From f891011a5a1133229024a7051205b59027894546 Mon Sep 17 00:00:00 2001
From: Solange Emmenegger <solange.emmenegger@hslu.ch>
Date: Mon, 24 Oct 2022 14:37:36 +0000
Subject: [PATCH] Auto-saving for solange.emmenegger@hslu.ch on branch master
from commit a4710a6
---
.../00a Python Self Study.ipynb | 6 +-
.../00b Python Exercises.ipynb | 1126 ++++++++--------
.../00c Pandas Exercises.ipynb | 1181 +++++------------
3 files changed, 936 insertions(+), 1377 deletions(-)
diff --git a/notebooks/00 Python Tutorial/00a Python Self Study.ipynb b/notebooks/00 Python Tutorial/00a Python Self Study.ipynb
index a95ced6..32534c6 100644
--- a/notebooks/00 Python Tutorial/00a Python Self Study.ipynb
+++ b/notebooks/00 Python Tutorial/00a Python Self Study.ipynb
@@ -3677,7 +3677,7 @@
}
},
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -3691,9 +3691,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.6"
+ "version": "3.9.12"
}
},
"nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
}
diff --git a/notebooks/00 Python Tutorial/00b Python Exercises.ipynb b/notebooks/00 Python Tutorial/00b Python Exercises.ipynb
index bf18786..bae9a85 100644
--- a/notebooks/00 Python Tutorial/00b Python Exercises.ipynb
+++ b/notebooks/00 Python Tutorial/00b Python Exercises.ipynb
@@ -24,21 +24,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 63,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Hello World!\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"print(\"Hello World!\")"
]
@@ -55,26 +58,30 @@
"execution_count": null,
"metadata": {
"solution2": "hidden",
- "solution2_first": true
+ "solution2_first": true,
+ "tags": []
},
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 64,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Hello World!\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"def hello():\n",
" print(\"Hello World!\")\n",
@@ -98,22 +105,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 65,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Hello World!\n",
- "None\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"return_value = hello()\n",
"print(return_value)"
@@ -137,21 +146,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 66,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Hello World!\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"def hello():\n",
" return \"Hello World!\"\n",
@@ -175,21 +187,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 67,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Hello World!\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"def hello(output):\n",
" print(output)\n",
@@ -213,21 +228,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 68,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Hello World!\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"def hello(output=\"Hello World!\"):\n",
" print(output)\n",
@@ -251,25 +269,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 69,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Hello World!\n",
- "Hello World!\n",
- "Hello World!\n",
- "Hello World!\n",
- "Hello World!\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# the _ variable is used by convention as a placeholder for a throwaway variable\n",
"# we can use it here since we don't need the numbers 0 to 4 that we loop over\n",
@@ -286,7 +303,7 @@
},
{
"cell_type": "code",
- "execution_count": 70,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -303,25 +320,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 71,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 print\n",
- "2 this\n",
- "3 element\n",
- "4 by\n",
- "5 ellement\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"for i, elem in enumerate(data):\n",
" print(i+1, elem)\n",
@@ -352,11 +368,22 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 72,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
"outputs": [],
"source": [
@@ -380,24 +407,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 73,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[2, 4, 6, 8, 10]"
- ]
- },
- "execution_count": 73,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"even_ints[0:5]"
]
@@ -419,24 +446,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 74,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[12, 14, 16, 18, 20, 22, 24, 26, 28, 30]"
- ]
- },
- "execution_count": 74,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"even_ints[5:15]"
]
@@ -458,24 +485,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 75,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[92, 94, 96, 98, 100]"
- ]
- },
- "execution_count": 75,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"even_ints[-5:]"
]
@@ -497,48 +524,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 76,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[4,\n",
- " 8,\n",
