From 7e73d62f1fa8de931e5984f44b00b9effcb4ed1e Mon Sep 17 00:00:00 2001
From: eyrei123 <88923476+eyrei123@users.noreply.github.com>
Date: Tue, 14 Nov 2023 20:22:05 +0000
Subject: [PATCH 1/5] Add files via upload

---
 Solution.ipynb               | 567 +++++++++++++++++++++++++++++++++++
 james_bond_data.csv          |  28 ++
 james_bond_data.json         |   1 +
 james_bond_data.parquet      | Bin 0 -> 11050 bytes
 james_bond_data.xlsx         | Bin 0 -> 11269 bytes
 james_bond_data_cleansed.csv |  26 ++
 6 files changed, 622 insertions(+)
 create mode 100644 Solution.ipynb
 create mode 100644 james_bond_data.csv
 create mode 100644 james_bond_data.json
 create mode 100644 james_bond_data.parquet
 create mode 100644 james_bond_data.xlsx
 create mode 100644 james_bond_data_cleansed.csv

diff --git a/Solution.ipynb b/Solution.ipynb
new file mode 100644
index 0000000000..356b6682e7
--- /dev/null
+++ b/Solution.ipynb
@@ -0,0 +1,567 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "83ad2114-5ed8-4a90-85fa-adea5eda4392",
+   "metadata": {},
+   "source": [
+    "## Reading Data From CSV Files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a360772e-7829-4c15-9af9-d4596efc7351",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! python -m pip install pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1148ca58-9a4d-42ed-a43a-e3b8b3359c2d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "james_bond_df = pd.read_csv(\"james_bond_data.csv\")\n",
+    "james_bond_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e47c1f9b-b390-4035-956b-622615b57f32",
+   "metadata": {},
+   "source": [
+    "## Reading Data From Other Sources"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47a0e4a6-0ed9-4253-9833-0ad22c49b968",
+   "metadata": {},
+   "source": [
+    "### Reading Excel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a0364b81-64a0-4098-89fc-e58bd6d68257",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! python -m pip install openpyxl"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8302139f-52dc-4f95-aa9a-96040ae5d82b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import openpyxl\n",
+    "import pandas as pd\n",
+    "\n",
+    "james_bond_df_excel = pd.read_excel(\"james_bond_data.xlsx\")\n",
+    "james_bond_df_excel.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d85aee9-cfeb-460b-9fe8-f3c7e7dfb764",
+   "metadata": {},
+   "source": [
+    "### Reading JSON"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7465cd11-dad4-4741-9372-f825b28c33d6",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "james_bond_df_json = pd.read_json(\"james_bond_data.json\")\n",
+    "james_bond_df_json.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69f884c2-92e8-4db3-bd63-84007f654808",
+   "metadata": {},
+   "source": [
+    "### Scraping HTML"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b902722d-9648-4124-80b0-64004342170d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!python -m pip install lxml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1fb2ff9c-3030-4f4a-be30-c2ab68452a21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "james_bond_df_html = pd.read_html(\n",
+    "    \"https://en.wikipedia.org/wiki/List_of_James_Bond_novels_and_short_stories\"\n",
+    ")\n",
+    "james_bond_df_html[1].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be4a1143-c966-4056-8a5e-3bdebe2a9b1f",
+   "metadata": {},
+   "source": [
+    "### Reading Parquet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f36ef600-e6ba-4cc6-9ee3-0cbf369a4be2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!python -m pip install pyarrow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bd6f496d-aa94-43ce-9e97-6f01108df47b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "james_bond_df_parquet = pd.read_parquet(\"james_bond_data.parquet\")\n",
+    "james_bond_df_parquet.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e432b28e-257b-422b-b2f8-06f41608391b",
+   "metadata": {},
+   "source": [
+    "## Dealing With Missing Data and Invalid Data Types"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38eb1abb-9f89-4a53-9e77-f7c71dbeff18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "james_bond_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b29d5a34-c930-4ce2-898c-b9e8aa7f771d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "james_bond_df[james_bond_df.isna().any(axis=\"columns\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1db9201a-11c1-4cdd-9625-d70cee736191",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "james_bond_df.at[10, \"Avg_User_IMDB\"] = 7.1\n",
+    "james_bond_df.at[10, \"Avg_User_Rtn_Tom\"] = 6.8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "001996e3-2fce-4228-a873-b78eef613bba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "james_bond_df[[\"US_Gross\", \"World_Gross\", \"Budget ($ 000s)\", \"Film_Length\"]].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "880e4710-1c11-4de2-a2c3-97a9672ce6f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "james_bond_df[\"US_Gross\"] = (\n",
+    "    james_bond_df[\"US_Gross\"].replace(\"[$,]\", \"\", regex=True)\n",
+    ").astype(float)\n",
+    "\n",
+    "james_bond_df[\"World_Gross\"] = (\n",
+    "    james_bond_df[\"World_Gross\"].replace(\"[$,]\", \"\", regex=True)\n",
+    ").astype(float)\n",
+    "\n",
+    "james_bond_df[\"Budget ($ 000s)\"] = (\n",
+    "    james_bond_df[\"Budget ($ 000s)\"].replace(\"[$,]\", \"\", regex=True)\n",
+    ").astype(float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ae9c1d1b-a620-43c5-a199-eb6a7bff7ce2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "james_bond_df[\"Film_Length\"] = (\n",
