n",
" 2021-12-10T00:00:00 | \n",
" rectangle | \n",
" Several minutes | \n",
" Occurred : 12/10/2021 00:00 (Entered as : 12/... | \n",
" http://www.nuforc.org/webreports/165/S165810.html | \n",
" seen dark rectangle moving slowly thru the sky... | \n",
" 2021-12-19T00:00:00 | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" 3 | \n",
" One red light moving switly west to east, beco... | \n",
" Knoxville | \n",
" TN | \n",
" 2021-12-10T19:30:00 | \n",
" triangle | \n",
" 20-30 seconds | \n",
" Occurred : 12/10/2021 19:30 (Entered as : 12/... | \n",
" http://www.nuforc.org/webreports/165/S165825.html | \n",
" One red light moving switly west to east, beco... | \n",
" 2021-12-19T00:00:00 | \n",
" 35.961561 | \n",
" -83.980115 | \n",
"
\n",
" \n",
" 4 | \n",
" Bright, circular Fresnel-lens shaped light sev... | \n",
" Alexandria | \n",
" VA | \n",
" 2021-12-07T08:00:00 | \n",
" circle | \n",
" NaN | \n",
" Occurred : 12/7/2021 08:00 (Entered as : 12/0... | \n",
" http://www.nuforc.org/webreports/165/S165754.html | \n",
" Bright, circular Fresnel-lens shaped light sev... | \n",
" 2021-12-19T00:00:00 | \n",
" 38.798958 | \n",
" -77.095133 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" summary city state \\\n",
"0 Viewed some red lights in the sky appearing to... Visalia CA \n",
"1 Look like 1 or 3 crafts from North traveling s... Cincinnati OH \n",
"2 seen dark rectangle moving slowly thru the sky... Tecopa CA \n",
"3 One red light moving switly west to east, beco... Knoxville TN \n",
"4 Bright, circular Fresnel-lens shaped light sev... Alexandria VA \n",
"\n",
" date_time shape duration \\\n",
"0 2021-12-15T21:45:00 light 2 minutes \n",
"1 2021-12-16T09:45:00 triangle 14 seconds \n",
"2 2021-12-10T00:00:00 rectangle Several minutes \n",
"3 2021-12-10T19:30:00 triangle 20-30 seconds \n",
"4 2021-12-07T08:00:00 circle NaN \n",
"\n",
" stats \\\n",
"0 Occurred : 12/15/2021 21:45 (Entered as : 12/... \n",
"1 Occurred : 12/16/2021 09:45 (Entered as : 12/... \n",
"2 Occurred : 12/10/2021 00:00 (Entered as : 12/... \n",
"3 Occurred : 12/10/2021 19:30 (Entered as : 12/... \n",
"4 Occurred : 12/7/2021 08:00 (Entered as : 12/0... \n",
"\n",
" report_link \\\n",
"0 http://www.nuforc.org/webreports/165/S165881.html \n",
"1 http://www.nuforc.org/webreports/165/S165888.html \n",
"2 http://www.nuforc.org/webreports/165/S165810.html \n",
"3 http://www.nuforc.org/webreports/165/S165825.html \n",
"4 http://www.nuforc.org/webreports/165/S165754.html \n",
"\n",
" text posted \\\n",
"0 Viewed some red lights in the sky appearing to... 2021-12-19T00:00:00 \n",
"1 Look like 1 or 3 crafts from North traveling s... 2021-12-19T00:00:00 \n",
"2 seen dark rectangle moving slowly thru the sky... 2021-12-19T00:00:00 \n",
"3 One red light moving switly west to east, beco... 2021-12-19T00:00:00 \n",
"4 Bright, circular Fresnel-lens shaped light sev... 2021-12-19T00:00:00 \n",
"\n",
" city_latitude city_longitude \n",
"0 36.356650 -119.347937 \n",
"1 39.174503 -84.481363 \n",
"2 NaN NaN \n",
"3 35.961561 -83.980115 \n",
"4 38.798958 -77.095133 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Чтобы загрузить данные из CSV файла:\n",
"\n",
"df = pd.read_csv('datasets/nuforc_reports.csv')\n",
"df.head()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Получение сведений о датафрейме с данными"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"RangeIndex: 136940 entries, 0 to 136939\n",
"Data columns (total 12 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 summary 136866 non-null object \n",
" 1 city 136558 non-null object \n",
" 2 state 127595 non-null object \n",
" 3 date_time 134272 non-null object \n",
" 4 shape 131018 non-null object \n",
" 5 duration 130448 non-null object \n",
" 6 stats 136940 non-null object \n",
" 7 report_link 136940 non-null object \n",
" 8 text 136902 non-null object \n",
" 9 posted 134272 non-null object \n",
" 10 city_latitude 110136 non-null float64\n",
" 11 city_longitude 110136 non-null float64\n",
"dtypes: float64(2), object(10)\n",
"memory usage: 12.5+ MB\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" city_latitude | \n",