- " 12,\n",
- " 16,\n",
- " 20,\n",
- " 24,\n",
- " 28,\n",
- " 32,\n",
- " 36,\n",
- " 40,\n",
- " 44,\n",
- " 48,\n",
- " 52,\n",
- " 56,\n",
- " 60,\n",
- " 64,\n",
- " 68,\n",
- " 72,\n",
- " 76,\n",
- " 80,\n",
- " 84,\n",
- " 88,\n",
- " 92,\n",
- " 96,\n",
- " 100]"
- ]
- },
- "execution_count": 76,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"even_ints[1::2]"
]
@@ -560,73 +563,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 77,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[100,\n",
- " 98,\n",
- " 96,\n",
- " 94,\n",
- " 92,\n",
- " 90,\n",
- " 88,\n",
- " 86,\n",
- " 84,\n",
- " 82,\n",
- " 80,\n",
- " 78,\n",
- " 76,\n",
- " 74,\n",
- " 72,\n",
- " 70,\n",
- " 68,\n",
- " 66,\n",
- " 64,\n",
- " 62,\n",
- " 60,\n",
- " 58,\n",
- " 56,\n",
- " 54,\n",
- " 52,\n",
- " 50,\n",
- " 48,\n",
- " 46,\n",
- " 44,\n",
- " 42,\n",
- " 40,\n",
- " 38,\n",
- " 36,\n",
- " 34,\n",
- " 32,\n",
- " 30,\n",
- " 28,\n",
- " 26,\n",
- " 24,\n",
- " 22,\n",
- " 20,\n",
- " 18,\n",
- " 16,\n",
- " 14,\n",
- " 12,\n",
- " 10,\n",
- " 8,\n",
- " 6,\n",
- " 4,\n",
- " 2]"
- ]
- },
- "execution_count": 77,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"even_ints[::-1]"
]
@@ -648,24 +602,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 78,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'!dlroW olleH'"
- ]
- },
- "execution_count": 78,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"\"Hello World!\"[::-1] # strings can be treated as lists because they implement the iterator inteface"
]
@@ -679,7 +633,7 @@
},
{
"cell_type": "code",
- "execution_count": 79,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -696,24 +650,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 80,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[1, 2, 3, 4, 5]"
- ]
- },
- "execution_count": 80,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"a.append(5)\n",
"a"
@@ -736,24 +690,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 81,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[1, 2, 3, 4, 5, 6, 7, 8]"
- ]
- },
- "execution_count": 81,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"a.extend([6,7,8])\n",
"a"
@@ -776,24 +730,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 82,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
- ]
- },
- "execution_count": 82,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"a = a + [9,10]\n",
"a"
@@ -818,44 +772,44 @@
},
{
"cell_type": "code",
- "execution_count": 83,
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "True"
- ]
- },
- "execution_count": 83,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"5 in a"
]
},
{
"cell_type": "code",
- "execution_count": 84,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "False"
- ]
- },
- "execution_count": 84,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"11 in a"
]
@@ -877,24 +831,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 85,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[2, 4, 6, 8, 10, 12, 14, 16, 18, 20]"
- ]
- },
- "execution_count": 85,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"[elem*2 for elem in a]"
]
@@ -911,29 +865,30 @@
"execution_count": null,
"metadata": {
"solution2": "hidden",
- "solution2_first": true
+ "solution2_first": true,
+ "tags": []
},
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 86,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['2', '4', '6', '8', '10']"
- ]
- },
- "execution_count": 86,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"[str(i) for i in a if i%2==0]"
]
@@ -955,24 +910,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 87,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(1, 2, 3)"
- ]
- },
- "execution_count": 87,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"a = (1,2,3)\n",
"a"
@@ -995,25 +950,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 88,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "ename": "TypeError",
- "evalue": "'tuple' object does not support item assignment",
- "output_type": "error",
- "traceback": [
- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
- "\u001b[1;32m<ipython-input-88-7d02990d0abc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
- ]
- }
- ],
+ "outputs": [],
"source": [
"a[0] = 0"
]
@@ -1035,24 +989,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 89,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{1, 2, 3, 4, 5}"
- ]
- },
- "execution_count": 89,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"a = set((1,1,2,2,3,4,5))\n",
"a"
@@ -1084,24 +1038,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 90,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'a': 1, 2: '2', 16: [3, 4, 5]}"
- ]
- },
- "execution_count": 90,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"d = {'a':1, 2:'2', 16:[3,4,5]}\n",
