├── champions.csv
├── scrap.ipynb
├── .ipynb_checkpoints
    └── scrap-checkpoint.ipynb
└── football_analysis.ipynb


/champions.csv:
--------------------------------------------------------------------------------
 1 | Rank,Team,Participations,Titles,Pld,W,D,L,GF,GA,GD,Pts
 2 | 1,Brazil,21,5,109,73,18,18,229,105,124,237
 3 | 2,Germany,19,4,109,67,20,22,226,125,101,221
 4 | 3,Italy,18,4,83,45,21,17,128,77,51,156
 5 | 4,Argentina,17,2,81,43,15,23,137,93,44,144
 6 | 5,France,15,2,66,34,13,19,120,77,43,115
 7 | 6,England,15,1,69,29,21,19,91,64,27,108
 8 | 7,Spain,15,1,63,30,15,18,99,72,27,105
 9 | 8,Uruguay,13,2,56,24,12,20,87,74,13,84
10 | 


--------------------------------------------------------------------------------
/scrap.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 77,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "from bs4 import BeautifulSoup as bs\n",
 10 |     "import requests\n",
 11 |     "import pandas as pd\n",
 12 |     "import csv\n"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "code",
 17 |    "execution_count": 78,
 18 |    "metadata": {},
 19 |    "outputs": [],
 20 |    "source": [
 21 |     "url='https://en.wikipedia.org/wiki/FIFA_World_Cup'\n",
 22 |     "headers = {\"User-Agent\":\"Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:106.0) Gecko/20100101 Firefox/106.0\"}\n",
 23 |     "data=requests.get(url,headers=headers)\n",
 24 |     "soup=bs(data.text,'html.parser')\n",
 25 |     "\n",
 26 |     "#soup.prettify()"
 27 |    ]
 28 |   },
 29 |   {
 30 |    "cell_type": "code",
 31 |    "execution_count": 79,
 32 |    "metadata": {},
 33 |    "outputs": [],
 34 |    "source": [
 35 |     "\n",
 36 |     "resp_table=soup.select('table.wikitable')[7]\n",
 37 |     "#resp_table\n"
 38 |    ]
 39 |   },
 40 |   {
 41 |    "cell_type": "markdown",
 42 |    "metadata": {},
 43 |    "source": [
 44 |     "### listing only the headers\n"
 45 |    ]
 46 |   },
 47 |   {
 48 |    "cell_type": "code",
 49 |    "execution_count": 80,
 50 |    "metadata": {
 51 |     "scrolled": true
 52 |    },
 53 |    "outputs": [],
 54 |    "source": [
 55 |     "headers=[]\n",
 56 |     "for h in resp_table.find_all('th'):\n",
 57 |     "    headers.append(h.text.replace('\\n',\"\"))\n",
 58 |     "#print(headers)\n",
 59 |     "\n"
 60 |    ]
 61 |   },
 62 |   {
 63 |    "cell_type": "code",
 64 |    "execution_count": 84,
 65 |    "metadata": {},
 66 |    "outputs": [
 67 |     {
 68 |      "data": {
 69 |       "text/html": [
 70 |        "<div>\n",
 71 |        "<style scoped>\n",
 72 |        "    .dataframe tbody tr th:only-of-type {\n",
 73 |        "        vertical-align: middle;\n",
 74 |        "    }\n",
 75 |        "\n",
 76 |        "    .dataframe tbody tr th {\n",
 77 |        "        vertical-align: top;\n",
 78 |        "    }\n",
 79 |        "\n",
 80 |        "    .dataframe thead th {\n",
 81 |        "        text-align: right;\n",
 82 |        "    }\n",
 83 |        "</style>\n",
 84 |        "<table border=\"1\" class=\"dataframe\">\n",
 85 |        "  <thead>\n",
 86 |        "    <tr style=\"text-align: right;\">\n",
 87 |        "      <th></th>\n",
 88 |        "      <th>Rank</th>\n",
 89 |        "      <th>Team</th>\n",
 90 |        "      <th>Participations</th>\n",
 91 |        "      <th>Titles</th>\n",
 92 |        "      <th>Pld</th>\n",
 93 |        "      <th>W</th>\n",
 94 |        "      <th>D</th>\n",
 95 |        "      <th>L</th>\n",
 96 |        "      <th>GF</th>\n",
 97 |        "      <th>GA</th>\n",
 98 |        "      <th>GD</th>\n",
 99 |        "      <th>Pts</th>\n",
100 |        "    </tr>\n",
101 |        "  </thead>\n",
102 |        "  <tbody>\n",
103 |        "  </tbody>\n",
104 |        "</table>\n",
105 |        "</div>"
106 |       ],
107 |       "text/plain": [
108 |        "Empty DataFrame\n",
109 |        "Columns: [Rank, Team, Participations, Titles, Pld, W, D, L, GF, GA, GD, Pts]\n",
110 |        "Index: []"
111 |       ]
112 |      },
