final_data_github.ipynb

{"cells":[{"cell_type":"code","metadata":{"tags":[],"output_cleared":false,"source_hash":"614f40be","execution_millis":272,"deepnote_to_be_reexecuted":false,"execution_start":1609826257721,"cell_id":"00000-a0b432d3-52ef-441b-90df-4f9ebcfc8777","deepnote_cell_type":"code"},"source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport statistics as st\n\nswipe_data = pd.read_csv('Counts_Avg Meal_Participation.csv')\nsurvey_data = pd.read_csv('HUDS Survey to On-campus Students_November 28, 2020_00.33.csv')\n\n#print(swipe_data.head())\n#print(list(survey_data.columns))\n\nquestion_bank = {'Q2.1': 'Where do you live on campus?',\n                 'Q2.2': 'Which class year are you in?',\n                 'Q3.1': 'On average, how many servings of the main entree do you take each time you get LUNCH at the dining hall? Note: A serving is one “box” of food.',\n                 'Q3.2': 'On average, how many servings of the main entree do you take each time you get DINNER at the dining hall? Note: A serving is one “box” of food.',\n                 'Q3.3': 'How many additional containers (from sides, salads, soup, etc.) do you take on average each time you visit the dining hall?',\n                 'Q4.2_1': 'Rank in terms of importance - Sustainability',\n                 'Q4.2_2': 'Rank in terms of importance - Convenience',\n                 'Q4.2_3': 'Rank in terms of importance - Safety',\n                 'Q13_1': 'I would be willing to switch to the reusable container system outlined above.',\n                 'Q13_2': 'Switching to reusable containers would be environmentally beneficial.',\n                 'Q13_3': 'A reusable container program would be difficult for students to maintain and comply with.',\n                 'Q13_4': 'I would feel safe about eating from reusable containers that are washed and disinfected by HUDS.'}\n\nclean_dict = {}\nfor key in question_bank:\n    clean_dict[key] = list(survey_data[key])[2: ]\n\nsurvey_df = pd.DataFrame(data=clean_dict)\n\nlunch_count = clean_dict['Q3.1']\ndinner_count = clean_dict['Q3.2']\nadditional_count = clean_dict['Q3.3']\n\n\n# clean nan / nulls\nlunch_count = [i for i in lunch_count if str(i) != 'nan']\ndinner_count = [i for i in dinner_count if str(i) != 'nan']\nadditional_count = [i for i in additional_count if str(i) != 'nan']\n\nfor i, s in enumerate(additional_count):\n    if s == '4+':\n        additional_count[i] = 4\n\n# cast to int\nlunch_count = list(map(int,lunch_count))\ndinner_count = list(map(int,dinner_count))\nadditional_count = list(map(int,additional_count))\n\n# average and standard deviation\navg_lunch = st.mean(lunch_count)\navg_dinner = st.mean(dinner_count)\navg_additional = st.mean(additional_count)\n\nstd_lunch = np.std(lunch_count)\nstd_dinner = np.std(dinner_count)\nstd_additional = np.std(additional_count)\n\n\nprint(\"Lunch average amount of containers taken (survey):\")\nprint(avg_lunch)\n\nprint(\"\\nDinner average amount of containers taken (survey):\")\nprint(avg_dinner)\n\nprint(\"\\nAdditional Containers average amount of containers taken (survey):\")\nprint(avg_additional)","execution_count":null,"outputs":[{"name":"stdout","text":"Lunch average amount of containers taken (survey):\n1.0818181818181818\n\nDinner average amount of containers taken (survey):\n1.1363636363636365\n\nAdditional Containers average amount of containers taken (survey):\n1.3727272727272728\n","output_type":"stream"}]},{"cell_type":"code","metadata":{"tags":[],"output_cleared":false,"source_hash":"91fe4fcf","execution_millis":1,"deepnote_to_be_reexecuted":false,"execution_start":1609826258001,"cell_id":"00001-ab23c9f6-1208-4d02-8d97-70153bff918a","deepnote_cell_type":"code"},"source":"\n# other notebook\navg_lunch_swipes = 864.2857142857143\navg_dinner_swipes = 898.2857142857143\n\nlunch_containers_per_day = avg_lunch * avg_lunch_swipes\ndinner_containers_per_day = avg_lunch * avg_dinner_swipes\n\nprint(lunch_containers_per_day)\nprint(dinner_containers_per_day)","execution_count":null,"outputs":[{"name":"stdout","text":"935.0\n971.7818181818182\n","output_type":"stream"}]},{"cell_type":"code","metadata":{"tags":[],"output_cleared":false,"source_hash":"54425817","execution_millis":359,"is_code_hidden":false,"deepnote_to_be_reexecuted":false,"execution_start":1609826258037,"cell_id":"00002-4590f7fc-e312-40b2-a097-939d2ce78cbc","deepnote_cell_type":"code"},"source":"housing_options = ['Adams House', 'Cabot House', 'Currier House', 'Dunster House', 'Eliot House', 'Kirkland House', 'Leverett House', 'Lowell House', 'Mather House', 'Pforzheimer House', 'Quincy House', 'Winthrop House', 'The Yard (freshman housing)']\n\nlunch = {}\ndinner = {}\nadditional = {}\n\nlunch_tot = {}\ndinner_tot = {}\nadditional_tot = {}\n\nfor house in housing_options:\n    lunch[house] = 0\n    dinner[house] = 0\n    additional[house] = 0\n\n    lunch_tot[house] = 0\n    dinner_tot[house] = 0\n    additional_tot[house] = 0\n\nfor i in range(1, len(survey_df)):\n    for house in housing_options:\n        if survey_df.iloc[i, 0] == house:\n            if not str(survey_df.iloc[i, 2]) == 'nan':\n                lunch[house] += int(survey_df.iloc[i, 2])\n                lunch_tot[house] += 1\n            if not str(survey_df.iloc[i, 3]) == 'nan':\n                dinner[house] += int(survey_df.iloc[i, 3])\n                dinner_tot[house] += 1\n            if not str(survey_df.iloc[i, 4]) == 'nan':\n                additional_tot[house] += 1\n                if str(survey_df.iloc[i, 4]) == '4+':\n                    additional[house] += 4\n                else:\n                    additional[house] += int(survey_df.iloc[i, 4])\n\ndf = pd.DataFrame({'lunch': lunch, 'dinner': dinner, 'additional': additional}, index = housing_options)\ndf.plot.bar()","execution_count":null,"outputs":[{"output_type":"execute_result","execution_count":3,"data":{"text/plain":"<AxesSubplot:>"},"metadata":{}},{"data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"},"output_type":"display_data"}]},{"cell_type":"code","metadata":{"tags":[],"output_cleared":false,"source_hash":"c07e3a79","execution_millis":413,"deepnote_to_be_reexecuted":false,"execution_start":1609826258401,"cell_id":"00003-ae8d2971-0aaf-4590-8183-0865798e7a55","deepnote_cell_type":"code"},"source":"valid_columns = []\nfor column in list(swipe_data.columns):\n        if \"Unnamed\" not in column:\n            valid_columns.append(column)\n\ncolumns = swipe_data[valid_columns]\n\nlunch_swipes = columns.iloc[range(4, 47, 4)] \ndinner_swipes = columns.iloc[range(5, 47, 4)] \n\nprint(lunch_swipes['Adams'])\n\nlunch_swipe_totals = {}\ndinner_swipe_totals = {}\n\nfor col in valid_columns:\n    lunch_swipe_totals[col] = 0\n    dinner_swipe_totals[col] = 0\n\nfor col in valid_columns:\n    for i in range(len(lunch_swipes)):\n        lunch_swipe_totals[col] += int(lunch_swipes[col].iloc[i])\n        dinner_swipe_totals[col] += int(dinner_swipes[col].iloc[i])\n\nlunch_avg = {}\ndinner_avg = {}\nadditional_avg = {}\n\nfor house in housing_options:\n    lunch_avg[house] = lunch[house]/lunch_tot[house]\n    dinner_avg[house] = dinner[house]/dinner_tot[house]\n    additional_avg[house] = additional[house]/additional_tot[house]\n\nlunch_final = {}\ndinner_final = {}\nadditional_final = {}\n\nfor col in valid_columns:\n    if col == 'Annenberg':\n        house = 'The Yard (freshman housing)'\n    else:\n        house = col + \" House\"\n    lunch_final[house] = lunch_swipe_totals[col] * lunch_avg[house]\n    dinner_final[house] = dinner_swipe_totals[col] * dinner_avg[house]\n    additional_final[house] = 0.5 * lunch_swipe_totals[col] * additional_avg[house] + 0.5 * dinner_swipe_totals[col] * additional_avg[house]\n\ndf = pd.DataFrame({'lunch': lunch_final, 'dinner': dinner_final, 'additional': additional_final}, index = housing_options)\ndf.plot.bar()","execution_count":null,"outputs":[{"name":"stdout","text":"4     24\n8     26\n12    24\n16    28\n20    26\n24    27\n28    26\n32    20\n36    39\n40    23\n44    29\nName: Adams, dtype: object\n","output_type":"stream"},{"output_type":"execute_result","execution_count":4,"data":{"text/plain":"<AxesSubplot:>"},"metadata":{}},{"data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"},"output_type":"display_data"}]},{"cell_type":"code","metadata":{"tags":[],"deepnote_to_be_reexecuted":false,"source_hash":"136fd83","execution_millis":495,"allow_embed":false,"execution_start":1609826258823,"cell_id":"00004-dcb98be2-4d20-4fed-903e-56fe13e4f47f","deepnote_cell_type":"code"},"source":"import plotly.express as px\n\ndf = pd.DataFrame({'lunch': lunch_final, 'dinner': dinner_final, 'additional': additional_final}, index = housing_options)\nb_chart = px.bar(df, barmode = 'group', labels = {'value':'Number of Containers', 'index': 'Dining Hall', 'variable': ''}, \n# 570101\n# barmode = 'stack'\n# how to you get rid of the white borders between bars\ncolor_discrete_sequence=['#FF9586', '#eb4e42', '#b0171f'], title = \"Container consumption per dining hall per meal\")\n\nb_chart.update_traces(marker_line_width=0)\nb_chart.show()","execution_count":null,"outputs":[{"data":{"text/html":"<html>\n<head><meta charset=\"utf-8\" /></head>\n<body>\n    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 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{\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Container consumption per dining hall per meal\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Dining Hall\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Number of Containers\"}}},                        {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = 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</div>\n</body>\n</html>"},"metadata":{},"output_type":"display_data"}]},{"cell_type":"code","metadata":{"tags":[],"output_cleared":false,"source_hash":"9584da4c","execution_millis":2202,"deepnote_to_be_reexecuted":false,"execution_start":1609826259323,"cell_id":"00005-14f1ac11-57b3-4e53-ad8d-cbafae19fe4a","deepnote_cell_type":"code"},"source":"pip install plotly.express\n","execution_count":null,"outputs":[{"name":"stdout","text":"\u001b[33mWARNING: The directory '/home/jovyan/.cache/pip' or its parent directory is not owned or is not writable by the current user. The cache has been disabled. Check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.\u001b[0m\nRequirement already satisfied: plotly.express in /usr/local/lib/python3.7/site-packages (0.4.1)\nRequirement already satisfied: statsmodels>=0.9.0 in /usr/local/lib/python3.7/site-packages (from plotly.express) (0.12.1)\nRequirement already satisfied: patsy>=0.5 in /usr/local/lib/python3.7/site-packages (from plotly.express) (0.5.1)\nRequirement already satisfied: pandas>=0.20.0 in /opt/venv/lib/python3.7/site-packages (from plotly.express) (1.0.5)\nRequirement already satisfied: numpy>=1.11 in /opt/venv/lib/python3.7/site-packages (from plotly.express) (1.19.4)\nRequirement already satisfied: scipy>=0.18 in /opt/venv/lib/python3.7/site-packages (from plotly.express) (1.5.4)\nRequirement already satisfied: plotly>=4.1.0 in /opt/venv/lib/python3.7/site-packages (from plotly.express) (4.12.0)\nRequirement already satisfied: six in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from patsy>=0.5->plotly.express) (1.15.0)\nRequirement already satisfied: pytz>=2017.2 in /opt/venv/lib/python3.7/site-packages (from pandas>=0.20.0->plotly.express) (2020.4)\nRequirement already satisfied: python-dateutil>=2.6.1 in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from pandas>=0.20.0->plotly.express) (2.8.1)\nRequirement already satisfied: retrying>=1.3.3 in /opt/venv/lib/python3.7/site-packages (from plotly>=4.1.0->plotly.express) (1.3.3)\n\u001b[33mWARNING: You are using pip version 20.2.4; however, version 20.3.3 is available.\nYou should consider upgrading via the '/usr/local/bin/python -m pip install --upgrade pip' command.\u001b[0m\nNote: you may need to restart the kernel to use updated packages.