+    "    james_bond_df[\"Film_Length\"].str.rstrip(\"mins\").astype(int)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ed0ead0e-7310-4c82-86d5-2480a95f1525",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "james_bond_df[\"Release\"] = pd.to_datetime(james_bond_df[\"Release\"], format=\"%B, %Y\")\n",
+    "james_bond_df[\"Release_Year\"] = james_bond_df[\"Release\"].dt.year"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89653d81-3bcd-4078-83cb-ad4b2fa560e6",
+   "metadata": {},
+   "source": [
+    "## Dealing With Inconsistencies in Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc483320-7895-4368-a672-b98f8d0c9755",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "james_bond_df[\"Budget ($ 000s)\"] = james_bond_df[\"Budget ($ 000s)\"] * 1000\n",
+    "james_bond_df.rename(columns={\"Budget ($ 000s)\": \"Budget\"}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e129b32-5e66-41cb-b938-8fd58bb94116",
+   "metadata": {},
+   "source": [
+    "## Removing Duplicate Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be7aad8b-ef3f-48a6-a9a0-de909133921f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "james_bond_df[\"Movie\"].value_counts().head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "20067efb-e7c7-4690-b483-1d29847ad24f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "duplicate_movies = [\"The Man with the Golden Gun\", \"The Living Daylights\"]\n",
+    "james_bond_df[james_bond_df[\"Movie\"].isin(duplicate_movies)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c98c7640-1472-4869-9fdd-f070d665ae1d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "james_bond_df.drop_duplicates(inplace=True, ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8bdaa8b1-9f2e-46a5-b53a-c1ae4c201c99",
+   "metadata": {},
+   "source": [
+    "## Removing Typos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e442e51a-28fd-42d7-94b0-aaf1abe5d9a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "james_bond_df[\"Bond\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f9863aa7-b5db-4ab1-be63-727ff437b63b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "james_bond_df[\"Bond\"] = james_bond_df[\"Bond\"].str.replace(\"Shawn\", \"Sean\")\n",
+    "james_bond_df[\"Bond\"] = james_bond_df[\"Bond\"].str.replace(\"MOORE\", \"Moore\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25da4a99-6b90-4785-aaa4-48bed819e9be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "james_bond_df[\"Bond\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a26b138d-72e5-4e15-a875-ee65023545d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "james_bond_df[\"Bond_Car_MFG\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8260f6b1-6d7f-4338-95b7-8946d69a92e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "james_bond_df[\"Bond_Car_MFG\"] = james_bond_df[\"Bond_Car_MFG\"].str.replace(\"Astin\", \"Aston\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50c80bc8-fdb9-4c28-af5a-cd6b66c7a01d",
+   "metadata": {},
+   "source": [
+    "## Checking for Invalid Outliers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "81338285-2067-46fc-82f8-360d92ec7153",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "james_bond_df[[\"Film_Length\", \"Martinis\"]].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d84acfc8-fb2d-4c45-bfac-20469fc1de97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "james_bond_df[\"Film_Length\"].replace(1200, 120, inplace=True)\n",
+    "james_bond_df[\"Martinis\"].replace(-6, 6, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52db1351-36ed-4104-a999-345ebbc62214",
+   "metadata": {},
+   "source": [
+    "## Storing Your Cleansed Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "575a774e-6913-41fb-8ff9-4d786f478007",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "james_bond_df.to_csv(\"james_bond_data_cleansed.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f50918ee-e61f-46b2-b0c2-1ffa2c62bbc0",
+   "metadata": {},
+   "source": [
+    "## Using Python for Data Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bee6d6cb-e418-4c1d-8b75-604b9ab2e63d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!python -m pip install matplotlib scikit-learn numpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0246dcb1-88fc-4a3e-acc1-571037390e09",
+   "metadata": {},
+   "source": [
+    "## Performing a Regression Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27d0a3dd-e71a-4b8a-883c-40cb5c001f7e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "#x = james_bond_df[\"Avg_User_IMDB\"].values.reshape(-1, 1)\n",
+    "#y = james_bond_df[\"Avg_User_Rtn_Tom\"].values.reshape(-1, 1)\n",
+    "\n",
+    "x = james_bond_df[\"Avg_User_IMDB\"].array.reshape(-1, 1)\n",
+    "y = james_bond_df[\"Avg_User_Rtn_Tom\"].array.reshape(-1, 1)\n",
+    "\n",
+    "plt.title(\"Scatter Plot of Ratings.\")\n",
+    "plt.xlabel(\"Average IMDb Rating\")\n",
+    "plt.ylabel(\"Average Rotten Tomatoes Rating\")\n",
+    "plt.scatter(x, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "669fb9d7-d744-4e6b-899e-a69aebec53ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "x = james_bond_df[\"Avg_User_IMDB\"].array.reshape(-1, 1)\n",
+    "y = james_bond_df[\"Avg_User_Rtn_Tom\"].array.reshape(-1, 1)\n",
+    "\n",
+    "model = LinearRegression()\n",
+    "model.fit(x, y)\n",
+    "\n",
+    "r_squared = f\"R-Squared: {round(model.score(x, y),2)}\"\n",
+    "best_fit_equation = f\"y={round(model.coef_[0][0], 4)}x{round(model.intercept_[0], 4)}\"\n",
+    "y_pred = model.predict(x)\n",
