diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
new file mode 100644
index 0000000..105ce2d
--- /dev/null
+++ b/.idea/inspectionProfiles/profiles_settings.xml
@@ -0,0 +1,6 @@
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/lab2.iml b/.idea/lab2.iml
new file mode 100644
index 0000000..d0876a7
--- /dev/null
+++ b/.idea/lab2.iml
@@ -0,0 +1,8 @@
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
new file mode 100644
index 0000000..a971a2c
--- /dev/null
+++ b/.idea/misc.xml
@@ -0,0 +1,4 @@
+
+
+
+
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
new file mode 100644
index 0000000..530e0f6
--- /dev/null
+++ b/.idea/modules.xml
@@ -0,0 +1,8 @@
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
new file mode 100644
index 0000000..35eb1dd
--- /dev/null
+++ b/.idea/vcs.xml
@@ -0,0 +1,6 @@
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
new file mode 100644
index 0000000..5a9c72e
--- /dev/null
+++ b/.idea/workspace.xml
@@ -0,0 +1,42 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 1711477545007
+
+
+ 1711477545007
+
+
+
+
\ No newline at end of file
diff --git a/correlation.ipynb b/correlation.ipynb
index 51c1fea..24d9653 100644
--- a/correlation.ipynb
+++ b/correlation.ipynb
@@ -79,12 +79,12 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
- "brainFrame = pd.read_csv(?, delimiter='\\t')"
+ "brainFrame = pd.read_csv('titanic.csv', delimiter=',')"
]
},
{
@@ -102,7 +102,7 @@
"metadata": {},
"outputs": [],
"source": [
- "brainFrame.?()"
+ "brainFrame.head()"
]
},
{
@@ -118,7 +118,7 @@
"metadata": {},
"outputs": [],
"source": [
- "brainFrame.head(?)"
+ "brainFrame.head(10)"
]
},
{
@@ -134,7 +134,7 @@
"metadata": {},
"outputs": [],
"source": [
- "?"
+ "brainFrame.tail(8)"
]
},
{
@@ -159,7 +159,7 @@
"metadata": {},
"outputs": [],
"source": [
- "brainFrame.?()"
+ "brainFrame.describe()"
]
},
{
@@ -176,7 +176,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -195,12 +195,12 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
- "menDf = brainFrame[?]\n",
- "womenDf = brainFrame[?]"
+ "menDf = brainFrame[brainFrame['Sex'] == 'male']\n",
+ "womenDf = brainFrame[brainFrame['Sex'] == 'female']"
]
},
{
@@ -244,11 +244,10 @@
"source": [
"# Ячейка для кода № 7\n",
"# Постройка графика диаграммы рассеяния для кадра данных с женскими записями\n",
- "womenMeanSmarts = ???\n",
- "plt.scatter(???)\n",
- "\n",
- "#\n",
- "#"
+ "womenMeanSmarts =womenDf[[\"PIQ\", \"FSIQ\", \"VIQ\"]].mean(axis=1)\n",
+ "plt.scatter(womenMeanSmarts, womenDf[\"MRI_Count\"])\n",
+ "plt.show()\n",
+ "%matplotlib inline\n"
]
},
{
@@ -272,7 +271,7 @@
"metadata": {},
"outputs": [],
"source": [
- "brainFrame.?(method='pearson')"
+ "brainFrame.corr(method='pearson')"
]
},
{
@@ -286,7 +285,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "(ответ)"
+ " Корреляция переменной с самой собой всегда равна 1, потому что это полная линейная зависимость"
]
},
{
@@ -300,7 +299,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "(ответ)"
+ "Корреляция между переменными A и B также равна корреляции между переменными B и A"
]
},
{
@@ -316,7 +315,7 @@
"metadata": {},
"outputs": [],
