{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Vce</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00</th>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>4.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.33</th>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.62</th>\n",
       "      <td>4.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.69</th>\n",
       "      <td>2.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.86</th>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.72</th>\n",
       "      <td>2.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.78</th>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.73</th>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.90</th>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.03</th>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.10</th>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Vce\n",
       "Vp        \n",
       "0.00  5.00\n",
       "0.50  4.99\n",
       "1.00  0.11\n",
       "0.33  5.00\n",
       "0.62  4.45\n",
       "0.69  2.98\n",
       "0.86  0.15\n",
       "0.72  2.24\n",
       "0.78  0.59\n",
       "0.73  1.83\n",
       "0.90  0.13\n",
       "1.03  0.11\n",
       "1.10  0.10"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series([0, .5, 1, .33, .62, .69, .86, .72, .78, .73, .9, 1.03, 1.1])\n",
    "s2 = pd.Series([5,4.99,0.11,5,4.45,2.98,0.15, 2.24, 0.59, 1.83, 0.13, 0.11, 0.1])\n",
    "df1 = pd.DataFrame({'Vp': s1, 'Vce':s2}).set_index('Vp')\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df1.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Vce</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00</th>\n",
       "      <td>5.00</td>\n",
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       "    <tr>\n",
       "      <th>0.33</th>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>4.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.62</th>\n",
       "      <td>4.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.69</th>\n",
       "      <td>2.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.72</th>\n",
       "      <td>2.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.73</th>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.78</th>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.86</th>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.90</th>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.03</th>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.10</th>\n",
       "      <td>0.10</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Vce\n",
       "Vp        \n",
       "0.00  5.00\n",
       "0.33  5.00\n",
       "0.50  4.99\n",
       "0.62  4.45\n",
       "0.69  2.98\n",
       "0.72  2.24\n",
       "0.73  1.83\n",
       "0.78  0.59\n",
       "0.86  0.15\n",
       "0.90  0.13\n",
       "1.00  0.11\n",
       "1.03  0.11\n",
       "1.10  0.10"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df1.plot(legend=None, marker='o', figsize=(9,7))\n",
    "ax.set_title(\"NPN Transistor - Common Emmitter\")\n",
    "ax.set_ylabel('Vce')\n",
    "ax.grid()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "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>Vce</th>\n",
       "      <th>Ib</th>\n",
       "      <th>Ic</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.62</th>\n",
       "      <td>4.45</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.00055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.69</th>\n",
       "      <td>2.98</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.00202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.72</th>\n",
       "      <td>2.24</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.00276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.73</th>\n",
       "      <td>1.83</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.00317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.78</th>\n",
       "      <td>0.59</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.00441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.86</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.00485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.90</th>\n",
       "      <td>0.13</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.00487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.00489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.03</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.00489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.10</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.00490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Vce        Ib       Ic\n",
       "Vp                           \n",
       "0.62  4.45  0.000002  0.00055\n",
       "0.69  2.98  0.000009  0.00202\n",
       "0.72  2.24  0.000012  0.00276\n",
       "0.73  1.83  0.000013  0.00317\n",
       "0.78  0.59  0.000018  0.00441\n",
       "0.86  0.15  0.000026  0.00485\n",
       "0.90  0.13  0.000030  0.00487\n",
       "1.00  0.11  0.000040  0.00489\n",
       "1.03  0.11  0.000043  0.00489\n",
       "1.10  0.10  0.000050  0.00490"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Ib'] = (df1.index - 0.6) / 10000\n",
    "df1['Ic'] = (5 - df1['Vce'])/1000\n",
    "df2 = df1[df1['Ib']> 0]\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd501d8a048>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.plot(x='Ib', y='Ic', marker='o', figsize=(9,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "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>Vb</th>\n",
       "      <th>Vce</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>0.20</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.17</th>\n",
       "      <td>0.27</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.21</th>\n",
       "      <td>0.31</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.34</th>\n",
       "      <td>0.43</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.36</th>\n",
       "      <td>0.46</td>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.38</th>\n",
       "      <td>0.48</td>\n",
       "      <td>1.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.47</th>\n",
       "      <td>0.56</td>\n",
       "      <td>1.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>0.58</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.51</th>\n",
       "      <td>0.59</td>\n",
       "      <td>1.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.55</th>\n",
       "      <td>0.63</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>0.78</td>\n",
       "      <td>1.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.95</th>\n",
       "      <td>1.02</td>\n",
       "      <td>1.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.25</th>\n",
       "      <td>1.31</td>\n",
       "      <td>1.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.51</th>\n",
       "      <td>2.53</td>\n",
       "      <td>3.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.02</th>\n",
       "      <td>3.02</td>\n",
       "      <td>3.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.81</th>\n",
