Pdf Cnoverter Practice 1 and Turtle Code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 119,
   "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>SL</th>\n",
       "      <th>D escription</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Actuals upto</th>\n",
       "      <th>% of Actuals to Budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>estimates</td>\n",
       "      <td>DEC</td>\n",
       "      <td>Estimates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Corresponding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "      <td>Current previous year 2019-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>REVENUE RECEIPTS</td>\n",
       "      <td>178697.42</td>\n",
       "      <td>72322.49</td>\n",
       "      <td>40.47 53.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>a) Tax Revenue (i + ii + iii + iv + v + vi + vii)</td>\n",
       "      <td>110271.18</td>\n",
       "      <td>57161.00</td>\n",
       "      <td>51.84 64.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>i) Goods and Service Tax</td>\n",
       "      <td>36485.62</td>\n",
       "      <td>19133.41</td>\n",
       "      <td>52.44 107.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>ii) Stamps and registration Fees</td>\n",
       "      <td>6600.00</td>\n",
       "      <td>4047.37</td>\n",
       "      <td>61.32 83.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>iii) Land Revenue</td>\n",
       "      <td>300.00</td>\n",
       "      <td>15.43</td>\n",
       "      <td>5.14 10.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>iv)  Sales Tax</td>\n",
       "      <td>28000.00</td>\n",
       "      <td>14627.53</td>\n",
       "      <td>52.24 42.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SL                                       D escription     Budget  \\\n",
       "0   No                                                NaN  estimates   \n",
       "1  NaN                                                NaN  2019-2020   \n",
       "2  NaN                                                NaN        NaN   \n",
       "3  NaN                                                NaN        NaN   \n",
       "4    1                                   REVENUE RECEIPTS  178697.42   \n",
       "5  NaN  a) Tax Revenue (i + ii + iii + iv + v + vi + vii)  110271.18   \n",
       "6  NaN                           i) Goods and Service Tax   36485.62   \n",
       "7  NaN                   ii) Stamps and registration Fees    6600.00   \n",
       "8  NaN                                  iii) Land Revenue     300.00   \n",
       "9  NaN                                     iv)  Sales Tax   28000.00   \n",
       "\n",
       "  Actuals upto         % of Actuals to Budget  \n",
       "0          DEC                      Estimates  \n",
       "1          NaN                  Corresponding  \n",
       "2         2019  Current previous year 2019-20  \n",
       "3          NaN                        2018-19  \n",
       "4     72322.49                    40.47 53.49  \n",
       "5     57161.00                    51.84 64.53  \n",
       "6     19133.41                   52.44 107.61  \n",
       "7      4047.37                    61.32 83.99  \n",
       "8        15.43                     5.14 10.36  \n",
       "9     14627.53                    52.24 42.07  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53, 5)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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: 3</th>\n",
       "      <th>Actuals upto</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>DEC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Actuals upto</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NOVEMBER</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>72322.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>57161.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>19133.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>63750.41</td>\n",
       "      <td>4047.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>51302.70</td>\n",
       "      <td>15.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16786.82</td>\n",
       "      <td>14627.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3636.89</td>\n",
       "      <td>3526.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14.99</td>\n",
       "      <td>13120.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13748.77</td>\n",
       "      <td>2690.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3341.15</td>\n",
       "      <td>2173.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>($) 11314.52</td>\n",
       "      <td>12987.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2459.56</td>\n",
       "      <td>40456.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1936.11</td>\n",
       "      <td>46.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10511.60</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>35039.59</td>\n",
       "      <td>40410.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>43.30</td>\n",
       "      <td>112778.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.00</td>\n",
       "      <td>101729.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>34996.29</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>98790.00</td>\n",
       "      <td>44909.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>88891.71</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10778.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>39465.91</td>\n",
       "      <td>26247.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12753.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9656.51</td>\n",
       "      <td>7040.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>23496.06</td>\n",
       "      <td>5805.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>11414.61</td>\n",
       "      <td>5364. 65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4858.62</td>\n",
       "      <td>440.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>(*) 4737.50</td>\n",
       "      <td>107532.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>NaN</td>\n",
       "      <td>31230.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>4347.15</td>\n",
       "      <td>30906.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>390.35</td>\n",
       "      <td>323.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>93626.55</td>\n",
       "      <td>53361.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>27746.03</td>\n",
       "      <td>52303.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>27604.34</td>\n",