"source": [
- "womenDf.?(method='pearson')"
+ "womenDf.corr(method='pearson')"
]
},
{
@@ -332,7 +331,7 @@
"metadata": {},
"outputs": [],
"source": [
- "# Используйте corr() для расчёта критерия корреляции Пирсона для кадра данных с мужчинами\n"
+ "menDf.corr(method='pearson')\n"
]
},
{
@@ -404,10 +403,10 @@
"metadata": {},
"outputs": [],
"source": [
- "# Ячейка для кода № 14\n",
- "mcorr = ???\n",
- "#\n",
- "#"
+ "import seaborn as sns\n",
+ "\n",
+ "mcorr = menDf.corr()\n",
+ "sns.heatmap(wcorr)"
]
},
{
@@ -421,7 +420,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "(ответ)"
+ "Это означает, что между этими переменными нет линейной зависимости"
]
},
{
@@ -435,7 +434,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "(ответ)"
+ "Для удобного анализа"
]
},
{
@@ -470,7 +469,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.5"
+ "version": "3.12.2"
}
},
"nbformat": 4,
diff --git a/pandas.ipynb b/pandas.ipynb
index bd16b4f..c883d6d 100644
--- a/pandas.ipynb
+++ b/pandas.ipynb
@@ -16,10 +16,23 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"id": "7484df51-b002-414c-ae42-75a2df57c78d",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Requirement already satisfied: pandas in c:\\users\\хозяин\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (2.2.1)\n",
+ "Requirement already satisfied: numpy<2,>=1.26.0 in c:\\users\\хозяин\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pandas) (1.26.4)\n",
+ "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\хозяин\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pandas) (2.9.0.post0)\n",
+ "Requirement already satisfied: pytz>=2020.1 in c:\\users\\хозяин\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pandas) (2024.1)\n",
+ "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\хозяин\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pandas) (2024.1)\n",
+ "Requirement already satisfied: six>=1.5 in c:\\users\\хозяин\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n"
+ ]
+ }
+ ],
"source": [
"!pip install pandas"
]
@@ -35,14 +48,14 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
"id": "bfc3346f-3843-4aff-aec0-54321b9774f0",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
- "# write your code here"
+ "import pandas as pd"
]
},
{
@@ -57,14 +70,13 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"id": "fa5deec6-c85e-4d88-89df-bea7d75fcbba",
"metadata": {},
"outputs": [],
"source": [
- "# Чтение данных из файла 'titanic.csv'\n",
- "# Используйте метод pd.read_csv()\n",
- "# write your code here"
+ "\n",
+ "data = pd.read_csv('titanic.csv')"
]
},
{
@@ -78,14 +90,41 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"id": "216e104c-259f-4ecd-9cd4-40362f61ca4e",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " PassengerId Survived Pclass \\\n",
+ "0 1 0 3 \n",
+ "1 2 1 1 \n",
+ "2 3 1 3 \n",
+ "3 4 1 1 \n",
+ "4 5 0 3 \n",
+ "\n",
+ " Name Sex Age SibSp \\\n",
+ "0 Braund, Mr. Owen Harris male 22.0 1 \n",
+ "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
+ "2 Heikkinen, Miss. Laina female 26.0 0 \n",
+ "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