       "      <td>3.79</td>\n",
       "      <td>4.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.42</th>\n",
       "      <td>4.39</td>\n",
       "      <td>4.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.74</th>\n",
       "      <td>4.69</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.00</th>\n",
       "      <td>4.96</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Vb   Vce\n",
       "Vp              \n",
       "0.00  0.10  0.76\n",
       "0.10  0.20  0.86\n",
       "0.17  0.27  0.93\n",
       "0.21  0.31  0.97\n",
       "0.34  0.43  1.09\n",
       "0.36  0.46  1.12\n",
       "0.38  0.48  1.14\n",
       "0.47  0.56  1.22\n",
       "0.50  0.58  1.25\n",
       "0.51  0.59  1.26\n",
       "0.55  0.63  1.30\n",
       "0.70  0.78  1.45\n",
       "0.95  1.02  1.68\n",
       "1.25  1.31  1.97\n",
       "2.51  2.53  3.19\n",
       "3.02  3.02  3.68\n",
       "3.81  3.79  4.44\n",
       "4.42  4.39  4.97\n",
       "4.74  4.69  5.00\n",
       "5.00  4.96  5.00"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series([0, .10, .17, .21, .34, .36, .38, .47, .50, .51, .55, .70, .95, 1.25,  2.51, 3.02, 3.81, 4.42, 4.74, 5])\n",
    "s2 = pd.Series([0.1, 0.2, .27, .31, .43, .46, .48, .56, .58, .59, .63, .78, 1.02, 1.31,  2.53, 3.02, 3.79, 4.39, 4.69, 4.96])\n",
    "s3 = pd.Series([.76, .86, .93, .97, 1.09, 1.12, 1.14, 1.22, 1.25, 1.26, 1.30, 1.45, 1.68, 1.97,  3.19, 3.68, 4.44, 4.97, 5, 5])\n",
    "df3 = pd.DataFrame({'Vp': s1, 'Vb': s2, 'Vce':s3}).set_index('Vp')\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df3.plot(marker='o', figsize=(9,7))\n",
    "ax.set_title(\"PNP Transistor - Common Emmitter\")\n",
    "ax.set_ylabel('Vce')\n",
    "ax.grid()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "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>Vb</th>\n",
       "      <th>Vce</th>\n",
       "      <th>Ib</th>\n",
       "      <th>Ic</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.76</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>4.24</td>\n",
       "      <td>-0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>0.20</td>\n",
       "      <td>0.86</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>4.14</td>\n",
       "      <td>-0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.17</th>\n",
       "      <td>0.27</td>\n",
       "      <td>0.93</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>4.07</td>\n",
       "      <td>-0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.21</th>\n",
       "      <td>0.31</td>\n",
       "      <td>0.97</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>4.03</td>\n",
       "      <td>-0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.34</th>\n",
       "      <td>0.43</td>\n",
       "      <td>1.09</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>3.91</td>\n",
       "      <td>-0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.36</th>\n",
       "      <td>0.46</td>\n",
       "      <td>1.12</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>3.88</td>\n",
       "      <td>-0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.38</th>\n",
       "      <td>0.48</td>\n",
       "      <td>1.14</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>3.86</td>\n",
       "      <td>-0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.47</th>\n",
       "      <td>0.56</td>\n",
       "      <td>1.22</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>3.78</td>\n",
       "      <td>-0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>0.58</td>\n",
       "      <td>1.25</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>3.75</td>\n",
       "      <td>-0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.51</th>\n",
       "      <td>0.59</td>\n",
       "      <td>1.26</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>3.74</td>\n",
       "      <td>-0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.55</th>\n",
       "      <td>0.63</td>\n",
       "      <td>1.30</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>3.70</td>\n",
       "      <td>-0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>0.78</td>\n",
       "      <td>1.45</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>3.55</td>\n",
       "      <td>-0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.95</th>\n",
       "      <td>1.02</td>\n",
       "      <td>1.68</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>3.32</td>\n",
       "      <td>-0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.25</th>\n",
       "      <td>1.31</td>\n",
       "      <td>1.97</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>3.03</td>\n",
       "      <td>-0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.51</th>\n",
       "      <td>2.53</td>\n",
       "      <td>3.19</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1.81</td>\n",
       "      <td>-0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.02</th>\n",
       "      <td>3.02</td>\n",
       "      <td>3.68</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.32</td>\n",
       "      <td>-0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.81</th>\n",
       "      <td>3.79</td>\n",
       "      <td>4.44</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.42</th>\n",
       "      <td>4.39</td>\n",
       "      <td>4.97</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.74</th>\n",
       "      <td>4.69</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.00</th>\n",
       "      <td>4.96</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Vb   Vce    Ib    Ic  diff\n",
       "Vp                                \n",
       "0.00  0.10  0.76 -10.0  4.24 -0.66\n",
       "0.10  0.20  0.86 -10.0  4.14 -0.66\n",
       "0.17  0.27  0.93 -10.0  4.07 -0.66\n",
       "0.21  0.31  0.97 -10.0  4.03 -0.66\n",
       "0.34  0.43  1.09  -9.0  3.91 -0.66\n",
       "0.36  0.46  1.12 -10.0  3.88 -0.66\n",
       "0.38  0.48  1.14 -10.0  3.86 -0.66\n",
       "0.47  0.56  1.22  -9.0  3.78 -0.66\n",
       "0.50  0.58  1.25  -8.0  3.75 -0.67\n",
       "0.51  0.59  1.26  -8.0  3.74 -0.67\n",
       "0.55  0.63  1.30  -8.0  3.70 -0.67\n",
       "0.70  0.78  1.45  -8.0  3.55 -0.67\n",
       "0.95  1.02  1.68  -7.0  3.32 -0.66\n",
       "1.25  1.31  1.97  -6.0  3.03 -0.66\n",
       "2.51  2.53  3.19  -2.0  1.81 -0.66\n",
       "3.02  3.02  3.68   0.0  1.32 -0.66\n",
       "3.81  3.79  4.44   2.0  0.56 -0.65\n",
       "4.42  4.39  4.97   3.0  0.03 -0.58\n",
       "4.74  4.69  5.00   5.0  0.00 -0.31\n",
       "5.00  4.96  5.00   4.0  0.00 -0.04"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['Ib'] = (df3.index-df3['Vb'])*100 # microamps\n",
    "df3['Ic'] = (5 - df3['Vce']) # milliamps\n",
    "df3['diff']=df3['Vb'] - df3['Vce']\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df3.plot(x='Ib', y='Ic', marker='o', figsize=(9,7))\n",
    "ax.set_ylabel('Ic (milliamps)')\n",
    "ax.set_xlabel('Ib (microamps)')\n",
    "ax.set_title(\"PNP Transistor - Common Emmitter\")\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}