Biomage analysis survey_comparison with 2020.ipynb

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   "source": [
    "### **Comparison with 2020 data**\n",
    "\n",
    "The 2022 bioimage analysis survey data was compared with the data from 2020."
   ]
  },
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   "source": [
    "#importing the necessary modules\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import plotly.io as pio\n",
    "import kaleido\n",
    "from textwrap import wrap\n",
    "from utils import * \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reading data from another notebook \n",
    "%store -r data"
   ]
  },
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   "source": [
    "#### Role"
   ]
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   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pie chart for the roles of the survey participants \n",
    "role_2021 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\Which_of_the_following_roles_best_describes_you.txt')\n",
    "role_2021= role_2021.rename(columns={'0':'role'})\n",
    "role_2021['category'] = role_2021['role'].apply(lambda x: 'Other' if x not in ['Postdoctoral fellow','Undergraduate/Graduate student', 'Principal investigator', 'Facility staff', 'Research scientist', 'Image analyst', 'Facility director', 'Clinician'] else x)\n",
    "role_2021['category_1'] = role_2021['category'].apply(lambda x: 'Image/data analyst' if x=='Image analyst' else x)\n",
    "\n"
   ]
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    "#combined figure\n",
    "role_2021_2022 = go.Figure()\n",
    "\n",
    "role_2021_2022.add_traces(go.Bar(name='2020', x=role_2021[\"category_1\"].value_counts().index, y=role_2021[\"category_1\"].value_counts().values, orientation='v', text=role_2021[\"category_1\"].value_counts().values))\n",
    "role_2021_2022.add_traces(go.Bar(name='2022', x=data['Role'].value_counts().index, y=data['Role'].value_counts().values, orientation='v', text=data['Role'].value_counts().values))\n",
    "role_2021_2022.update_layout(width=600, height=500, title='Role', title_x=0.5, title_y=0.85, font=dict(family='Helvetica', color=\"Black\", size=16), legend=dict(title_font_family = 'Helvetica', font=dict(size=16, color=\"Black\")))\n",
    "role_2021_2022.update_yaxes(categoryorder = 'total ascending')\n"
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   "source": [
    "#### Location"
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         "text": "Location",
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         "y": 0.85
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        "width": 500
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     },
     "metadata": {},
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   "source": [
    "#location \n",
    "location_2021 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\Where_do_you_currently_primarily_work.txt')\n",
    "location_2021= location_2021.rename(columns={'0':'location'})\n",
    "\n",
    "#combined figure\n",
    "loc = go.Figure()\n",
    "\n",
    "loc.add_traces(go.Bar(name='2020', x=location_2021['location'].value_counts().index, y=location_2021['location'].value_counts().values, text=location_2021['location'].value_counts().values))\n",
    "loc.add_traces(go.Bar(name='2022', x=data['Location'].value_counts().index, y=data['Location'].value_counts().values, text=data['Location'].value_counts().values))\n",
    "loc.update_layout(width=500, height=500, title='Location', title_x=0.5, title_y=0.85, font=dict(family='Helvetica', color=\"Black\", size=16), legend=dict(title_font_family = 'Helvetica', font=dict(size=16, color=\"Black\")))\n"
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   "source": [
    "#### Work type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# work type_2021\n",
    "work_type_2021 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\How_would_you_describe_your_work.txt')\n",
    "work_type_2021= work_type_2021.rename(columns={'0':'work_type'})\n",
    "work_type_2021.loc[work_type_2021[\"work_type\"] < 3, \"Work type\" ] = \"Imaging\"\n",
    "work_type_2021.loc[(work_type_2021[\"work_type\"] >= 3) & (work_type_2021[\"work_type\"] < 5), \"Work type\"] = \"Balanced\"\n",
    "work_type_2021.loc[work_type_2021[\"work_type\"] >= 5, \"Work type\"] = \"Analyst\"\n",
    "work_type_counts_2021 = work_type_2021[\"Work type\"].value_counts()\n",
    "work_type_counts_2021 \n",
    "#knowledge in computational skills_2021 \n",
    "comp_skill_2021 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\How_would_you_rate_your_computational_skills.txt')\n",
    "comp_skill_2021= comp_skill_2021.rename(columns={'0':'comp_skill'})\n",
    "\n",
    "comp_skill_2021.loc[comp_skill_2021[\"comp_skill\"] < 3, \"Knowledge of computational skills\"] = \"Low skill\"\n",
    "comp_skill_2021.loc[(comp_skill_2021[\"comp_skill\"] >= 3) & (comp_skill_2021[\"comp_skill\"] < 5), \"Knowledge of computational skills\"] = \"Medium skill\"\n",
    "comp_skill_2021.loc[comp_skill_2021[\"comp_skill\"] >= 5, \"Knowledge of computational skills\"] = \"High skill\"\n",
    "computational_knowledge_2021 = comp_skill_2021[\"Knowledge of computational skills\"].value_counts()\n"
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  {
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           "bgcolor": "#E5ECF6",
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          },
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           "ticks": "",
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     },
     "metadata": {},
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   ],
   "source": [
    "%store -r work_type_counts\n",
    "# comparing 2021 and 2022 work type\n",
    "work_21_22 = go.Figure()\n",
    "\n",
    "work_21_22.add_trace(go.Bar(name=2020, x=work_type_counts_2021.index, y=work_type_counts_2021.values, text=work_type_counts_2021.values))\n",
    "work_21_22.add_trace(go.Bar(name=2022, x=work_type_counts.index , y=work_type_counts.values, text=work_type_counts.values))\n",
    "\n",
    "work_21_22.update_layout(title = 'Work type', title_x=0.5, width=400, height=400, font=dict(family='Helvetica', color=\"Black\", size=16), legend=dict(title_font_family = 'Helvetica', font=dict(size=16, color=\"Black\")))\n",
    "# comparing 2021 and 2022 work type and comp skill in percentage\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "work_21_22_percent= go.Figure()\n",
    "work_21_22_percent = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])\n",
    "\n",
    "work_21_22_percent.add_trace(go.Pie(labels=work_type_counts_2021.index, values=work_type_counts_2021.values), 1,1)\n",
    "work_21_22_percent.add_trace(go.Pie(labels = work_type_counts.index , values=work_type_counts.values),1,2)\n",
    "work_21_22_percent.update_traces(hole=.4, hoverinfo=\"label+percent+name\")\n",
    "work_21_22_percent.update_layout(title_x=0.5, title_y=0.80, title_text=\"Work type\", annotations=[dict(text='2020', x=0.18, y=0.5, font_size=16, showarrow=False),dict(text='2022', x=0.82, y=0.5, font_size=16, showarrow=False)])\n",
    "work_21_22_percent.update_layout(font=dict(family='Helvetica', color=\"Black\", size=16), legend=dict(title_font_family = 'Helvetica', font=dict(size=16, color=\"Black\")))\n",
    "work_21_22_percent.update_layout(width=600, height=350)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Computational knowledge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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         "type": "bar",
         "x": [
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         "y": [
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           "bgcolor": "#E5ECF6",
           "caxis": {
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          },
          "title": {
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        },
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      }
     },
     "metadata": {},
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   ],
   "source": [
    "%store -r computational_knowledge\n",
    "# comparing 2021 and 2022 comp skill\n",
    "comp_skill_21_22 = go.Figure()\n",
    "\n",
    "comp_skill_21_22.add_trace(go.Bar(name=2020, x= computational_knowledge_2021.index, y=computational_knowledge_2021.values, text=computational_knowledge_2021.values))\n",
    "comp_skill_21_22.add_trace(go.Bar(name=2022, x=computational_knowledge.index , y=computational_knowledge.values, text=computational_knowledge.values))\n",
    "\n",
    "comp_skill_21_22.update_layout(title = 'Computational knowledge', title_x=0.5, width=400, height=400, font=dict(family='Helvetica', color=\"Black\", size=16), legend=dict(title_font_family = 'Helvetica', font=dict(size=16, color=\"Black\")))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# comfort in developing new computational skills \n",
    "comf_2021 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\How_would_you_rate_your_comfort_in_developing_new_computational_skills.txt')\n",
    "comf_2021= comf_2021.rename(columns={'0':'comfort'})\n",
    "\n",
    "# Comfort in developing new computational skills\n",
    "comf_2021.loc[comf_2021[\"comfort\"] < 3, 'Comfort'] = 'Low comfort'\n",
    "comf_2021.loc[(comf_2021['comfort'] >= 3) & (comf_2021['comfort'] < 5), 'Comfort'] = \"Medium comfort\"\n",
    "comf_2021.loc[comf_2021['comfort'] >= 5 , 'Comfort'] = \"High comfort\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Comfort'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\ssivagur\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3628\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3629\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3630\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\ssivagur\\Anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\ssivagur\\Anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\ssivagur\\Anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine._get_loc_duplicates\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\ssivagur\\Anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine._maybe_get_bool_indexer\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\ssivagur\\Anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index._unpack_bool_indexer\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Comfort'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_24584\\3398713573.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mcomf_21_22\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_trace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2020\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mcomf_2021\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Comfort'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcomf_2021\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Comfort'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtext\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcomf_2021\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Comfort'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mcomf_21_22\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_trace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2022\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Comfort'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Comfort'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtext\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Comfort'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mcomf_21_22\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate_layout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtitle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'Comfort in developing <br> new computational skills'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtitle_x\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m400\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m400\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfont\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfamily\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Helvetica'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Black\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m14\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlegend\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtitle_font_family\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'Helvetica'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfont\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m16\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Black\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\ssivagur\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3503\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3504\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3505\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3506\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3507\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\ssivagur\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3629\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3630\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3631\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3632\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3633\u001b[0m                 \u001b[1;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Comfort'"