+    "\n",
+    "plt.title(\"Scatter Plot of Ratings.\")\n",
+    "plt.xlabel(\"Average IMDb Rating\")\n",
+    "plt.ylabel(\"Average Rotten Tomatoes Rating\")\n",
+    "plt.scatter(x, y)\n",
+    "plt.text(7.25, 5.5, r_squared, fontsize=10)\n",
+    "plt.text(7.25, 7, best_fit_equation, fontsize=10)\n",
+    "plt.plot(x, y_pred, color=\"red\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b38df412-c320-49fb-93ae-e253405537a8",
+   "metadata": {},
+   "source": [
+    "## Investigating a Statistical Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "938e5942-e57f-4e41-99f1-215cfb37d0df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "film_length_groups = james_bond_df[\"Film_Length\"].value_counts(bins=7, sort=False)\n",
+    "film_length_groups.plot(kind=\"bar\", title=\"Film Length Distribution\").set(\n",
+    "    xlabel=\"Time Range (mins)\", ylabel=\"Count\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ff4e9955-baf4-48eb-b032-fbf55f439194",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "james_bond_df[\"Film_Length\"].agg([\"mean\", \"max\", \"min\", \"std\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b14c433-c3a6-4484-bc0a-26825bd1e870",
+   "metadata": {},
+   "source": [
+    "## Finding No Relationship"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2bb83374-347f-4cf6-bc21-8180a003371d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = james_bond_df[\"Avg_User_IMDB\"].array.reshape(-1, 1)\n",
+    "y = james_bond_df[\"Kills_Bond\"].array.reshape(-1, 1)\n",
+    "\n",
+    "plt.title(\"Scatter Plot of Kills vs Ratings.\")\n",
+    "plt.xlabel(\"Average IMDb Rating\")\n",
+    "plt.ylabel(\"Kills By Bond\")\n",
+    "plt.scatter(x, y)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/james_bond_data.csv b/james_bond_data.csv
new file mode 100644
index 0000000000..4a983b2201
--- /dev/null
+++ b/james_bond_data.csv
@@ -0,0 +1,28 @@
+Release,Movie,Bond,Bond_Car_MFG,US_Gross,World_Gross,Budget ($ 000s),Film_Length,Avg_User_IMDB,Avg_User_Rtn_Tom,Martinis,Kills_Bond
+"June, 1962",Dr. No,Sean Connery,Sunbeam," $16,067,035.00 "," $59,567,035.00 "," $1,000.00 ",110 mins,7.3,7.7,2,4
+"August, 1963",From Russia with Love,Sean Connery,Bently," $24,800,000.00 "," $78,900,000.00 "," $2,000.00 ",115 mins,7.5,8,0,11
+"May, 1964",Goldfinger,Sean Connery,Aston Martin," $51,100,000.00 "," $124,900,000.00 "," $3,000.00 ",110 mins,7.8,8.4,1,9
+"September, 1965",Thunderball,Sean Connery,Aston Martin," $63,600,000.00 "," $141,200,000.00 "," $9,000.00 ",130 mins,7,6.8,0,20
+"November, 1967",You Only Live Twice,Sean Connery,Toyota," $43,100,000.00 "," $111,600,000.00 "," $9,500.00 ",117 mins,6.9,6.3,1,21
+"July, 1969",On Her Majesty's Secret Service,George Lazenby,Mercury," $22,800,000.00 "," $82,000,000.00 "," $8,000.00 ",142 mins,6.8,6.7,1,5
+"March, 1971",Diamonds Are Forever,Shawn Connery,Ford," $43,800,000.00 "," $116,000,000.00 "," $7,200.00 ",1200 mins,6.7,6.3,0,7
+"August, 1973",Live and Let Die,Roger Moore,AMC," $35,400,000.00 "," $161,800,000.00 "," $7,000.00 ",121 mins,6.8,5.9,0,8
+"July, 1974",The Man with the Golden Gun,Roger Moore,AMC," $21,000,000.00 "," $97,600,000.00 "," $7,000.00 ",125 mins,6.7,5.1,0,1
+"July, 1974",The Man with the Golden Gun,Roger Moore,AMC," $21,000,000.00 "," $97,600,000.00 "," $7,000.00 ",125 mins,6.7,5.1,0,1
+"April, 1977",The Spy Who Loved Me,Roger Moore,Lotus," $46,800,000.00 "," $185,400,000.00 "," $14,000.00 ",125 mins,,,1,31
+"October, 1979",Moonraker,Roger Moore,Lotus," $70,300,000.00 "," $210,300,000.00 "," $31,000.00 ",126 mins,6.2,5.7,1,12
+"June, 1981",For Your Eyes Only,Roger MOORE,Citroen," $54,800,000.00 "," $195,300,000.00 "," $28,000.00 ",127 mins,6.8,6.3,0,18
+"March, 1983",Octopussy,Roger Moore,Bajaj," $67,900,000.00 "," $187,500,000.00 "," $27,500.00 ",131 mins,6.5,5.3,0,15
+"October, 1985",A View to a Kill,Roger Moore,Rolls Royce," $50,327,960.00 "," $152,627,960.00 "," $30,000.00 ",131 mins,6.2,4.7,0,5
+"May, 1987",The Living Daylights,Timothy Dalton,Rolls Royce," $51,185,000.00 "," $191,200,000.00 "," $40,000.00 ",130 mins,6.7,6.3,2,13
+"May, 1987",The Living Daylights,Timothy Dalton,Rolls Royce," $51,185,000.00 "," $191,200,000.00 "," $40,000.00 ",130 mins,6.7,6.3,2,13
+"January, 1989",License to Kill,Timothy Dalton,Aston Martin," $34,667,015.00 "," $156,167,015.00 "," $42,000.00 ",133 mins,6.5,6,1,10
+"September, 1995",GoldenEye,Pierce Brosnan,BMW," $106,429,941.00 "," $356,429,941.00 "," $60,000.00 ",130 mins,7.2,6.9,1,47
+"July, 1997",Tomorrow Never Dies,Pierce Brosnan,Aston Martin," $125,304,276.00 "," $339,504,276.00 "," $110,000.00 ",119 mins,6.4,6,1,30
+"June, 1999",The World Is Not Enough,Pierce Brosnan,BMW," $126,930,660.00 "," $361,730,660.00 "," $135,000.00 ",128 mins,6.3,5.7,1,27
+"August, 2002",Die Another Day,Pierce Brosnan,Aston Martin," $160,942,139.00 "," $431,942,139.00 "," $142,000.00 ",133 mins,6,6.1,2,31
+"February, 2006",Casino Royale,Daniel Craig,Astin Martin," $167,365,000.00 "," $596,365,000.00 "," $102,000.00 ",144 mins,7.9,7.8,3,11
+"December, 2008",Quantum of Solace,Daniel Craig,Aston Martin," $169,368,427.00 "," $591,692,078.00 "," $230,000.00 ",106 mins,6.7,6.1,-6,16
+"November, 2012",Skyfall,Daniel Craig,Astin Martin," $304,360,277.00 "," $1,108,561,108.00 "," $200,000.00 ",143 mins,7.8,8.2,1,26
+"September, 2015",Spectre,Daniel Craig,Aston Martin," $200,074,175.00 "," $879,620,923.00 "," $245,000.00 ",148 mins,6.8,6.4,1,30
+"November, 2021",No Time to Die,Daniel Craig,Aston Martin," $160,891,007.00 "," $759,959,662.00 "," $275,000.00 ",163 mins,7.3,7.3,1,14
diff --git a/james_bond_data.json b/james_bond_data.json