113 |      "execution_count": 84,
114 |      "metadata": {},
115 |      "output_type": "execute_result"
116 |     }
117 |    ],
118 |    "source": [
119 |     "df=pd.DataFrame(columns=headers)\n",
120 |     "#df\n"
121 |    ]
122 |   },
123 |   {
124 |    "cell_type": "code",
125 |    "execution_count": 86,
126 |    "metadata": {},
127 |    "outputs": [],
128 |    "source": [
129 |     "rows=[]\n",
130 |     "row_tags=rows=resp_table.find_all('tr')\n",
131 |     "for r in row_tags[1:]:\n",
132 |     "    dirty_rows=r.find_all('td')\n",
133 |     "    beauty_rows=[rw.text.strip() for rw in dirty_rows]\n",
134 |     "    #print(beauty_rows)\n",
135 |     "    length=len(df)\n",
136 |     "    df.loc[length]=beauty_rows\n",
137 |     "    \n",
138 |     "    "
139 |    ]
140 |   },
141 |   {
142 |    "cell_type": "code",
143 |    "execution_count": 87,
144 |    "metadata": {},
145 |    "outputs": [
146 |     {
147 |      "data": {
148 |       "text/html": [
149 |        "<div>\n",
150 |        "<style scoped>\n",
151 |        "    .dataframe tbody tr th:only-of-type {\n",
152 |        "        vertical-align: middle;\n",
153 |        "    }\n",
154 |        "\n",
155 |        "    .dataframe tbody tr th {\n",
156 |        "        vertical-align: top;\n",
157 |        "    }\n",
158 |        "\n",
159 |        "    .dataframe thead th {\n",
160 |        "        text-align: right;\n",
161 |        "    }\n",
162 |        "</style>\n",
163 |        "<table border=\"1\" class=\"dataframe\">\n",
164 |        "  <thead>\n",
165 |        "    <tr style=\"text-align: right;\">\n",
166 |        "      <th></th>\n",
167 |        "      <th>Rank</th>\n",
168 |        "      <th>Team</th>\n",
169 |        "      <th>Participations</th>\n",
170 |        "      <th>Titles</th>\n",
171 |        "      <th>Pld</th>\n",
172 |        "      <th>W</th>\n",
173 |        "      <th>D</th>\n",
174 |        "      <th>L</th>\n",
175 |        "      <th>GF</th>\n",
176 |        "      <th>GA</th>\n",
177 |        "      <th>GD</th>\n",
178 |        "      <th>Pts</th>\n",
179 |        "    </tr>\n",
180 |        "  </thead>\n",
181 |        "  <tbody>\n",
182 |        "    <tr>\n",
183 |        "      <th>0</th>\n",
184 |        "      <td>1</td>\n",
185 |        "      <td>Brazil</td>\n",
186 |        "      <td>21</td>\n",
187 |        "      <td>5</td>\n",
188 |        "      <td>109</td>\n",
189 |        "      <td>73</td>\n",
190 |        "      <td>18</td>\n",
191 |        "      <td>18</td>\n",
192 |        "      <td>229</td>\n",
193 |        "      <td>105</td>\n",
194 |        "      <td>124</td>\n",
195 |        "      <td>237</td>\n",
196 |        "    </tr>\n",
197 |        "    <tr>\n",
198 |        "      <th>1</th>\n",
199 |        "      <td>2</td>\n",
200 |        "      <td>Germany[124]</td>\n",
201 |        "      <td>19</td>\n",
202 |        "      <td>4</td>\n",
203 |        "      <td>109</td>\n",
204 |        "      <td>67</td>\n",
205 |        "      <td>20</td>\n",
206 |        "      <td>22</td>\n",
207 |        "      <td>226</td>\n",
208 |        "      <td>125</td>\n",
209 |        "      <td>101</td>\n",
210 |        "      <td>221</td>\n",
211 |        "    </tr>\n",
212 |        "    <tr>\n",
213 |        "      <th>2</th>\n",
214 |        "      <td>3</td>\n",
215 |        "      <td>Italy</td>\n",
216 |        "      <td>18</td>\n",
217 |        "      <td>4</td>\n",
218 |        "      <td>83</td>\n",
219 |        "      <td>45</td>\n",
220 |        "      <td>21</td>\n",
221 |        "      <td>17</td>\n",
222 |        "      <td>128</td>\n",
223 |        "      <td>77</td>\n",
224 |        "      <td>51</td>\n",
225 |        "      <td>156</td>\n",
226 |        "    </tr>\n",
227 |        "    <tr>\n",
228 |        "      <th>3</th>\n",
229 |        "      <td>4</td>\n",
230 |        "      <td>Argentina</td>\n",
231 |        "      <td>17</td>\n",
232 |        "      <td>2</td>\n",
233 |        "      <td>81</td>\n",