     ]
    }
   ],
   "source": [
    "#Comparing comfort in developing new computational skills\n",
    "comf_21_22 = go.Figure()\n",
    "\n",
    "comf_21_22.add_trace(go.Bar(name=2020, x= comf_2021['Comfort'].value_counts().index, y=comf_2021['Comfort'].value_counts().values, text=comf_2021['Comfort'].value_counts().values))\n",
    "comf_21_22.add_trace(go.Bar(name=2022, x=data['Comfort'].value_counts().index, y=data['Comfort'].value_counts().values, text=data['Comfort'].value_counts().values))\n",
    "\n",
    "comf_21_22.update_layout(title = 'Comfort in developing <br> new computational skills', title_x=0.5, width=400, height=400, font=dict(family='Helvetica', color=\"Black\", size=14), legend=dict(title_font_family = 'Helvetica', font=dict(size=16, color=\"Black\")))\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Approach to image analysis problems"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#solving image analysis problems- 2020\n",
    "\n",
    "sol_2021 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\How_do_you_generally_go_about_solving_an_image_analysis_problem_Check_the_approaches_you_use_the_most.txt')\n",
    "sol_2021 = sol_2021.rename(columns={'0':'solution'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_other(series, list_to_include=[]):\n",
    "    list = sol_2021['solution'].dropna().tolist()\n",
    "    list_split = [i.split(',') for i in list]\n",
    "    list_flat = [i for innerlist in list_split for i in innerlist]\n",
    "    list_wo_spaces = [i.lstrip() for i in list_flat] #removing the leading spaces in the string \n",
    "    list_counts = dict((x,list_wo_spaces.count(x)) for x in set(list_wo_spaces))\n",
    "\n",
    "    filtered_dict = {k:v for k,v in list_counts.items() if k in list_to_include} \n",
    "    filtered_dict_other = {k:v for k,v in list_counts.items() if k not in list_to_include}  # filtering out the 'Other'category\n",
    "    df = pd.DataFrame.from_dict(filtered_dict, orient='index')\n",
    "    other_df = pd.DataFrame.from_dict(filtered_dict_other, orient ='index')\n",
    "    df = df.reset_index()\n",
    "    df = df.rename(columns={0:'counts'})\n",
    "\n",
    "    #Other category is added  as a separate row along with the sum \n",
    "    other = {'index':'Other', 'counts':other_df[0].sum()}\n",
    "    df = df.append(other, ignore_index=True)\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ssivagur\\AppData\\Local\\Temp\\ipykernel_24584\\3258138431.py:17: FutureWarning:\n",
      "\n",
      "The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sol_2020_df = count_other(sol_2021['solution'], list_to_include =['Sit down with a tool I know and start playing with the data', 'Ask a friend or colleague to help me', 'Ask on forum.image.sc', 'Look up solutions generally on the internet (Google)',\n",
    "'Look up solutions on a particular website (please list below)', 'Look up solutions in the scientific literature'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def barchart_df(series):\n",
    "    #creating the dcitionary of the unique values with the counts \n",
    "    list = series.dropna(how='all').tolist()\n",
    "    list_split = [re.split(r',\\s*(?![^()]*\\))', i) for i in list]   #splitting the list based on comma but not the ones inside the bracket \n",
    "    list_flat = [i for innerlist in list_split for i in innerlist]  #converting a nested list to a flat list \n",
    "    list_wo_spaces = [i.lstrip() for i in list_flat] #removing the leading spaces in the string \n",
    "    dict_count = dict((x,list_wo_spaces.count(x)) for x in set(list_wo_spaces)) # creating a dictionary with counts of each unique values \n",
    "    \n",
    "\n",
    "    #creating a dataframe from the modified dictionary \n",
    "    df = pd.DataFrame.from_dict(dict_count, orient='index')\n",
    "    df = df.rename(columns={0:'counts'})\n",
    "    return df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "sol_2022_df = barchart_df(data[\"Approach to solutions\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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   "source": [
    "\n",
    "#Comparing - approaches to image analysis problems \n",
    "sol_21_22 = go.Figure()\n",
    "\n",
    "sol_21_22.add_trace(go.Bar(name=2020, y= sol_2020_df['index'], x=sol_2020_df['counts'], text=sol_2020_df['counts'], orientation='h'))\n",
    "sol_21_22.add_trace(go.Bar(name=2022, y=sol_2022_df.index, x=sol_2022_df['counts'], text=sol_2022_df['counts'], orientation='h'))\n",
    "sol_21_22.update_layout(title = 'Approaches to image analysis problems', title_x=0.5, width=700, height=400, font=dict(family='Helvetica', color=\"Black\", size=16), legend=dict(title_font_family = 'Helvetica', font=dict(size=16, color=\"Black\")))\n",
    "sol_21_22.update_yaxes(categoryorder='total ascending')\n"
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  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ask a friend or colleague to help me</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ask a friend or colleague to help me</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ask a friend or colleague to help me</td>\n",
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       "      <td>Ask a friend or colleague to help me</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ask a friend or colleague to help me</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>Sit down with a tool I know and start playing ...</td>\n",
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       "      <th>480</th>\n",
       "      <td>Sit down with a tool I know and start playing ...</td>\n",
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       "      <th>481</th>\n",
       "      <td>Sit down with a tool I know and start playing ...</td>\n",
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       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>Sit down with a tool I know and start playing ...</td>\n",
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       "      <th>483</th>\n",
       "      <td>sit down with a software (Fiji) and try differ...</td>\n",
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       "<p>484 rows × 1 columns</p>\n",
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       "                                              solution\n",
       "0                 Ask a friend or colleague to help me\n",
       "1                 Ask a friend or colleague to help me\n",
       "2                 Ask a friend or colleague to help me\n",
       "3                 Ask a friend or colleague to help me\n",
       "4                 Ask a friend or colleague to help me\n",
       "..                                                 ...\n",
       "479  Sit down with a tool I know and start playing ...\n",
       "480  Sit down with a tool I know and start playing ...\n",
       "481  Sit down with a tool I know and start playing ...\n",
       "482  Sit down with a tool I know and start playing ...\n",
       "483  sit down with a software (Fiji) and try differ...\n",
       "\n",
       "[484 rows x 1 columns]"
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     },
     "execution_count": 18,
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   ],
   "source": [
    "## Usage of image sc forum in different work type\n",
    "sol_2021"
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  },
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   "metadata": {},
   "source": [
    "#### Notifications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Notifications of the future workshops\n",
    "notifications_2021 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\How_would_you_prefer_to_be_notified_about_image_analysis_workshops_sessions_or_conferences_being_planned.txt')\n",
    "notifications_2021 = notifications_2021.rename(columns={'0':'Notifications'})\n",
    "notifications_2021['notify'] = notifications_2021['Notifications'].apply(lambda x: 'Other' if x not in ['Email list', 'Postings on image.sc forum or microforum', 'Twitter', 'My local microscopy facility', 'Word of mouth'] else x)\n"
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   "source": [
    "#reading data from other notebook\n",
    "%store -r counts_notifications\n",
    "\n",
    "#Comparison - 2020 ad 2022\n",
    "not_21_22 = go.Figure()\n",
    "\n",
    "not_21_22.add_trace(go.Bar(name='2020', y=notifications_2021['notify'].value_counts().index, x=notifications_2021['notify'].value_counts().values, orientation='h', text=notifications_2021['notify'].value_counts().values))\n",
    "not_21_22.add_trace(go.Bar(name='2022', y=counts_notifications['notify'].value_counts().index, x=counts_notifications['notify'].value_counts().values, orientation='h', text=counts_notifications['notify'].value_counts().values))\n",
    "not_21_22.update_layout(title='Notifications for the future image analysis workshops', title_x=0.5, title_y=0.9, width=600, height=500)\n",
    "not_21_22.update_yaxes(categoryorder='total ascending')\n",
    "not_21_22.update_layout(font=dict(family='Helvetica', color=\"Black\", size=16), legend=dict(title_font_family = 'Helvetica', font=dict(size=16, color=\"Black\")))\n"
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   "source": [
    "#### Most used image analysis tools "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#most used image analysis tools\n",
    "most_used_2021 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\What_image_analysis_tools_do_you_use_the_most.txt')\n",
    "most_used_2021 = most_used_2021.rename(columns={'0':'most_used_tools'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tools_count_df(series):\n",
    "#creating the dcitionary of the unique values with the counts \n",
    "    list = series.dropna(how='all').tolist()\n",
    "    list_split = [re.split(r',\\s*(?![^()]*\\))', i) for i in list]   #splitting the list based on comma but not the ones inside the bracket \n",
    "    list_flat = [i for innerlist in list_split for i in innerlist]  #converting a nested list to a flat list \n",
    "    list_wo_spaces = [i.rstrip() for i in list_flat] #removing the leading spaces in the string \n",
    "    dict_count = dict((x,list_wo_spaces.count(x)) for x in set(list_wo_spaces)) # creating a dictionary with counts of each unique values \n",
    "   \n",
    "    #shortening the index to avoid having lengthy axis labels\n",
    "    final_key=[]\n",
    "    for i in dict_count.keys():\n",
    "        new_key = i.split('(')\n",
    "        new_key = [new_key[0]]\n",
    "        new_key = [i.rstrip() for i in new_key]\n",
    "        final_key = final_key + new_key\n",
    "        \n",
    "    #changing the key values in a dictionary \n",
    "    list = dict_count.values()\n",
    "    final_dict = dict(zip(final_key, list))\n",
    "    \n",
    "    #creating a dataframe from the modified dictionary \n",
    "    tools_df = pd.DataFrame.from_dict(final_dict, orient='index')\n",
    "    tools_df = tools_df.rename(columns={0:'counts'})\n",
    "    tools_df = tools_df.drop('None')\n",
    "    return tools_df"
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "most_2022_df = tools_count_df(data['Most used image analysis tools'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
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    "\n",
    "#using the following instead of teh value counts since the legends couldn't be shortened \n",
    "most_used_2021['Commercial software that comes with my microscope'] = most_used_2021['most_used_tools'].apply(lambda x:True if x == 'Commercial software that comes with my microscope (ie Columbus, Elements, Softworx, etc)' else False)\n",
    "most_used_2021['Other commercial software'] = most_used_2021['most_used_tools'].apply(lambda x: True if x == 'Other commercial software (Imaris, Volocity, etc)' else False)\n",
    "most_used_2021['Open source point-and-click software'] = most_used_2021['most_used_tools'].apply(lambda x: True if x == 'Open source point-and-click software (ImageJ, FIJI, Icy, CellProfiler, etc)'else False)\n",
    "most_used_2021['Computational libraries and scripts'] = most_used_2021['most_used_tools'].apply(lambda x:True if x == 'Computational libraries and scripts (scikit-image, MATLAB, etc)' else False)\n",
    "most_used_2021['None'] = most_used_2021['most_used_tools'].apply(lambda x:True if x == 'None' else False)\n",
    "most_used_2021_subset = most_used_2021.iloc[:, 1:6]\n",
    "most_used_2021_subset\n",
    "\n",
    "#creating a subset\n",
    "most_used_2021_df = pd.DataFrame(most_used_2021_subset.sum())\n",
    "most_used_2021_df = most_used_2021_df.reset_index()\n",
    "most_used_2021_df = most_used_2021_df.rename(columns={'index':'tools', 0 :'counts'})\n",
    "most_used_2021_df\n",
    "\n",
    "#comparing with 2022\n",
    "most_used_21_22 = go.Figure()\n",
    "\n",
    "most_used_21_22.add_trace(go.Bar(name='2020', x= most_used_2021_df['counts'], y=most_used_2021_df['tools'], orientation='h', text=most_used_2021_df['counts']))\n",
    "most_used_21_22.add_trace(go.Bar(name='2022', x=most_2022_df['counts'], y=most_2022_df.index, orientation='h', text=most_2022_df['counts']))\n",
    "most_used_21_22.update_layout(title = 'Most used image analysis tools', title_x=0.5,title_y = 0.95, width=700, height=400)\n",
