nlp  visulazation senario.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ImpleMinetation of matplotlib function \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "labels = ['Month1', 'Month2', 'Month3', 'Month4'] \n",
    "\n",
    "mine = [21, 52, 33, 54] \n",
    "others = [54, 23, 32, 41] \n",
    "Mine_std = [2, 3, 4, 1] \n",
    "Others_std = [3, 5, 2, 3] \n",
    "width = 0.3\n",
    "\n",
    "fig, ax = plt.subplots() \n",
    "\n",
    "ax.barh(labels, mine, width, \n",
    "xerr = Mine_std, label ='Mine') \n",
    "\n",
    "ax.barh(labels, others, width, xerr = Others_std, left = mine, label ='Others') \n",
    "\n",
    "ax.set_xlabel('Articles') \n",
    "ax.legend() \n",
    "\n",
    "ax.set_title('matplotlib.axes.Axes.barh Example') \n",
    "\n",
    "plt.show() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          speed  lifespan  size\n",
      "snail       0.1       2.0     3\n",
      "pig        17.5       8.0    15\n",
      "elephant   40.0      70.0   100\n",
      "rabbit     48.0       1.5     8\n",
      "giraffe    52.0      25.0   150\n",
      "coyote     69.0      12.0    18\n",
      "horse      88.0      28.0    49\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "speed = [0.1, 17.5, 40, 48, 52, 69, 88]\n",
    "lifespan = [2, 8, 70, 1.5, 25, 12, 28]\n",
    "size = [3, 15, 100, 8, 150, 18, 49]\n",
    "index = ['snail', 'pig', 'elephant','rabbit', 'giraffe', 'coyote', 'horse']\n",
    "df = pd.DataFrame({'speed': speed,'lifespan': lifespan,'size':size}, index=index)\n",
    "print(df)\n",
    "ax = df.plot.barh()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydf=pd.read_csv(\"my final.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Names</th>\n",
       "      <th>java</th>\n",
       "      <th>aws</th>\n",
       "      <th>lambda</th>\n",
       "      <th>emr</th>\n",
       "      <th>spark</th>\n",
       "      <th>scala</th>\n",
       "      <th>python</th>\n",
       "      <th>networking</th>\n",
       "      <th>ai</th>\n",
       "      <th>bigdata</th>\n",
       "      <th>objective c</th>\n",
       "      <th>c++</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>resumes\\0505150a-ac29-403d-bf36-1a2c3f3bb6bb.docx</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>resumes\\1de77962-90d6-460a-9007-0a65071cddd9.docx</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>resumes\\6434f5cc-6cb5-412e-b593-dfc1b89cc234.docx</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                              Names  java  aws  \\\n",
       "0           0  resumes\\0505150a-ac29-403d-bf36-1a2c3f3bb6bb.docx     3    0   \n",
       "1           1  resumes\\1de77962-90d6-460a-9007-0a65071cddd9.docx     0    7   \n",
       "2           2  resumes\\6434f5cc-6cb5-412e-b593-dfc1b89cc234.docx     0    0   \n",
       "\n",
       "   lambda  emr  spark  scala  python  networking  ai  bigdata  objective c  \\\n",
       "0       0    0      0      0       0           2   0        0            0   \n",
       "1       2    2      5      2       2           0   0        0            0   \n",
       "2       0    0      0      0       0           0   0        0            0   \n",
       "\n",
       "   c++  \n",
       "0    0  \n",
       "1    0  \n",
       "2    0  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydf.rename(columns = {'Unnamed: 0':'Index'}, inplace = True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydf.set_index('Names',inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydf.drop(['Index'], axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['resumes\\\\0505150a-ac29-403d-bf36-1a2c3f3bb6bb.docx',\n",
       " 'resumes\\\\1de77962-90d6-460a-9007-0a65071cddd9.docx',\n",
       " 'resumes\\\\6434f5cc-6cb5-412e-b593-dfc1b89cc234.docx']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylabels=[]\n",
    "for i in mydf['Names']:\n",
    "    mylabels.append(i)\n",
    "mylabels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = mydf.plot.barh()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                   java  aws  lambda\n",
      "resumes\\0505150a-ac29-403d-bf36-1a2c3f3bb6bb.docx   0.1    2       3\n",
      "resumes\\1de77962-90d6-460a-9007-0a65071cddd9.docx  17.5    8      15\n",
      "resumes\\6434f5cc-6cb5-412e-b593-dfc1b89cc234.docx  40.0   70     100\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "java = [0.1, 17.5, 40]\n",
    "aws = [2, 8, 70]\n",
    "lambda1 = [3, 15, 100]\n",
    "index = mylabels\n",
    "df = pd.DataFrame({'java': java,'aws': aws,'lambda':lambda1}, index=index)\n",
    "print(df)\n",
    "ax = df.plot.barh()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}