234 |        "      <td>43</td>\n",
235 |        "      <td>15</td>\n",
236 |        "      <td>23</td>\n",
237 |        "      <td>137</td>\n",
238 |        "      <td>93</td>\n",
239 |        "      <td>44</td>\n",
240 |        "      <td>144</td>\n",
241 |        "    </tr>\n",
242 |        "    <tr>\n",
243 |        "      <th>4</th>\n",
244 |        "      <td>5</td>\n",
245 |        "      <td>France</td>\n",
246 |        "      <td>15</td>\n",
247 |        "      <td>2</td>\n",
248 |        "      <td>66</td>\n",
249 |        "      <td>34</td>\n",
250 |        "      <td>13</td>\n",
251 |        "      <td>19</td>\n",
252 |        "      <td>120</td>\n",
253 |        "      <td>77</td>\n",
254 |        "      <td>43</td>\n",
255 |        "      <td>115</td>\n",
256 |        "    </tr>\n",
257 |        "    <tr>\n",
258 |        "      <th>5</th>\n",
259 |        "      <td>6</td>\n",
260 |        "      <td>England</td>\n",
261 |        "      <td>15</td>\n",
262 |        "      <td>1</td>\n",
263 |        "      <td>69</td>\n",
264 |        "      <td>29</td>\n",
265 |        "      <td>21</td>\n",
266 |        "      <td>19</td>\n",
267 |        "      <td>91</td>\n",
268 |        "      <td>64</td>\n",
269 |        "      <td>27</td>\n",
270 |        "      <td>108</td>\n",
271 |        "    </tr>\n",
272 |        "    <tr>\n",
273 |        "      <th>6</th>\n",
274 |        "      <td>7</td>\n",
275 |        "      <td>Spain</td>\n",
276 |        "      <td>15</td>\n",
277 |        "      <td>1</td>\n",
278 |        "      <td>63</td>\n",
279 |        "      <td>30</td>\n",
280 |        "      <td>15</td>\n",
281 |        "      <td>18</td>\n",
282 |        "      <td>99</td>\n",
283 |        "      <td>72</td>\n",
284 |        "      <td>27</td>\n",
285 |        "      <td>105</td>\n",
286 |        "    </tr>\n",
287 |        "    <tr>\n",
288 |        "      <th>7</th>\n",
289 |        "      <td>8</td>\n",
290 |        "      <td>Uruguay</td>\n",
291 |        "      <td>13</td>\n",
292 |        "      <td>2</td>\n",
293 |        "      <td>56</td>\n",
294 |        "      <td>24</td>\n",
295 |        "      <td>12</td>\n",
296 |        "      <td>20</td>\n",
297 |        "      <td>87</td>\n",
298 |        "      <td>74</td>\n",
299 |        "      <td>13</td>\n",
300 |        "      <td>84</td>\n",
301 |        "    </tr>\n",
302 |        "  </tbody>\n",
303 |        "</table>\n",
304 |        "</div>"
305 |       ],
306 |       "text/plain": [
307 |        "  Rank          Team Participations Titles  Pld   W   D   L   GF   GA   GD  \\\n",
308 |        "0    1        Brazil             21      5  109  73  18  18  229  105  124   \n",
309 |        "1    2  Germany[124]             19      4  109  67  20  22  226  125  101   \n",
310 |        "2    3         Italy             18      4   83  45  21  17  128   77   51   \n",
311 |        "3    4     Argentina             17      2   81  43  15  23  137   93   44   \n",
312 |        "4    5        France             15      2   66  34  13  19  120   77   43   \n",
313 |        "5    6       England             15      1   69  29  21  19   91   64   27   \n",
314 |        "6    7         Spain             15      1   63  30  15  18   99   72   27   \n",
315 |        "7    8       Uruguay             13      2   56  24  12  20   87   74   13   \n",
316 |        "\n",
317 |        "   Pts  \n",
318 |        "0  237  \n",
319 |        "1  221  \n",
320 |        "2  156  \n",
321 |        "3  144  \n",
322 |        "4  115  \n",
323 |        "5  108  \n",
324 |        "6  105  \n",
325 |        "7   84  "
326 |       ]
327 |      },
328 |      "execution_count": 87,
329 |      "metadata": {},
330 |      "output_type": "execute_result"
331 |     }
332 |    ],
333 |    "source": [
334 |     "df\n",
335 |     "    "
336 |    ]
337 |   },
338 |   {
339 |    "cell_type": "code",
340 |    "execution_count": 88,
341 |    "metadata": {},
342 |    "outputs": [],
343 |    "source": [