    "most_used_21_22.update_layout(font=dict(family='Helvetica', color=\"Black\", size=16), legend=dict(title_font_family = 'Helvetica', font=dict(size=16, color=\"Black\")))\n",
    "most_used_21_22.update_yaxes(categoryorder='total ascending')"
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   "source": [
    "#### Commonly used image analysis tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "com_tool_df = tools_count_df(data['Commonly used image analysis tools'])"
   ]
  },
  {
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   "execution_count": null,
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           "bgcolor": "#E5ECF6",
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     },
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   ],
   "source": [
    "#What image analysis tools have you used before?\n",
    "com_tool_2021 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\What_image_analysis_tools_have_you_used_before_check_all_that_apply.txt')\n",
    "com_tool_2021 = com_tool_2021.rename(columns={'0':'com_used_tools'})\n",
    "\n",
    "#Commonly used image analysis tools\n",
    "com_tool_2021['Commonly used image analysis tools'] = com_tool_2021['com_used_tools'].astype(str) # this is done to ignore the Nan for analysis \n",
    "com_tool_2021[\"Commercial software that comes with my microscope\"] = com_tool_2021['com_used_tools'].apply(lambda x: True if 'Columbus' in x else False)\n",
    "com_tool_2021[\"Other commercial software\"] = com_tool_2021['com_used_tools'].apply(lambda x: True if 'Imaris' in x else False)\n",
    "com_tool_2021[\"Open source point-and-click software\"] = com_tool_2021[\"com_used_tools\"].apply(lambda x: True if 'FIJI' in x else False)\n",
    "com_tool_2021[\"Computational libraries and scripts\"] = com_tool_2021[\"com_used_tools\"].apply(lambda x: True if 'MATLAB' in x else False)\n",
    "com_tool_2021[\"Tools_none\"] = com_tool_2021[\"com_used_tools\"].apply(lambda x: True if 'None' in x else False)\n",
    "\n",
    "# creating a subset of the dataframe\n",
    "com_tool_2021_subset = com_tool_2021.iloc[:, 2:7]\n",
    "com_tool_2021_df = pd.DataFrame(com_tool_2021_subset.sum())\n",
    "com_tool_2021_df = com_tool_2021_df.reset_index()\n",
    "com_tool_2021_df = com_tool_2021_df.rename(columns={'index':'tools', 0:'counts'})\n",
    "com_tool_2021_df\n",
    "# comparison of the commonly used image analysis tools - 2020 and 2022\n",
    "com_tool_21_22 = go.Figure()\n",
    "\n",
    "com_tool_21_22.add_trace(go.Bar(name='2020', y=com_tool_2021_df['tools'], x=com_tool_2021_df['counts'], text=com_tool_2021_df['counts'], orientation='h'))\n",
    "com_tool_21_22.add_trace(go.Bar(name='2022', x=com_tool_df['counts'], y=com_tool_df.index, text=com_tool_df['counts'], orientation='h'))\n",
    "\n",
    "com_tool_21_22.update_layout(width=700, height=400, title=\"Commonly used image analysis tools\", title_x=0.5, font=dict(family='Helvetica', color=\"Black\", size=16), legend=dict(title_font_family = 'Helvetica', font=dict(size=16, color=\"Black\")))\n",
    "com_tool_21_22.update_yaxes(categoryorder = 'total ascending')\n"
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  },
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   "source": [
    "#### Kinds of images analyzed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#what type of images do you commonly want to analyze?\n",
    "\n",
    "# Brightfield manually acquired-2020\n",
    "BF_manual_2020 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\What_kinds_of_images_do_you_commonly_want_to_analyze_Brightfield_DIC_phase-contrast_of_cells_or_organisms_from_manually_selected_fields.txt')\n",
    "BF_manual_2020 = BF_manual_2020.rename(columns={'0':'Brightfield/DIC/phase-contrast manually acquired'})\n",
    "\n",
    "# Brightfield automated-2020\n",
    "BF_automated_2020 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\What_kinds_of_images_do_you_commonly_want_to_analyze_Brightfield_DIC_phase-contrast_of_cells_or_organisms_from_an_automated_microscope.txt')\n",
    "BF_automated_2020 = BF_automated_2020.rename(columns={'0':'Brightfield/DIC/phase-contrast automated'})\n",
    "\n",
    "#fluorescent manual -2020\n",
    "fluorescent_manual_2020 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\What_kinds_of_images_do_you_commonly_want_to_analyze_Fluorescent_images_of_cells_organisms_from_manually_selected_fields.txt')\n",
    "fluorescent_manual_2020 = fluorescent_manual_2020.rename(columns={'0':'Fluorescent images manually acquired'})\n",
    "\n",
    "#fluorescent automated -2020\n",
    "fluorescent_automated_2020 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\What_kinds_of_images_do_you_commonly_want_to_analyze_Fluorescent_images_of_cells_organisms_from_an_automated_microscope.txt')\n",
    "fluorescent_automated_2020 = fluorescent_automated_2020.rename(columns={'0':'Fluorescent images automated'})\n",
    "\n",
    "#histology in tissue sections-2020\n",
    "histology_2020 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\What_kinds_of_images_do_you_commonly_want_to_analyze_Histologically_stained_tissue_sections.txt')\n",
    "histology_2020 = histology_2020.rename(columns={'0':'Histological tissue sections'})\n",
    "\n",
    "#fluorescence in tissue sections-2020\n",
    "flo_2020 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\What_kinds_of_images_do_you_commonly_want_to_analyze_Fluorescently_stained_tissue_sections.txt')\n",
    "flo_2020 = flo_2020.rename(columns={'0':'Fluorescence in tissue sections'})\n",
    "\n",
    "#Electron microscopy -2020\n",
    "elec_2020 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\What_kinds_of_images_do_you_commonly_want_to_analyze_Electron_microscopy.txt')\n",
    "elec_2020 = elec_2020.rename(columns={'0':'Electron microscopy'})\n",
    "\n",
    "#Imaging mass spectrometry - 2020\n",
    "ms_2020 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\What_kinds_of_images_do_you_commonly_want_to_analyze_Imaging_mass_spectrometry.txt')\n",
    "ms_2020 = ms_2020.rename(columns={'0':'Imaging mass spectrometry'})\n",
    "\n",
    "#Imaging flow cytometry - 2020\n",
    "fc_2020 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\What_kinds_of_images_do_you_commonly_want_to_analyze_Imaging_flow_cytometry.txt')\n",
    "fc_2020 = fc_2020.rename(columns={'0':'Imaging flow cytometry'})\n",
    "\n",
    "#super resolution - 2020\n",
    "sr_2020 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\What_kinds_of_images_do_you_commonly_want_to_analyze_Superresolution_PALM_STORM.txt')\n",
    "sr_2020 = sr_2020.rename(columns={'0':'Superresolution (PALM/STORM)'})\n",
    "\n",
    "#FLIM -2020\n",
    "flim_2020 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\What_kinds_of_images_do_you_commonly_want_to_analyze_Autofluorescence_imaging_ie_FLIM.txt')\n",
    "flim_2020 = flim_2020.rename(columns={'0':'Autofluorescence imaging (ie FLIM)'})\n",
    "\n",
    "# combining all the dataframes - modality - 2020\n",
    "modality_type_combined_2020 = pd.concat([BF_manual_2020,BF_automated_2020,fluorescent_manual_2020,fluorescent_automated_2020,histology_2020,\n",
    "flo_2020,elec_2020,ms_2020,fc_2020,sr_2020,flim_2020], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
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    "stacked_barchart(modality_type_combined_2020, title='Kinds_of_images_analyzed_2020', order_of_stacks=['2D', '2D+time','3D(<3000x3000x100)', '3D+time', '3Dlargevolume+time', '3D(SPIM/largevolume)'])"
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   "source": [
    "#### Topics of interest - comparison"
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  {
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   "execution_count": 19,
   "metadata": {},
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       "      <th>interest</th>\n",
       "      <th>Image analysis theory</th>\n",
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       "      <td>238</td>\n",
       "      <td>234</td>\n",
       "      <td>37.6</td>\n",
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       "      <td>94</td>\n",
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       "      <td>28</td>\n",
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       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>24</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                interest  Image analysis theory  Image analysis practices  \\\n",
       "0        Very interested                    182                       248   \n",
       "1  Moderately interested                    147                       162   \n",
       "2    Somewhat interested                    126                        68   \n",
       "3  Not at all interested                     29                         6   \n",
       "\n",
       "   Practices related to particular field  Specific software tool  \\\n",
       "0                                    360                     238   \n",
       "1                                     94                     149   \n",
       "2                                     28                      83   \n",
       "3                                      2                      14   \n",
       "\n",
       "   Deep learning for image analysis  percent_theory  percent_practices  \\\n",
       "0                               234            37.6               51.2   \n",
       "1                               138            30.4               33.5   \n",
       "2                                88            26.0               14.0   \n",
       "3                                24             6.0                1.2   \n",
       "\n",
       "   percent_particular  percent_sptool  percent_DL  \n",
       "0                74.4            49.2        48.3  \n",
       "1                19.4            30.8        28.5  \n",
       "2                 5.8            17.1        18.2  \n",
       "3                 0.4             2.9         5.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Topics of interest for the tutorials - 2020 and 2022\n",
    "int_2020_theory = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\How_interested_are_you_in_learning_more_about_the_following_topics_Image_analysis_theory_.txt')\n",
    "int_2020_theory = int_2020_theory.rename(columns={'0':'theory'})\n",
    "int_2020_theory = int_2020_theory['theory'].value_counts().to_frame()\n",
    "\n",
    "int_2020_practices = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\How_interested_are_you_in_learning_more_about_the_following_topics_General_image_analysis_practices.txt')\n",
    "int_2020_practices = int_2020_practices.rename(columns={'0':'practices'})\n",
    "int_2020_practices = int_2020_practices['practices'].value_counts().to_frame()\n",
    "\n",
    "int_2020_par_field = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\How_interested_are_you_in_learning_more_about_the_following_topics_Image_analysis_practices_particular_to_my_field.txt')\n",
    "int_2020_par_field = int_2020_par_field.rename(columns={'0':'Practices related to particular field'})\n",
    "int_2020_par_field = int_2020_par_field['Practices related to particular field'].value_counts().to_frame()\n",
    "\n",
    "int_2020_spc_tool = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\How_interested_are_you_in_learning_more_about_the_following_topics_Learning_to_use_a_particular_software_tool.txt')\n",
    "int_2020_spc_tool = int_2020_spc_tool.rename(columns={'0':'Specific software tool'})\n",
    "int_2020_spc_tool = int_2020_spc_tool['Specific software tool'].value_counts().to_frame()\n",
    "\n",
    "int_2020_DL = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\How_interested_are_you_in_learning_more_about_the_following_topics_Deep_learning_as_applied_to_image_analysis.txt')\n",
    "int_2020_DL = int_2020_DL.rename(columns={'0':'Deep learning for image analysis'})\n",
    "int_2020_DL = int_2020_DL['Deep learning for image analysis'].value_counts().to_frame()\n",
    "int_2020_com = pd.concat([int_2020_theory, int_2020_practices, int_2020_par_field, int_2020_spc_tool, int_2020_DL], axis=1)\n",
    "int_2020_com = int_2020_com.rename(columns={'theory':'Image analysis theory', 'practices':'Image analysis practices'})\n",
    "int_2020_com = int_2020_com.reset_index()\n",
    "int_2020_com = int_2020_com.rename(columns={'index':'interest'})\n",
    "\n",
    "#changing the value of the row since the it is 'A little interested' in 2020 and 'somewhat interested' in 2022\n",
    "int_2020_com.loc[2,'interest'] = 'Somewhat interested'\n",
    "\n",
    "#getting the percentage \n",
    "int_2020_com['percent_theory'] = (int_2020_com['Image analysis theory']/int_2020_com['Image analysis theory'].sum()) *100\n",
    "int_2020_com['percent_practices'] = (int_2020_com['Image analysis practices']/int_2020_com['Image analysis practices'].sum()) *100\n",
    "int_2020_com['percent_particular'] = (int_2020_com['Practices related to particular field']/int_2020_com['Practices related to particular field'].sum()) *100\n",
    "int_2020_com['percent_sptool'] = (int_2020_com['Specific software tool']/int_2020_com['Specific software tool'].sum()) *100\n",
    "int_2020_com['percent_DL'] = (int_2020_com['Deep learning for image analysis']/int_2020_com['Deep learning for image analysis'].sum()) *100\n",
    "\n",
    "#rounding up the decimals\n",
    "int_2020_com['percent_theory']= int_2020_com['percent_theory'].round(decimals=1)\n",
