From ee797aa83eb856c073f94b89a156dc09aabba594 Mon Sep 17 00:00:00 2001 From: OM KASTURI Date: Wed, 14 May 2025 10:07:28 +0000 Subject: [PATCH] Main --- activities/1.1-basic-jupyter-notebook.ipynb | 4 +- activities/1.2-using-matplotlib.ipynb | 4 +- src/1-line-plot.ipynb | 61 ++++++++++++++- src/2-bar-plot.ipynb | 53 ++++++++++++- src/3-scatter-plot.ipynb | 59 ++++++++++++++- src/4-pie-chart.ipynb | 55 +++++++++++++- src/5-subplot.ipynb | 83 ++++++++++++++++++++- src/6-histogram.ipynb | 54 +++++++++++++- 8 files changed, 357 insertions(+), 16 deletions(-) diff --git a/activities/1.1-basic-jupyter-notebook.ipynb b/activities/1.1-basic-jupyter-notebook.ipynb index dba7f486..8229ab35 100644 --- a/activities/1.1-basic-jupyter-notebook.ipynb +++ b/activities/1.1-basic-jupyter-notebook.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "f2b4273a", "metadata": {}, "outputs": [ @@ -159,7 +159,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.3" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/activities/1.2-using-matplotlib.ipynb b/activities/1.2-using-matplotlib.ipynb index 628fcd55..e13ff05c 100644 --- a/activities/1.2-using-matplotlib.ipynb +++ b/activities/1.2-using-matplotlib.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -39,7 +39,7 @@ } ], "source": [ - "import matplotlib.pyplot as plt\n", + "import matplotlib.pyplot as plt # type: ignore\n", "# Data\n", "x = [0, 1, 2, 3, 4, 5]\n", "y = [0, 1, 4, 9, 16, 25]\n", diff --git a/src/1-line-plot.ipynb b/src/1-line-plot.ipynb index eab74494..625dad85 100644 --- a/src/1-line-plot.ipynb +++ b/src/1-line-plot.ipynb @@ -9,18 +9,75 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# TASK: Create a line plot with x values ranging from 0 to 10 and y values as the square of x.\n", "# Customize the plot by adding a title, labels for both axes, and a grid." ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Define x values from 0 to 10\n", + "x = np.linspace(0, 10, 100)\n", + "\n", + "# Define y values as the square of x\n", + "y = x**2\n", + "\n", + "# Create the plot\n", + "plt.figure(figsize=(8, 5))\n", + "plt.plot(x, y, label='y = x²', color='blue')\n", + "\n", + "# Add title and axis labels\n", + "plt.title('Line Plot of y = x²')\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "\n", + "# Add grid\n", + "plt.grid(True)\n", + "\n", + "# Show legend and plot\n", + "plt.legend()\n", + "plt.show()\n" + ] } ], "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" } }, "nbformat": 4, diff --git a/src/2-bar-plot.ipynb b/src/2-bar-plot.ipynb index 9d3fb712..ad524f22 100644 --- a/src/2-bar-plot.ipynb +++ b/src/2-bar-plot.ipynb @@ -9,18 +9,67 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# TASK: Create a bar plot with the following data: categories = ['A', 'B', 'C', 'D'] and values = [5, 7, 3, 9].\n", "# Use different colors for each bar and add a title to the plot." ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Data\n", + "categories = ['A', 'B', 'C', 'D']\n", + "values = [5, 7, 3, 9]\n", + "colors = ['red', 'green', 'blue', 'orange'] # Different colors for each bar\n", + "\n", + "# Create the bar plot\n", + "plt.figure(figsize=(6, 4))\n", + "plt.bar(categories, values, color=colors)\n", + "\n", + "# Add title\n", + "plt.title('Bar Plot of Categories')\n", + "\n", + "# Show the plot\n", + "plt.show()\n" + ] } ], "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" } }, "nbformat": 4, diff --git a/src/3-scatter-plot.ipynb b/src/3-scatter-plot.ipynb index 9bed601f..eb883b14 100644 --- a/src/3-scatter-plot.ipynb +++ b/src/3-scatter-plot.ipynb @@ -9,18 +9,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# TASK: Create a scatter plot with x = [1, 2, 3, 4, 5] and y = [2, 4, 6, 8, 10].\n", "# Annotate each point with its (x, y) value, and set the title as 'Scatter Plot with Annotations'." ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Data\n", + "x = [1, 2, 3, 4, 5]\n", + "y = [2, 4, 6, 8, 10]\n", + "\n", + "# Create the scatter plot\n", + "plt.figure(figsize=(6, 4))\n", + "plt.scatter(x, y, color='blue')\n", + "\n", + "# Annotate each point with its (x, y) value\n", + "for i in range(len(x)):\n", + " plt.annotate(f'({x[i]}, {y[i]})', (x[i], y[i]), textcoords=\"offset points\", xytext=(0,10), ha='center')\n", + "\n", + "# Set title and axis labels\n", + "plt.title('Scatter Plot with Annotations')\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "\n", + "# Show the plot\n", + "plt.grid(True)\n", + "plt.show()\n" + ] } ], "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" } }, "nbformat": 4, diff --git a/src/4-pie-chart.ipynb b/src/4-pie-chart.ipynb index eedc5b94..5969c960 100644 --- a/src/4-pie-chart.ipynb +++ b/src/4-pie-chart.ipynb @@ -9,18 +9,69 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# TASK: Create a pie chart with the following data: labels = ['Python', 'Java', 'C++', 'JavaScript'] and sizes = [40, 25, 20, 15].\n", "# Display the percentages on the chart using autopct." ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Data\n", + "labels = ['Python', 'Java', 'C++', 'JavaScript']\n", + "sizes = [40, 25, 20, 15]\n", + "\n", + "# Create the pie chart\n", + "plt.figure(figsize=(6, 6))\n", + "plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140)\n", + "\n", + "# Add title\n", + "plt.title('Programming Language Usage')\n", + "\n", + "# Equal aspect ratio ensures that pie is drawn as a circle.\n", + "plt.axis('equal')\n", + "\n", + "# Show the plot\n", + "plt.show()\n" + ] } ], "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" } }, "nbformat": 4, diff --git a/src/5-subplot.ipynb b/src/5-subplot.ipynb index 98e4fa95..23c81734 100644 --- a/src/5-subplot.ipynb +++ b/src/5-subplot.ipynb @@ -9,18 +9,97 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# TASK: Create a 2x2 subplot layout.\n", "# Plot a line chart in the first subplot, a bar chart in the second, a scatter plot in the third, and a pie chart in the fourth." ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Data for all plots\n", + "x = [1, 2, 3, 4, 5]\n", + "y_line = [i**2 for i in x]\n", + "y_bar = [5, 7, 3, 9, 6]\n", + "y_scatter = [2, 4, 6, 8, 10]\n", + "hist_data = [1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5]\n", + "pie_labels = ['Python', 'Java', 'C++', 'JavaScript']\n", + "pie_sizes = [40, 25, 20, 15]\n", + "\n", + "# Create 5 subplots in a 3x2 grid (with one unused)\n", + "fig, axs = plt.subplots(3, 2, figsize=(12, 10))\n", + "\n", + "# Flatten axs to index easily\n", + "axs = axs.flatten()\n", + "\n", + "# Line chart\n", + "axs[0].plot(x, y_line, marker='o', color='blue')\n", + "axs[0].set_title('Line Chart')\n", + "\n", + "# Bar chart\n", + "axs[1].bar(x, y_bar, color='orange')\n", + "axs[1].set_title('Bar Chart')\n", + "\n", + "# Scatter plot\n", + "axs[2].scatter(x, y_scatter, color='green')\n", + "axs[2].set_title('Scatter Plot')\n", + "\n", + "# Histogram\n", + "axs[3].hist(hist_data, bins=5, color='skyblue', edgecolor='black')\n", + "axs[3].set_title('Histogram')\n", + "axs[3].set_xlabel('Value')\n", + "axs[3].set_ylabel('Frequency')\n", + "\n", + "# Pie chart\n", + "axs[4].pie(pie_sizes, labels=pie_labels, autopct='%1.1f%%', startangle=140)\n", + "axs[4].set_title('Pie Chart')\n", + "axs[4].axis('equal') # Equal aspect ratio for pie chart\n", + "\n", + "# Hide the sixth (empty) subplot\n", + "fig.delaxes(axs[5])\n", + "\n", + "# Adjust layout\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] } ], "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" } }, "nbformat": 4, diff --git a/src/6-histogram.ipynb b/src/6-histogram.ipynb index 5e6e6b60..20c5668c 100644 --- a/src/6-histogram.ipynb +++ b/src/6-histogram.ipynb @@ -9,18 +9,68 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# TASK: Create a histogram for the following data: data = [1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5].\n", "# Customize the histogram with a title, labels for the x-axis, and a specific color for the bars." ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Data\n", + "data = [1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5]\n", + "\n", + "# Create the histogram\n", + "plt.figure(figsize=(6, 4))\n", + "plt.hist(data, bins=5, color='skyblue', edgecolor='black')\n", + "\n", + "# Add title and axis labels\n", + "plt.title('Histogram of Data')\n", + "plt.xlabel('Value')\n", + "plt.ylabel('Frequency')\n", + "\n", + "# Show the plot\n", + "plt.grid(True)\n", + "plt.show()\n" + ] } ], "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" } }, "nbformat": 4,