diff --git a/02_activities/assignments/assignment_3/Bar Chart by Python Screenshot.png b/02_activities/assignments/assignment_3/Bar Chart by Python Screenshot.png new file mode 100644 index 000000000..8f47b4810 Binary files /dev/null and b/02_activities/assignments/assignment_3/Bar Chart by Python Screenshot.png differ diff --git a/02_activities/assignments/assignment_3/Bar Chart by Python.ipynb b/02_activities/assignments/assignment_3/Bar Chart by Python.ipynb new file mode 100644 index 000000000..bcba8b73f --- /dev/null +++ b/02_activities/assignments/assignment_3/Bar Chart by Python.ipynb @@ -0,0 +1,124 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 7, + "id": "fcf6f1ed", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "40bde6e6", + "metadata": {}, + "outputs": [], + "source": [ + "file_path = r\"C:\\Users\\pca_9\\Desktop\\visualization\\02_activities\\assignments\\assignment_3\\Marriage Licence Statistics Data.csv\"\n", + "df = pd.read_csv(file_path)\n", + "\n", + "df['DATE'] = pd.to_datetime(df['TIME_PERIOD'])\n", + "df = df[(df['DATE'].dt.year >= 2011) & (df['DATE'].dt.year <= 2024)]\n", + "\n", + "city_monthly_totals = df.groupby('DATE')['MARRIAGE_LICENSES'].sum().reset_index()\n", + "city_monthly_totals['Month'] = city_monthly_totals['DATE'].dt.month_name()\n", + "\n", + "month_order = ['January', 'February', 'March', 'April', 'May', 'June', \n", + " 'July', 'August', 'September', 'October', 'November', 'December']\n", + "\n", + "seasonal_averages = city_monthly_totals.groupby('Month')['MARRIAGE_LICENSES'].mean().reindex(month_order).reset_index()\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b498e772", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\pca_9\\AppData\\Local\\Temp\\ipykernel_14904\\173376014.py:2: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " bar_plot = sns.barplot(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 6))\n", + "bar_plot = sns.barplot(\n", + " data=seasonal_averages, \n", + " x='Month', \n", + " y='MARRIAGE_LICENSES', \n", + " palette='viridis' # Uses a clear, high-contrast color scale\n", + ")\n", + "\n", + "for p in bar_plot.patches:\n", + " bar_plot.annotate(f'{int(p.get_height())}', \n", + " (p.get_x() + p.get_width() / 2., p.get_height()), \n", + " ha='center', va='center', \n", + " xytext=(0, 9), \n", + " textcoords='offset points',\n", + " fontsize=10, fontweight='bold')\n", + " \n", + " plt.title('Average Marriage Licenses Issued by Month in Toronto (2011-2024)', fontsize=16, pad=20)\n", + "plt.xlabel('Month', fontsize=12)\n", + "plt.ylabel('Average Number of Licenses', fontsize=12)\n", + "plt.xticks(rotation=45)\n", + "plt.ylim(0, seasonal_averages['MARRIAGE_LICENSES'].max() * 1.15) # Leave space for labels\n", + "plt.grid(axis='y', linestyle='--', alpha=0.3)\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "66f285b6", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "visualization-env", + "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": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/02_activities/assignments/assignment_3/Bar Chart by Python.md b/02_activities/assignments/assignment_3/Bar Chart by Python.md new file mode 100644 index 000000000..b8b7945e5 --- /dev/null +++ b/02_activities/assignments/assignment_3/Bar Chart by Python.md @@ -0,0 +1,47 @@ +# Data Visualization + +## Assignment 3: Final Project + + +- For each visualization, describe and justify: + + > What software did you use to create your data visualization? + + Answer: I used Python with the Pandas library for data manipulation, and Matplotlib and Seaborn for generating the bar chart. + + > Who is your intended audience? + + Answer: The intended audience includes Toronto city planners, municipal service staff, and local wedding industry stakeholders (venues, florists, and photographers) who need to allocate resources based on seasonal demand. + + > What information or message are you trying to convey with your visualization? + + Answer: The chart highlights the extreme seasonality of marriage licenses in Toronto. By averaging data from 2011–2024, it clearly shows that demand peaks significantly in the summer months (July and August) and reaches its lowest point in January and February. + + > What aspects of design did you consider when making your visualization? How did you apply them? With what elements of your plots? + + Answer: I chose a bar chart to make categorical comparisons between months intuitive. I applied the "Viridis" color palette for a professional look and added direct numerical labels on top of each bar so the exact average is immediately visible without needing to check the Y-axis. + + > How did you ensure that your data visualizations are reproducible? If the tool you used to make your data visualization is not reproducible, how will this impact your data visualization? + + Answer: The visualization is fully reproducible through a Python script. The code handles every step from loading the raw CSV to filtering years and calculating averages, ensuring anyone with the dataset can regenerate the exact same chart. + + > How did you ensure that your data visualization is accessible? + + Answer: I used a color-blind friendly palette (Viridis) and included bold data labels on top of every bar. This ensures the information is accessible to users who may have difficulty distinguishing colors or reading fine-print axis scales. + + > Who are the individuals and communities who might be impacted by your visualization? + + Answer: Small business owners in the hospitality sector are impacted as they use this data for seasonal staffing. Additionally, couples are impacted as the data reveals when competition for city services and wedding dates is highest. + + > How did you choose which features of your chosen dataset to include or exclude from your visualization? + + Answer: I included the MARRIAGE_LICENSES and TIME_PERIOD (converted to month names) to focus on the seasonality