2nd March 2023.ipynb

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    "## 2nd March Assignment\n",
    "### Matplotlib"
   ]
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    "---"
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    "```\n",
    "Q1. What is Matplotlib? Why is it used? Name five plots that can be plotted using the Pyplot module of Matplotlib.\n",
    "\n",
    "Ans. Matplotlib is a popular data visualization library in Python that provides a wide range of functionalities for creating high-quality plots, graphs, and charts. It is widely used for visualizing and analyzing data in various fields such as data science, machine learning, statistics, and more.\n",
    "\n",
    "Here are five plots that can be created using the Pyplot module of Matplotlib:\n",
    "\n",
    "1. Line Plot: A line plot is used to represent data points connected by straight lines. It is useful for visualizing trends and patterns over a continuous variable or time.\n",
    "\n",
    "2. Scatter Plot: A scatter plot displays individual data points as markers on a two-dimensional plane. It is helpful for examining the relationship between two variables and identifying any patterns or clusters.\n",
    "\n",
    "3. Bar Plot: A bar plot (or bar chart) uses rectangular bars to represent categorical data. It is commonly used to compare and visualize the distribution or frequency of different categories.\n",
    "\n",
    "4. Histogram: A histogram displays the distribution of a continuous variable by dividing it into bins and representing the frequency or count of data points falling into each bin. It helps visualize the shape, central tendency, and spread of the data.\n",
    "\n",
    "5. Pie Chart: A pie chart represents data as a circular graph divided into slices, where each slice represents a proportion or percentage of the whole. It is suitable for displaying categorical data and comparing different categories based on their relative sizes.\n",
    "\n",
    "These are just a few examples of the plots that can be created using the Pyplot module of Matplotlib. Matplotlib provides many other types of plots, such as box plots, violin plots, heatmaps, and more, allowing users to choose the appropriate visualization technique based on their data and analysis goals.\n",
    "```"
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    "```\n",
    "Q2. What is a scatter plot? Use the following code to generate data for x and y. Using this generated data\n",
    "plot a scatter plot.\n",
    "```\n",
    "```python\n",
    "import numpy as np\n",
    "\n",
    "np.random.seed(3)\n",
    "\n",
    "x = 3 + np.random.normal(0, 2, 50)\n",
    "\n",
    "y = 3 + np.random.normal(0, 2, len(x))\n",
    "```\n",
    "```\n",
    "Note: Also add title, xlabel, and ylabel to the plot.\n",
    "\n",
    "Ans. A scatter plot is a type of plot used to display the relationship between two numerical variables. It represents individual data points as markers on a two-dimensional plane, with one variable plotted along the x-axis and the other variable plotted along the y-axis.\n",
    "\n",
    "The scatter plot is particularly useful for examining the correlation or association between the two variables and identifying any patterns or trends in the data. Each data point on the plot represents a unique combination of values for the two variables, allowing for the visualization of the distribution and clustering of the data.\n",
    "\n",
    "Scatter plots can provide insights into several aspects of the relationship between variables, including:\n",
    "\n",
    "1. Correlation: By observing the general trend of the data points on the scatter plot, you can determine whether there is a positive, negative, or no correlation between the two variables. A positive correlation means that as one variable increases, the other tends to increase as well, while a negative correlation indicates an inverse relationship.\n",
    "\n",
    "2. Outliers: Scatter plots can reveal outliers, which are data points that deviate significantly from the overall pattern. Outliers may indicate errors in data collection or represent unique cases that require further investigation.\n",
    "\n",
    "3. Clustering: Patterns or clusters of data points on the scatter plot can suggest the presence of groups or subgroups within the data. This can be helpful for identifying distinct patterns or segments in the dataset.\n",
    "\n",
    "4. Distribution: Scatter plots provide a visual representation of the distribution of data points. The concentration and spread of the points can give insights into the variability and density of the data.\n",
    "\n",
    "Scatter plots can be enhanced with additional features such as color-coding, size variation, or regression lines to provide further information or highlight specific characteristics in the data.\n",
    "```"
   ]
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.random.seed(3)\n",
    "\n",
    "x = 3 + np.random.normal(0, 2, 50)\n",
    "\n",
    "y = 3 + np.random.normal(0, 2, len(x))\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(x,y)\n",
    "\n",
    "plt.show(\"svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76276f5c-fe5f-46b7-85d3-212c04d9e636",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "036566d3-e4dd-4954-bf06-e9ba18ad9aaf",
   "metadata": {},
   "source": [
    "```\n",
    "Q3. Why is the subplot() function used? Draw four line plots using the subplot() function.\n",
    "\n",
    "Use the following data:\n",
    "```\n",
    "```python\n",
    "import numpy as np\n",
    "\n",
    "For line 1: x = np.array([0, 1, 2, 3, 4, 5]) and y = np.array([0, 100, 200, 300, 400, 500])\n",