new file mode 100644
index 0000000000..852810b38e
--- /dev/null
+++ b/james_bond_data.json
@@ -0,0 +1 @@
+{"Release":{"0":"June, 1962","1":"August, 1963","2":"May, 1964","3":"September, 1965","4":"November, 1967","5":"July, 1969","6":"March, 1971","7":"August, 1973","8":"July, 1974","9":"July, 1974","10":"April, 1977","11":"October, 1979","12":"June, 1981","13":"March, 1983","14":"October, 1985","15":"May, 1987","16":"May, 1987","17":"January, 1989","18":"September, 1995","19":"July, 1997","20":"June, 1999","21":"August, 2002","22":"February, 2006","23":"December, 2008","24":"November, 2012","25":"September, 2015","26":"November, 2021"},"Movie":{"0":"Dr. No","1":"From Russia with Love","2":"Goldfinger","3":"Thunderball","4":"You Only Live Twice","5":"On Her Majesty's Secret Service","6":"Diamonds Are Forever","7":"Live and Let Die","8":"The Man with the Golden Gun","9":"The Man with the Golden Gun","10":"The Spy Who Loved Me","11":"Moonraker","12":"For Your Eyes Only","13":"Octopussy","14":"A View to a Kill","15":"The Living Daylights","16":"The Living Daylights","17":"License to Kill","18":"GoldenEye","19":"Tomorrow Never Dies","20":"The World Is Not Enough","21":"Die Another Day","22":"Casino Royale","23":"Quantum of Solace","24":"Skyfall","25":"Spectre","26":"No Time to Die"},"Bond":{"0":"Sean Connery","1":"Sean Connery","2":"Sean Connery","3":"Sean Connery","4":"Sean Connery","5":"George Lazenby","6":"Shawn Connery","7":"Roger Moore","8":"Roger Moore","9":"Roger Moore","10":"Roger Moore","11":"Roger Moore","12":"Roger MOORE","13":"Roger Moore","14":"Roger Moore","15":"Timothy Dalton","16":"Timothy Dalton","17":"Timothy Dalton","18":"Pierce Brosnan","19":"Pierce Brosnan","20":"Pierce Brosnan","21":"Pierce Brosnan","22":"Daniel Craig","23":"Daniel Craig","24":"Daniel Craig","25":"Daniel Craig","26":"Daniel Craig"},"Bond_Car_MFG":{"0":"Sunbeam","1":"Bently","2":"Aston Martin","3":"Aston Martin","4":"Toyota","5":"Mercury","6":"Ford","7":"AMC","8":"AMC","9":"AMC","10":"Lotus","11":"Lotus","12":"Citroen","13":"Bajaj","14":"Rolls Royce","15":"Rolls Royce","16":"Rolls Royce","17":"Aston Martin","18":"BMW","19":"Aston Martin","20":"BMW","21":"Aston Martin","22":"Astin Martin","23":"Aston Martin","24":"Astin Martin","25":"Aston Martin","26":"Aston Martin"},"US_Gross":{"0":" $16,067,035.00 ","1":" $24,800,000.00 ","2":" $51,100,000.00 ","3":" $63,600,000.00 ","4":" $43,100,000.00 ","5":" $22,800,000.00 ","6":" $43,800,000.00 ","7":" $35,400,000.00 ","8":" $21,000,000.00 ","9":" $21,000,000.00 ","10":" $46,800,000.00 ","11":" $70,300,000.00 ","12":" $54,800,000.00 ","13":" $67,900,000.00 ","14":" $50,327,960.00 ","15":" $51,185,000.00 ","16":" $51,185,000.00 ","17":" $34,667,015.00 ","18":" $106,429,941.00 ","19":" $125,304,276.00 ","20":" $126,930,660.00 ","21":" $160,942,139.00 ","22":" $167,365,000.00 ","23":" $169,368,427.00 ","24":" $304,360,277.00 ","25":" $200,074,175.00 ","26":" $160,891,007.00 "},"World_Gross":{"0":" $59,567,035.00 ","1":" $78,900,000.00 ","2":" $124,900,000.00 ","3":" $141,200,000.00 ","4":" $111,600,000.00 ","5":" $82,000,000.00 ","6":" $116,000,000.00 ","7":" $161,800,000.00 ","8":" $97,600,000.00 ","9":" $97,600,000.00 ","10":" $185,400,000.00 ","11":" $210,300,000.00 ","12":" $195,300,000.00 ","13":" $187,500,000.00 ","14":" $152,627,960.00 ","15":" $191,200,000.00 ","16":" $191,200,000.00 ","17":" $156,167,015.00 ","18":" $356,429,941.00 ","19":" $339,504,276.00 ","20":" $361,730,660.00 ","21":" $431,942,139.00 ","22":" $596,365,000.00 ","23":" $591,692,078.00 ","24":" $1,108,561,108.00 ","25":" $879,620,923.00 ","26":" $759,959,662.00 "},"Budget ($ 000s)":{"0":" $1,000.00 ","1":" $2,000.00 ","2":" $3,000.00 ","3":" $9,000.00 ","4":" $9,500.00 ","5":" $8,000.00 ","6":" $7,200.00 ","7":" $7,000.00 ","8":" $7,000.00 ","9":" $7,000.00 ","10":" $14,000.00 ","11":" $31,000.00 ","12":" $28,000.00 ","13":" $27,500.00 ","14":" $30,000.00 ","15":" $40,000.00 ","16":" $40,000.00 ","17":" $42,000.00 ","18":" $60,000.00 ","19":" $110,000.00 ","20":" $135,000.00 ","21":" $142,000.00 ","22":" $102,000.00 ","23":" $230,000.00 ","24":" $200,000.00 ","25":" $245,000.00 ","26":" $275,000.00 "},"Film_Length":{"0":"110 mins","1":"115 mins","2":"110 mins","3":"130 mins","4":"117 mins","5":"142 mins","6":"1200 mins","7":"121 mins","8":"125 mins","9":"125 mins","10":"125 mins","11":"126 mins","12":"127 mins","13":"131 mins","14":"131 mins","15":"130 mins","16":"130 mins","17":"133 mins","18":"130 mins","19":"119 mins","20":"128 mins","21":"133 mins","22":"144 mins","23":"106 mins","24":"143 mins","25":"148 mins","26":"163 mins"},"Avg_User_IMDB":{"0":7.3,"1":7.5,"2":7.8,"3":7.0,"4":6.9,"5":6.8,"6":6.7,"7":6.8,"8":6.7,"9":6.7,"10":null,"11":6.2,"12":6.8,"13":6.5,"14":6.2,"15":6.7,"16":6.7,"17":6.5,"18":7.2,"19":6.4,"20":6.3,"21":6.0,"22":7.9,"23":6.7,"24":7.8,"25":6.8,"26":7.3},"Avg_User_Rtn_Tom":{"0":7.7,"1":8.0,"2":8.4,"3":6.8,"4":6.3,"5":6.7,"6":6.3,"7":5.9,"8":5.1,"9":5.1,"10":null,"11":5.7,"12":6.3,"13":5.3,"14":4.7,"15":6.3,"16":6.3,"17":6.0,"18":6.9,"19":6.0,"20":5.7,"21":6.1,"22":7.8,"23":6.1,"24":8.2,"25":6.4,"26":7.3},"Martinis":{"0":2,"1":0,"2":1,"3":0,"4":1,"5":1,"6":0,"7":0,"8":0,"9":0,"10":1,"11":1,"12":0,"13":0,"14":0,"15":2,"16":2,"17":1,"18":1,"19":1,"20":1,"21":2,"22":3,"23":-6,"24":1,"25":1,"26":1},"Kills_Bond":{"0":4,"1":11,"2":9,"3":20,"4":21,"5":5,"6":7,"7":8,"8":1,"9":1,"10":31,"11":12,"12":18,"13":15,"14":5,"15":13,"16":13,"17":10,"18":47,"19":30,"20":27,"21":31,"22":11,"23":16,"24":26,"25":30,"26":14}}
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HcmV?d00001