+ "4 Allen, Mr. William Henry male 35.0 0 \n",
+ "\n",
+ " Parch Ticket Fare Cabin Embarked \n",
+ "0 0 A/5 21171 7.2500 NaN S \n",
+ "1 0 PC 17599 71.2833 C85 C \n",
+ "2 0 STON/O2. 3101282 7.9250 NaN S \n",
+ "3 0 113803 53.1000 C123 S \n",
+ "4 0 373450 8.0500 NaN S \n"
+ ]
+ }
+ ],
"source": [
- "# Вывод первых 5 строк данных\n",
- "# Используйте метод .head()\n",
- "# write your code here"
+ "\n",
+ "data = pd.read_csv('titanic.csv')\n",
+ "print(data.head(5))"
]
},
{
@@ -99,11 +138,42 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"id": "43650b4f-f3e7-4480-b874-b5552f564383",
"metadata": {},
- "outputs": [],
- "source": []
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "RangeIndex: 891 entries, 0 to 890\n",
+ "Data columns (total 12 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 PassengerId 891 non-null int64 \n",
+ " 1 Survived 891 non-null int64 \n",
+ " 2 Pclass 891 non-null int64 \n",
+ " 3 Name 891 non-null object \n",
+ " 4 Sex 891 non-null object \n",
+ " 5 Age 714 non-null float64\n",
+ " 6 SibSp 891 non-null int64 \n",
+ " 7 Parch 891 non-null int64 \n",
+ " 8 Ticket 891 non-null object \n",
+ " 9 Fare 891 non-null float64\n",
+ " 10 Cabin 204 non-null object \n",
+ " 11 Embarked 889 non-null object \n",
+ "dtypes: float64(2), int64(5), object(5)\n",
+ "memory usage: 83.7+ KB\n",
+ "None\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "data = pd.read_csv('titanic.csv')\n",
+ "print(data.info())"
+ ]
},
{
"cell_type": "markdown",
@@ -118,16 +188,52 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 6,
"id": "f7910fde-24f7-4cf8-991d-01f08bc45b63",
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket \\\n",
+ "0 False False False False False False False False False \n",
+ "1 False False False False False False False False False \n",
+ "2 False False False False False False False False False \n",
+ "3 False False False False False False False False False \n",
+ "4 False False False False False False False False False \n",
+ ".. ... ... ... ... ... ... ... ... ... \n",
+ "886 False False False False False False False False False \n",
+ "887 False False False False False False False False False \n",
+ "888 False False False False False True False False False \n",
+ "889 False False False False False False False False False \n",
+ "890 False False False False False False False False False \n",
+ "\n",
+ " Fare Cabin Embarked \n",
+ "0 False True False \n",
+ "1 False False False \n",
+ "2 False True False \n",
+ "3 False False False \n",
+ "4 False True False \n",
+ ".. ... ... ... \n",
+ "886 False True False \n",
+ "887 False False False \n",
+ "888 False True False \n",
+ "889 False False False \n",
+ "890 False True False \n",
+ "\n",
+ "[891 rows x 12 columns]\n"
+ ]
+ }
+ ],
"source": [
- "# Проверка на наличие NaN в DataFrame\n",
- "# Используйте метод .isna()\n",
- "# write your code here"
+ "\n",
+ "data = pd.read_csv('titanic.csv')\n",
+ "\n",
+ "nan_check = data.isna()\n",
+ "print(nan_check)"
]
},
{
@@ -140,14 +246,63 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"id": "7d901187-75a9-497e-8774-6e0dde584197",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " PassengerId Survived Pclass \\\n",