344 |     "df.to_csv('champions.csv',index=False)"
345 |    ]
346 |   },
347 |   {
348 |    "cell_type": "code",
349 |    "execution_count": null,
350 |    "metadata": {},
351 |    "outputs": [],
352 |    "source": []
353 |   }
354 |  ],
355 |  "metadata": {
356 |   "kernelspec": {
357 |    "display_name": "Python 3 (ipykernel)",
358 |    "language": "python",
359 |    "name": "python3"
360 |   },
361 |   "language_info": {
362 |    "codemirror_mode": {
363 |     "name": "ipython",
364 |     "version": 3
365 |    },
366 |    "file_extension": ".py",
367 |    "mimetype": "text/x-python",
368 |    "name": "python",
369 |    "nbconvert_exporter": "python",
370 |    "pygments_lexer": "ipython3",
371 |    "version": "3.11.0"
372 |   },
373 |   "vscode": {
374 |    "interpreter": {
375 |     "hash": "d3e10ef16274dd72e574b8fa73b58450b957d8421a2901baded3cca26fcf5dda"
376 |    }
377 |   }
378 |  },
379 |  "nbformat": 4,
380 |  "nbformat_minor": 2
381 | }
382 | 


--------------------------------------------------------------------------------
/.ipynb_checkpoints/scrap-checkpoint.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "from bs4 import BeautifulSoup as bs\n",
 10 |     "import requests\n",
 11 |     "import pandas\n"
 12 |    ]
 13 |   },
 14 |   {
 15 |    "cell_type": "code",
 16 |    "execution_count": 2,
 17 |    "metadata": {},
 18 |    "outputs": [],
 19 |    "source": [
 20 |     "url='https://en.wikipedia.org/wiki/FIFA_World_Cup'\n",
 21 |     "headers = {\"User-Agent\":\"Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:106.0) Gecko/20100101 Firefox/106.0\"}\n",
 22 |     "data=requests.get(url,headers=headers)\n",
 23 |     "soup=bs(data.text,'html.parser')"
 24 |    ]
 25 |   },
 26 |   {
 27 |    "cell_type": "code",
 28 |    "execution_count": 3,
 29 |    "metadata": {},
 30 |    "outputs": [],
 31 |    "source": [
 32 |     "#print(soup.prettify())\n",
 33 |     "\n",
 34 |     "#access the titles of the website\n",
 35 |     "#tit=soup.title.text\n",
 36 |     "#print(tit)"
 37 |    ]
 38 |   },
 39 |   {
 40 |    "cell_type": "code",
 41 |    "execution_count": 4,
 42 |    "metadata": {
 43 |     "scrolled": true
 44 |    },
 45 |    "outputs": [
 46 |     {
 47 |      "name": "stdout",
 48 |      "output_type": "stream",
 49 |      "text": [
 50 |       "<table class=\"wikitable sortable\" style=\"text-align:center;\">\n",
 51 |       "<tbody><tr>\n",
 52 |       "<th>Rank\n",
 53 |       "</th>\n",
 54 |       "<th>Team\n",
 55 |       "</th>\n",
 56 |       "<th>Participations\n",
 57 |       "</th>\n",
 58 |       "<th>Titles\n",
 59 |       "</th>\n",
 60 |       "<th>Pld\n",
 61 |       "</th>\n",
 62 |       "<th>W\n",
 63 |       "</th>\n",
 64 |       "<th>D\n",
 65 |       "</th>\n",
 66 |       "<th>L\n",
 67 |       "</th>\n",
 68 |       "<th>GF\n",
 69 |       "</th>\n",
 70 |       "<th>GA\n",
 71 |       "</th>\n",
 72 |       "<th>GD\n",
 73 |       "</th>\n",
 74 |       "<th>Pts\n",
 75 |       "</th></tr>\n",
 76 |       "<tr>\n",
 77 |       "<td>1\n",
 78 |       "</td>\n",
 79 |       "<td align=\"left\"><span style=\"white-space:nowrap\"><span class=\"flagicon\"><img alt=\"\" class=\"thumbborder\" data-file-height=\"504\" data-file-width=\"720\" decoding=\"async\" height=\"15\" src=\"//upload.wikimedia.org/wikipedia/en/thumb/0/05/Flag_of_Brazil.svg/22px-Flag_of_Brazil.svg.png\" srcset=\"//upload.wikimedia.org/wikipedia/en/thumb/0/05/Flag_of_Brazil.svg/33px-Flag_of_Brazil.svg.png 1.5x, //upload.wikimedia.org/wikipedia/en/thumb/0/05/Flag_of_Brazil.svg/43px-Flag_of_Brazil.svg.png 2x\" width=\"22\"/> </span><a href=\"/wiki/Brazil_national_football_team\" title=\"Brazil national football team\">Brazil</a></span>\n",
 80 |       "</td>\n",
 81 |       "<td>21\n",
 82 |       "</td>\n",
 83 |       "<td>5\n",
 84 |       "</td>\n",
 85 |       "<td>109\n",
 86 |       "</td>\n",
 87 |       "<td>73\n",
 88 |       "</td>\n",
 89 |       "<td>18\n",
 90 |       "</td>\n",
 91 |       "<td>18\n",
 92 |       "</td>\n",