    "\n",
    "For line 2: x = np.array([0, 1, 2, 3, 4, 5]) and y = np.array([50, 20, 40, 20, 60, 70])\n",
    "\n",
    "For line 3: x = np.array([0, 1, 2, 3, 4, 5]) and y = np.array([10, 20, 30, 40, 50, 60])\n",
    "\n",
    "For line 4: x = np.array([0, 1, 2, 3, 4, 5]) and y = np.array([200, 350, 250, 550, 450, 150])\n",
    "```\n",
    "```\n",
    "Ans. The subplot() function in Matplotlib is used to create a grid of subplots within a single figure. It allows you to divide the figure into multiple smaller plots, each with its own axes and content. The subplot() function is helpful in several scenarios:\n",
    "\n",
    "1. Creating Multiple Plots: The subplot() function provides a convenient way to create multiple plots within a single figure. By specifying the number of rows, number of columns, and the position of each subplot in the grid, you can easily arrange and visualize multiple plots side by side.\n",
    "\n",
    "2. Comparing Data: When you have multiple datasets or variations of data that you want to compare, using subplots can be beneficial. Each subplot can represent a different aspect of the data, allowing for easier visual comparison and analysis.\n",
    "\n",
    "3. Customizing Layout: The subplot() function allows you to have control over the layout of the subplots. You can specify the size, position, and spacing of each subplot, enabling you to create complex arrangements of plots according to your specific requirements.\n",
    "\n",
    "4. Grouping Related Plots: Subplots are useful when you want to group related plots together. For example, you may want to display multiple views or different visualizations of the same data in a single figure. Subplots provide a concise way to organize and present such information.\n",
    "\n",
    "5. Storytelling and Presentation: Subplots are often used in presentations or reports to present a series of related visualizations or to show the progression of data over time. They can be used to tell a data-driven story or present different aspects of an analysis in a structured manner.\n",
    "\n",
    "By utilizing the subplot() function, you can create more complex and informative figures by combining multiple plots into a single layout. This allows for better visualization, comparison, and presentation of data and enhances the overall understanding of the information being conveyed.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b92d10d3-67fc-4336-846c-a8b7ba9ccb74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#Line 1\n",
    "\n",
    "x1 = np.array([0, 1, 2, 3, 4, 5]) \n",
    "y1 = np.array([0, 100, 200, 300, 400, 500])\n",
    "\n",
    "#Line 2\n",
    "x2 = np.array([0, 1, 2, 3, 4, 5])\n",
    "y2 = np.array([50, 20, 40, 20, 60, 70])\n",
    "\n",
    "#Line 3\n",
    "x3 = np.array([0, 1, 2, 3, 4, 5])  \n",
    "y3 = np.array([10, 20, 30, 40, 50, 60])\n",
    "\n",
    "#Line 4\n",
    "x4 = np.array([0, 1, 2, 3, 4, 5])\n",
    "y4 = np.array([200, 350, 250, 550, 450, 150])\n",
    "\n",
    "# Create figure\n",
    "fig = plt.figure()\n",
    "\n",
    "# Plot on each subplot\n",
    "plt.plot(x1, y1, label = 'Line 1')\n",
    "plt.plot(x2, y2, label = 'Line 2')\n",
    "plt.plot(x3, y3, label = 'Line 3')\n",
    "plt.plot(x4, y4, label = 'Line 4')\n",
    "plt.legend()\n",
    "# Set title\n",
    "plt.title(\"Multiple Line Plot\")\n",
    "\n",
    "# Display the figure\n",
    "plt.show(\"svg\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b0b1495-963c-4f92-96f0-f2b3b8030c18",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dc46d51-ea10-42b9-b168-3277c0a36f16",
   "metadata": {},
   "source": [
    "```\n",
    "Q4. What is a bar plot? Why is it used? Using the following data plot a bar plot and a horizontal bar plot.\n",
    "```\n",
    "```python\n",
    "import numpy as np\n",
    "\n",
    "company = np.array([\"Apple\", \"Microsoft\", \"Google\", \"AMD\"])\n",
    "\n",
    "profit = np.array([3000, 8000, 1000, 10000])\n",
    "```\n",
    "```\n",
    "Ans. A bar plot, also known as a bar chart, is a visualization tool used to represent categorical data with rectangular bars. It is used to compare and display the distribution or frequency of different categories or groups.\n",
    "\n",
    "The primary purpose of a bar plot is to provide a visual summary of categorical data and highlight any disparities or patterns among the categories. Here are some key characteristics and uses of bar plots:\n",
    "\n",
    "1. Categorical Comparison: Bar plots are effective in comparing the values of different categories or groups. The length or height of each bar represents the quantity or frequency of the category it represents, allowing for easy visual comparison.\n",
    "\n",
    "2. Data Distribution: Bar plots provide insights into the distribution of data across categories. They help identify the categories with the highest or lowest values, allowing for quick identification of trends or outliers.\n",
    "\n",
    "3. Data Exploration: Bar plots are useful for exploring data and identifying patterns or relationships between categories. They can reveal patterns such as trends, seasonality, or variations in data across different groups.\n",
    "\n",
    "4. Data Comparison: Bar plots can be used to compare data across multiple variables or subgroups within categories. By using different colors or grouping bars together, it becomes possible to compare values across different dimensions or conditions.\n",
    "\n",
    "5. Communication and Presentation: Bar plots are commonly used for data visualization in presentations, reports, and publications. They provide a clear and intuitive representation of categorical data, making it easier for the audience to understand and interpret the information.\n",