    "int_2020_com['percent_practices'] = int_2020_com['percent_practices'].round(decimals=1)\n",
    "int_2020_com['percent_particular'] = int_2020_com['percent_particular'].round(decimals=1)\n",
    "int_2020_com['percent_sptool'] = int_2020_com['percent_sptool'].round(decimals=1)\n",
    "int_2020_com['percent_DL'] = int_2020_com['percent_DL'].round(decimals=1)\n",
    "int_2020_com\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "trial_radar = data[\"Image analysis theory\"].value_counts().to_frame()\n",
    "trial_radar_1=data[\"Image analysis practices\"].value_counts().to_frame()\n",
    "trial_radar_2=data[\"Topics related to sub discipline\"].value_counts().to_frame()\n",
    "trial_radar_3=data[\"Specific software tool\"].value_counts().to_frame()\n",
    "trial_radar_4=data[\"Deep learning for image analysis\"].value_counts().to_frame()\n",
    "trial_radar_5=data[\"Analyzing large images\"].value_counts().to_frame()\n",
    "trial_radar_6=data[\"Visualization of results\"].value_counts().to_frame()\n",
    "\n",
    "#concatenating all the related columns for topics of interest\n",
    "trial = pd.concat([trial_radar, trial_radar_1, trial_radar_2, trial_radar_3, trial_radar_4, trial_radar_5, trial_radar_6], axis=1)\n",
    "\n",
    "#Reseting the index so that the column can be renamed\n",
    "trial =trial.reset_index()\n",
    "trial = trial.rename(columns={'index':'interest'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>percent_practices</th>\n",
       "      <th>percent_particular</th>\n",
       "      <th>percent_sptool</th>\n",
       "      <th>percent_DL</th>\n",
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       "  <tbody>\n",
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       "      <td>Very interested</td>\n",
       "      <td>157</td>\n",
       "      <td>227</td>\n",
       "      <td>341</td>\n",
       "      <td>223</td>\n",
       "      <td>207</td>\n",
       "      <td>31.8</td>\n",
       "      <td>46.0</td>\n",
       "      <td>69.2</td>\n",
       "      <td>45.2</td>\n",
       "      <td>42.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Moderately interested</td>\n",
       "      <td>150</td>\n",
       "      <td>188</td>\n",
       "      <td>109</td>\n",
       "      <td>171</td>\n",
       "      <td>143</td>\n",
       "      <td>30.4</td>\n",
       "      <td>38.1</td>\n",
       "      <td>22.1</td>\n",
       "      <td>34.7</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Somewhat interested</td>\n",
       "      <td>147</td>\n",
       "      <td>69</td>\n",
       "      <td>36</td>\n",
       "      <td>82</td>\n",
       "      <td>114</td>\n",
       "      <td>29.8</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>16.6</td>\n",
       "      <td>23.1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Not at all interested</td>\n",
       "      <td>39</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>29</td>\n",
       "      <td>7.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.9</td>\n",
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      "text/plain": [
       "                interest  Image analysis theory  Image analysis practices  \\\n",
       "0        Very interested                    157                       227   \n",
       "1  Moderately interested                    150                       188   \n",
       "2    Somewhat interested                    147                        69   \n",
       "3  Not at all interested                     39                         9   \n",
       "\n",
       "   Topics related to sub discipline  Specific software tool  \\\n",
       "0                               341                     223   \n",
       "1                               109                     171   \n",
       "2                                36                      82   \n",
       "3                                 7                      17   \n",
       "\n",
       "   Deep learning for image analysis  percent_theory  percent_practices  \\\n",
       "0                               207            31.8               46.0   \n",
       "1                               143            30.4               38.1   \n",
       "2                               114            29.8               14.0   \n",
       "3                                29             7.9                1.8   \n",
       "\n",
       "   percent_particular  percent_sptool  percent_DL  \n",
       "0                69.2            45.2        42.0  \n",
       "1                22.1            34.7        29.0  \n",
       "2                 7.3            16.6        23.1  \n",
       "3                 1.4             3.4         5.9  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#editing the 2022 column \n",
    "trial_subset = trial.iloc[:, 0:6]\n",
    "trial_subset = trial_subset.rename(columns={'Interes in topics Image analysis theory':'Image analysis theory', 'Interest in topics Image analysis practices':'Image analysis practices','Interest in topics related to sub discipline':'Practices related to particular field','Interest in topics Specific software tool':'Specific software tool', 'Interest in topics Deep learning for image analysis':'Deep learning for image analysis'})\n",
    "trial_subset\n",
    "\n",
    "#getting the percentage \n",
    "trial_subset['percent_theory'] = (trial_subset['Image analysis theory']/trial_subset['Image analysis theory'].sum()) *100\n",
    "trial_subset['percent_practices'] = (trial_subset['Image analysis practices']/trial_subset['Image analysis practices'].sum()) *100\n",
    "trial_subset['percent_particular'] = (trial_subset['Topics related to sub discipline']/trial_subset['Topics related to sub discipline'].sum()) *100\n",
    "trial_subset['percent_sptool'] = (trial_subset['Specific software tool']/trial_subset['Specific software tool'].sum()) *100\n",
    "trial_subset['percent_DL'] = (trial_subset['Deep learning for image analysis']/trial_subset['Deep learning for image analysis'].sum()) *100\n",
    "\n",
    "#rounding up the decimals\n",
    "trial_subset['percent_theory']= trial_subset['percent_theory'].round(decimals=1)\n",
    "trial_subset['percent_practices'] = trial_subset['percent_practices'].round(decimals=1)\n",
    "trial_subset['percent_particular'] = trial_subset['percent_particular'].round(decimals=1)\n",
    "trial_subset['percent_sptool'] = trial_subset['percent_sptool'].round(decimals=1)\n",
    "trial_subset['percent_DL'] = trial_subset['percent_DL'].round(decimals=1)\n",
    "trial_subset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "#comparison - topics of interest for the tutorials \n",
    "int_tut_20_22 = go.Figure()\n",
    "\n",
    "int_tut_20_22 = make_subplots(rows=4, cols=2,shared_yaxes='all', subplot_titles=('Image analysis theory', 'Image analysis practices', 'Image analysis practices <br> related to particular field', 'Specific software tool', 'Deep learning for image analysis'))\n",
    "\n",
    "\n",
    "int_tut_20_22.add_trace(go.Bar(name=\"2020\", x=int_2020_com['interest'], y=int_2020_com['percent_theory'], text=int_2020_com['percent_theory'],marker_color= '#636EFA', legendgroup='group1', yaxis='y1'), row=1, col=1)\n",
    "int_tut_20_22.add_trace(go.Bar(name=\"2022\", x=trial_subset['interest'], y=trial_subset['percent_theory'], text=trial_subset['percent_theory'],marker_color= 'rgba(255, 0, 0, 0.6)', legendgroup='group1'), row=1, col=1)\n",
    "\n",
    "int_tut_20_22.add_trace(go.Bar(x=int_2020_com['interest'], y=int_2020_com['percent_practices'], text=int_2020_com['percent_practices'],marker_color= '#636EFA', showlegend=False),row=1, col=2)\n",
    "int_tut_20_22.add_trace(go.Bar(x=trial_subset['interest'], y=trial_subset['percent_practices'], text=trial_subset['percent_practices'],marker_color= 'rgba(255, 0, 0, 0.6)', showlegend=False),row=1, col=2)\n",
    "\n",
    "int_tut_20_22.add_trace(go.Bar(x=int_2020_com['interest'], y=int_2020_com['percent_particular'], text=int_2020_com['percent_particular'],marker_color= '#636EFA',showlegend=False, yaxis='y3'),row=2, col=1)\n",
    "int_tut_20_22.add_trace(go.Bar(x=trial_subset['interest'], y=trial_subset['percent_particular'], text=trial_subset['percent_particular'],marker_color= 'rgba(255, 0, 0, 0.6)',showlegend=False),row=2, col=1)\n",
    "\n",
    "int_tut_20_22.add_trace(go.Bar(x=int_2020_com['interest'], y=int_2020_com['percent_sptool'], text=int_2020_com['percent_sptool'],marker_color= '#636EFA',showlegend=False),row=2, col=2)\n",
    "int_tut_20_22.add_trace(go.Bar(x=trial_subset['interest'], y=trial_subset['percent_sptool'], text=trial_subset['percent_sptool'],marker_color= 'rgba(255, 0, 0, 0.6)',showlegend=False),row=2, col=2)\n",
    "\n",
    "int_tut_20_22.add_trace(go.Bar(x=int_2020_com['interest'], y=int_2020_com['percent_DL'], text=int_2020_com['percent_DL'],marker_color= '#636EFA',showlegend=False, yaxis='y5'),row=3, col=1)\n",
    "int_tut_20_22.add_trace(go.Bar(x=trial_subset['interest'], y=trial_subset['percent_DL'], text=trial_subset['percent_DL'],marker_color= 'rgba(255, 0, 0, 0.6)', showlegend=False),row=3, col=1)\n",
    "\n",
    "\n",
    "int_tut_20_22.update_layout(width=650, height=1200, font=dict(family='Helvetica', color=\"Black\", size=14), legend=dict(title_font_family = 'Helvetica', font=dict(size=16, color=\"Black\")))\n",
    "int_tut_20_22.update_layout(yaxis1=(dict(title='Percent')), yaxis3=(dict(title='Percent')), yaxis5=(dict(title='Percent')))\n",
    "int_tut_20_22.update_layout(title='Topics of interest for the future workshops', title_x =0.5)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fold change in the interests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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           "zerolinewidth": 2
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        },
        "title": {
         "text": "Fold change in topics of interest with respect to 2020 data"
        },
        "width": 700,
        "yaxis": {
         "title": {
          "text": "Fold change"
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     },
     "metadata": {},
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   ],
   "source": [
    "# Creating dataframes to understand the fold change in the topics of interest for future workshops\n",
    "int_2020_com\n",
    "trial_subset\n",
    "#2020 columns \n",
    "int_2020_com_sub = int_2020_com.drop(int_2020_com.columns[[1,2,3,4,5]], axis=1)\n",
    "int_2020_com_sub.columns = [i+'20' for i in int_2020_com_sub.columns]\n",
    "int_2020_com_sub\n",
    "#2022 columns \n",
    "trial_subset_sub = trial_subset.drop(trial_subset.columns[[1,2,3,4,5]], axis=1)\n",
    "trial_subset_sub.columns = [i+'22' for i in trial_subset_sub.columns]\n",
    "trial_subset_sub\n",
    "#combining dataframes \n",
    "int_22_20 = pd.concat([trial_subset_sub, int_2020_com_sub], axis=1)\n",
    "int_22_20\n",
    "# calculating the fold change\n",
    "for i in range(1,6):\n",
    "    col_name=int_22_20.columns[i]\n",
    "    int_22_20[col_name+'fold']=(int_22_20.iloc[:, i]-int_22_20.iloc[:, i+6])/int_22_20.iloc[:, i+6]\n",
    "    int_22_20[col_name+'fold']  = int_22_20[col_name+'fold'].round(decimals=2)\n",
    "#selecting the desired columns from the dataframe \n",
    "int_22_20_1 = int_22_20.iloc[:,0]\n",
    "int_22_20_2 = int_22_20.iloc[:,12:18]\n",
    "combined = pd.concat([int_22_20_1, int_22_20_2], axis=1)\n",
    "combined = combined.set_index('interest22').transpose()\n",
    "combined = combined.reset_index()\n",
    "combined\n",
    "#replacing the values\n",
    "combined = combined.replace('percent_theory22fold', 'Image analysis theory')\n",
    "combined = combined.replace('percent_practices22fold', 'Image analysis practices')\n",
    "combined = combined.replace('percent_particular22fold', 'Image analysis practices related to particular subdiscipline')\n",
    "combined = combined.replace('percent_sptool22fold', 'Particular software tool')\n",
    "combined = combined.replace('percent_DL22fold', 'Deep learning for image analysis')\n",
    "combined\n",
    "#fold change bar chart\n",
    "int_fold_bar = go.Figure()\n",
    "\n",
    "int_fold_bar.add_trace(go.Bar(name='Very interested',x=combined['index'], y=combined['Very interested'], marker_color='royalblue'))\n",
    "int_fold_bar.add_trace(go.Bar(name='Moderately interested',x=combined['index'], y=combined['Moderately interested'], marker_color='dodgerblue'))\n",
    "int_fold_bar.add_trace(go.Bar(name='Somewhat interested',x=combined['index'], y=combined['Somewhat interested'], marker_color='cornflowerblue'))\n",
    "int_fold_bar.add_trace(go.Bar(name='Not at all interested',x=combined['index'], y=combined['Not at all interested'], marker_color='skyblue'))\n",
    "int_fold_bar.update_layout(barmode='group',title='Fold change in topics of interest with respect to 2020 data', width=700, height=600, font=dict(family='Helvetica', color=\"Black\", size=14), legend=dict(title_font_family = 'Helvetica', font=dict(size=16, color=\"Black\")))\n",
    "int_fold_bar.update_yaxes(title='Fold change')\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Preferable instructional methods - comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Preferable instructional methods - 2020\n",