 93 |       "<td>229\n",
 94 |       "</td>\n",
 95 |       "<td>105\n",
 96 |       "</td>\n",
 97 |       "<td>124\n",
 98 |       "</td>\n",
 99 |       "<td>237\n",
100 |       "</td></tr>\n",
101 |       "<tr>\n",
102 |       "<td>2\n",
103 |       "</td>\n",
104 |       "<td align=\"left\"><span style=\"white-space:nowrap\"><span class=\"flagicon\"><img alt=\"\" class=\"thumbborder\" data-file-height=\"600\" data-file-width=\"1000\" decoding=\"async\" height=\"14\" src=\"//upload.wikimedia.org/wikipedia/en/thumb/b/ba/Flag_of_Germany.svg/23px-Flag_of_Germany.svg.png\" srcset=\"//upload.wikimedia.org/wikipedia/en/thumb/b/ba/Flag_of_Germany.svg/35px-Flag_of_Germany.svg.png 1.5x, //upload.wikimedia.org/wikipedia/en/thumb/b/ba/Flag_of_Germany.svg/46px-Flag_of_Germany.svg.png 2x\" width=\"23\"/> </span><a href=\"/wiki/Germany_national_football_team\" title=\"Germany national football team\">Germany</a></span><sup class=\"reference\" id=\"cite_ref-125\"><a href=\"#cite_note-125\">[123]</a></sup>\n",
105 |       "</td>\n",
106 |       "<td>19\n",
107 |       "</td>\n",
108 |       "<td>4\n",
109 |       "</td>\n",
110 |       "<td>109\n",
111 |       "</td>\n",
112 |       "<td>67\n",
113 |       "</td>\n",
114 |       "<td>20\n",
115 |       "</td>\n",
116 |       "<td>22\n",
117 |       "</td>\n",
118 |       "<td>226\n",
119 |       "</td>\n",
120 |       "<td>125\n",
121 |       "</td>\n",
122 |       "<td>101\n",
123 |       "</td>\n",
124 |       "<td>221\n",
125 |       "</td></tr>\n",
126 |       "<tr>\n",
127 |       "<td>3\n",
128 |       "</td>\n",
129 |       "<td align=\"left\"><span style=\"white-space:nowrap\"><span class=\"flagicon\"><img alt=\"\" class=\"thumbborder\" data-file-height=\"1000\" data-file-width=\"1500\" decoding=\"async\" height=\"15\" src=\"//upload.wikimedia.org/wikipedia/en/thumb/0/03/Flag_of_Italy.svg/23px-Flag_of_Italy.svg.png\" srcset=\"//upload.wikimedia.org/wikipedia/en/thumb/0/03/Flag_of_Italy.svg/35px-Flag_of_Italy.svg.png 1.5x, //upload.wikimedia.org/wikipedia/en/thumb/0/03/Flag_of_Italy.svg/45px-Flag_of_Italy.svg.png 2x\" width=\"23\"/> </span><a href=\"/wiki/Italy_national_football_team\" title=\"Italy national football team\">Italy</a></span>\n",
130 |       "</td>\n",
131 |       "<td>18\n",
132 |       "</td>\n",
133 |       "<td>4\n",
134 |       "</td>\n",
135 |       "<td>83\n",
136 |       "</td>\n",
137 |       "<td>45\n",
138 |       "</td>\n",
139 |       "<td>21\n",
140 |       "</td>\n",
141 |       "<td>17\n",
142 |       "</td>\n",
143 |       "<td>128\n",
144 |       "</td>\n",
145 |       "<td>77\n",
146 |       "</td>\n",
147 |       "<td>51\n",
148 |       "</td>\n",
149 |       "<td>156\n",
150 |       "</td></tr>\n",
151 |       "<tr>\n",
152 |       "<td>4\n",
153 |       "</td>\n",
154 |       "<td align=\"left\"><span style=\"white-space:nowrap\"><span class=\"flagicon\"><img alt=\"\" class=\"thumbborder\" data-file-height=\"500\" data-file-width=\"800\" decoding=\"async\" height=\"14\" src=\"//upload.wikimedia.org/wikipedia/commons/thumb/1/1a/Flag_of_Argentina.svg/23px-Flag_of_Argentina.svg.png\" srcset=\"//upload.wikimedia.org/wikipedia/commons/thumb/1/1a/Flag_of_Argentina.svg/35px-Flag_of_Argentina.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/1/1a/Flag_of_Argentina.svg/46px-Flag_of_Argentina.svg.png 2x\" width=\"23\"/> </span><a href=\"/wiki/Argentina_national_football_team\" title=\"Argentina national football team\">Argentina</a></span>\n",
155 |       "</td>\n",
156 |       "<td>17\n",
157 |       "</td>\n",
158 |       "<td>2\n",
159 |       "</td>\n",
160 |       "<td>81\n",
161 |       "</td>\n",
162 |       "<td>43\n",
163 |       "</td>\n",
164 |       "<td>15\n",
165 |       "</td>\n",
166 |       "<td>23\n",
167 |       "</td>\n",
168 |       "<td>137\n",
169 |       "</td>\n",
170 |       "<td>93\n",
171 |       "</td>\n",
172 |       "<td>44\n",
173 |       "</td>\n",
174 |       "<td>144\n",
175 |       "</td></tr>\n",
176 |       "<tr>\n",