"d"
@@ -1124,24 +1078,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 91,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'2'"
- ]
- },
- "execution_count": 91,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"d[2]"
]
@@ -1158,29 +1112,30 @@
"execution_count": null,
"metadata": {
"solution2": "hidden",
- "solution2_first": true
+ "solution2_first": true,
+ "tags": []
},
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 92,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'a': 1, 2: '2'}"
- ]
- },
- "execution_count": 92,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"del d[16]\n",
"d"
@@ -1203,24 +1158,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 93,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'a': 1, 2: '2', 42: 42}"
- ]
- },
- "execution_count": 93,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"d[42] = 42\n",
"d"
@@ -1243,24 +1198,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 94,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['1', '2', '42']"
- ]
- },
- "execution_count": 94,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"[str(d[i]) for i in d.keys()]"
]
@@ -1282,25 +1237,25 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 95,
+ "execution_count": null,
"metadata": {
"code_folding": [],
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{1: 'a', '2': 2, 42: 42}"
- ]
- },
- "execution_count": 95,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"{v:k for k, v in d.items()}\n",
"\n",
@@ -1328,11 +1283,22 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
"outputs": [],
"source": [
@@ -1356,11 +1322,22 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
"outputs": [],
"source": [
@@ -1384,11 +1361,22 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
"outputs": [],
"source": [
@@ -1421,11 +1409,22 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
"outputs": [],
"source": [
@@ -1458,11 +1457,22 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
"outputs": [],
"source": [
@@ -1481,7 +1491,7 @@
},
{
"cell_type": "code",
- "execution_count": 101,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -1498,24 +1508,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 107,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(2, 1)"
- ]
- },
- "execution_count": 107,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"def divide(dividend, divisor):\n",
" return floor(dividend/divisor), dividend%divisor\n",
@@ -1540,11 +1550,22 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
"outputs": [],
"source": [
@@ -1626,11 +1647,22 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
"outputs": [],
"source": [
@@ -1685,11 +1717,22 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
"outputs": [],
"source": [
@@ -1718,14 +1761,32 @@
" ..."
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {
+ "jupyter": {
+ "source_hidden": true
+ },
"run_control": {
"marked": false
},
- "solution2": "hidden"
+ "solution2": "hidden",
+ "tags": []
},
"outputs": [],
"source": [
@@ -1747,7 +1808,7 @@
},
{
"cell_type": "code",
- "execution_count": 110,
+ "execution_count": null,
"metadata": {
"solution2": "hidden",
"solution2_first": true
@@ -1763,9 +1824,27 @@
},
{
"cell_type": "code",
- "execution_count": 111,
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
"outputs": [],
"source": [
@@ -1782,20 +1861,9 @@
},
{
"cell_type": "code",
- "execution_count": 112,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[1, 1, 2, 3, 5, 8, 13, 21, 34, 55]"
- ]
- },
- "execution_count": 112,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"fib(10)"
]
@@ -1811,7 +1879,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -1825,9 +1893,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.6"
+ "version": "3.9.12"
}
},
"nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
}
diff --git a/notebooks/00 Python Tutorial/00c Pandas Exercises.ipynb b/notebooks/00 Python Tutorial/00c Pandas Exercises.ipynb
index fa86936..1cbd75d 100644
--- a/notebooks/00 Python Tutorial/00c Pandas Exercises.ipynb
+++ b/notebooks/00 Python Tutorial/00c Pandas Exercises.ipynb
@@ -31,11 +31,22 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
"outputs": [],
"source": [
@@ -52,7 +63,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {
"solution2": "hidden",
"solution2_first": true
@@ -64,9 +75,27 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
"outputs": [],
"source": [
@@ -91,24 +120,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(1000, 12)"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"df.shape"
]
@@ -131,258 +160,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "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>Rank</th>\n",
- " <th>Title</th>\n",
- " <th>Genre</th>\n",
- " <th>Description</th>\n",
- " <th>Director</th>\n",