" city_longitude | \n",
"
\n",
" \n",
" \n",
" \n",
" count | \n",
" 110136.000000 | \n",
" 110136.000000 | \n",
"
\n",
" \n",
" mean | \n",
" 38.704608 | \n",
" -95.185792 | \n",
"
\n",
" \n",
" std | \n",
" 5.752186 | \n",
" 18.310088 | \n",
"
\n",
" \n",
" min | \n",
" -32.055500 | \n",
" -170.494000 | \n",
"
\n",
" \n",
" 25% | \n",
" 34.238375 | \n",
" -113.901810 | \n",
"
\n",
" \n",
" 50% | \n",
" 39.257500 | \n",
" -89.161450 | \n",
"
\n",
" \n",
" 75% | \n",
" 42.317739 | \n",
" -80.363444 | \n",
"
\n",
" \n",
" max | \n",
" 64.845276 | \n",
" 130.850580 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" city_latitude city_longitude\n",
"count 110136.000000 110136.000000\n",
"mean 38.704608 -95.185792\n",
"std 5.752186 18.310088\n",
"min -32.055500 -170.494000\n",
"25% 34.238375 -113.901810\n",
"50% 39.257500 -89.161450\n",
"75% 42.317739 -80.363444\n",
"max 64.845276 130.850580"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Получить общую информацию о датафрейме можно с помощью:\n",
"df.info()\n",
"#Для получения статистического описания числовых колонок:\n",
"df.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Получение сведений о колонках датафрейма"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['summary', 'city', 'state', 'date_time', 'shape', 'duration', 'stats',\n",
" 'report_link', 'text', 'posted', 'city_latitude', 'city_longitude'],\n",
" dtype='object')\n"
]
}
],
"source": [
"#Вывести названия колонок:\n",
"print(df.columns)\n",
"\n",
"#Получить уникальные значения в колонке:\n",
"unique_values = df['city'].unique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Вывод отельных строки и столбцов из датафрейма"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"\n",
"#Для вывода отдельных строк можно использовать iloc или loc:\n",
"\n",
"# Вывод первой строки\n",
"first_row = df.iloc[0]\n",
"\n",
"# Вывод строк с 0 по 4\n",
"first_five_rows = df.iloc[0:5]\n",
"\n",
"# Вывод по метке индекса\n",
"row_by_label = df.loc[0]\n",
"\n",
"# Вывод определенного столбца\n",
"specific_column = df['city']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Группировка и агрегация данных в датафрейме"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"#Для группировки данных можно использовать groupby:\n",
"grouped = df.groupby('city').agg({'state': 'sum'})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### сортировка данных в датафрейме"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"#Для сортировки данных по определенной колонке:\n",
"sorted_df = df.sort_values(by='city', ascending=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### удаление строк/столбцов"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"#Для удаления строк:\n",
"\n",
"# Удаление строки по индексу\n",
"df = df.drop(24)\n",
"\n",
"# Удаление нескольких строк\n",
"df = df.drop([1, 2, 3])\n",
"\n",
"\n",
"#Для удаления столбцов:\n",
"\n",
"# Удаление столбца\n",
"df = df.drop(\"summary\", axis=1)\n",
"\n",
"# Удаление нескольких столбцов\n",
"df = df.drop(['shape', 'duration'], axis=1)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### создание новых столбцов на основе данных из существующих столбцов датафрейма"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"#Создание нового столбца на основе существующих:\n",
"df['new_columnStateCity'] = df['state'] + df['city']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### удаление строк с пустыми значениями"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"#Для удаления строк с хотя бы одним пустым значением:\n",
"df = df.dropna()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### заполнение пустых значений на основе существующих данных"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# Заполнение средним значением\n",
"df['city_latitude'] = df['city_latitude'].fillna(df['city_latitude'].mean())\n",
"\n",
"# Заполнение фиксированным значением\n",
"df['state'] = df['state'].fillna(0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Линейная диаграмма (plot)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
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