    "art_20 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\For_any_topics_you_re_interested_in_how_interested_would_you_be_in_learning_about_them_in_the_following_ways_Scholarly_best_practices_article.txt')\n",
    "art_20 = art_20.rename(columns={'0':'article'})\n",
    "art_20 = art_20['article'].value_counts().to_frame()\n",
    "\n",
    "wrt_tut_20 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\For_any_topics_you_re_interested_in_how_interested_would_you_be_in_learning_about_them_in_the_following_ways_Written_step-by-step_tutorial.txt')\n",
    "wrt_tut_20 = wrt_tut_20.rename(columns={'0':'written tutorial'})\n",
    "wrt_tut_20 = wrt_tut_20['written tutorial'].value_counts().to_frame()\n",
    "\n",
    "vid_tut_20 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\For_any_topics_you_re_interested_in_how_interested_would_you_be_in_learning_about_them_in_the_following_ways_Video_tutorial.txt')\n",
    "vid_tut_20 = vid_tut_20.rename(columns={'0':'video tutorial'})\n",
    "vid_tut_20 = vid_tut_20['video tutorial'].value_counts().to_frame()\n",
    "\n",
    "int_web_20 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\For_any_topics_you_re_interested_in_how_interested_would_you_be_in_learning_about_them_in_the_following_ways_Interactive_webinar.txt')\n",
    "int_web_20 = int_web_20.rename(columns={'0':'interactive webinar'})\n",
    "int_web_20 = int_web_20['interactive webinar'].value_counts().to_frame()\n",
    "\n",
    "off_hr_20 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\For_any_topics_you_re_interested_in_how_interested_would_you_be_in_learning_about_them_in_the_following_ways_One-on-one_office_hours_with_an_expert.txt')\n",
    "off_hr_20 = off_hr_20.rename(columns={'0':'office hour'})\n",
    "off_hr_20 = off_hr_20['office hour'].value_counts().to_frame()\n",
    "\n",
    "one_day_20 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\For_any_topics_you_re_interested_in_how_interested_would_you_be_in_learning_about_them_in_the_following_ways_In_person_seminar_tutorial_lasting_lessthan1_day.txt')\n",
    "one_day_20 = one_day_20.rename(columns={'0':'one day'})\n",
    "one_day_20 = one_day_20['one day'].value_counts().to_frame()\n",
    "\n",
    "mul_day_20 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\For_any_topics_you_re_interested_in_how_interested_would_you_be_in_learning_about_them_in_the_following_ways_Multiday_workshop.txt')\n",
    "mul_day_20 = mul_day_20.rename(columns={'0':'multi day'})\n",
    "mul_day_20 = mul_day_20['multi day'].value_counts().to_frame()\n",
    "#Combining all the dataframes \n",
    "pre_tut_20 = pd.concat([art_20,wrt_tut_20,vid_tut_20,int_web_20,off_hr_20,one_day_20,mul_day_20], axis=1)\n",
    "pre_tut_20 = pre_tut_20.reset_index()\n",
    "pre_tut_20.loc[2, 'index'] = 'Somewhat interested'\n",
    "\n",
    "#creating percentage columns \n",
    "pre_tut_20['percent_article'] = (pre_tut_20['article']/pre_tut_20['article'].sum())* 100\n",
    "pre_tut_20['percent_article']= pre_tut_20['percent_article'].round(decimals=1)\n",
    "\n",
    "pre_tut_20['percent_written'] = (pre_tut_20['written tutorial']/pre_tut_20['written tutorial'].sum())*100\n",
    "pre_tut_20['percent_written']=pre_tut_20['percent_written'].round(decimals=1)\n",
    "\n",
    "pre_tut_20['percent_video'] = (pre_tut_20['video tutorial']/pre_tut_20['video tutorial'].sum())*100\n",
    "pre_tut_20['percent_video']= pre_tut_20['percent_video'].round(decimals=1)\n",
    "\n",
    "pre_tut_20['percent_web'] = (pre_tut_20['interactive webinar']/pre_tut_20['interactive webinar'].sum())*100\n",
    "pre_tut_20['percent_web']=pre_tut_20['percent_web'].round(decimals=1)\n",
    "\n",
    "pre_tut_20['percent_off'] = (pre_tut_20['office hour']/pre_tut_20['office hour'].sum())*100\n",
    "pre_tut_20['percent_off']=pre_tut_20['percent_off'].round(decimals=1)\n",
    "\n",
    "pre_tut_20['percent_one'] = (pre_tut_20['one day']/pre_tut_20['one day'].sum())*100\n",
    "pre_tut_20['percent_one']=pre_tut_20['percent_one'].round(decimals=1)\n",
    "\n",
    "pre_tut_20['percent_mul'] = (pre_tut_20['multi day']/pre_tut_20['multi day'].sum())*100\n",
    "pre_tut_20['percent_mul']=pre_tut_20['percent_mul'].round(decimals=1)\n",
    "\n",
    "pre_tut_20\n",
    "#preferable instructional methods - 2022\n",
    "art = data[\"Best practices articles\"].value_counts().to_frame()\n",
    "wrt = data[\"Written tutorials\"].value_counts().to_frame()\n",
    "vid = data[\"Video tutorial\"].value_counts().to_frame()\n",
    "web = data[\"Interactive webinar\"].value_counts().to_frame()\n",
    "off = data[\"Office hours\"].value_counts().to_frame()\n",
    "one = data[\"One day seminar\"].value_counts().to_frame()\n",
    "mul = data[\"Multiday workshop\"].value_counts().to_frame()\n",
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    "#combining all the dataframes\n",
    "mtds = pd.concat([art, wrt, vid, web, off, one, mul], axis=1)\n",
    "mtds = mtds.reset_index()\n",
    "mtds['index'] = ['Somewhat interested', 'Moderately interested', 'Very interested', 'Not at all interested']\n",
    "\n",
    "#creating percentage columns \n",
    "mtds['per_art'] = (mtds['Best practices articles']/mtds['Best practices articles'].sum())*100\n",
    "mtds['per_art'] = mtds['per_art'].round(decimals=1)\n",
    "\n",
    "mtds['per_wrt'] = (mtds['Written tutorials']/mtds['Written tutorials'].sum())*100\n",
    "mtds['per_wrt'] =mtds['per_wrt'].round(decimals=1)\n",
    "\n",
    "mtds['per_vid'] = (mtds['Video tutorial']/mtds['Video tutorial'].sum())*100\n",
    "mtds['per_vid'] = mtds['per_vid'].round(decimals=1)\n",
    "\n",
    "mtds['per_web'] = (mtds['Interactive webinar']/mtds['Interactive webinar'].sum())*100\n",
    "mtds['per_web'] =mtds['per_web'].round(decimals=1)\n",
    "\n",
    "mtds['per_off'] = (mtds['Office hours']/mtds['Office hours'].sum())*100\n",
    "mtds['per_off'] = mtds['per_off'].round(decimals=1)\n",
    "\n",
    "mtds['per_one'] = (mtds['One day seminar']/mtds['One day seminar'].sum())*100\n",
    "mtds['per_one'] = mtds['per_one'].round(decimals=1)\n",
    "\n",
    "mtds['per_mul'] = (mtds['Multiday workshop']/mtds['Multiday workshop'].sum())*100\n",
    "mtds['per_mul'] = mtds['per_mul'].round(decimals=1)\n"
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   "source": [
    "\n",
    "#comparison-graphs \n",
    "int_mtd_20_22 = go.Figure()\n",
    "\n",
    "int_mtd_20_22 = make_subplots(rows=4, cols=2,shared_yaxes='all', subplot_titles=('Best practices articles', 'Written tutorial', 'Video tutorial', 'Interactive webinar', 'Office hours', 'In-person/one day workshop', 'Multi-day workshop'))\n",
    "\n",
    "int_mtd_20_22.add_trace(go.Bar(name='2020',x=pre_tut_20['index'], y=pre_tut_20['percent_article'],text=pre_tut_20['percent_article'], marker_color= '#636EFA', yaxis='y1'),1,1)\n",
    "int_mtd_20_22.add_trace(go.Bar(name='2022', x=mtds['index'], y=mtds['per_art'],text=mtds['per_art'], marker_color= 'rgba(255, 0, 0, 0.6)'),1,1) \n",
    "\n",
    "int_mtd_20_22.add_trace(go.Bar(x=pre_tut_20['index'], y=pre_tut_20['percent_written'],text=pre_tut_20['percent_written'], marker_color= '#636EFA', showlegend=False),1,2)\n",
    "int_mtd_20_22.add_trace(go.Bar(x=mtds['index'], y=mtds['per_wrt'],text=mtds['per_wrt'], marker_color= 'rgba(255, 0, 0, 0.6)', showlegend=False),1,2) \n",
    "\n",
    "int_mtd_20_22.add_trace(go.Bar(x=pre_tut_20['index'], y=pre_tut_20['percent_video'],text=pre_tut_20['percent_video'], marker_color= '#636EFA', showlegend=False, yaxis='y3'),2,1)\n",
    "int_mtd_20_22.add_trace(go.Bar(x=mtds['index'], y=mtds['per_vid'],text=mtds['per_vid'], marker_color= 'rgba(255, 0, 0, 0.6)', showlegend=False),2,1) \n",
    "\n",
    "int_mtd_20_22.add_trace(go.Bar(x=pre_tut_20['index'], y=pre_tut_20['percent_web'],text=pre_tut_20['percent_web'], marker_color= '#636EFA', showlegend=False),2,2)\n",
    "int_mtd_20_22.add_trace(go.Bar(x=mtds['index'], y=mtds['per_web'],text=mtds['per_web'], marker_color= 'rgba(255, 0, 0, 0.6)', showlegend=False),2,2)\n",
    "\n",
    "int_mtd_20_22.add_trace(go.Bar(x=pre_tut_20['index'], y=pre_tut_20['percent_off'],text=pre_tut_20['percent_off'], marker_color= '#636EFA', showlegend=False, yaxis='y5'),3,1)\n",
    "int_mtd_20_22.add_trace(go.Bar(x=mtds['index'], y=mtds['per_off'],text=mtds['per_off'], marker_color= 'rgba(255, 0, 0, 0.6)', showlegend=False),3,1)\n",
    "\n",
    "int_mtd_20_22.add_trace(go.Bar(x=pre_tut_20['index'], y=pre_tut_20['percent_one'],text=pre_tut_20['percent_one'], marker_color= '#636EFA', showlegend=False),3,2)\n",
    "int_mtd_20_22.add_trace(go.Bar(x=mtds['index'], y=mtds['per_one'],text=mtds['per_one'], marker_color= 'rgba(255, 0, 0, 0.6)',showlegend=False),3,2)\n",
    "\n",
    "int_mtd_20_22.add_trace(go.Bar(x=pre_tut_20['index'], y=pre_tut_20['percent_mul'],text=pre_tut_20['percent_mul'], marker_color= '#636EFA', showlegend=False, yaxis='y7'),4,1)\n",
    "int_mtd_20_22.add_trace(go.Bar(x=mtds['index'], y=mtds['per_mul'],text=mtds['per_mul'], marker_color= 'rgba(255, 0, 0, 0.6)', showlegend=False),4,1)\n",
    "\n",
    "int_mtd_20_22.update_layout(width=650, height=1250, font=dict(family='Helvetica', color=\"Black\", size=14), legend=dict(title_font_family = 'Helvetica', font=dict(size=16, color=\"Black\")))\n",
    "int_mtd_20_22.update_layout(yaxis1=(dict(title='Percent')), yaxis3=(dict(title='Percent')), yaxis5=(dict(title='Percent')), yaxis7=(dict(title='Percent')))\n",
    "int_mtd_20_22.update_xaxes(categoryorder='array',categoryarray = ['Very interested', 'Moderately interested', 'Somewhat interested','Not at all interested'])\n",
    "int_mtd_20_22.update_layout(title='Preferable instructional methods', title_x =0.5)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fold change in the preferable instructional methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "      <td>38.9</td>\n",
       "      <td>23.3</td>\n",
       "      <td>18.1</td>\n",
       "      <td>Very interested</td>\n",
       "      <td>29.5</td>\n",
       "      <td>...</td>\n",
       "      <td>33.3</td>\n",
       "      <td>29.8</td>\n",
       "      <td>30.8</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 index22  per_art22  per_wrt22  per_vid22  per_web22  \\\n",
       "0  Moderately interested       33.5       29.8       27.6       34.9   \n",
       "1  Not at all interested        9.9        0.6        6.5       10.8   \n",
       "2    Somewhat interested       38.5       10.8       19.5       33.7   \n",
       "3        Very interested       18.1       58.8       46.5       20.7   \n",
       "\n",
       "   per_off22  per_one22  per_mul22                  index  percent_article  \\\n",
       "0       24.5       34.3       27.8  Moderately interested             43.4   \n",
       "1       11.2       12.4       17.0  Not at all interested              5.2   \n",
       "2       25.4       30.0       37.1    Somewhat interested             21.9   \n",
       "3       38.9       23.3       18.1        Very interested             29.5   \n",
       "\n",
       "   ...  percent_off  percent_one  percent_mul  per_art22fold  per_wrt22fold  \\\n",
       "0  ...         27.5         33.3         31.8          -0.23           0.05   \n",
       "1  ...         11.4         11.4          9.9           0.90          -0.65   \n",
       "2  ...         27.9         25.6         27.5           0.76           0.54   \n",
       "3  ...         33.3         29.8         30.8          -0.39          -0.06   \n",
       "\n",
       "   per_vid22fold  per_web22fold  per_off22fold  per_one22fold  per_mul22fold  \n",
       "0           0.19           0.13          -0.11           0.03          -0.13  \n",
       "1           0.44           0.80          -0.02           0.09           0.72  \n",
       "2           0.29           0.58          -0.09           0.17           0.35  \n",
       "3          -0.19          -0.50           0.17          -0.22          -0.41  \n",
       "\n",
       "[4 rows x 23 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dipalying the fold change - 2020 and 2022\n",
    "pre_tut_20\n",
    "mtds\n",
    "\n",
    "#creating dataframes for the fold change \n",
    "fold_df_20 = pre_tut_20.drop(pre_tut_20.columns[[1, 2,3,4,5,6,7]], axis=1)\n",
    "fold_df_22 = mtds.drop(mtds.columns[[1,2,3,4,5,6,7]], axis=1)\n",
    "\n",
    "\n",
    "fold_df_20 = fold_df_20.sort_values(by='index').reset_index(drop=True)\n",
    "fold_df_22.columns = [(i+'22') for i in fold_df_22.columns]\n",
    "fold_df_22 = fold_df_22.sort_values(by='index22').reset_index(drop=True)\n",
    "\n",
    "fold_df_22_20 = pd.concat([fold_df_22, fold_df_20], axis=1)\n",
    "fold_df_22_20\n",
    "#Calculating the fold change \n",
    "for i in range(1,8):\n",
    "    col_name=fold_df_22_20.columns[i]\n",
    "    fold_df_22_20[col_name+'fold']=(fold_df_22_20.iloc[:, i]-fold_df_22_20.iloc[:, i+8])/fold_df_22_20.iloc[:, i+8]\n",
    "    fold_df_22_20[col_name+'fold']  = fold_df_22_20[col_name+'fold'].round(decimals=2)\n",
    "\n",
    "fold_df_22_20\n",
    "fold_df_22_20_sub_1 = fold_df_22_20.iloc[:, 0]\n",
    "fold_df_22_20_sub_2 = fold_df_22_20.iloc[:, 16:]\n",
    "fold_df_22_20_sub = pd.concat([fold_df_22_20_sub_1, fold_df_22_20_sub_2], axis=1)\n",
    "fold_df_22_20_sub = fold_df_22_20_sub.set_index('index22').transpose()\n",
    "fold_df_22_20_sub = fold_df_22_20_sub.reset_index()\n",
    "\n",
    "#Changing the column names \n",
    "fold_df_22_20_sub = fold_df_22_20_sub.replace('per_art22fold', 'Best practices articles')\n",