- " <th>Actors</th>\n",
- " <th>Year</th>\n",
- " <th>Runtime (Minutes)</th>\n",
- " <th>Rating</th>\n",
- " <th>Votes</th>\n",
- " <th>Revenue (Millions)</th>\n",
- " <th>Metascore</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>1</td>\n",
- " <td>Guardians of the Galaxy</td>\n",
- " <td>Action,Adventure,Sci-Fi</td>\n",
- " <td>A group of intergalactic criminals are forced ...</td>\n",
- " <td>James Gunn</td>\n",
- " <td>Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...</td>\n",
- " <td>2014</td>\n",
- " <td>121</td>\n",
- " <td>8.1</td>\n",
- " <td>757074</td>\n",
- " <td>333.13</td>\n",
- " <td>76.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>2</td>\n",
- " <td>Prometheus</td>\n",
- " <td>Adventure,Mystery,Sci-Fi</td>\n",
- " <td>Following clues to the origin of mankind, a te...</td>\n",
- " <td>Ridley Scott</td>\n",
- " <td>Noomi Rapace, Logan Marshall-Green, Michael Fa...</td>\n",
- " <td>2012</td>\n",
- " <td>124</td>\n",
- " <td>7.0</td>\n",
- " <td>485820</td>\n",
- " <td>126.46</td>\n",
- " <td>65.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>3</td>\n",
- " <td>Split</td>\n",
- " <td>Horror,Thriller</td>\n",
- " <td>Three girls are kidnapped by a man with a diag...</td>\n",
- " <td>M. Night Shyamalan</td>\n",
- " <td>James McAvoy, Anya Taylor-Joy, Haley Lu Richar...</td>\n",
- " <td>2016</td>\n",
- " <td>117</td>\n",
- " <td>7.3</td>\n",
- " <td>157606</td>\n",
- " <td>138.12</td>\n",
- " <td>62.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>4</td>\n",
- " <td>Sing</td>\n",
- " <td>Animation,Comedy,Family</td>\n",
- " <td>In a city of humanoid animals, a hustling thea...</td>\n",
- " <td>Christophe Lourdelet</td>\n",
- " <td>Matthew McConaughey,Reese Witherspoon, Seth Ma...</td>\n",
- " <td>2016</td>\n",
- " <td>108</td>\n",
- " <td>7.2</td>\n",
- " <td>60545</td>\n",
- " <td>270.32</td>\n",
- " <td>59.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>5</td>\n",
- " <td>Suicide Squad</td>\n",
- " <td>Action,Adventure,Fantasy</td>\n",
- " <td>A secret government agency recruits some of th...</td>\n",
- " <td>David Ayer</td>\n",
- " <td>Will Smith, Jared Leto, Margot Robbie, Viola D...</td>\n",
- " <td>2016</td>\n",
- " <td>123</td>\n",
- " <td>6.2</td>\n",
- " <td>393727</td>\n",
- " <td>325.02</td>\n",
- " <td>40.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>6</td>\n",
- " <td>The Great Wall</td>\n",
- " <td>Action,Adventure,Fantasy</td>\n",
- " <td>European mercenaries searching for black powde...</td>\n",
- " <td>Yimou Zhang</td>\n",
- " <td>Matt Damon, Tian Jing, Willem Dafoe, Andy Lau</td>\n",
- " <td>2016</td>\n",
- " <td>103</td>\n",
- " <td>6.1</td>\n",
- " <td>56036</td>\n",
- " <td>45.13</td>\n",
- " <td>42.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>7</td>\n",
- " <td>La La Land</td>\n",
- " <td>Comedy,Drama,Music</td>\n",
- " <td>A jazz pianist falls for an aspiring actress i...</td>\n",
- " <td>Damien Chazelle</td>\n",
- " <td>Ryan Gosling, Emma Stone, Rosemarie DeWitt, J....</td>\n",
- " <td>2016</td>\n",
- " <td>128</td>\n",
- " <td>8.3</td>\n",
- " <td>258682</td>\n",
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- " <td>Mindhorn</td>\n",
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- " <th>9</th>\n",
- " <td>10</td>\n",
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- " <td>Morten Tyldum</td>\n",
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- "0 1 Guardians of the Galaxy Action,Adventure,Sci-Fi \n",
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- "9 10 Passengers Adventure,Drama,Romance \n",
- "\n",
- " Description Director \\\n",
- "0 A group of intergalactic criminals are forced ... James Gunn \n",
- "1 Following clues to the origin of mankind, a te... Ridley Scott \n",
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- "3 In a city of humanoid animals, a hustling thea... Christophe Lourdelet \n",
- "4 A secret government agency recruits some of th... David Ayer \n",
- "5 European mercenaries searching for black powde... Yimou Zhang \n",
- "6 A jazz pianist falls for an aspiring actress i... Damien Chazelle \n",
- "7 A has-been actor best known for playing the ti... Sean Foley \n",
- "8 A true-life drama, centering on British explor... James Gray \n",
- "9 A spacecraft traveling to a distant colony pla... Morten Tyldum \n",
- "\n",
- " Actors Year Runtime (Minutes) \\\n",
- "0 Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S... 2014 121 \n",
- "1 Noomi Rapace, Logan Marshall-Green, Michael Fa... 2012 124 \n",
- "2 James McAvoy, Anya Taylor-Joy, Haley Lu Richar... 2016 117 \n",
- "3 Matthew McConaughey,Reese Witherspoon, Seth Ma... 2016 108 \n",
- "4 Will Smith, Jared Leto, Margot Robbie, Viola D... 2016 123 \n",
- "5 Matt Damon, Tian Jing, Willem Dafoe, Andy Lau 2016 103 \n",