       "      <td>1058.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>141.69</td>\n",
       "      <td>22939.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>46052.72</td>\n",
       "      <td>18518.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>45253.44</td>\n",
       "      <td>4421.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>799.28</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>19827.45</td>\n",
       "      <td>107535.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>16033.58</td>\n",
       "      <td>5243.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>3793.87</td>\n",
       "      <td>-29407.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.35</td>\n",
       "      <td>-40410.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>93629.21</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>5160.79</td>\n",
       "      <td>-29631.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>-25141.30</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>-34996.29</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>-25339.78</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 3 Actuals upto\n",
       "0            NaN          DEC\n",
       "1   Actuals upto          NaN\n",
       "2       NOVEMBER         2019\n",
       "3            NaN          NaN\n",
       "4           2019     72322.49\n",
       "5            NaN     57161.00\n",
       "6            NaN     19133.41\n",
       "7       63750.41      4047.37\n",
       "8       51302.70        15.43\n",
       "9       16786.82     14627.53\n",
       "10       3636.89      3526.37\n",
       "11         14.99     13120.67\n",
       "12      13748.77      2690.22\n",
       "13       3341.15      2173.52\n",
       "14  ($) 11314.52     12987.97\n",
       "15       2459.56     40456.36\n",
       "16       1936.11        46.34\n",
       "17      10511.60         0.00\n",
       "18      35039.59     40410.02\n",
       "19         43.30    112778.85\n",
       "20          0.00    101729.87\n",
       "21      34996.29          NaN\n",
       "22      98790.00     44909.91\n",
       "23      88891.71          NaN\n",
       "24           NaN     10778.71\n",
       "25      39465.91     26247.26\n",
       "26           NaN     12753.98\n",
       "27       9656.51      7040.01\n",
       "28      23496.06      5805.24\n",
       "29      11414.61     5364. 65\n",
       "30       4858.62       440.59\n",
       "31   (*) 4737.50    107532.45\n",
       "32           NaN     31230.21\n",
       "33       4347.15     30906.74\n",
       "34        390.35       323.47\n",
       "35      93626.55     53361.98\n",
       "36      27746.03     52303.96\n",
       "37      27604.34      1058.02\n",
       "38        141.69     22939.91\n",
       "39      46052.72     18518.82\n",
       "40      45253.44      4421.09\n",
       "41        799.28         0.35\n",
       "42      19827.45    107535.11\n",
       "43      16033.58      5243.74\n",
       "44       3793.87    -29407.38\n",
       "45          0.35    -40410.02\n",
       "46      93629.21          NaN\n",
       "47       5160.79    -29631.31\n",
       "48     -25141.30          NaN\n",
       "49           NaN          NaN\n",
       "50     -34996.29          NaN\n",
       "51           NaN          NaN\n",
       "52     -25339.78          NaN"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3=pd.concat([data1.iloc[:,3],data2.iloc[:,3]],axis=1)\n",
    "data3                 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_output = pd.concat([data1[\"Unnamed: 3\"],data2[\"Actuals upto\"]])\n",
    "print(df_output)\n",
    "df_output.to_excel(\"output2.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2=data2.values\n",
    "type(data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2[:,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1=data1.values\n",
    "type(data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1[:,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data3=data1[:,3]+data2[:,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.values\n",
    "print(data[:,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(df_first_shift['Product'])\n",
    "\n",
    "df_all = pd.concat([df_first_shift, df_second_shift, df_third_shift])\n",
    "print(df_all)\n",
    "\n",
    "pivot = df_all.groupby(['Shift']).mean()\n",
    "shift_productivity = pivot.loc[:,\"Production Run Time (Min)\":\"Products Produced (Units)\"]\n",
    "\n",
    "print(shift_productivity)\n",
    "\n",
    "#shift_productivity.plot(kind='bar')\n",
    "#plt.show()\n",
    "\n",
    "df_all.to_excel(\"output.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fruit=\"banana\"\n",
    "index=0\n",
    "while index<len(fruit):\n",
    "    print(index,fruit[index])\n",
    "    index+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for letter in fruit:\n",
    "    print(letter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "count=0\n",
    "fruit=\"A DataFrame with mixed type columns(e.g., str/object, int64, float32) results in an ndarray of the broadest type that accommodates these mixed types\"\n",
    "for letter in fruit:\n",
    "    if letter==\"a\" or letter==\"A\":\n",
    "        count+=1\n",
    "print(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "word=\"jack fruit\"\n",
    "\n",
    "if word==\"banana\":\n",
    "    print(\"yes it is banana\")\n",
    "elif word< \"banana\":\n",
    "    print(\"your\",word,\"comes before banana in dictionary\")\n",
    "elif word > \"banana\":\n",
    "    print(\"your\",word,\"comes after banana in dictionary\")\n",
    "else:\n",
    "    print(\"its banana\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "lame=\"Hello\"\n",
    "dir(lame)\n",
    "print(lame.lower())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# dir(lame)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import turtle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "win=turtle.Screen()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "win.bgcolor(\"lightgreen\")\n",
    "a=turtle.Turtle()\n",
    "\n",
    "a.speed(4)\n",
    "a.shape(\"turtle\")\n",
    "a.color(\"red\")\n",
    "\n",
    "i=100\n",
    "dist=5\n",
    "for j in range(10):\n",
    "    #a.stamp()\n",
    "    \n",
    "    a.forward(i)\n",
    "    a.left(90)\n",
    "    a.forward(i)\n",
    "    a.left(90)\n",
    "    a.forward(i)\n",
    "    a.left(90)\n",
    "    a.forward(i+10)\n",
    "win.exitonclick()\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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