diff --git a/james_bond_data_cleansed.csv b/james_bond_data_cleansed.csv
new file mode 100644
index 0000000000..04d4f213b9
--- /dev/null
+++ b/james_bond_data_cleansed.csv
@@ -0,0 +1,26 @@
+Release,Movie,Bond,Bond_Car_MFG,US_Gross,World_Gross,Budget,Film_Length,Avg_User_IMDB,Avg_User_Rtn_Tom,Martinis,Kills_Bond,Release_Year
+1962-06-01,Dr. No,Sean Connery,Sunbeam,16067035.0,59567035.0,1000000.0,110,7.3,7.7,2,4,1962
+1963-08-01,From Russia with Love,Sean Connery,Bently,24800000.0,78900000.0,2000000.0,115,7.5,8.0,0,11,1963
+1964-05-01,Goldfinger,Sean Connery,Aston Martin,51100000.0,124900000.0,3000000.0,110,7.8,8.4,1,9,1964
+1965-09-01,Thunderball,Sean Connery,Aston Martin,63600000.0,141200000.0,9000000.0,130,7.0,6.8,0,20,1965
+1967-11-01,You Only Live Twice,Sean Connery,Toyota,43100000.0,111600000.0,9500000.0,117,6.9,6.3,1,21,1967
+1969-07-01,On Her Majesty's Secret Service,George Lazenby,Mercury,22800000.0,82000000.0,8000000.0,142,6.8,6.7,1,5,1969
+1971-03-01,Diamonds Are Forever,Sean Connery,Ford,43800000.0,116000000.0,7200000.0,120,6.7,6.3,0,7,1971
+1973-08-01,Live and Let Die,Roger Moore,AMC,35400000.0,161800000.0,7000000.0,121,6.8,5.9,0,8,1973
+1974-07-01,The Man with the Golden Gun,Roger Moore,AMC,21000000.0,97600000.0,7000000.0,125,6.7,5.1,0,1,1974
+1977-04-01,The Spy Who Loved Me,Roger Moore,Lotus,46800000.0,185400000.0,14000000.0,125,7.1,6.8,1,31,1977
+1979-10-01,Moonraker,Roger Moore,Lotus,70300000.0,210300000.0,31000000.0,126,6.2,5.7,1,12,1979
+1981-06-01,For Your Eyes Only,Roger Moore,Citroen,54800000.0,195300000.0,28000000.0,127,6.8,6.3,0,18,1981
+1983-03-01,Octopussy,Roger Moore,Bajaj,67900000.0,187500000.0,27500000.0,131,6.5,5.3,0,15,1983
+1985-10-01,A View to a Kill,Roger Moore,Rolls Royce,50327960.0,152627960.0,30000000.0,131,6.2,4.7,0,5,1985
+1987-05-01,The Living Daylights,Timothy Dalton,Rolls Royce,51185000.0,191200000.0,40000000.0,130,6.7,6.3,2,13,1987
+1989-01-01,License to Kill,Timothy Dalton,Aston Martin,34667015.0,156167015.0,42000000.0,133,6.5,6.0,1,10,1989
+1995-09-01,GoldenEye,Pierce Brosnan,BMW,106429941.0,356429941.0,60000000.0,130,7.2,6.9,1,47,1995
+1997-07-01,Tomorrow Never Dies,Pierce Brosnan,Aston Martin,125304276.0,339504276.0,110000000.0,119,6.4,6.0,1,30,1997
+1999-06-01,The World Is Not Enough,Pierce Brosnan,BMW,126930660.0,361730660.0,135000000.0,128,6.3,5.7,1,27,1999
+2002-08-01,Die Another Day,Pierce Brosnan,Aston Martin,160942139.0,431942139.0,142000000.0,133,6.0,6.1,2,31,2002
+2006-02-01,Casino Royale,Daniel Craig,Aston Martin,167365000.0,596365000.0,102000000.0,144,7.9,7.8,3,11,2006
+2008-12-01,Quantum of Solace,Daniel Craig,Aston Martin,169368427.0,591692078.0,230000000.0,106,6.7,6.1,6,16,2008
+2012-11-01,Skyfall,Daniel Craig,Aston Martin,304360277.0,1108561108.0,200000000.0,143,7.8,8.2,1,26,2012
+2015-09-01,Spectre,Daniel Craig,Aston Martin,200074175.0,879620923.0,245000000.0,148,6.8,6.4,1,30,2015
+2021-11-01,No Time to Die,Daniel Craig,Aston Martin,160891007.0,759959662.0,275000000.0,163,7.3,7.3,1,14,2021