+ "0 1 0 3 \n",
+ "1 2 1 1 \n",
+ "2 3 1 3 \n",
+ "3 4 1 1 \n",
+ "4 5 0 3 \n",
+ ".. ... ... ... \n",
+ "886 887 0 2 \n",
+ "887 888 1 1 \n",
+ "888 889 0 3 \n",
+ "889 890 1 1 \n",
+ "890 891 0 3 \n",
+ "\n",
+ " Name Sex Age SibSp \\\n",
+ "0 Braund, Mr. Owen Harris male 22.0 1 \n",
+ "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
+ "2 Heikkinen, Miss. Laina female 26.0 0 \n",
+ "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
+ "4 Allen, Mr. William Henry male 35.0 0 \n",
+ ".. ... ... ... ... \n",
+ "886 Montvila, Rev. Juozas male 27.0 0 \n",
+ "887 Graham, Miss. Margaret Edith female 19.0 0 \n",
+ "888 Johnston, Miss. Catherine Helen \"Carrie\" female 0.0 1 \n",
+ "889 Behr, Mr. Karl Howell male 26.0 0 \n",
+ "890 Dooley, Mr. Patrick male 32.0 0 \n",
+ "\n",
+ " Parch Ticket Fare Cabin Embarked \n",
+ "0 0 A/5 21171 7.2500 0 S \n",
+ "1 0 PC 17599 71.2833 C85 C \n",
+ "2 0 STON/O2. 3101282 7.9250 0 S \n",
+ "3 0 113803 53.1000 C123 S \n",
+ "4 0 373450 8.0500 0 S \n",
+ ".. ... ... ... ... ... \n",
+ "886 0 211536 13.0000 0 S \n",
+ "887 0 112053 30.0000 B42 S \n",
+ "888 2 W./C. 6607 23.4500 0 S \n",
+ "889 0 111369 30.0000 C148 C \n",
+ "890 0 370376 7.7500 0 Q \n",
+ "\n",
+ "[891 rows x 12 columns]\n"
+ ]
+ }
+ ],
"source": [
- "# Заполнение NaN определенным значением (например, нулем)\n",
- "# Используйте метод .fillna()\n",
- "# write your code here"
+ "\n",
+ "data = pd.read_csv('titanic.csv')\n",
+ "\n",
+ "data_filled = data.fillna(0)\n",
+ "print(data_filled)"
]
},
{
@@ -160,16 +315,65 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 9,
"id": "4d188deb-0818-4b01-b3a5-9d20d2166d10",
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " PassengerId Survived Pclass \\\n",
+ "1 2 1 1 \n",
+ "3 4 1 1 \n",
+ "6 7 0 1 \n",
+ "10 11 1 3 \n",
+ "11 12 1 1 \n",
+ ".. ... ... ... \n",
+ "871 872 1 1 \n",
+ "872 873 0 1 \n",
+ "879 880 1 1 \n",
+ "887 888 1 1 \n",
+ "889 890 1 1 \n",
+ "\n",
+ " Name Sex Age SibSp \\\n",
+ "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
+ "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
+ "6 McCarthy, Mr. Timothy J male 54.0 0 \n",
+ "10 Sandstrom, Miss. Marguerite Rut female 4.0 1 \n",
+ "11 Bonnell, Miss. Elizabeth female 58.0 0 \n",
+ ".. ... ... ... ... \n",
+ "871 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47.0 1 \n",
+ "872 Carlsson, Mr. Frans Olof male 33.0 0 \n",
+ "879 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56.0 0 \n",
+ "887 Graham, Miss. Margaret Edith female 19.0 0 \n",
+ "889 Behr, Mr. Karl Howell male 26.0 0 \n",
+ "\n",
+ " Parch Ticket Fare Cabin Embarked \n",
+ "1 0 PC 17599 71.2833 C85 C \n",
+ "3 0 113803 53.1000 C123 S \n",
+ "6 0 17463 51.8625 E46 S \n",
+ "10 1 PP 9549 16.7000 G6 S \n",
+ "11 0 113783 26.5500 C103 S \n",
+ ".. ... ... ... ... ... \n",
+ "871 1 11751 52.5542 D35 S \n",
+ "872 0 695 5.0000 B51 B53 B55 S \n",
+ "879 1 11767 83.1583 C50 C \n",
+ "887 0 112053 30.0000 B42 S \n",
+ "889 0 111369 30.0000 C148 C \n",
+ "\n",
+ "[183 rows x 12 columns]\n"