    "fold_df_22_20_sub = fold_df_22_20_sub.replace('per_wrt22fold', 'Written tutorials')\n",
    "fold_df_22_20_sub = fold_df_22_20_sub.replace('per_vid22fold', 'Video tutorials')\n",
    "fold_df_22_20_sub = fold_df_22_20_sub.replace('per_web22fold', 'Interactive webinar')\n",
    "fold_df_22_20_sub = fold_df_22_20_sub.replace('per_off22fold', 'Office hours')\n",
    "fold_df_22_20_sub = fold_df_22_20_sub.replace('per_one22fold', 'In-person seminar/ One day workshop')\n",
    "fold_df_22_20_sub = fold_df_22_20_sub.replace('per_mul22fold', 'Multiday workshop')\n",
    "fold_df_22_20\n"
   ]
  },
  {
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         "text": "Fold change in the preferable instructional <br>methods with respect to 2020 data"
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   ],
   "source": [
    "#plotting fold change \n",
    "fold_bar = go.Figure()\n",
    "\n",
    "fold_bar.add_trace(go.Bar(name='Very interested',x=fold_df_22_20_sub['index'], y=fold_df_22_20_sub['Very interested'], marker_color='royalblue'))\n",
    "fold_bar.add_trace(go.Bar(name='Moderately interested',x=fold_df_22_20_sub['index'], y=fold_df_22_20_sub['Moderately interested'], marker_color='dodgerblue'))\n",
    "fold_bar.add_trace(go.Bar(name='Somewhat interested',x=fold_df_22_20_sub['index'], y=fold_df_22_20_sub['Somewhat interested'], marker_color='cornflowerblue'))\n",
    "fold_bar.add_trace(go.Bar(name='Not at all interested',x=fold_df_22_20_sub['index'], y=fold_df_22_20_sub['Not at all interested'], marker_color='skyblue'))\n",
    "fold_bar.update_layout(barmode='group',title='Fold change in the preferable instructional <br>methods with respect to 2020 data', width=700, height=500, font=dict(family='Helvetica', color=\"Black\", size=14), legend=dict(title_font_family = 'Helvetica', font=dict(size=16, color=\"Black\")))\n",
    "fold_bar.update_yaxes(title='Fold change')\n"
   ]
  },
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   "source": [
    "#### Combined fold change - interest and the preferable instructional methods"
   ]
  },
  {
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   "execution_count": 28,
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   "source": [
    "#Combinbing the fold change charts of the preferable methods and the topic of interest for the tutorial \n",
    "comb_df_1 = combined.loc[:,['index','Very interested']]\n",
    "comb_df_1['Category'] = 'Interest for <br> future workshops'\n",
    "comb_df_2 = fold_df_22_20_sub.loc[:, ['index', 'Very interested']]\n",
    "comb_df_2['Category'] = 'Preferable <br> instructional methods'\n",
    "comb_fold_df = pd.concat([comb_df_1, comb_df_2])\n",
    "comb_fold_df\n",
    "#creating bar chart for the combined fold \n",
    "comb_fold_bar = go.Figure()\n",
    "colors=['blue','blue','blue', 'blue', 'blue', 'crimson','crimson', 'crimson', 'crimson', 'crimson', 'crimson', 'crimson'] \n",
    "comb_fold_bar.add_trace(go.Bar(x=comb_fold_df['index'], y=comb_fold_df['Very interested'],marker_color=colors, orientation='v'))\n",
    "comb_fold_bar.update_layout(barmode='group',width=700, height=700, font=dict(family='Helvetica', color=\"Black\", size=14), legend=dict(title_font_family = 'Helvetica', font=dict(size=16, color=\"Black\")))\n",
    "comb_fold_bar.update_xaxes(title='Fold change', categoryorder ='total ascending')\n",
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   ],
   "source": [
    "#creating bar chart for the combined fold \n",
    "comb_fold_bar= px.bar(x=comb_fold_df['index'], y=comb_fold_df['Very interested'], color = comb_fold_df['Category'],labels={'y':'Fold change', 'x':' '}, orientation='v')\n",
    "comb_fold_bar.update_layout(barmode='group',width=700, height=700, font=dict(family='Helvetica', color=\"Black\", size=14), legend=dict(title_font_family = 'Helvetica', font=dict(size=16, color=\"Black\")))\n",
    "comb_fold_bar.update_layout(legend=dict(yanchor=\"bottom\",y=0.02,xanchor=\"left\",x=0.62), legend_title = '')\n",
    "comb_fold_bar['data'][0]['x'][2] = 'Image analysis practices <br> related to particular subdiscipline'\n",
    "comb_fold_bar['data'][0]['x'][4] = 'Deep learning for <br> image analysis'\n",
    "comb_fold_bar['data'][1]['x'][5] = 'In-person seminar/<br> One day workshop'\n",
    "comb_fold_bar.update_xaxes(categoryorder ='total ascending')\n",
    "comb_fold_bar.update_xaxes(tickangle =90)\n",
    "comb_fold_bar.write_image('C:\\\\Users\\\\ssivagur\\\\Documents\\\\GitHub\\\\2023_ImageAnalysisSurvey\\\\Graphs\\\\svg_files\\\\fold_int_mtd.svg')\n",
    "comb_fold_bar\n"
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  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating bar chart for the interest \n",
    "fold_bar_int= px.bar(x=comb_df_1['index'], y=comb_df_1['Very interested'],labels={'y':'Percent change', 'x':' '}, orientation='v')\n",
    "fold_bar_int.update_layout(barmode='group',width=500, height=500, font=dict(family='Helvetica', color=\"Black\", size=14), legend=dict(title_font_family = 'Helvetica', font=dict(size=16, color=\"Black\")))\n",
    "fold_bar_int.update_layout(legend=dict(yanchor=\"bottom\",y=0.02,xanchor=\"left\",x=0.62), legend_title = '')\n",
    "fold_bar_int.update_xaxes(tickangle = 90, categoryorder = 'total ascending')\n",
    "fold_bar_int['data'][0]['x'][2] = 'Image analysis practices <br> related to particular subdiscipline'\n",
    "fold_bar_int['data'][0]['x'][4] ='Deep learning for <br> image analysis'\n",
    "fold_bar_int.write_image('percentchange_interest.svg')\n"
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  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating bar chart for the topics\n",
    "fold_bar_top= px.bar(x=comb_df_2['index'], y=comb_df_2['Very interested'],labels={'y':'Percent change', 'x':' '}, orientation='v')\n",
    "fold_bar_top.update_layout(barmode='group',width=500, height=500, font=dict(family='Helvetica', color=\"Black\", size=14), legend=dict(title_font_family = 'Helvetica', font=dict(size=16, color=\"Black\")))\n",
    "fold_bar_top.update_layout(legend=dict(yanchor=\"bottom\",y=0.02,xanchor=\"left\",x=0.62), legend_title = '')\n",
    "fold_bar_top.update_xaxes(tickangle = 90, categoryorder='total ascending')\n",
    "fold_bar_top['data'][0]['x'][5] = 'In-person seminar/<br> One day workshop'\n",
    "fold_bar_top.write_image('Percentchange_topic.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "int_top_subplots = make_subplots(rows=1, cols=2, shared_yaxes='all', subplot_titles=('Interest for future workshops', 'Preferable instructional methods'))\n",
    "\n",
    "int_top_subplots.add_trace(go.Bar(x=comb_df_1['index'], y=comb_df_1['Very interested'], orientation='v',showlegend=False, marker_color='royalblue', yaxis='y1'), 1,1)\n",
    "int_top_subplots.add_trace(go.Bar(x=comb_df_2['index'], y=comb_df_2['Very interested'],orientation='v',showlegend=False, marker_color='royalblue'), 1,2)\n",
    "\n",
    "int_top_subplots.update_layout(width=1000, height=500)\n",
    "int_top_subplots.update_xaxes(categoryorder ='total ascending', tickangle = 90)\n",
    "int_top_subplots.update_layout(yaxis1 =(dict(title='Fold change')))\n",
    "int_top_subplots['data'][0]['x'][2] = 'Image analysis practices <br> related to particular<br> subdiscipline'\n",
    "int_top_subplots['data'][0]['x'][4] = 'Deep learning <br> for image analysis'\n",
    "int_top_subplots['data'][1]['x'][0] = 'Best practices <br> articles'\n",
    "int_top_subplots['data'][1]['x'][5] = 'In-person seminar/<br> One day workshop'\n",
    "int_top_subplots.update_layout(font=dict(family='Helvetica', color=\"Black\", size=14), legend=dict(title_font_family = 'Helvetica', font=dict(size=16, color=\"Black\")))\n",
    "int_top_subplots.write_image('fold_sharedaxes.svg')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Wordclouds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ssivagur\\Documents\\GitHub\\2023_ImageAnalysisSurvey\\utils.py:228: FutureWarning:\n",
      "\n",
      "The default value of regex will change from True to False in a future version.\n",
      "\n",
      "c:\\Users\\ssivagur\\Documents\\GitHub\\2023_ImageAnalysisSurvey\\utils.py:228: FutureWarning:\n",
      "\n",
      "The default value of regex will change from True to False in a future version.\n",
      "\n",
      "c:\\Users\\ssivagur\\Documents\\GitHub\\2023_ImageAnalysisSurvey\\utils.py:228: FutureWarning:\n",
      "\n",
      "The default value of regex will change from True to False in a future version.\n",
      "\n",
      "c:\\Users\\ssivagur\\Documents\\GitHub\\2023_ImageAnalysisSurvey\\utils.py:228: FutureWarning:\n",
      "\n",
      "The default value of regex will change from True to False in a future version.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# wordcloud for well solved image analysis problems_2020\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "well_solved_2021 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\What_image_analysis_problems_ie_finding_nuclei_tissue_analysis_analysis_of_super-resolution_data_etc_do_you_think_are_generally_well-solved.txt')\n",
    "well_solved_2021 = well_solved_2021.rename(columns= {'0':'well_solved-2020'})\n",
    "wordcloud(well_solved_2021[\"well_solved-2020\"], extra_stopwords=['finding nuclei','finding', 'nuclei', 'tissue','analysis', 'analysis of super-resolution data', 'cell', 'cells', 'etc', 'image', 'images', 'well', \n",
    "             'solved', 'imaging', 'better', 'simple', 'different', 'non', 'within', 'depend', 'none', 'things', 'easy', 'many', 'almost', 'common', \n",
    "             'though', 'n', 'depends', 'number', 'made', 'seem', 'show', 'particularly', 'co', 'size', 'types', 'every', 'possible', 'based', 'generally', 'semi', 'work', 'don', 't', 'basic'])\n",
    "# wordcloud for image analysis problems which need better solutions -2021\n",
    "better_sol_2021 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\What_image_analysis_problems_ie_finding_nuclei_tissue_analysis_analysis_of_super-resolution_data_etc_do_you_wish_had_easier_better_solutions.txt')\n",
    "better_sol_2021 = better_sol_2021.rename(columns= {'0':'Image analysis problems that need better solutions-2020'})\n",
    "wordcloud(better_sol_2021[\"Image analysis problems that need better solutions-2020\"], extra_stopwords=['finding nuclei','finding', 'nuclei', 'tissue', 'analysis', 'analysis of super-resolution data', 'image', 'images', 'imaging', 'e', 'g', \n",
    "             'cell', 'em', 'non', 'etc', 'cells', 'better', 'find', 'easy', 'sample', 'well', 'don', 't', 'different', 'without','many', 'change', 'high', 'especially', \n",
    "             'low', 'good', 'tool', 'based', 'things', 'changes','within', 'problem', 'small', 'working', 'data', \n",
    "             'analyzing','set', 'often', 'easier', 'clearly', 'types', 'general', 'anything', 'co', 'work', 'highly', 'need', 'way', 're'])\n",
    "# wordcloud for creators role -2020\n",
    "creator_2021 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\What_do_you_think_analysis_tool_CREATORS_such_as_software_developers_could_should_do_to_make_image_analysis_better_and_more_successful_How_best_could_we_encourage_them_to_do_it.txt')\n",
    "creator_2021 = creator_2021.rename(columns= {'0':'Creators role - 2020'})\n",
    "wordcloud(creator_2021[\"Creators role - 2020\"],extra_stopwords=['tool','make','analysis', 'better', 'more', 'software', 'tools', 'user', 'step', 'users', 'use', 'image', 'don', 't', 's', 'etc', \n",
    "                 'used', 'clearly', 'doesn', 'best', 'touch', 'give', 'non', 'less', 'images', 'asap', 'high','using', 'well', 'work', 'even', 'end',\n",
    "                 'others','everyone','everything','know','really','without','something','always','one','want', 'exactly', 've','m','certain', 're','instead',\n",
    "                 'us','maybe','sc', 'kind','going', 'different','see','especially', 'lack','much', 'past', 'selves', 'running', 'either','require', 'happens', 'lot', 'put', 'perhaps', \n",
    "                 'properly', 'many', 'behave', 'good', 'data', 'away', 'tell', 'will'] )\n",
    "# wordcloud for users role -2020\n",
    "user_2021 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\What_do_you_think_analysis_tool_USERS_such_as_microscopists_could_should_do_to_make_image_analysis_better_and_more_successful__How_best_could_we_encourage_them_to_do_it.txt')\n",
    "user_2021 = user_2021.rename(columns= {'0':'Users role - 2020'})\n",
    "\n",
    "wordcloud(user_2021[\"Users role - 2020\"], extra_stopwords=['tool','make','analysis', 'better', 'more', 'software', 'tools', 'user', 'step', 'users', 'use', 'don', 'image', 'imaging', 'images', \n",
    "                  'data', 'think', 'best', 'need', 'encourage', 'etc', 't', 'needs', 'basic', 'able', 'keep', 'used', 'help', 'high', 'try', 'first', 'clearly',\n",