    "\n",
    "6. Decision Making: Bar plots aid decision-making processes by providing a visual summary of categorical data. They help stakeholders identify patterns, make comparisons, and draw insights that can support informed decision-making.\n",
    "\n",
    "Bar plots can be customized to enhance their visual appeal and convey additional information. For example, labels, titles, and color-coding can be added to provide context and improve interpretability. Bar plots are versatile and widely used in various domains, including business, marketing, social sciences, and data analysis, to present and analyze categorical data effectively.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "905ad9dd-984f-4ce8-9e65-903d646c127a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "company = np.array([\"Apple\", \"Microsoft\", \"Google\", \"AMD\"])\n",
    "\n",
    "profit = np.array([3000, 8000, 1000, 10000])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2c806fb5-7ade-44c3-bc27-a6cd4a8f9926",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Company and Their Profits')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar Plot\n",
    "\n",
    "plt.bar(company, profit)\n",
    "plt.xlabel(\"Companies\")\n",
    "plt.ylabel(\"Profit\")\n",
    "plt.title(\"Company and Their Profits\")\n",
    "plt.show(\"svg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "52b5995a-8936-4d73-90b0-95eb3e652280",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Company and Their Profits')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Horizontal Bar Plot\n",
    "\n",
    "plt.barh(company, profit)\n",
    "plt.ylabel(\"Companies\")\n",
    "plt.xlabel(\"Profit\")\n",
    "plt.title(\"Company and Their Profits\")\n",
    "plt.show(\"svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a10dc29-c728-4398-a078-d89afec45824",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1156fa9-2e5d-4029-924e-c8585da4af50",
   "metadata": {},
   "source": [
    "```\n",
    "Q5. What is a box plot? Why is it used? Using the following data plot a box plot.\n",
    "```\n",
    "```python\n",
    "box1 = np.random.normal(100, 10, 200)\n",
    "\n",
    "box2 = np.random.normal(90, 20, 200)\n",
    "```\n",
    "```\n",
    "Ans. A box plot, also known as a box-and-whisker plot, is a statistical visualization tool that provides a summary of the distribution of numerical data. It displays key statistical measures and identifies potential outliers in a dataset. Box plots are commonly used for the following purposes:\n",
    "\n",
    "1. Data Summary: A box plot provides a concise summary of the distribution of a dataset. It displays key statistical measures such as the median, quartiles (25th and 75th percentiles), and the range of the data. The central box in the plot represents the interquartile range (IQR), which contains the middle 50% of the data. The line inside the box represents the median value.\n",
    "\n",
    "2. Outlier Detection: Box plots are effective in identifying potential outliers in a dataset. Outliers are data points that significantly deviate from the rest of the data and may indicate anomalies or errors. Box plots help to visually identify values that fall beyond the whiskers (lines extending from the box), which are typically set to 1.5 times the IQR.\n",
    "\n",
    "3. Comparison of Groups: Box plots are useful for comparing the distributions of multiple groups or categories. By placing multiple box plots side by side, it becomes easy to compare the central tendency, spread, and skewness of the data across different groups. This allows for the detection of variations, patterns, or differences between groups.\n",
    "\n",
    "4. Skewness and Symmetry: Box plots provide insights into the skewness and symmetry of the data. If the box is symmetrical, it indicates that the data is evenly distributed around the median. Skewness can be observed when one whisker is longer than the other, suggesting that the data is positively or negatively skewed.\n",
    "\n",
    "5. Data Visualization and Communication: Box plots are a popular choice for visualizing and communicating data in various fields such as statistics, data analysis, and research. They provide a clear and standardized representation of the distribution and key statistics, enabling easy understanding and comparison of datasets.\n",
    "\n",
    "Box plots are versatile and can be customized to include additional information such as labels, titles, and annotations to provide context and enhance interpretability. They are particularly useful when dealing with large datasets, comparing groups, or analyzing distributions, making them a valuable tool for data exploration and analysis.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1e737b91-1d86-451b-86af-7c23493d37b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Box Plot 2')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "box1 = np.random.normal(100, 10, 200)\n",
    "\n",
    "box2 = np.random.normal(90, 20, 200)\n",
    "\n",
    "ax1= fig.add_subplot(1,2,1)\n",
    "ax1.boxplot(box1)\n",
    "plt.title(\"Box Plot 1\")\n",
    "\n",
    "ax2= fig.add_subplot(1,2,2)\n",
    "ax2.boxplot(box2)\n",
    "plt.title(\"Box Plot 2\")\n",
    "plt.show(\"svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e59ee13d-717f-409c-af57-3bb78e7f4305",
   "metadata": {},
   "source": [
    "---"
   ]
  }
 ],
 "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": "3.10.8"
  }
 },
 "nbformat": 4,
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