- "6 Ryan Gosling, Emma Stone, Rosemarie DeWitt, J.... 2016 128 \n",
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- "8 Charlie Hunnam, Robert Pattinson, Sienna Mille... 2016 141 \n",
- "9 Jennifer Lawrence, Chris Pratt, Michael Sheen,... 2016 116 \n",
- "\n",
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- "3 7.2 60545 270.32 59.0 \n",
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- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"df.head(n=10)"
]
@@ -400,168 +195,30 @@
"metadata": {
"lines_to_next_cell": 2,
"solution2": "hidden",
- "solution2_first": true
+ "solution2_first": true,
+ "tags": []
},
"outputs": [],
"source": []
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+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
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"cell_type": "code",
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+ "execution_count": null,
"metadata": {
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- " Rank Title Genre \\\n",
- "995 996 Secret in Their Eyes Crime,Drama,Mystery \n",
- "996 997 Hostel: Part II Horror \n",
- "997 998 Step Up 2: The Streets Drama,Music,Romance \n",
- "998 999 Search Party Adventure,Comedy \n",
- "999 1000 Nine Lives Comedy,Family,Fantasy \n",
- "\n",
- " Description Director \\\n",
- "995 A tight-knit team of rising investigators, alo... Billy Ray \n",
- "996 Three American college students studying abroa... Eli Roth \n",
- "997 Romantic sparks occur between two dance studen... Jon M. Chu \n",
- "998 A pair of friends embark on a mission to reuni... Scot Armstrong \n",
- "999 A stuffy businessman finds himself trapped ins... Barry Sonnenfeld \n",
- "\n",
- " Actors Year \\\n",
- "995 Chiwetel Ejiofor, Nicole Kidman, Julia Roberts... 2015 \n",
- "996 Lauren German, Heather Matarazzo, Bijou Philli... 2007 \n",
- "997 Robert Hoffman, Briana Evigan, Cassie Ventura,... 2008 \n",
- "998 Adam Pally, T.J. Miller, Thomas Middleditch,Sh... 2014 \n",
- "999 Kevin Spacey, Jennifer Garner, Robbie Amell,Ch... 2016 \n",
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- "998 93 5.6 4881 NaN 22.0 \n",
- "999 87 5.3 12435 19.64 11.0 "
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"df.tail()"
]
@@ -584,11 +241,22 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
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+ },
+ "solution2": "hidden",
+ "tags": []
},
"outputs": [],
"source": [
@@ -617,27 +285,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['Rank', 'Title', 'Genre', 'Description', 'Director', 'Actors', 'Year',\n",
- " 'Runtime (Minutes)', 'Rating', 'Votes', 'Revenue (Millions)',\n",
- " 'Metascore'],\n",
- " dtype='object')"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"df.columns"
]
@@ -660,24 +325,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "RangeIndex(start=0, stop=999, step=1)"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"df.index"
]
@@ -695,40 +360,30 @@
"metadata": {
"lines_to_next_cell": 2,
"solution2": "hidden",
- "solution2_first": true
+ "solution2_first": true,
+ "tags": []
},
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0 2014\n",
- "1 2012\n",
- "2 2016\n",
- "3 2016\n",
- "4 2016\n",
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- "995 2015\n",
- "996 2007\n",
- "997 2008\n",
- "998 2014\n",
- "Name: Year, Length: 999, dtype: int64"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"df.Year # or df['Year'] or df.loc[:, 'Year']"
]
@@ -751,24 +406,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(2006, 2016)"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"df.Year.min(), df.Year.max()"
]
@@ -791,36 +446,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Rank int64\n",
- "Title object\n",
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- "Runtime (Minutes) int64\n",
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- "Votes int64\n",
- "Revenue (Millions) float64\n",
- "Metascore float64\n",
- "dtype: object"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"df.dtypes"
]
@@ -838,16 +481,28 @@
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"lines_to_next_cell": 2,
"solution2": "hidden",
- "solution2_first": true
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"source": []
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+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
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{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
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+ },
+ "solution2": "hidden",
+ "tags": []
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"outputs": [],
"source": [
@@ -856,153 +511,9 @@
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- " <td>62.0</td>\n",
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- " <td>270.32</td>\n",