+ ]
+ }
+ ],
"source": [
- "# Удаление строк, содержащих NaN\n",
- "# Используйте метод .dropna()\n",
- "# write your code here"
+ "\n",
+ "data = pd.read_csv('titanic.csv')\n",
+ "\n",
+ "data_without_na = data.dropna()\n",
+ "print(data_without_na)"
]
},
{
@@ -184,28 +388,104 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 10,
"id": "d11f6114-ce4b-4e71-afec-adf1d8c1ec6e",
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0 22.0\n",
+ "1 38.0\n",
+ "2 26.0\n",
+ "3 35.0\n",
+ "4 35.0\n",
+ " ... \n",
+ "886 27.0\n",
+ "887 19.0\n",
+ "888 NaN\n",
+ "889 26.0\n",
+ "890 32.0\n",
+ "Name: Age, Length: 891, dtype: float64\n",
+ " Name Sex\n",
+ "0 Braund, Mr. Owen Harris male\n",
+ "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female\n",
+ "2 Heikkinen, Miss. Laina female\n",
+ "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female\n",
+ "4 Allen, Mr. William Henry male\n",
+ ".. ... ...\n",
+ "886 Montvila, Rev. Juozas male\n",
+ "887 Graham, Miss. Margaret Edith female\n",
+ "888 Johnston, Miss. Catherine Helen \"Carrie\" female\n",
+ "889 Behr, Mr. Karl Howell male\n",
+ "890 Dooley, Mr. Patrick male\n",
+ "\n",
+ "[891 rows x 2 columns]\n",
+ "PassengerId 1\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: 0, dtype: object\n",
+ " PassengerId Survived Pclass Name \\\n",
+ "4 5 0 3 Allen, Mr. William Henry \n",
+ "6 7 0 1 McCarthy, Mr. Timothy J \n",
+ "13 14 0 3 Andersson, Mr. Anders Johan \n",
+ "20 21 0 2 Fynney, Mr. Joseph J \n",
+ "21 22 1 2 Beesley, Mr. Lawrence \n",
+ ".. ... ... ... ... \n",
+ "867 868 0 1 Roebling, Mr. Washington Augustus II \n",
+ "872 873 0 1 Carlsson, Mr. Frans Olof \n",
+ "873 874 0 3 Vander Cruyssen, Mr. Victor \n",
+ "881 882 0 3 Markun, Mr. Johann \n",
+ "890 891 0 3 Dooley, Mr. Patrick \n",
+ "\n",
+ " Sex Age SibSp Parch Ticket Fare Cabin Embarked \n",
+ "4 male 35.0 0 0 373450 8.0500 NaN S \n",
+ "6 male 54.0 0 0 17463 51.8625 E46 S \n",
+ "13 male 39.0 1 5 347082 31.2750 NaN S \n",
+ "20 male 35.0 0 0 239865 26.0000 NaN S \n",
+ "21 male 34.0 0 0 248698 13.0000 D56 S \n",
+ ".. ... ... ... ... ... ... ... ... \n",
+ "867 male 31.0 0 0 PC 17590 50.4958 A24 S \n",
+ "872 male 33.0 0 0 695 5.0000 B51 B53 B55 S \n",
+ "873 male 47.0 0 0 345765 9.0000 NaN S \n",
+ "881 male 33.0 0 0 349257 7.8958 NaN S \n",
+ "890 male 32.0 0 0 370376 7.7500 NaN Q \n",
+ "\n",
+ "[202 rows x 12 columns]\n"
+ ]
+ }
+ ],
"source": [
- "# Выбор столбца по метке\n",
- "# Используйте синтаксис DataFrame['название_столбца']\n",
- "# write your code here\n",
"\n",
- "# Выбор нескольких столбцов\n",
- "# Используйте синтаксис DataFrame[['столбец_1', 'столбец_2']]\n",
- "# write your code here\n",
+ "data = pd.read_csv('titanic.csv')\n",
+ "\n",
+ "#Выбор по метке\n",
+ "selected_column = data['Age']\n",
+ "print(selected_column)\n",
+ "\n",
+ "#Выбор нескольких столбцов\n",
+ "selected_columns = data[['Name', 'Sex']]\n",
+ "print(selected_columns)\n",
"\n",
- "# Выбор строк по индексу\n",
- "# Используйте метод .loc[]\n",
- "# write your code here\n",
+ "#Выбор по индексу\n",
+ "selected_row = data.loc[0] \n",