From 04f5a12a883bd694e2b413113afb316c71925124 Mon Sep 17 00:00:00 2001
From: gahjelle <geirarne@gmail.com>
Date: Wed, 15 Nov 2023 12:34:23 +0100
Subject: [PATCH 2/5] Move data analysis files into separate folder

---
 .../Solution.ipynb                                  |   0
 .../james_bond_data.csv                             |   0
 .../james_bond_data.json                            |   0
 .../james_bond_data.parquet                         | Bin
 .../james_bond_data.xlsx                            | Bin
 .../james_bond_data_cleansed.csv                    |   0
 6 files changed, 0 insertions(+), 0 deletions(-)
 rename Solution.ipynb => python-data-analysis/Solution.ipynb (100%)
 rename james_bond_data.csv => python-data-analysis/james_bond_data.csv (100%)
 rename james_bond_data.json => python-data-analysis/james_bond_data.json (100%)
 rename james_bond_data.parquet => python-data-analysis/james_bond_data.parquet (100%)
 rename james_bond_data.xlsx => python-data-analysis/james_bond_data.xlsx (100%)
 rename james_bond_data_cleansed.csv => python-data-analysis/james_bond_data_cleansed.csv (100%)

diff --git a/Solution.ipynb b/python-data-analysis/Solution.ipynb
similarity index 100%
rename from Solution.ipynb
rename to python-data-analysis/Solution.ipynb
diff --git a/james_bond_data.csv b/python-data-analysis/james_bond_data.csv
similarity index 100%
rename from james_bond_data.csv
rename to python-data-analysis/james_bond_data.csv
diff --git a/james_bond_data.json b/python-data-analysis/james_bond_data.json
similarity index 100%
rename from james_bond_data.json
rename to python-data-analysis/james_bond_data.json
diff --git a/james_bond_data.parquet b/python-data-analysis/james_bond_data.parquet
similarity index 100%
rename from james_bond_data.parquet
rename to python-data-analysis/james_bond_data.parquet
diff --git a/james_bond_data.xlsx b/python-data-analysis/james_bond_data.xlsx
similarity index 100%
rename from james_bond_data.xlsx
rename to python-data-analysis/james_bond_data.xlsx
diff --git a/james_bond_data_cleansed.csv b/python-data-analysis/james_bond_data_cleansed.csv
similarity index 100%
rename from james_bond_data_cleansed.csv
rename to python-data-analysis/james_bond_data_cleansed.csv

From 085ad86a93b87cf21c2d3329323ea1f0a19de77f Mon Sep 17 00:00:00 2001
From: gahjelle <geirarne@gmail.com>
Date: Wed, 15 Nov 2023 12:34:38 +0100
Subject: [PATCH 3/5] Format notebook with Black

---
 python-data-analysis/Solution.ipynb | 25 +++++++++++++++++--------
 1 file changed, 17 insertions(+), 8 deletions(-)

diff --git a/python-data-analysis/Solution.ipynb b/python-data-analysis/Solution.ipynb
index 356b6682e7..afaa57346b 100644
--- a/python-data-analysis/Solution.ipynb
+++ b/python-data-analysis/Solution.ipynb
@@ -200,7 +200,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "james_bond_df[[\"US_Gross\", \"World_Gross\", \"Budget ($ 000s)\", \"Film_Length\"]].head()"
+    "james_bond_df[\n",
+    "    [\"US_Gross\", \"World_Gross\", \"Budget ($ 000s)\", \"Film_Length\"]\n",
+    "].head()"
    ]
   },
   {
@@ -242,7 +244,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "james_bond_df[\"Release\"] = pd.to_datetime(james_bond_df[\"Release\"], format=\"%B, %Y\")\n",
+    "james_bond_df[\"Release\"] = pd.to_datetime(\n",
+    "    james_bond_df[\"Release\"], format=\"%B, %Y\"\n",
+    ")\n",
     "james_bond_df[\"Release_Year\"] = james_bond_df[\"Release\"].dt.year"
    ]
   },
@@ -290,7 +294,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
     "duplicate_movies = [\"The Man with the Golden Gun\", \"The Living Daylights\"]\n",
     "james_bond_df[james_bond_df[\"Movie\"].isin(duplicate_movies)]"
    ]
@@ -361,7 +364,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "james_bond_df[\"Bond_Car_MFG\"] = james_bond_df[\"Bond_Car_MFG\"].str.replace(\"Astin\", \"Aston\")"
+    "james_bond_df[\"Bond_Car_MFG\"] = james_bond_df[\"Bond_Car_MFG\"].str.replace(\n",
+    "    \"Astin\", \"Aston\"\n",
+    ")"
    ]
   },
   {
@@ -446,8 +451,8 @@
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "#x = james_bond_df[\"Avg_User_IMDB\"].values.reshape(-1, 1)\n",
-    "#y = james_bond_df[\"Avg_User_Rtn_Tom\"].values.reshape(-1, 1)\n",
+    "# x = james_bond_df[\"Avg_User_IMDB\"].values.reshape(-1, 1)\n",
+    "# y = james_bond_df[\"Avg_User_Rtn_Tom\"].values.reshape(-1, 1)\n",
     "\n",
     "x = james_bond_df[\"Avg_User_IMDB\"].array.reshape(-1, 1)\n",
     "y = james_bond_df[\"Avg_User_Rtn_Tom\"].array.reshape(-1, 1)\n",
@@ -475,7 +480,9 @@
     "model.fit(x, y)\n",
     "\n",
     "r_squared = f\"R-Squared: {round(model.score(x, y),2)}\"\n",
-    "best_fit_equation = f\"y={round(model.coef_[0][0], 4)}x{round(model.intercept_[0], 4)}\"\n",
+    "best_fit_equation = (\n",
+    "    f\"y={round(model.coef_[0][0], 4)}x{round(model.intercept_[0], 4)}\"\n",
+    ")\n",
     "y_pred = model.predict(x)\n",
     "\n",
     "plt.title(\"Scatter Plot of Ratings.\")\n",
@@ -502,7 +509,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "film_length_groups = james_bond_df[\"Film_Length\"].value_counts(bins=7, sort=False)\n",
+    "film_length_groups = james_bond_df[\"Film_Length\"].value_counts(\n",
+    "    bins=7, sort=False\n",
+    ")\n",
     "film_length_groups.plot(kind=\"bar\", title=\"Film Length Distribution\").set(\n",
     "    xlabel=\"Time Range (mins)\", ylabel=\"Count\"\n",
     ")"

From 022fbe3b9fc623c24a2563c904a7ca955fc2a7dd Mon Sep 17 00:00:00 2001
From: gahjelle <geirarne@gmail.com>
Date: Wed, 15 Nov 2023 12:37:02 +0100
Subject: [PATCH 4/5] Add README file

---
 python-data-analysis/README.md | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 python-data-analysis/README.md

diff --git a/python-data-analysis/README.md b/python-data-analysis/README.md
new file mode 100644
index 0000000000..4284962772
--- /dev/null
+++ b/python-data-analysis/README.md
@@ -0,0 +1 @@
+# Using Python for Data Analysis

