VdaEx6Task1.ipynb

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       "                        [{\"dimensions\": [{\"label\": \"pPKCG_N\", \"range\": [0, 3.3], \"values\": [1.576342548, 1.56658151, 1.5802937, 1.485143739, 1.491349481, 1.487809452, 1.402571953, 1.388595438, 1.439985227, 1.316857189, 1.356652292, 1.356866538, 1.218259895, 1.267063418, 1.269944519, 1.536630142, 1.514555831, 1.512177038, 1.492524256, 1.528286789, 1.50774855, 1.449491807, 1.409907121, 1.453024319, 1.347932817, 1.386038031, 1.429331603, 1.407551642, 1.495464659, 1.440596606, 1.332472515, 1.366758961, 1.354046457, 1.327137187, 1.318514947, 1.335305164, 1.315923738, 1.311132255, 1.354486661, 1.316484969, 1.302066251, 1.324726262, 1.319478403, 1.347006422, 1.328985507, 1.246167595, 1.220304291, 1.253875969, 1.221793376, 1.185599567, 1.172856118, 1.171629916, 1.152635181, 1.184656146, 1.174923547, 1.163442498, 1.174625038, 1.140701622, 1.149032137, 1.126410835, 1.161428571, 1.146174075, 1.153280908, 1.096652086, 1.078396072, 1.071723682, 1.00369155, 0.986309644, 1.00688592, 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   "source": [
    "\"\"\"\n",
    "Created on Sat May 11 14:28:18 2019\n",
    "\n",
    "#Mahpara Hyder Chowdhury\n",
    "#Maryam Assaedi\n",
    "#Mst. Mahfuja Akter\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "#a\n",
    "\n",
    "import pandas as pd  \n",
    "import numpy as np\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot \n",
    "\n",
    "init_notebook_mode(connected=True) \n",
    "\n",
    "import cufflinks as cf  \n",
    "cf.go_offline() \n",
    "\n",
    "\n",
    "\n",
    "df = pd.read_excel(\"Data_Cortex_Nuclear.xls\")\n",
    "#df.mean()\n",
    "df2 = df.fillna(0)\n",
    "\n",
    "\n",
    "col_names = list(df2) # get column names from existing dataframe\n",
    "groups = df2.groupby('class') # creates a new object of groups of data\n",
    "output_df = pd.DataFrame(columns = col_names)\n",
    "\n",
    "\n",
    "df3 = df2[df2.loc[:, 'class'].isin(df[df['class'] == 'c-SC-s']['class'])]\n",
    "df3 = df3.append(df2[df2.loc[:, 'class'].isin(df[df['class'] == 't-SC-s']['class'])])\n",
    "\n",
    "\n",
    "\n",
    "df3['class'].replace(['c-SC-s','t-SC-s'],[1,2], inplace = True)\n",
    "finaldf = df3[['pPKCG_N', 'pP70S6_N', 'pS6_N', 'pGSK3B_N', 'ARC_N', 'class']]\n",
    "\n",
    "\n",
    "data = [\n",
    "    go.Parcoords(\n",
    "        line = dict(color = finaldf['class']),\n",
    "        dimensions = list([\n",
    "            dict(range = [0,3.3],\n",
    "                label = 'pPKCG_N', values = finaldf['pPKCG_N']),\n",
    "            dict(range = [0,3.3],\n",
    "                label = 'pP70S6_N', values = finaldf['pP70S6_N']),\n",
    "            dict(range = [0,3.3],\n",
    "                label = 'pS6_N', values = finaldf['pS6_N']),\n",
    "            dict(range = [0,3.3],\n",
    "                label = 'pGSK3B_N', values = finaldf['pGSK3B_N']),\n",
    "            dict(range = [0,3.3],\n",
    "                label = 'ARC_N', values = finaldf['ARC_N'])\n",
    "        ])\n",
    "    )\n",
    "]\n",
    "\n",
    "layout = go.Layout(\n",
    "    plot_bgcolor = '#E5E5E5',\n",
    "    paper_bgcolor = '#E5E5E5'\n",
    ")\n",
    "\n",
    "fig = go.Figure(data = data, layout = layout)\n",
    "iplot(fig)"
   ]
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