    "                  'bit', 'will', 'different', 'field', 'non', 'ask', 'good', 'know'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'stoplist' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_24584\\869319900.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m#unigrams from 2021\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfeature_extraction\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtext\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mCountVectorizer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mc_vec\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCountVectorizer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstop_words\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstoplist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mngram_range\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[1;31m# matrix of ngrams\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[0mngrams\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mc_vec\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtopic_interest_data_2021\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"interest\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'stoplist' is not defined"
     ]
    }
   ],
   "source": [
    "#Comparing with the previous years data (topics of interest for the image analysis tutorials)\n",
    "\n",
    "topic_interest_data_2021 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\What_specific_topics_ie_overviews_of_a_particular_tool_comparisons_between_pieces_of_software_or_how_to_use_a_certain_tool_for_a_certain_kind_of_experiment_would_you_like_to_s.txt')\n",
    "topic_interest_data_2021= topic_interest_data_2021.rename(columns={'0':'interest'})\n",
    "#unigrams from 2021\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "c_vec = CountVectorizer(stop_words=stoplist, ngram_range=(1,1))\n",
    "# matrix of ngrams\n",
    "ngrams = c_vec.fit_transform(topic_interest_data_2021[\"interest\"])\n",
    "# count frequency of ngrams\n",
    "count_values = ngrams.toarray().sum(axis=0)\n",
    "# list of ngrams\n",
    "vocab = c_vec.vocabulary_\n",
    "df_2021_unigram = pd.DataFrame(sorted([(count_values[i],k) for k,i in vocab.items()], reverse=True)\n",
    "            ).rename(columns={0: 'frequency', 1:'unigram'})\n",
    "#bigrams from 2021\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "c_vec = CountVectorizer(stop_words=stoplist, ngram_range=(2,2))\n",
    "# matrix of ngrams\n",
    "ngrams = c_vec.fit_transform(topic_interest_data_2021[\"interest\"])\n",
    "# count frequency of ngrams\n",
    "count_values = ngrams.toarray().sum(axis=0)\n",
    "# list of ngrams\n",
    "vocab = c_vec.vocabulary_\n",
    "df_2021_bigram = pd.DataFrame(sorted([(count_values[i],k) for k,i in vocab.items()], reverse=True)\n",
    "            ).rename(columns={0: 'frequency', 1:'bigram'})\n",
    "#topics of interest for the tutorials with 2021 data \n",
    "topic_interest_data_2021[\"Python/coding/script\"] = topic_interest_data_2021[\"interest\"].str.contains('python|coding|script', case=False)\n",
    "topic_interest_data_2021[\"Segmentation\"] = topic_interest_data_2021['interest'].str.contains('segmentation|cell segmentation', case=False)\n",
    "topic_interest_data_2021[\"napari\"] = topic_interest_data_2021['interest'].str.contains('napari', case=False)\n",
    "topic_interest_data_2021[\"Certain tools\"] = topic_interest_data_2021['interest'].str.contains('certain tool', case=False)\n",
    "topic_interest_data_2021[\"Open source\"] = topic_interest_data_2021['interest'].str.contains('open source', case=False)\n",
    "topic_interest_data_2021[\"Deep/machine learning\"] = topic_interest_data_2021['interest'].str.contains('deep|machine', case=False)\n",
    "topic_interest_data_2021[\"Fiji/ImageJ/Macro\"] = topic_interest_data_2021['interest'].str.contains('imagej|fiji|macro', case=False)\n",
    "topic_interest_data_2021[\"Best practices\"] = topic_interest_data_2021['interest'].str.contains('best practices', case=False)\n",
    "topic_interest_data_2021[\"Cellprofiler\"] = topic_interest_data_2021['interest'].str.contains('cellprofiler', case=False)\n",
    "topic_interest_data_2021[\"Comparisons\"] = topic_interest_data_2021['interest'].str.contains('comparison', case=False)\n",
    "\n",
    "topic_data_2021 = topic_interest_data_2021.drop(columns=['interest', 'Fiji/ImageJ/macro'])\n",
    "topic_data_2021_sum = topic_data_2021.sum()\n",
    "\n",
    "#chart for the topics of interest for the future workshop\n",
    "topic_data_2021_chart = px.bar(topic_data_2021_sum, labels= {'value':'counts', 'index':''}, text_auto=True)\n",
    "topic_data_2021_chart.update_layout(title='Topics of interest for the image analysis tutorials', title_x=0.5, width = 500, height=500, showlegend=False)\n",
    "topic_data_2021_chart.update_xaxes(categoryorder='total descending')\n",
    "# Topics of interest for future tutorials - 2020 and 2022\n",
    "top_20 = topic_data_2021_sum.to_frame()\n",
    "top_20 = top_20.rename(columns={0:'counts'})\n",
    "top_20 = top_20.reset_index()\n",
    "top_20 = top_20.rename(columns={'index':'topics'})\n",
    "\n",
    "top_22 = topic_data_sum.to_frame()\n",
    "top_22 = top_22.rename(columns={0:'counts'})\n",
    "top_22 = top_22.reset_index()\n",
    "top_22 = top_22.rename(columns={'index':'topics'})\n",
    "\n",
    "\n",
    "# bar chart\n",
    "top_comp = go.Figure()\n",
    "\n",
    "top_comp.add_trace(go.Bar(name='2020',x=top_20['topics'], y=top_20['counts'], text=top_20['counts']))\n",
    "top_comp.add_trace(go.Bar(name='2022', x=top_22['topics'], y=top_22['counts'], text=top_22['counts']))\n",
    "\n",
    "top_comp.update_layout(title='Topics of interest for the image analysis tutorials', title_x=0.5, width = 500, height=500, font=dict(family='Helvetica', color=\"Black\", size=16))\n",
    "top_comp.update_xaxes(categoryorder='total descending')\n",
    "#Conf-2020\n",
    "\n",
    "conf_20 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\Please_select_any_of_the_following_you_have_attended_in_the_past.txt')\n",
    "conf_20= conf_20.rename(columns={'0':'conf'})\n",
    "conf_20\n",
    "conf_20 = conf_20.dropna()\n",
    "conf_20['Workshop'] = conf_20['conf'].apply(lambda x: True if 'Workshop' in x else False)\n",
    "conf_20['Conference session'] = conf_20['conf'].apply(lambda x: True if 'session' in x else False)\n",
    "conf_20['Conference'] = conf_20['conf'].apply(lambda x: True if 'dedicated' in x else False)\n",
    "\n",
    "\n",
    "conf_20 = conf_20.iloc[:, 1:4]\n",
    "conf_tot = conf_20.sum()\n",
    "conf_tot = conf_tot.to_frame()\n",
    "conf_tot\n",
    "conf_tot = conf_tot.rename(columns={0:'counts'})\n",
    "conf_tot = conf_tot.reset_index()\n",
    "conf_tot = conf_tot.rename(columns={'index':'topics'})\n",
    "\n",
    "pre_df_tot = pre_df_tot.rename(columns={0:'counts'})\n",
    "pre_df_tot = pre_df_tot.reset_index()\n",
    "pre_df_tot = pre_df_tot.rename(columns={'index':'topics'})\n",
    "pre_df_tot\n",
    "pre_con_20_22 = go.Figure()\n",
    "\n",
    "pre_con_20_22.add_trace(go.Bar(name='2020', x=conf_tot['topics'], y=conf_tot['counts'], text=conf_tot['counts']))\n",
    "pre_con_20_22.add_trace(go.Bar(name='2022', x=pre_df_tot['topics'], y=pre_df_tot['counts'], text=pre_df_tot['counts']))\n",
    "\n",
    "pre_con_20_22.update_layout(width = 500, height=500, font=dict(family='Helvetica', color=\"Black\", size=16))\n",
    "pre_con_20_22.update_xaxes(categoryorder='total descending')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Are there any image analysis workshops, tutorials, or conferences that you have participated in and found particularly helpful? If yes, what made them beneficial?\n",
    "work_20 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\Are_there_any_image_analysis_workshops_tutorials_or_conferences_that_you_have_participated_in_and_found_particularly_helpful__If_yes_what_made_them_beneficial.txt')\n",
    "work_20= work_20.rename(columns={'0':'work'})\n",
    "work_20 = work_20['work'].str.lower().str.strip()\n",
    "work_20 = pd.DataFrame(work_20)\n",
    "work_20\n",
    "\n",
    "\n",
    "work_20[\"NEUBIAS\"] = work_20['work'].apply(lambda x: True if 'neubias' in x else False)\n",
    "work_20[\"Fiji/ImageJ\"] = work_20['work'].apply(lambda x: True if ('imagej' in x) or ('fiji' in x) else False)\n",
    "work_20[\"Cellprofiler\"] = work_20['work'].apply(lambda x: True if 'cellprofiler' in x else False)\n",
    "work_20[\"Imaris\"] = work_20['work'].apply(lambda x: True if 'imaris' in x else False)\n",
    "work_20[\"Robert Hasse\"] = work_20['work'].apply(lambda x: True if 'robert' in x else False)\n",
    "work_20[\"AQLM\"] = work_20['work'].apply(lambda x: True if 'aqlm' in x else False)\n",
    "work_20[\"CSHL\"] = work_20['work'].apply(lambda x: True if 'cshl' in x else False)\n",
    "work_20[\"I2K\"] = work_20['work'].apply(lambda x: True if 'i2k' in x else False)\n",
    "work_20[\"EMBL\"] = work_20['work'].apply(lambda x: True if 'embl' in x else False)\n",
    "work_20\n",
    "\n",
    "work_20_sum = work_20.sum().to_frame()\n",
    "work_20_sum = work_20_sum.drop('work')\n",
    "work_20_sum = work_20_sum.rename(columns={0:'counts'})\n",
    "work_20_sum = work_20_sum.reset_index()\n",
    "work_20_sum = work_20_sum.rename(columns={'index':'conferences'})\n",
    "work_20_sum\n",
    "\n",
    "#altering 2022 dataframe \n",
    "workshops_for_chart = workshops_for_chart.rename(columns={0:'counts'})\n",
    "workshops_for_chart = workshops_for_chart.reset_index()\n",
    "workshops_for_chart = workshops_for_chart.rename(columns={'index':'conferences'})\n",
    "workshops_for_chart\n",
    "#comparison chart \n",
    "work_20_22 = go.Figure()\n",
    "\n",
    "work_20_22.add_trace(go.Bar(name='2020', x=work_20_sum['conferences'], y=work_20_sum['counts'], text=work_20_sum['counts']))\n",
    "work_20_22.add_trace(go.Bar(name='2022', x=workshops_for_chart['conferences'], y=workshops_for_chart['counts'], text=workshops_for_chart['counts']))\n",
    "\n",
    "work_20_22.update_layout(title_x=0.5, title='Workshops attended by the participants', width=500, height=500, font=dict(family='Helvetica', color=\"Black\", size=16))\n",
    "work_20_22.update_xaxes(categoryorder = 'total descending')\n",
    "\n",
    "#image analysis addition for the conferences - 2020\n",
    "\n",
    "ia_20 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\Are_there_any_conferences_you_ve_attended_in_the_past_that_you_think_would_particularly_benefit_from_the_addition_expansion_of_image_analysis_offerings.txt')\n",
    "ia_20= ia_20.rename(columns={'0':'ia'})\n",
    "ia_20 = ia_20['ia'].str.lower().str.strip()\n",
    "ia_20 = pd.DataFrame(ia_20)\n",
    "ia_20\n",
    "# Creating a dataframe of unigrams \n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "c_vec = CountVectorizer(stop_words=stoplist, ngram_range=(1,1))\n",
    "# matrix of ngrams\n",
    "ngrams = c_vec.fit_transform(ia_20['ia'])\n",
    "# count frequency of ngrams\n",
    "count_values = ngrams.toarray().sum(axis=0)\n",
    "# list of ngrams\n",
    "vocab = c_vec.vocabulary_\n",
    "ia_unigram = pd.DataFrame(sorted([(count_values[i],k) for k,i in vocab.items()], reverse=True)\n",
    "            ).rename(columns={0: 'frequency', 1:'unigram'})\n",
    "# Creating a dataframe of bigrams \n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "c_vec = CountVectorizer(stop_words=stoplist, ngram_range=(2,2))\n",
    "# matrix of ngrams\n",
    "ngrams = c_vec.fit_transform(ia_20['ia'])\n",
    "# count frequency of ngrams\n",
    "count_values = ngrams.toarray().sum(axis=0)\n",
    "# list of ngrams\n",
    "vocab = c_vec.vocabulary_\n",
    "ia_bigram = pd.DataFrame(sorted([(count_values[i],k) for k,i in vocab.items()], reverse=True)\n",
    "            ).rename(columns={0: 'frequency', 1:'bigram'})\n",
    "# Creating a dataframe of trigrams \n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "c_vec = CountVectorizer(stop_words=stoplist, ngram_range=(3,3))\n",
    "# matrix of ngrams\n",
    "ngrams = c_vec.fit_transform(ia_20['ia'])\n",
    "# count frequency of ngrams\n",
    "count_values = ngrams.toarray().sum(axis=0)\n",
    "# list of ngrams\n",
    "vocab = c_vec.vocabulary_\n",
    "ia_trigram = pd.DataFrame(sorted([(count_values[i],k) for k,i in vocab.items()], reverse=True)\n",
    "            ).rename(columns={0: 'frequency', 1:'trigram'})\n",
    "data_df_20 = [['ASCB', 29], ['ELMI', 7], ['ABRF', 0], ['NEUBIAS', 8], ['MMC', 5], ['Biophysical society', 9], ['Developmental biology conference', 2]]\n",
    "conf_df_20 = pd.DataFrame(data_df_20, columns=['Conferences', 'Counts'])\n",
    "\n",
    "\n",
    "#chart for the conferences that need more image analysis sessions\n",
    "ia_20_22 = go.Figure()\n",
    "\n",
    "ia_20_22.add_trace(go.Bar(name='2020', x=conf_df_20['Conferences'], y=conf_df_20['Counts'], text=conf_df_20['Counts']))\n",
    "ia_20_22.add_trace(go.Bar(name='2022', x=conf_df['Conferences'], y=conf_df['Counts'], text=conf_df['Counts'] ))\n",
    "ia_20_22.update_layout(width=500, height=600, font=dict(family='Helvetica', color=\"Black\", size=16))\n",
    "ia_20_22.update_xaxes(categoryorder='total descending')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# wordcloud for creators role -2020\n",
    "creator_2021 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\What_do_you_think_analysis_tool_CREATORS_such_as_software_developers_could_should_do_to_make_image_analysis_better_and_more_successful_How_best_could_we_encourage_them_to_do_it.txt')\n",
    "creator_2021 = creator_2021.rename(columns= {'0':'Creators role - 2020'})\n",
    "wordcloud(creator_2021[\"Creators role - 2020\"],extra_stopwords=['tool','make','analysis', 'better', 'more', 'software', 'tools', 'user', 'step', 'users', 'use', 'image', 'don', 't', 's', 'etc', \n",
    "                 'used', 'clearly', 'doesn', 'best', 'touch', 'give', 'non', 'less', 'images', 'asap', 'high','using', 'well', 'work', 'even', 'end',\n",