177 |       "<td>5\n",
178 |       "</td>\n",
179 |       "<td align=\"left\"><span style=\"white-space:nowrap\"><span class=\"flagicon\"><img alt=\"\" class=\"thumbborder\" data-file-height=\"600\" data-file-width=\"900\" decoding=\"async\" height=\"15\" src=\"//upload.wikimedia.org/wikipedia/en/thumb/c/c3/Flag_of_France.svg/23px-Flag_of_France.svg.png\" srcset=\"//upload.wikimedia.org/wikipedia/en/thumb/c/c3/Flag_of_France.svg/35px-Flag_of_France.svg.png 1.5x, //upload.wikimedia.org/wikipedia/en/thumb/c/c3/Flag_of_France.svg/45px-Flag_of_France.svg.png 2x\" width=\"23\"/> </span><a href=\"/wiki/France_national_football_team\" title=\"France national football team\">France</a></span>\n",
180 |       "</td>\n",
181 |       "<td>15\n",
182 |       "</td>\n",
183 |       "<td>2\n",
184 |       "</td>\n",
185 |       "<td>66\n",
186 |       "</td>\n",
187 |       "<td>34\n",
188 |       "</td>\n",
189 |       "<td>13\n",
190 |       "</td>\n",
191 |       "<td>19\n",
192 |       "</td>\n",
193 |       "<td>120\n",
194 |       "</td>\n",
195 |       "<td>77\n",
196 |       "</td>\n",
197 |       "<td>43\n",
198 |       "</td>\n",
199 |       "<td>115\n",
200 |       "</td></tr>\n",
201 |       "<tr>\n",
202 |       "<td>6\n",
203 |       "</td>\n",
204 |       "<td align=\"left\"><span style=\"white-space:nowrap\"><span class=\"flagicon\"><img alt=\"\" class=\"thumbborder\" data-file-height=\"480\" data-file-width=\"800\" decoding=\"async\" height=\"14\" src=\"//upload.wikimedia.org/wikipedia/en/thumb/b/be/Flag_of_England.svg/23px-Flag_of_England.svg.png\" srcset=\"//upload.wikimedia.org/wikipedia/en/thumb/b/be/Flag_of_England.svg/35px-Flag_of_England.svg.png 1.5x, //upload.wikimedia.org/wikipedia/en/thumb/b/be/Flag_of_England.svg/46px-Flag_of_England.svg.png 2x\" width=\"23\"/> </span><a href=\"/wiki/England_national_football_team\" title=\"England national football team\">England</a></span>\n",
205 |       "</td>\n",
206 |       "<td>15\n",
207 |       "</td>\n",
208 |       "<td>1\n",
209 |       "</td>\n",
210 |       "<td>69\n",
211 |       "</td>\n",
212 |       "<td>29\n",
213 |       "</td>\n",
214 |       "<td>21\n",
215 |       "</td>\n",
216 |       "<td>19\n",
217 |       "</td>\n",
218 |       "<td>91\n",
219 |       "</td>\n",
220 |       "<td>64\n",
221 |       "</td>\n",
222 |       "<td>27\n",
223 |       "</td>\n",
224 |       "<td>108\n",
225 |       "</td></tr>\n",
226 |       "<tr>\n",
227 |       "<td>7\n",
228 |       "</td>\n",
229 |       "<td align=\"left\"><span style=\"white-space:nowrap\"><span class=\"flagicon\"><img alt=\"\" class=\"thumbborder\" data-file-height=\"500\" data-file-width=\"750\" decoding=\"async\" height=\"15\" src=\"//upload.wikimedia.org/wikipedia/en/thumb/9/9a/Flag_of_Spain.svg/23px-Flag_of_Spain.svg.png\" srcset=\"//upload.wikimedia.org/wikipedia/en/thumb/9/9a/Flag_of_Spain.svg/35px-Flag_of_Spain.svg.png 1.5x, //upload.wikimedia.org/wikipedia/en/thumb/9/9a/Flag_of_Spain.svg/45px-Flag_of_Spain.svg.png 2x\" width=\"23\"/> </span><a href=\"/wiki/Spain_national_football_team\" title=\"Spain national football team\">Spain</a></span>\n",
230 |       "</td>\n",
231 |       "<td>15\n",
232 |       "</td>\n",
233 |       "<td>1\n",
234 |       "</td>\n",
235 |       "<td>63\n",
236 |       "</td>\n",
237 |       "<td>30\n",
238 |       "</td>\n",
239 |       "<td>15\n",
240 |       "</td>\n",
241 |       "<td>18\n",
242 |       "</td>\n",
243 |       "<td>99\n",
244 |       "</td>\n",
245 |       "<td>72\n",
246 |       "</td>\n",
247 |       "<td>27\n",
248 |       "</td>\n",
249 |       "<td>105\n",
250 |       "</td></tr>\n",
251 |       "<tr>\n",
252 |       "<td>8\n",
253 |       "</td>\n",