story. I excluded the _id and the individual CIVIC_CENTRE names to focus on a unified, city-wide average that is easier for a general audience to digest. + + > What ‘underwater labour’ contributed to your final data visualization product? + + Answer: The 'underwater labour' involved cleaning the date strings, summing data from four different civic centers to get city-wide totals, and performing a multi-step aggregation to calculate the mean for each month across a 14-year period. + + +- This assignment is intentionally open-ended - you are free to create static or dynamic data visualizations, maps, or whatever form of data visualization you think best communicates your information to your audience of choice! +- Total word count should not exceed **(as a maximum) 1000 words** + diff --git a/02_activities/assignments/assignment_3/Marriage Licence Statistics Data.csv b/02_activities/assignments/assignment_3/Marriage Licence Statistics Data.csv new file mode 100644 index 000000000..e5c4e63ee --- 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diff --git a/02_activities/assignments/assignment_3/Marriage Licence Statistics Data.xlsx b/02_activities/assignments/assignment_3/Marriage Licence Statistics Data.xlsx new file mode 100644 index 000000000..27b18365a Binary files /dev/null and b/02_activities/assignments/assignment_3/Marriage Licence Statistics Data.xlsx differ diff --git a/02_activities/assignments/assignment_3/Pie Chart by Excel .png b/02_activities/assignments/assignment_3/Pie Chart by Excel .png new file mode 100644 index 000000000..bea69d624 Binary files /dev/null and b/02_activities/assignments/assignment_3/Pie Chart by Excel .png differ diff --git a/02_activities/assignments/assignment_3/Pie Chart by Excel.md b/02_activities/assignments/assignment_3/Pie Chart by Excel.md new file mode 100644 index 000000000..92b268ea1 --- /dev/null +++ b/02_activities/assignments/assignment_3/Pie Chart by Excel.md @@ -0,0 +1,47 @@ +# Data Visualization + +## Assignment 3: Final Project + + +- For each visualization, describe and justify: + + > What software did you use to create your data visualization? + + Answer: I used Microsoft Excel to create this pie chart, utilizing its built-in charting engine to calculate the proportional distribution of the data. + + > Who is your intended audience? + + Answer: The intended audience includes City of Toronto administrators and departmental managers who are responsible for resource allocation and staffing across the city’s various municipal service locations. + + > What information or message are you trying to convey with your visualization? + + Answer: The visualization conveys the dominance of the Toronto (Downtown) location in handling marriage license requests. It shows that more than half of the city's marriage license volume is concentrated in a single civic centre, while the remaining volume is split among the other three districts. + + > What aspects of design did you consider when making your visualization? How did you apply them? With what elements of your plots? + + Answer: I chose a pie chart to highlight "part-to-whole" relationships. I applied clear data labels that show both the category name and the percentage to provide immediate context. I also used distinct colors for each slice to ensure that the boundaries between the different civic centres were visually clear. + + > How did you ensure that your data visualizations are reproducible? If the tool you used to make your data visualization is not reproducible, how will this impact your data visualization? + + Answer: This Excel visualization is not fully reproducible because it relies on manual user-interface interactions (pointing and clicking) rather than code. This impacts the visualization because if the dataset is updated, the chart must be manually recreated or adjusted, which increases the risk of human error compared to a scripted approach. + + > How did you ensure that your data visualization is accessible? + + Answer: I ensured accessibility by including text labels and percentage values directly on the chart slices. This prevents the "legend-scanning" problem, where a user has to look back and forth between a color key and the chart, making it easier for people with color vision deficiencies or cognitive processing challenges to understand the data. + + > Who are the individuals and communities who might be impacted by your visualization? + + Answer: Municipal employees at the Toronto (Downtown) centre are most impacted, as the chart justifies their higher workload. Additionally, residents in Etobicoke or Scarborough might see this and realize their local centres are less crowded, potentially influencing where they choose to apply for their own licenses. + + > How did you choose which features of your chosen dataset to include or exclude from your visualization? + + Answer: I included the CIVIC_CENTRE and the total sum of MARRIAGE_LICENSES. I excluded the time-series data (TIME_PERIOD) because the goal of this specific visualization was to show geographic distribution rather than changes over time. + + > What ‘underwater labour’ contributed to your final data visualization product? + + Answer: The 'underwater labour' involved cleaning the dataset to remove any incomplete records, manually aggregating 14 years of monthly data into four distinct category totals, and translating the technical abbreviations (ET, NY, SC, TO) into user-friendly names for the chart labels. + + +- This assignment is intentionally open-ended - you are free to create static or dynamic data visualizations, maps, or whatever form of data visualization you think best communicates your information to your audience of choice! +- Total word count should not exceed **(as a maximum) 1000 words** + diff --git a/python_marriage_trend.png b/python_marriage_trend.png new file mode 100644 index 000000000..a382f17f6 Binary files /dev/null and b/python_marriage_trend.png differ