From 87e24405841d35a380dfb91bd51039a52558beb8 Mon Sep 17 00:00:00 2001
From: eyrei123 <88923476+eyrei123@users.noreply.github.com>
Date: Sun, 19 Nov 2023 15:38:54 +0000
Subject: [PATCH 5/5] Delete python-data-analysis/Solution.ipynb

Removal of solution notebook.  Now obsolete.
---
 python-data-analysis/Solution.ipynb | 576 ----------------------------
 1 file changed, 576 deletions(-)
 delete mode 100644 python-data-analysis/Solution.ipynb

diff --git a/python-data-analysis/Solution.ipynb b/python-data-analysis/Solution.ipynb
deleted file mode 100644
index afaa57346b..0000000000
--- a/python-data-analysis/Solution.ipynb
+++ /dev/null
@@ -1,576 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "83ad2114-5ed8-4a90-85fa-adea5eda4392",
-   "metadata": {},
-   "source": [
-    "## Reading Data From CSV Files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a360772e-7829-4c15-9af9-d4596efc7351",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "! python -m pip install pandas"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1148ca58-9a4d-42ed-a43a-e3b8b3359c2d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "james_bond_df = pd.read_csv(\"james_bond_data.csv\")\n",
-    "james_bond_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e47c1f9b-b390-4035-956b-622615b57f32",
-   "metadata": {},
-   "source": [
-    "## Reading Data From Other Sources"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "47a0e4a6-0ed9-4253-9833-0ad22c49b968",
-   "metadata": {},
-   "source": [
-    "### Reading Excel"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a0364b81-64a0-4098-89fc-e58bd6d68257",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "! python -m pip install openpyxl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8302139f-52dc-4f95-aa9a-96040ae5d82b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import openpyxl\n",
-    "import pandas as pd\n",
-    "\n",
-    "james_bond_df_excel = pd.read_excel(\"james_bond_data.xlsx\")\n",
-    "james_bond_df_excel.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1d85aee9-cfeb-460b-9fe8-f3c7e7dfb764",
-   "metadata": {},
-   "source": [
-    "### Reading JSON"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7465cd11-dad4-4741-9372-f825b28c33d6",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "james_bond_df_json = pd.read_json(\"james_bond_data.json\")\n",
-    "james_bond_df_json.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "69f884c2-92e8-4db3-bd63-84007f654808",
-   "metadata": {},
-   "source": [
-    "### Scraping HTML"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b902722d-9648-4124-80b0-64004342170d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!python -m pip install lxml"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1fb2ff9c-3030-4f4a-be30-c2ab68452a21",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "james_bond_df_html = pd.read_html(\n",
-    "    \"https://en.wikipedia.org/wiki/List_of_James_Bond_novels_and_short_stories\"\n",
-    ")\n",
-    "james_bond_df_html[1].head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "be4a1143-c966-4056-8a5e-3bdebe2a9b1f",
-   "metadata": {},
-   "source": [
-    "### Reading Parquet"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f36ef600-e6ba-4cc6-9ee3-0cbf369a4be2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!python -m pip install pyarrow"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bd6f496d-aa94-43ce-9e97-6f01108df47b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "james_bond_df_parquet = pd.read_parquet(\"james_bond_data.parquet\")\n",
-    "james_bond_df_parquet.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e432b28e-257b-422b-b2f8-06f41608391b",
-   "metadata": {},
-   "source": [
-    "## Dealing With Missing Data and Invalid Data Types"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "38eb1abb-9f89-4a53-9e77-f7c71dbeff18",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "james_bond_df.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b29d5a34-c930-4ce2-898c-b9e8aa7f771d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "james_bond_df[james_bond_df.isna().any(axis=\"columns\")]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1db9201a-11c1-4cdd-9625-d70cee736191",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "james_bond_df.at[10, \"Avg_User_IMDB\"] = 7.1\n",
-    "james_bond_df.at[10, \"Avg_User_Rtn_Tom\"] = 6.8"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "001996e3-2fce-4228-a873-b78eef613bba",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "james_bond_df[\n",
-    "    [\"US_Gross\", \"World_Gross\", \"Budget ($ 000s)\", \"Film_Length\"]\n",
-    "].head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "880e4710-1c11-4de2-a2c3-97a9672ce6f7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "james_bond_df[\"US_Gross\"] = (\n",
-    "    james_bond_df[\"US_Gross\"].replace(\"[$,]\", \"\", regex=True)\n",
-    ").astype(float)\n",
-    "\n",
-    "james_bond_df[\"World_Gross\"] = (\n",
-    "    james_bond_df[\"World_Gross\"].replace(\"[$,]\", \"\", regex=True)\n",
-    ").astype(float)\n",
-    "\n",
-    "james_bond_df[\"Budget ($ 000s)\"] = (\n",
-    "    james_bond_df[\"Budget ($ 000s)\"].replace(\"[$,]\", \"\", regex=True)\n",
-    ").astype(float)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ae9c1d1b-a620-43c5-a199-eb6a7bff7ce2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "james_bond_df[\"Film_Length\"] = (\n",
-    "    james_bond_df[\"Film_Length\"].str.rstrip(\"mins\").astype(int)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ed0ead0e-7310-4c82-86d5-2480a95f1525",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "james_bond_df[\"Release\"] = pd.to_datetime(\n",
-    "    james_bond_df[\"Release\"], format=\"%B, %Y\"\n",
-    ")\n",
-    "james_bond_df[\"Release_Year\"] = james_bond_df[\"Release\"].dt.year"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "89653d81-3bcd-4078-83cb-ad4b2fa560e6",
-   "metadata": {},
-   "source": [
-    "## Dealing With Inconsistencies in Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bc483320-7895-4368-a672-b98f8d0c9755",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "james_bond_df[\"Budget ($ 000s)\"] = james_bond_df[\"Budget ($ 000s)\"] * 1000\n",
-    "james_bond_df.rename(columns={\"Budget ($ 000s)\": \"Budget\"}, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3e129b32-5e66-41cb-b938-8fd58bb94116",
-   "metadata": {},
-   "source": [
