Project : Facial Expression Recognition with PyTorch Issue #697

Basics of Power Bi/Netflix/Readme.md

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+# Netflix Power BI Report
+
+## Overview
+
+This project involves analyzing Netflix data using Power BI to uncover insights and trends. The report includes various visualizations that highlight key metrics such as viewing trends, genre popularity, and user demographics.
+
+## Screenshots
+
+![Netflix_Report](image.png)
+![Sentiments](image-1.png)
+
+## Features
+
+- **Content Analysis**: Visualization of content distribution by genre, country, and release year.
+- **Trend Analysis**: Identification of trends in content production and user ratings over time.
+- **Top Content**: Highlighting the most popular shows and movies based on user ratings and watch counts.
+- **User Demographics**: Insights into user preferences by region and age group.
+- **Top Casts**: Highlighting the top Casts based on total contents.
+
+- ## Files
+- **Netflix.pbix**: The main Power BI file containing all the visualizations and dashboards.
+- **Netflix(Dataset)**: The folder containing the dataset used for the analysis.
+
+## Steps
+
+1. **Open the Power BI File**: Download and open the `Netflix_PowerBI_Report.pbix` file in Power BI Desktop.
+2. **Explore the Visualizations**: Navigate through the different pages to explore various insights.
+3. **Customize as Needed**: Modify the visualizations and data as needed to suit your requirements.
+
+## Getting Started
+
+To get started with the project, follow these steps:
+
+1. **Clone the Repository**:
+
+   ```sh
+   git clone https://github.com/your-username/Netflix-PowerBI-Report.git
+   ```
+
+2. **Open the Power BI Report**:
+   - Open `Netflix_PowerBI_Report.pbix` in Power BI Desktop.
+
+**Author**: Shubham Nakum  
+**Contact**: [email protected]

Basics of the Python/Basic of Python/2.TUPLES_LECTURE/DICTIONARIES.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 style=\"color:blue\">DICTIONARIES</h1>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<ul type=\"disc\">\n",
+    "<li>Consists of keys and key values separated by a colon</li>\n",
+    "<li> Dictionaries, also called dict can contain,\n",
+    "<ul type=\"square\">\n",
+    "<li>Strings and numbers</li>\n",
+    "<li>Tuples</li>\n",
+    "<li>lists</li>\n",
+    "<li>sets</li>\n",
+    "<li>dataframes</li>\n",
+    "<li>series</li>\n",
+    "<li>nested dictionaries</li>\n",
+    "</ul>\n",
+    "</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d1={}\n",
+    "type(d1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>Adding elements into dictionary</h2>\n",
+    "<p> Elements can be added to dictionary as a key, value pairs in the following way:</p>\n",
+    "<code> dictionary[key]=value </code>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'A': 10, 'B': 20, 'C': 30}\n"
+     ]
+    }
+   ],
+   "source": [
+    "d1['A']=10\n",
+    "d1['B']=20\n",
+    "d1['C']=30\n",
+    "print(d1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>Changing values in dictionary</h2>\n",
+    "<p>Dictionaries are mutable, meaning the values referenced by dictionary keys can be changed. This is illustrated in the following example...</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dictionaries before changing value of 'C' {'A': 10, 'B': 20, 'C': 1000}\n",
+      "Dictionaries after changing value of 'C' {'A': 10, 'B': 20, 'C': 1000}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Dictionaries before changing value of 'C'\",d1)\n",
+    "d1[\"C\"] = 1000\n",
+    "print(\"Dictionaries after changing value of 'C'\",d1)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<hr><br>We can also update a dictionary with elements of another dictionary as follows..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'robot': 40, 'car': 50, 'A': 10, 'B': 20, 'C': 1000}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "toys = {\"robot\": 40, \"car\": 50}\n",
+    "toys.update(d1)\n",
+    "toys\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2> Accessing the keys and values seperately</h2>\n",
+    "We can get the keys and values seperately using keys() and values() methods respectively. They can converted to a list by wrapping it inside the list() method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_values([40, 50, 20, 1000])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "toys.values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['robot', 'car', 'A', 'B', 'C'])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(toys.keys())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>Deleting an element from Dictionary</h2>\n",
+    "<p>To remove an key-value pair, we use pop(key)</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'robot': 40, 'car': 50, 'B': 20, 'C': 1000}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "toys.pop(\"A\")\n",
+    "toys\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "More examples..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'k1': [0, 1, 2, 3],\n",
+       " 'k2': (4, 5, 6, 7),\n",
+       " 'k3': (1, 2, 3, {'k4': [1, 2, 3, 'found you!', 4, 5]})}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "maze = {\"k1\": list(range(4)), \"k2\": tuple(range(4,8)),\n",
+    "\"k3\": (1,2, 3, {\"k4\": [1,2,3, \"found you!\", 4, 5]})}\n",
+    "maze\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'found you!'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "maze[\"k3\"][3][\"k4\"][3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "vars",
+   "language": "python",
+   "name": "vars"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}