\n","output_type":"stream"}]},{"cell_type":"code","metadata":{"tags":[],"output_cleared":false,"source_hash":"4a8b29da","execution_millis":825,"deepnote_to_be_reexecuted":false,"execution_start":1609826261533,"cell_id":"00006-8c9036bb-4727-4de9-8055-2ecce328cfd8","deepnote_cell_type":"code"},"source":"import seaborn as sns\nimport plotly.express as px\n\nsurvey_dataframe = pd.DataFrame(survey_data)\n\nimportance_ratings = survey_dataframe.iloc[3:, range(22, 25)]\nimportance_ratings.columns = ['Sustainability', 'Convenience', 'Safety']\n\nimportance_ratings_cleaned = pd.DataFrame.dropna(importance_ratings, axis=0, how=\"any\")\n# print(importance_ratings_cleaned)\n\na = importance_ratings_cleaned['Sustainability'].value_counts()\nprint(a)\n# hard coding it bc idk how not to\na1 = list(a)[2]\na2 = list(a)[0]\na3 = list(a)[1]\nprint([a1, a2, a3])\nb = importance_ratings_cleaned['Safety'].value_counts()\nprint(b)\nb1 = list(b)[2]\nb2 = list(b)[0]\nb3 = list(b)[1]\nprint([b1, b2, b3])\nc = importance_ratings_cleaned['Convenience'].value_counts()\nprint(c)\nc1 = list(c)[1]\nc2 = list(c)[2]\nc3 = list(c)[0]\nprint([c1, c2, c3])\nheatmap_array = [[a1, a2, a3], [b1, b2, b3], [c1, c2, c3]]\nheatmap_data = pd.DataFrame(heatmap_array)\n\nprint(heatmap_data)\nheatmap_data.index = ['Sustainability', 'Safety', 'Convenience']\nheatmap_data.columns = [1, 2, 3]\n\nheatmap = sns.heatmap(heatmap_data, cmap=\"Reds\", annot=True)\nheatmap.set_yticklabels(heatmap.get_yticklabels(), rotation=360)\nplt.xlabel(\"Rating (1: most important, 3: least important)\")\nplt.title(\"Factors in Terms of Importance\")\nplt.show()\n\n\nfig = px.imshow(heatmap_data, x=['Most important', 'Somewhat important', 'Less important'], color_continuous_scale=\"Reds\",title=\"Factors in Terms of Importance\")\n# , labels=dict(x=\"Rating\", y=\"Factor\", color=\"Frequency\")\nfig.update_xaxes(tickfont=dict(size=11))\nfig.show()","execution_count":null,"outputs":[{"name":"stdout","text":"2    40\n3    35\n1    18\nName: Sustainability, dtype: int64\n[18, 40, 35]\n1    46\n2    31\n3    16\nName: Safety, dtype: int64\n[16, 46, 31]\n3    42\n1    29\n2    22\nName: Convenience, dtype: int64\n[29, 22, 42]\n    0   1   2\n0  18  40  35\n1  16  46  31\n2  29  22  42\n","output_type":"stream"},{"data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Factors in Terms of Importance\"}, \"xaxis\": {\"constrain\": \"domain\", \"scaleanchor\": \"y\", \"tickfont\": {\"size\": 11}}, \"yaxis\": {\"autorange\": \"reversed\", \"constrain\": \"domain\"}},                        {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('39d8ffe9-565c-4ecd-aa61-0460f91c208e');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                            </script>        </div>\n</body>\n</html>"},"metadata":{},"output_type":"display_data"}]},{"cell_type":"code","metadata":{"tags":[],"output_cleared":false,"source_hash":"2d3d4a02","execution_millis":505,"deepnote_to_be_reexecuted":false,"execution_start":1609826262370,"cell_id":"00007-ea16b964-0175-400a-beb0-b4449629b292","deepnote_cell_type":"code"},"source":"\n\nagreement = survey_dataframe.iloc[3:, 25:]\nagreement.columns = ['Question 1', 'Question 2', 'Question 3', 'Question 4']\n\nagreement_cleaned = pd.DataFrame.dropna(agreement, axis=0, how=\"any\")\nprint(agreement_cleaned)\n\nq1 = agreement_cleaned['Question 1'].value_counts()\nprint(q1)\nq11 = q1['Strongly Disagree']\nq12 = q1['Disagree']\nq13 = q1['Somewhat disagree']\nq14 = q1['Neither agree nor disagree']\nq15 = q1['Somewhat agree']\nq16 = q1['Agree']\nq17 = q1['Strongly agree']\nq1_agreement = [q11, q12, q13, q14, q15, q16, q17]\n\nq2 = agreement_cleaned['Question 2'].value_counts()\nprint(q2)\nq21 = q2['Strongly Disagree']\nq22 = 0 #hard coded lol\nq23 = q2['Somewhat disagree']\nq24 = q2['Neither agree nor disagree']\nq25 = q2['Somewhat agree']\nq26 = q2['Agree']\nq27 = q2['Strongly agree']\nq2_agreement = [q21, q22, q23, q24, q25, q26, q27]\n\nq3 = agreement_cleaned['Question 3'].value_counts()\nq31 = q3['Strongly Disagree']\nq32 = q3['Disagree']\nq33 = q3['Somewhat disagree']\nq34 = q3['Neither agree nor disagree']\nq35 = q3['Somewhat agree']\nq36 = q3['Agree']\nq37 = q3['Strongly agree']\nq3_agreement = [q31, q32, q33, q34, q35, q36, q37]\nprint(q3)\n\nq4 = agreement_cleaned['Question 4'].value_counts()\nq41 = q4['Strongly Disagree']\nq42 = q4['Disagree']\nq43 = q4['Somewhat disagree']\nq44 = q4['Neither agree nor disagree']\nq45 = q4['Somewhat agree']\nq46 = q4['Agree']\nq47 = q4['Strongly agree']\nq4_agreement = [q41, q42, q43, q44, q45, q46, q47]\nprint(q4)\n\nagreement_data = pd.DataFrame([q1_agreement, q2_agreement, q3_agreement, q4_agreement])\n# agreement_data = agreement_data.T\n# Corresponding to Questions 1-4\nagreement_data.index = ['Willing to Switch', 'Environmentally Beneficial', 'Difficut', 'Safe'] # FIX LATER\nagreement_data.columns = ['Strongly disagree', 'Disagree', 'Somewhat disagree', 'Neither agree nor disagree', 'Somewhat agree', 'Agree', 'Strongly agree']\nprint(agreement_data)\n\n\nplt.figure(figsize = (15,5))\nagreement_heatmap = sns.heatmap(agreement_data, cmap=\"Reds\", annot=True)\nagreement_heatmap.set_yticklabels(agreement_heatmap.get_yticklabels(), rotation=360)\nplt.title(\"Student perspectives on proposed reusable container system\")\nplt.show()\n\nfig = px.imshow(agreement_data, color_continuous_scale=\"mint\", labels=dict(color=\"Frequency\"), title=\"Student perspectives on proposed reusable container system\")\n# , labels=dict(x=\"Rating\", y=\"Factor\", color=\"Frequency\")\nfig.update_layout(\n    autosize=False,\n    width=900,\n    height=600,\n)\nfig.update_xaxes(tickangle=90, tickfont=dict(size=12))\nfig.show()\n\n","execution_count":null,"outputs":[{"name":"stdout","text":"                     Question 1      Question 2                  Question 3  \\\n3                Somewhat agree  Strongly agree              Somewhat agree   \n4    Neither agree nor disagree  Strongly agree              Somewhat agree   \n5             Strongly Disagree           Agree                       Agree   \n6                Strongly agree  Strongly agree  Neither agree nor disagree   \n7             Strongly Disagree  Somewhat agree                       Agree   \n..                          ...             ...                         ...   \n99            Somewhat disagree  Somewhat agree                       Agree   \n100              Somewhat agree  Strongly agree                       Agree   \n101              Strongly agree  Strongly agree              Somewhat agree   \n102              Strongly agree  Strongly agree                    Disagree   \n103           Somewhat disagree  Somewhat agree                       Agree   \n\n            Question 4  \n3       Strongly agree  \n4    Somewhat disagree  \n5    Strongly Disagree  \n6                Agree  \n7    Strongly Disagree  \n..                 ...  \n99            Disagree  \n100              Agree  \n101     Strongly agree  \n102     Somewhat agree  \n103  Somewhat disagree  \n\n[101 rows x 4 columns]\nStrongly agree                32\nAgree                         25\nSomewhat agree                14\nStrongly Disagree             10\nSomewhat disagree              9\nNeither agree nor disagree     8\nDisagree                       3\nName: Question 1, dtype: int64\nStrongly agree                66\nAgree                         19\nSomewhat agree                13\nNeither agree nor disagree     1\nSomewhat disagree              1\nStrongly Disagree              1\nName: Question 2, dtype: int64\nSomewhat agree                32\nAgree                         24\nDisagree                      13\nStrongly agree                10\nStrongly Disagree              9\nSomewhat disagree              7\nNeither agree nor disagree     6\nName: Question 3, dtype: int64\nStrongly agree                31\nAgree                         26\nSomewhat disagree             15\nSomewhat agree                12\nDisagree                       8\nStrongly Disagree              6\nNeither agree nor disagree     3\nName: Question 4, dtype: int64\n                            Strongly disagree  Disagree  Somewhat disagree  \\\nWilling to Switch                          10         3                  9   \nEnvironmentally Beneficial                  1         0                  1   \nDifficut                                    9        13                  7   \nSafe                                        6         8                 15   \n\n                            Neither agree nor disagree  Somewhat agree  Agree  \\\nWilling to Switch                                    8              14     25   \nEnvironmentally Beneficial                           1              13     19   \nDifficut                                             6              32     24   \nSafe                                                 3              12     26   \n\n                            Strongly agree  \nWilling to Switch                       32  \nEnvironmentally Beneficial              66  \nDifficut                                10  \nSafe                                    31  \n","output_type":"stream"},{"data":{"text/plain":"<Figure size 1080x360 with 2 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\"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Student perspectives on proposed reusable container system\"}, \"width\": 900, \"xaxis\": {\"constrain\": \"domain\", \"scaleanchor\": \"y\", \"tickangle\": 90, \"tickfont\": {\"size\": 12}}, \"yaxis\": {\"autorange\": \"reversed\", \"constrain\": \"domain\"}},                        {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('34592d74-094b-4301-b2ff-93da36905ee6');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            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\nagreement_data2 = pd.DataFrame([q1_agreement, q3_agreement, q4_agreement])\nprint(agreement_data2)\n# Corresponding to Questions 1, 3, 4\nagreement_data2.index = ['Willing to Switch', 'Difficult', 'Safe'] # FIX LATER\nlst = ['Strongly disagree', 'Disagree', 'Somewhat disagree', 'Neither agree nor disagree', 'Somewhat agree', 'Agree', 'Strongly agree']\nagreement_data2.columns = lst\n\n\nplt.figure(figsize = (15,5))\nagreement_heatmap2 = sns.heatmap(agreement_data2, cmap=\"Reds\", annot=True)\nagreement_heatmap2.set_yticklabels(agreement_heatmap2.get_yticklabels(), rotation=360)\nplt.title(\"Student perspectives on proposed reusable container system: Heatmap\")\nplt.show()\n\nfig2 = px.imshow(agreement_data2, color_continuous_scale=\"reds\", labels=dict(color=\"Frequency\"), title=\"Student perspectives on proposed reusable container system\")\n# , labels=dict(x=\"Rating\", y=\"Factor\", color=\"Frequency\")\nfig2.update_layout(\n    autosize=False,\n    width=900,\n    height=600,\n)\n\n# 570101\n# 760000\n# BE1E26\n# D84742\n# f25246\n# FF6B61\n# FF9586\n\nfig2.update_xaxes(tickangle=270, tickfont=dict(size=12))\nfig2.show()\n\nq1_bar = q1_agreement.copy()\nq1_bar.reverse()\nq3_bar = q3_agreement.copy()\nq3_bar.reverse()\nq4_bar = q4_agreement.copy()\nq4_bar.reverse()\nlst_bar = lst.copy()\nlst_bar.reverse()\nbar_data = pd.DataFrame([q1_bar, q3_bar, q4_bar])\nbar_data.index = ['Willing to Switch', 'Difficult', 'Safe']\nbar_data.columns = lst_bar\nbar_chart = px.bar(bar_data, barmode = 'stack', labels = {'value':'Frequency', 'index': '', 'variable': ''}, \n# 570101\n# how to you get rid of the white borders between bars\ncolor_discrete_sequence=['#760000', '#b0171f', '#d23a35', '#eb4e42', '#ff7066', '#FF9586', '#ffafa3'], title=\"Student perspectives on proposed reusable container system: Segmented Bar Chart\")\n\nbar_chart.update_traces(marker_line_width=0)\nbar_chart.show()","execution_count":null,"outputs":[{"name":"stdout","text":"    0   1   2  3   4   5   6\n0  10   3   9  8  14  25  32\n1   9  13   7  6  32  24  10\n2   6   8  15  3  12  26  31\n","output_type":"stream"},{"data":{"text/plain":"<Figure size 1080x360 with 2 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\n"},"metadata":{"needs_background":"light"},"output_type":"display_data"},{"data":{"text/html":"<html>\n<head><meta 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