-    "## Removing Duplicate Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "be7aad8b-ef3f-48a6-a9a0-de909133921f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "james_bond_df[\"Movie\"].value_counts().head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "20067efb-e7c7-4690-b483-1d29847ad24f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "duplicate_movies = [\"The Man with the Golden Gun\", \"The Living Daylights\"]\n",
-    "james_bond_df[james_bond_df[\"Movie\"].isin(duplicate_movies)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c98c7640-1472-4869-9fdd-f070d665ae1d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "james_bond_df.drop_duplicates(inplace=True, ignore_index=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8bdaa8b1-9f2e-46a5-b53a-c1ae4c201c99",
-   "metadata": {},
-   "source": [
-    "## Removing Typos"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e442e51a-28fd-42d7-94b0-aaf1abe5d9a8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "james_bond_df[\"Bond\"].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f9863aa7-b5db-4ab1-be63-727ff437b63b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "james_bond_df[\"Bond\"] = james_bond_df[\"Bond\"].str.replace(\"Shawn\", \"Sean\")\n",
-    "james_bond_df[\"Bond\"] = james_bond_df[\"Bond\"].str.replace(\"MOORE\", \"Moore\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "25da4a99-6b90-4785-aaa4-48bed819e9be",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "james_bond_df[\"Bond\"].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a26b138d-72e5-4e15-a875-ee65023545d1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "james_bond_df[\"Bond_Car_MFG\"].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8260f6b1-6d7f-4338-95b7-8946d69a92e2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "james_bond_df[\"Bond_Car_MFG\"] = james_bond_df[\"Bond_Car_MFG\"].str.replace(\n",
-    "    \"Astin\", \"Aston\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "50c80bc8-fdb9-4c28-af5a-cd6b66c7a01d",
-   "metadata": {},
-   "source": [
-    "## Checking for Invalid Outliers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "81338285-2067-46fc-82f8-360d92ec7153",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "james_bond_df[[\"Film_Length\", \"Martinis\"]].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d84acfc8-fb2d-4c45-bfac-20469fc1de97",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "james_bond_df[\"Film_Length\"].replace(1200, 120, inplace=True)\n",
-    "james_bond_df[\"Martinis\"].replace(-6, 6, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "52db1351-36ed-4104-a999-345ebbc62214",
-   "metadata": {},
-   "source": [
-    "## Storing Your Cleansed Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "575a774e-6913-41fb-8ff9-4d786f478007",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "james_bond_df.to_csv(\"james_bond_data_cleansed.csv\", index=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f50918ee-e61f-46b2-b0c2-1ffa2c62bbc0",
-   "metadata": {},
-   "source": [
-    "## Using Python for Data Analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bee6d6cb-e418-4c1d-8b75-604b9ab2e63d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!python -m pip install matplotlib scikit-learn numpy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0246dcb1-88fc-4a3e-acc1-571037390e09",
-   "metadata": {},
-   "source": [
-    "## Performing a Regression Analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "27d0a3dd-e71a-4b8a-883c-40cb5c001f7e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# x = james_bond_df[\"Avg_User_IMDB\"].values.reshape(-1, 1)\n",
-    "# y = james_bond_df[\"Avg_User_Rtn_Tom\"].values.reshape(-1, 1)\n",
-    "\n",
-    "x = james_bond_df[\"Avg_User_IMDB\"].array.reshape(-1, 1)\n",
-    "y = james_bond_df[\"Avg_User_Rtn_Tom\"].array.reshape(-1, 1)\n",
-    "\n",
-    "plt.title(\"Scatter Plot of Ratings.\")\n",
-    "plt.xlabel(\"Average IMDb Rating\")\n",
-    "plt.ylabel(\"Average Rotten Tomatoes Rating\")\n",
-    "plt.scatter(x, y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "669fb9d7-d744-4e6b-899e-a69aebec53ed",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.linear_model import LinearRegression\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "x = james_bond_df[\"Avg_User_IMDB\"].array.reshape(-1, 1)\n",
-    "y = james_bond_df[\"Avg_User_Rtn_Tom\"].array.reshape(-1, 1)\n",
-    "\n",
-    "model = LinearRegression()\n",
-    "model.fit(x, y)\n",
-    "\n",
-    "r_squared = f\"R-Squared: {round(model.score(x, y),2)}\"\n",
-    "best_fit_equation = (\n",
-    "    f\"y={round(model.coef_[0][0], 4)}x{round(model.intercept_[0], 4)}\"\n",
-    ")\n",
-    "y_pred = model.predict(x)\n",
-    "\n",
-    "plt.title(\"Scatter Plot of Ratings.\")\n",
-    "plt.xlabel(\"Average IMDb Rating\")\n",
-    "plt.ylabel(\"Average Rotten Tomatoes Rating\")\n",
-    "plt.scatter(x, y)\n",
-    "plt.text(7.25, 5.5, r_squared, fontsize=10)\n",
-    "plt.text(7.25, 7, best_fit_equation, fontsize=10)\n",
-    "plt.plot(x, y_pred, color=\"red\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b38df412-c320-49fb-93ae-e253405537a8",
-   "metadata": {},
-   "source": [
-    "## Investigating a Statistical Distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "938e5942-e57f-4e41-99f1-215cfb37d0df",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "film_length_groups = james_bond_df[\"Film_Length\"].value_counts(\n",
-    "    bins=7, sort=False\n",
-    ")\n",
-    "film_length_groups.plot(kind=\"bar\", title=\"Film Length Distribution\").set(\n",
-    "    xlabel=\"Time Range (mins)\", ylabel=\"Count\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ff4e9955-baf4-48eb-b032-fbf55f439194",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "james_bond_df[\"Film_Length\"].agg([\"mean\", \"max\", \"min\", \"std\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b14c433-c3a6-4484-bc0a-26825bd1e870",
-   "metadata": {},
-   "source": [
-    "## Finding No Relationship"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2bb83374-347f-4cf6-bc21-8180a003371d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x = james_bond_df[\"Avg_User_IMDB\"].array.reshape(-1, 1)\n",
-    "y = james_bond_df[\"Kills_Bond\"].array.reshape(-1, 1)\n",
-    "\n",
-    "plt.title(\"Scatter Plot of Kills vs Ratings.\")\n",
-    "plt.xlabel(\"Average IMDb Rating\")\n",
-    "plt.ylabel(\"Kills By Bond\")\n",
-    "plt.scatter(x, y)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.12.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}