254 |       "<td align=\"left\"><span style=\"white-space:nowrap\"><span class=\"flagicon\"><img alt=\"\" class=\"thumbborder\" data-file-height=\"630\" data-file-width=\"945\" decoding=\"async\" height=\"15\" src=\"//upload.wikimedia.org/wikipedia/commons/thumb/f/fe/Flag_of_Uruguay.svg/23px-Flag_of_Uruguay.svg.png\" srcset=\"//upload.wikimedia.org/wikipedia/commons/thumb/f/fe/Flag_of_Uruguay.svg/35px-Flag_of_Uruguay.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/f/fe/Flag_of_Uruguay.svg/45px-Flag_of_Uruguay.svg.png 2x\" width=\"23\"/> </span><a href=\"/wiki/Uruguay_national_football_team\" title=\"Uruguay national football team\">Uruguay</a></span>\n",
255 |       "</td>\n",
256 |       "<td>13\n",
257 |       "</td>\n",
258 |       "<td>2\n",
259 |       "</td>\n",
260 |       "<td>56\n",
261 |       "</td>\n",
262 |       "<td>24\n",
263 |       "</td>\n",
264 |       "<td>12\n",
265 |       "</td>\n",
266 |       "<td>20\n",
267 |       "</td>\n",
268 |       "<td>87\n",
269 |       "</td>\n",
270 |       "<td>74\n",
271 |       "</td>\n",
272 |       "<td>13\n",
273 |       "</td>\n",
274 |       "<td>84\n",
275 |       "</td></tr></tbody></table>\n"
276 |      ]
277 |     }
278 |    ],
279 |    "source": [
280 |     "\n",
281 |     "resp_table=soup.select('table.wikitable')[7]\n",
282 |     "print(resp_table)\n"
283 |    ]
284 |   },
285 |   {
286 |    "cell_type": "markdown",
287 |    "metadata": {},
288 |    "source": [
289 |     "### listing only the headers"
290 |    ]
291 |   },
292 |   {
293 |    "cell_type": "code",
294 |    "execution_count": 27,
295 |    "metadata": {},
296 |    "outputs": [
297 |     {
298 |      "name": "stdout",
299 |      "output_type": "stream",
300 |      "text": [
301 |       "['Rank\\n', 'Team\\n', 'Participations\\n', 'Titles\\n', 'Pld\\n', 'W\\n', 'D\\n', 'L\\n', 'GF\\n', 'GA\\n', 'GD\\n', 'Pts\\n']\n"
302 |      ]
303 |     }
304 |    ],
305 |    "source": [
306 |     "headers=[]\n",
307 |     "for h in resp_table.find_all('th'):\n",
308 |     "    headers.append(h.text)\n",
309 |     "print(headers)"
310 |    ]
311 |   },
312 |   {
313 |    "cell_type": "code",
314 |    "execution_count": 29,
315 |    "metadata": {},
316 |    "outputs": [
317 |     {
318 |      "ename": "AttributeError",
319 |      "evalue": "ResultSet object has no attribute 'text'. You're probably treating a list of elements like a single element. Did you call find_all() when you meant to call find()?",
320 |      "output_type": "error",
321 |      "traceback": [
322 |       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
323 |       "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
324 |       "Cell \u001b[1;32mIn [29], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[39mfor\u001b[39;00m row \u001b[39min\u001b[39;00m resp_table\u001b[39m.\u001b[39mfind_all(\u001b[39m'\u001b[39m\u001b[39mtr\u001b[39m\u001b[39m'\u001b[39m)[\u001b[39m1\u001b[39m:]:\n\u001b[1;32m----> 2\u001b[0m     data\u001b[39m=\u001b[39mrow\u001b[39m.\u001b[39;49mfind_all(\u001b[39m'\u001b[39;49m\u001b[39mtd\u001b[39;49m\u001b[39m'\u001b[39;49m)\u001b[39m.\u001b[39;49mtext\n",
325 |       "File \u001b[1;32mc:\\Python\\Python310\\lib\\site-packages\\bs4\\element.py:2289\u001b[0m, in \u001b[0;36mResultSet.__getattr__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2287\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__getattr__\u001b[39m(\u001b[39mself\u001b[39m, key):\n\u001b[0;32m   2288\u001b[0m     \u001b[39m\"\"\"Raise a helpful exception to explain a common code fix.\"\"\"\u001b[39;00m\n\u001b[1;32m-> 2289\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m(\n\u001b[0;32m   2290\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mResultSet object has no attribute \u001b[39m\u001b[39m'\u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m. You\u001b[39m\u001b[39m'\u001b[39m\u001b[39mre probably treating a list of elements like a single element. Did you call find_all() when you meant to call find()?\u001b[39m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m key\n\u001b[0;32m   2291\u001b[0m     )\n",
326 |       "\u001b[1;31mAttributeError\u001b[0m: ResultSet object has no attribute 'text'. You're probably treating a list of elements like a single element. Did you call find_all() when you meant to call find()?"