+ "print(selected_row)\n",
"\n",
- "# Выбор строк и столбцов по условию\n",
- "# Используя логические операции, выберите мужчин старше 30\n",
- "# write your code here"
+ "#Выбор строк и столбцов \n",
+ "selected_data = data[(data['Sex'] == 'male') & (data['Age'] > 30)]\n",
+ "print(selected_data)"
]
},
{
@@ -219,16 +499,52 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 11,
"id": "de5e850c-e920-4ae4-aadb-3f1953438b09",
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " PassengerId Survived Pclass Name \\\n",
+ "803 804 1 3 Thomas, Master. Assad Alexander \n",
+ "755 756 1 2 Hamalainen, Master. Viljo \n",
+ "644 645 1 3 Baclini, Miss. Eugenie \n",
+ "469 470 1 3 Baclini, Miss. Helene Barbara \n",
+ "78 79 1 2 Caldwell, Master. Alden Gates \n",
+ ".. ... ... ... ... \n",
+ "859 860 0 3 Razi, Mr. Raihed \n",
+ "863 864 0 3 Sage, Miss. Dorothy Edith \"Dolly\" \n",
+ "868 869 0 3 van Melkebeke, Mr. Philemon \n",
+ "878 879 0 3 Laleff, Mr. Kristo \n",
+ "888 889 0 3 Johnston, Miss. Catherine Helen \"Carrie\" \n",
+ "\n",
+ " Sex Age SibSp Parch Ticket Fare Cabin Embarked \n",
+ "803 male 0.42 0 1 2625 8.5167 NaN C \n",
+ "755 male 0.67 1 1 250649 14.5000 NaN S \n",
+ "644 female 0.75 2 1 2666 19.2583 NaN C \n",
+ "469 female 0.75 2 1 2666 19.2583 NaN C \n",
+ "78 male 0.83 0 2 248738 29.0000 NaN S \n",
+ ".. ... ... ... ... ... ... ... ... \n",
+ "859 male NaN 0 0 2629 7.2292 NaN C \n",
+ "863 female NaN 8 2 CA. 2343 69.5500 NaN S \n",
+ "868 male NaN 0 0 345777 9.5000 NaN S \n",
+ "878 male NaN 0 0 349217 7.8958 NaN S \n",
+ "888 female NaN 1 2 W./C. 6607 23.4500 NaN S \n",
+ "\n",
+ "[891 rows x 12 columns]\n"
+ ]
+ }
+ ],
"source": [
- "# Сортировка данных по столбцу 'столбец_1' по возрастанию\n",
- "# Используйте метод .sort_values()\n",
- "# write your code here"
+ "\n",
+ "data = pd.read_csv('titanic.csv')\n",
+ "\n",
+ "sorted_data = data.sort_values(by='Age')\n",
+ "print(sorted_data)"
]
},
{
@@ -242,16 +558,30 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 12,
"id": "25ced901-0482-49a8-8c12-d192e84e3fb3",
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Pclass\n",
+ "1 0.629630\n",
+ "2 0.472826\n",
+ "3 0.242363\n",
+ "Name: Survived, dtype: float64\n"
+ ]
+ }
+ ],
"source": [
- "# Найдите долю выживших среди всех PClass\n",
- "# Используйте метод .groupby()\n",
- "# write your code here"
+ "\n",
+ "data = pd.read_csv('titanic.csv')\n",
+ "\n",
+ "survival_rate_by_pclass = data.groupby('Pclass')['Survived'].mean()\n",
+ "print(survival_rate_by_pclass)"
]
},
{
@@ -270,11 +600,122 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"id": "a1b4deaa-cd06-41b3-8084-5c2d3a867811",
"metadata": {},
- "outputs": [],
- "source": []
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Первые 10 строк данных:\n",
+ " PassengerId Survived Pclass \\\n",
+ "0 1 0 3 \n",
+ "1 2 1 1 \n",
+ "2 3 1 3 \n",
+ "3 4 1 1 \n",
+ "4 5 0 3 \n",
+ "5 6 0 3 \n",
+ "6 7 0 1 \n",
+ "7 8 0 3 \n",
+ "8 9 1 3 \n",
+ "9 10 1 2 \n",
+ "\n",
+ " Name Sex Age SibSp \\\n",
+ "0 Braund, Mr. Owen Harris male 22.0 1 \n",
+ "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