    "                 'others','everyone','everything','know','really','without','something','always','one','want', 'exactly', 've','m','certain', 're','instead',\n",
    "                 'us','maybe','sc', 'kind','going', 'different','see','especially', 'lack','much', 'past', 'selves', 'running', 'either','require', 'happens', 'lot', 'put', 'perhaps', \n",
    "                 'properly', 'many', 'behave', 'good', 'data', 'away', 'tell', 'will'] )\n",
    "# wordcloud for users role -2020\n",
    "user_2021 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\What_do_you_think_analysis_tool_USERS_such_as_microscopists_could_should_do_to_make_image_analysis_better_and_more_successful__How_best_could_we_encourage_them_to_do_it.txt')\n",
    "user_2021 = user_2021.rename(columns= {'0':'Users role - 2020'})\n",
    "\n",
    "wordcloud(user_2021[\"Users role - 2020\"], extra_stopwords=['tool','make','analysis', 'better', 'more', 'software', 'tools', 'user', 'step', 'users', 'use', 'don', 'image', 'imaging', 'images', \n",
    "                  'data', 'think', 'best', 'need', 'encourage', 'etc', 't', 'needs', 'basic', 'able', 'keep', 'used', 'help', 'high', 'try', 'first', 'clearly',\n",
    "                  'bit', 'will', 'different', 'field', 'non', 'ask', 'good', 'know'])\n",
    "#Comparing with the previous years data (topics of interest for the image analysis tutorials)\n",
    "\n",
    "topic_interest_data_2021 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\What_specific_topics_ie_overviews_of_a_particular_tool_comparisons_between_pieces_of_software_or_how_to_use_a_certain_tool_for_a_certain_kind_of_experiment_would_you_like_to_s.txt')\n",
    "topic_interest_data_2021= topic_interest_data_2021.rename(columns={'0':'interest'})\n",
    "#unigrams from 2021\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "c_vec = CountVectorizer(stop_words=stoplist, ngram_range=(1,1))\n",
    "# matrix of ngrams\n",
    "ngrams = c_vec.fit_transform(topic_interest_data_2021[\"interest\"])\n",
    "# count frequency of ngrams\n",
    "count_values = ngrams.toarray().sum(axis=0)\n",
    "# list of ngrams\n",
    "vocab = c_vec.vocabulary_\n",
    "df_2021_unigram = pd.DataFrame(sorted([(count_values[i],k) for k,i in vocab.items()], reverse=True)\n",
    "            ).rename(columns={0: 'frequency', 1:'unigram'})\n",
    "#bigrams from 2021\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "c_vec = CountVectorizer(stop_words=stoplist, ngram_range=(2,2))\n",
    "# matrix of ngrams\n",
    "ngrams = c_vec.fit_transform(topic_interest_data_2021[\"interest\"])\n",
    "# count frequency of ngrams\n",
    "count_values = ngrams.toarray().sum(axis=0)\n",
    "# list of ngrams\n",
    "vocab = c_vec.vocabulary_\n",
    "df_2021_bigram = pd.DataFrame(sorted([(count_values[i],k) for k,i in vocab.items()], reverse=True)\n",
    "            ).rename(columns={0: 'frequency', 1:'bigram'})\n",
    "#topics of interest for the tutorials with 2021 data \n",
    "topic_interest_data_2021[\"Python/coding/script\"] = topic_interest_data_2021[\"interest\"].str.contains('python|coding|script', case=False)\n",
    "topic_interest_data_2021[\"Segmentation\"] = topic_interest_data_2021['interest'].str.contains('segmentation|cell segmentation', case=False)\n",
    "topic_interest_data_2021[\"napari\"] = topic_interest_data_2021['interest'].str.contains('napari', case=False)\n",
    "topic_interest_data_2021[\"Certain tools\"] = topic_interest_data_2021['interest'].str.contains('certain tool', case=False)\n",
    "topic_interest_data_2021[\"Open source\"] = topic_interest_data_2021['interest'].str.contains('open source', case=False)\n",
    "topic_interest_data_2021[\"Deep/machine learning\"] = topic_interest_data_2021['interest'].str.contains('deep|machine', case=False)\n",
    "topic_interest_data_2021[\"Fiji/ImageJ/Macro\"] = topic_interest_data_2021['interest'].str.contains('imagej|fiji|macro', case=False)\n",
    "topic_interest_data_2021[\"Best practices\"] = topic_interest_data_2021['interest'].str.contains('best practices', case=False)\n",
    "topic_interest_data_2021[\"Cellprofiler\"] = topic_interest_data_2021['interest'].str.contains('cellprofiler', case=False)\n",
    "topic_interest_data_2021[\"Comparisons\"] = topic_interest_data_2021['interest'].str.contains('comparison', case=False)\n",
    "\n",
    "topic_data_2021 = topic_interest_data_2021.drop(columns=['interest', 'Fiji/ImageJ/macro'])\n",
    "topic_data_2021_sum = topic_data_2021.sum()\n",
    "\n",
    "#chart for the topics of interest for the future workshop\n",
    "topic_data_2021_chart = px.bar(topic_data_2021_sum, labels= {'value':'counts', 'index':''}, text_auto=True)\n",
    "topic_data_2021_chart.update_layout(title='Topics of interest for the image analysis tutorials', title_x=0.5, width = 500, height=500, showlegend=False)\n",
    "topic_data_2021_chart.update_xaxes(categoryorder='total descending')\n",
    "# Topics of interest for future tutorials - 2020 and 2022\n",
    "top_20 = topic_data_2021_sum.to_frame()\n",
    "top_20 = top_20.rename(columns={0:'counts'})\n",
    "top_20 = top_20.reset_index()\n",
    "top_20 = top_20.rename(columns={'index':'topics'})\n",
    "\n",
    "top_22 = topic_data_sum.to_frame()\n",
    "top_22 = top_22.rename(columns={0:'counts'})\n",
    "top_22 = top_22.reset_index()\n",
    "top_22 = top_22.rename(columns={'index':'topics'})\n",
    "\n",
    "\n",
    "# bar chart\n",
    "top_comp = go.Figure()\n",
    "\n",
    "top_comp.add_trace(go.Bar(name='2020',x=top_20['topics'], y=top_20['counts'], text=top_20['counts']))\n",
    "top_comp.add_trace(go.Bar(name='2022', x=top_22['topics'], y=top_22['counts'], text=top_22['counts']))\n",
    "\n",
    "top_comp.update_layout(title='Topics of interest for the image analysis tutorials', title_x=0.5, width = 500, height=500, font=dict(family='Helvetica', color=\"Black\", size=16))\n",
    "top_comp.update_xaxes(categoryorder='total descending')\n",
    "#Conf-2020\n",
    "\n",
    "conf_20 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\Please_select_any_of_the_following_you_have_attended_in_the_past.txt')\n",
    "conf_20= conf_20.rename(columns={'0':'conf'})\n",
    "conf_20\n",
    "conf_20 = conf_20.dropna()\n",
    "conf_20['Workshop'] = conf_20['conf'].apply(lambda x: True if 'Workshop' in x else False)\n",
    "conf_20['Conference session'] = conf_20['conf'].apply(lambda x: True if 'session' in x else False)\n",
    "conf_20['Conference'] = conf_20['conf'].apply(lambda x: True if 'dedicated' in x else False)\n",
    "\n",
    "\n",
    "conf_20 = conf_20.iloc[:, 1:4]\n",
    "conf_tot = conf_20.sum()\n",
    "conf_tot = conf_tot.to_frame()\n",
    "conf_tot\n",
    "conf_tot = conf_tot.rename(columns={0:'counts'})\n",
    "conf_tot = conf_tot.reset_index()\n",
    "conf_tot = conf_tot.rename(columns={'index':'topics'})\n",
    "\n",
    "pre_df_tot = pre_df_tot.rename(columns={0:'counts'})\n",
    "pre_df_tot = pre_df_tot.reset_index()\n",
    "pre_df_tot = pre_df_tot.rename(columns={'index':'topics'})\n",
    "pre_df_tot\n",
    "pre_con_20_22 = go.Figure()\n",
    "\n",
    "pre_con_20_22.add_trace(go.Bar(name='2020', x=conf_tot['topics'], y=conf_tot['counts'], text=conf_tot['counts']))\n",
    "pre_con_20_22.add_trace(go.Bar(name='2022', x=pre_df_tot['topics'], y=pre_df_tot['counts'], text=pre_df_tot['counts']))\n",
    "\n",
    "pre_con_20_22.update_layout(width = 500, height=500, font=dict(family='Helvetica', color=\"Black\", size=16))\n",
    "pre_con_20_22.update_xaxes(categoryorder='total descending')\n",
    "#Are there any image analysis workshops, tutorials, or conferences that you have participated in and found particularly helpful? If yes, what made them beneficial?\n",
    "work_20 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\Are_there_any_image_analysis_workshops_tutorials_or_conferences_that_you_have_participated_in_and_found_particularly_helpful__If_yes_what_made_them_beneficial.txt')\n",
    "work_20= work_20.rename(columns={'0':'work'})\n",
    "work_20 = work_20['work'].str.lower().str.strip()\n",
    "work_20 = pd.DataFrame(work_20)\n",
    "work_20\n",
    "\n",
    "\n",
    "work_20[\"NEUBIAS\"] = work_20['work'].apply(lambda x: True if 'neubias' in x else False)\n",
    "work_20[\"Fiji/ImageJ\"] = work_20['work'].apply(lambda x: True if ('imagej' in x) or ('fiji' in x) else False)\n",
    "work_20[\"Cellprofiler\"] = work_20['work'].apply(lambda x: True if 'cellprofiler' in x else False)\n",
    "work_20[\"Imaris\"] = work_20['work'].apply(lambda x: True if 'imaris' in x else False)\n",
    "work_20[\"Robert Haase\"] = work_20['work'].apply(lambda x: True if 'robert' in x else False)\n",
    "work_20[\"AQLM\"] = work_20['work'].apply(lambda x: True if 'aqlm' in x else False)\n",
    "work_20[\"CSHL\"] = work_20['work'].apply(lambda x: True if 'cshl' in x else False)\n",
    "work_20[\"I2K\"] = work_20['work'].apply(lambda x: True if 'i2k' in x else False)\n",
    "work_20[\"EMBL\"] = work_20['work'].apply(lambda x: True if 'embl' in x else False)\n",
    "work_20\n",
    "\n",
    "work_20_sum = work_20.sum().to_frame()\n",
    "work_20_sum = work_20_sum.drop('work')\n",
    "work_20_sum = work_20_sum.rename(columns={0:'counts'})\n",
    "work_20_sum = work_20_sum.reset_index()\n",
    "work_20_sum = work_20_sum.rename(columns={'index':'conferences'})\n",
    "work_20_sum\n",
    "\n",
    "#altering 2022 dataframe \n",
    "workshops_for_chart = workshops_for_chart.rename(columns={0:'counts'})\n",
    "workshops_for_chart = workshops_for_chart.reset_index()\n",
    "workshops_for_chart = workshops_for_chart.rename(columns={'index':'conferences'})\n",
    "workshops_for_chart\n",
    "#comparison chart \n",
    "work_20_22 = go.Figure()\n",
    "\n",
    "work_20_22.add_trace(go.Bar(name='2020', x=work_20_sum['conferences'], y=work_20_sum['counts'], text=work_20_sum['counts']))\n",
    "work_20_22.add_trace(go.Bar(name='2022', x=workshops_for_chart['conferences'], y=workshops_for_chart['counts'], text=workshops_for_chart['counts']))\n",
    "\n",
    "work_20_22.update_layout(title_x=0.5, title='Workshops attended by the participants', width=500, height=500, font=dict(family='Helvetica', color=\"Black\", size=16))\n",
    "work_20_22.update_xaxes(categoryorder = 'total descending')\n",
    "\n",
    "#image analysis addition for the conferences - 2020\n",
    "\n",
    "ia_20 = pd.read_csv('C:\\\\Users\\\\ssivagur\\\\Documents\\\\2021_Image_analysis_survey_data\\\\Are_there_any_conferences_you_ve_attended_in_the_past_that_you_think_would_particularly_benefit_from_the_addition_expansion_of_image_analysis_offerings.txt')\n",
    "ia_20= ia_20.rename(columns={'0':'ia'})\n",
    "ia_20 = ia_20['ia'].str.lower().str.strip()\n",
    "ia_20 = pd.DataFrame(ia_20)\n",
    "ia_20\n",
    "# Creating a dataframe of unigrams \n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "c_vec = CountVectorizer(stop_words=stoplist, ngram_range=(1,1))\n",
    "# matrix of ngrams\n",
    "ngrams = c_vec.fit_transform(ia_20['ia'])\n",
    "# count frequency of ngrams\n",
    "count_values = ngrams.toarray().sum(axis=0)\n",
    "# list of ngrams\n",
    "vocab = c_vec.vocabulary_\n",
    "ia_unigram = pd.DataFrame(sorted([(count_values[i],k) for k,i in vocab.items()], reverse=True)\n",
    "            ).rename(columns={0: 'frequency', 1:'unigram'})\n",
    "# Creating a dataframe of bigrams \n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "c_vec = CountVectorizer(stop_words=stoplist, ngram_range=(2,2))\n",
    "# matrix of ngrams\n",
    "ngrams = c_vec.fit_transform(ia_20['ia'])\n",
    "# count frequency of ngrams\n",
    "count_values = ngrams.toarray().sum(axis=0)\n",
    "# list of ngrams\n",
    "vocab = c_vec.vocabulary_\n",
    "ia_bigram = pd.DataFrame(sorted([(count_values[i],k) for k,i in vocab.items()], reverse=True)\n",
    "            ).rename(columns={0: 'frequency', 1:'bigram'})\n",
    "# Creating a dataframe of trigrams \n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "c_vec = CountVectorizer(stop_words=stoplist, ngram_range=(3,3))\n",
    "# matrix of ngrams\n",
    "ngrams = c_vec.fit_transform(ia_20['ia'])\n",
    "# count frequency of ngrams\n",
    "count_values = ngrams.toarray().sum(axis=0)\n",
    "# list of ngrams\n",
    "vocab = c_vec.vocabulary_\n",
    "ia_trigram = pd.DataFrame(sorted([(count_values[i],k) for k,i in vocab.items()], reverse=True)\n",
    "            ).rename(columns={0: 'frequency', 1:'trigram'})\n",
    "data_df_20 = [['ASCB', 29], ['ELMI', 7], ['ABRF', 0], ['NEUBIAS', 8], ['MMC', 5], ['Biophysical society', 9], ['Developmental biology conference', 2]]\n",
    "conf_df_20 = pd.DataFrame(data_df_20, columns=['Conferences', 'Counts'])\n",
    "\n",
    "\n",
    "#chart for the conferences that need more image analysis sessions\n",
    "ia_20_22 = go.Figure()\n",
    "\n",
    "ia_20_22.add_trace(go.Bar(name='2020', x=conf_df_20['Conferences'], y=conf_df_20['Counts'], text=conf_df_20['Counts']))\n",
    "ia_20_22.add_trace(go.Bar(name='2022', x=conf_df['Conferences'], y=conf_df['Counts'], text=conf_df['Counts'] ))\n",
    "ia_20_22.update_layout(width=500, height=600, font=dict(family='Helvetica', color=\"Black\", size=16))\n",
    "ia_20_22.update_xaxes(categoryorder='total descending')"
   ]
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