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- "2 Three girls are kidnapped by a man with a diag... M. Night Shyamalan \n",
- "3 In a city of humanoid animals, a hustling thea... Christophe Lourdelet \n",
- "4 A secret government agency recruits some of th... David Ayer \n",
- "\n",
- " Actors Year Runtime (Minutes) \\\n",
- "0 Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S... 2014 121 \n",
- "1 Noomi Rapace, Logan Marshall-Green, Michael Fa... 2012 124 \n",
- "2 James McAvoy, Anya Taylor-Joy, Haley Lu Richar... 2016 117 \n",
- "3 Matthew McConaughey,Reese Witherspoon, Seth Ma... 2016 108 \n",
- "4 Will Smith, Jared Leto, Margot Robbie, Viola D... 2016 123 \n",
- "\n",
- " Rating Votes Revenue (Millions) Metascore \n",
- "0 8.1 757074 333.13 76.0 \n",
- "1 7.0 485820 126.46 65.0 \n",
- "2 7.3 157606 138.12 62.0 \n",
- "3 7.2 60545 270.32 59.0 \n",
- "4 6.2 393727 325.02 40.0 "
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"df.head()"
]
@@ -1026,7 +537,8 @@
"execution_count": null,
"metadata": {
"solution2": "hidden",
- "solution2_first": true
+ "solution2_first": true,
+ "tags": []
},
"outputs": [],
"source": [
@@ -1036,27 +548,29 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0 [Action, Adventure, Sci-Fi]\n",
- "1 [Adventure, Mystery, Sci-Fi]\n",
- "2 [Horror, Thriller]\n",
- "3 [Animation, Comedy, Family]\n",
- "4 [Action, Adventure, Fantasy]\n",
- "Name: Genre, dtype: object"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"genres = df.Genre.str.split(',')\n",
"genres.head()"
@@ -1071,78 +585,9 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
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- "<style scoped>\n",
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- " <td>Mystery</td>\n",
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- "0 0 Action\n",
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- "4 1 Mystery"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"genres = genres.apply(pd.Series).stack().reset_index().iloc[:, [0,2]]\n",
"genres.columns = ['movie_id', 'genre']\n",
@@ -1167,82 +612,24 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
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+ "tags": []
},
- "outputs": [
- {
- "data": {
- "text/html": [
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- "<style scoped>\n",
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- " <td>Bradley Cooper</td>\n",
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- " <tr>\n",
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- " <td>0</td>\n",
- " <td>Zoe Saldana</td>\n",
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- " <tr>\n",
- " <th>4</th>\n",
- " <td>1</td>\n",
- " <td>Noomi Rapace</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " movie_id actor\n",
- "0 0 Chris Pratt\n",
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- "3 0 Zoe Saldana\n",
- "4 1 Noomi Rapace"
- ]
- },
- "execution_count": 23,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"actors = df.Actors.str.split(',').apply(pd.Series).stack().reset_index().iloc[:, [0,2]]\n",
"actors.columns = ['movie_id', 'actor']\n",
@@ -1268,11 +655,22 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
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+ },
+ "solution2": "hidden",
+ "tags": []
},
"outputs": [],
"source": [
@@ -1331,11 +729,22 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
"outputs": [],
"source": [
@@ -1348,7 +757,11 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
"outputs": [],
"source": [
@@ -1374,11 +787,22 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
"outputs": [],
"source": [
@@ -1403,11 +827,22 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
"outputs": [],
"source": [
@@ -1431,16 +866,28 @@
"metadata": {
"lines_to_next_cell": 2,
"solution2": "hidden",
- "solution2_first": true
+ "solution2_first": true,
+ "tags": []
},
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
"outputs": [],
"source": [
@@ -1465,11 +912,22 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
"outputs": [],
"source": [
@@ -1495,11 +953,22 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
"outputs": [],
"source": [
@@ -1524,11 +993,22 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
"outputs": [],
"source": [
@@ -1553,11 +1033,22 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Click on the three dots below to disply the solution"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "solution2": "hidden"
+ "jupyter": {
+ "source_hidden": true
+ },
+ "solution2": "hidden",
+ "tags": []
},
"outputs": [],
"source": [
@@ -1650,9 +1141,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.13"
+ "version": "3.9.12"
}
},
"nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
}
--
GitLab