+ "2 Heikkinen, Miss. Laina female 26.0 0 \n",
+ "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
+ "4 Allen, Mr. William Henry male 35.0 0 \n",
+ "5 Moran, Mr. James male 0.0 0 \n",
+ "6 McCarthy, Mr. Timothy J male 54.0 0 \n",
+ "7 Palsson, Master. Gosta Leonard male 2.0 3 \n",
+ "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 \n",
+ "9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 \n",
+ "\n",
+ " Parch Ticket Fare Cabin Embarked \n",
+ "0 0 A/5 21171 7.2500 0 S \n",
+ "1 0 PC 17599 71.2833 C85 C \n",
+ "2 0 STON/O2. 3101282 7.9250 0 S \n",
+ "3 0 113803 53.1000 C123 S \n",
+ "4 0 373450 8.0500 0 S \n",
+ "5 0 330877 8.4583 0 Q \n",
+ "6 0 17463 51.8625 E46 S \n",
+ "7 1 349909 21.0750 0 S \n",
+ "8 2 347742 11.1333 0 S \n",
+ "9 0 237736 30.0708 0 C \n",
+ "\n",
+ "Строки, где возраст больше 30, отсортированные по Fare:\n",
+ " PassengerId Survived Pclass \\\n",
+ "258 259 1 1 \n",
+ "679 680 1 1 \n",
+ "737 738 1 1 \n",
+ "438 439 0 1 \n",
+ "299 300 1 1 \n",
+ ".. ... ... ... \n",
+ "263 264 0 1 \n",
+ "179 180 0 3 \n",
+ "597 598 0 3 \n",
+ "822 823 0 1 \n",
+ "806 807 0 1 \n",
+ "\n",
+ " Name Sex Age SibSp \\\n",
+ "258 Ward, Miss. Anna female 35.0 0 \n",
+ "679 Cardeza, Mr. Thomas Drake Martinez male 36.0 0 \n",
+ "737 Lesurer, Mr. Gustave J male 35.0 0 \n",
+ "438 Fortune, Mr. Mark male 64.0 1 \n",
+ "299 Baxter, Mrs. James (Helene DeLaudeniere Chaput) female 50.0 0 \n",
+ ".. ... ... ... ... \n",
+ "263 Harrison, Mr. William male 40.0 0 \n",
+ "179 Leonard, Mr. Lionel male 36.0 0 \n",
+ "597 Johnson, Mr. Alfred male 49.0 0 \n",
+ "822 Reuchlin, Jonkheer. John George male 38.0 0 \n",
+ "806 Andrews, Mr. Thomas Jr male 39.0 0 \n",
+ "\n",
+ " Parch Ticket Fare Cabin Embarked \n",
+ "258 0 PC 17755 512.3292 0 C \n",
+ "679 1 PC 17755 512.3292 B51 B53 B55 C \n",
+ "737 0 PC 17755 512.3292 B101 C \n",
+ "438 4 19950 263.0000 C23 C25 C27 S \n",
+ "299 1 PC 17558 247.5208 B58 B60 C \n",
+ ".. ... ... ... ... ... \n",
+ "263 0 112059 0.0000 B94 S \n",
+ "179 0 LINE 0.0000 0 S \n",
+ "597 0 LINE 0.0000 0 S \n",
+ "822 0 19972 0.0000 0 S \n",
+ "806 0 112050 0.0000 A36 S \n",
+ "\n",
+ "[305 rows x 12 columns]\n",
+ "\n",
+ "Средний возраст для каждого класса:\n",
+ "Pclass\n",
+ "1 32.923241\n",
+ "2 28.091467\n",
+ "3 18.177026\n",
+ "Name: Age, dtype: float64\n"
+ ]
+ }
+ ],
+ "source": [
+ "data = pd.read_csv('titanic.csv')\n",
+ "\n",
+ "data_filled = data.fillna(0)\n",
+ "\n",
+ "print(\"Первые 10 строк данных:\")\n",
+ "print(data_filled.head(10))\n",
+ "\n",
+ "data_age_gt_30 = data_filled[data_filled['Age'] > 30]\n",
+ "\n",
+ "sorted_data_by_fare = data_age_gt_30.sort_values(by='Fare', ascending=False)\n",
+ "\n",
+ "print(\"\\nСтроки, где возраст больше 30, отсортированные по Fare:\")\n",
+ "print(sorted_data_by_fare)\n",
+ "\n",
+ "average_age_by_pclass = data_filled.groupby('Pclass')['Age'].mean()\n",
+ "\n",
+ "print(\"\\nСредний возраст для каждого класса:\")\n",
+ "print(average_age_by_pclass)"
+ ]
}
],
"metadata": {
@@ -293,7 +734,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.5"
+ "version": "3.12.2"
}
},
"nbformat": 4,