327 |      ]
328 |     }
329 |    ],
330 |    "source": [
331 |     "for row in resp_table.find_all('tr')[1:]:\n",
332 |     "    data=row.find_all('td').text\n",
333 |     "\n"
334 |    ]
335 |   }
336 |  ],
337 |  "metadata": {
338 |   "kernelspec": {
339 |    "display_name": "Python 3 (ipykernel)",
340 |    "language": "python",
341 |    "name": "python3"
342 |   },
343 |   "language_info": {
344 |    "codemirror_mode": {
345 |     "name": "ipython",
346 |     "version": 3
347 |    },
348 |    "file_extension": ".py",
349 |    "mimetype": "text/x-python",
350 |    "name": "python",
351 |    "nbconvert_exporter": "python",
352 |    "pygments_lexer": "ipython3",
353 |    "version": "3.11.0"
354 |   },
355 |   "vscode": {
356 |    "interpreter": {
357 |     "hash": "d3e10ef16274dd72e574b8fa73b58450b957d8421a2901baded3cca26fcf5dda"
358 |    }
359 |   }
360 |  },
361 |  "nbformat": 4,
362 |  "nbformat_minor": 2
363 | }
364 | 


--------------------------------------------------------------------------------
/football_analysis.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 27,
  6 |    "id": "eecc6fa6",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import pandas as pd\n",
 11 |     "import requests\n",
 12 |     "import matplotlib.pyplot as mp"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "code",
 17 |    "execution_count": 28,
 18 |    "id": "f07ee235",
 19 |    "metadata": {},
 20 |    "outputs": [
 21 |     {
 22 |      "data": {
 23 |       "text/html": [
 24 |        "<div>\n",
 25 |        "<style scoped>\n",
 26 |        "    .dataframe tbody tr th:only-of-type {\n",
 27 |        "        vertical-align: middle;\n",
 28 |        "    }\n",
 29 |        "\n",
 30 |        "    .dataframe tbody tr th {\n",
 31 |        "        vertical-align: top;\n",
 32 |        "    }\n",
 33 |        "\n",
 34 |        "    .dataframe thead th {\n",
 35 |        "        text-align: right;\n",
 36 |        "    }\n",
 37 |        "</style>\n",
 38 |        "<table border=\"1\" class=\"dataframe\">\n",
 39 |        "  <thead>\n",
 40 |        "    <tr style=\"text-align: right;\">\n",
 41 |        "      <th></th>\n",
 42 |        "      <th>Rank</th>\n",
 43 |        "      <th>Team</th>\n",
 44 |        "      <th>Participations</th>\n",
 45 |        "      <th>Titles</th>\n",
 46 |        "      <th>Pld</th>\n",
 47 |        "      <th>W</th>\n",
 48 |        "      <th>D</th>\n",
 49 |        "      <th>L</th>\n",
 50 |        "      <th>GF</th>\n",
 51 |        "      <th>GA</th>\n",
 52 |        "      <th>GD</th>\n",
 53 |        "      <th>Pts</th>\n",
 54 |        "    </tr>\n",
 55 |        "  </thead>\n",
 56 |        "  <tbody>\n",
 57 |        "    <tr>\n",
 58 |        "      <th>0</th>\n",
 59 |        "      <td>1</td>\n",
 60 |        "      <td>Brazil</td>\n",
 61 |        "      <td>21</td>\n",
 62 |        "      <td>5</td>\n",
 63 |        "      <td>109</td>\n",
 64 |        "      <td>73</td>\n",
 65 |        "      <td>18</td>\n",
 66 |        "      <td>18</td>\n",
 67 |        "      <td>229</td>\n",
 68 |        "      <td>105</td>\n",
 69 |        "      <td>124</td>\n",
 70 |        "      <td>237</td>\n",
 71 |        "    </tr>\n",
 72 |        "    <tr>\n",
 73 |        "      <th>1</th>\n",
 74 |        "      <td>2</td>\n",
 75 |        "      <td>Germany</td>\n",
 76 |        "      <td>19</td>\n",
 77 |        "      <td>4</td>\n",
 78 |        "      <td>109</td>\n",
 79 |        "      <td>67</td>\n",
 80 |        "      <td>20</td>\n",
 81 |        "      <td>22</td>\n",
 82 |        "      <td>226</td>\n",
 83 |        "      <td>125</td>\n",
 84 |        "      <td>101</td>\n",
 85 |        "      <td>221</td>\n",
 86 |        "    </tr>\n",
 87 |        "    <tr>\n",
 88 |        "      <th>2</th>\n",
 89 |        "      <td>3</td>\n",
 90 |        "      <td>Italy</td>\n",
 91 |        "      <td>18</td>\n",
 92 |        "      <td>4</td>\n",
 93 |        "      <td>83</td>\n",
 94 |        "      <td>45</td>\n",
 95 |        "      <td>21</td>\n",
 96 |        "      <td>17</td>\n",
 97 |        "      <td>128</td>\n",
 98 |        "      <td>77</td>\n",
 99 |        "      <td>51</td>\n",
100 |        "      <td>156</td>\n",
101 |        "    </tr>\n",
102 |        "    <tr>\n",
103 |        "      <th>3</th>\n",
104 |        "      <td>4</td>\n",
105 |        "      <td>Argentina</td>\n",
106 |        "      <td>17</td>\n",
107 |        "      <td>2</td>\n",
108 |        "      <td>81</td>\n",
109 |        "      <td>43</td>\n",
110 |        "      <td>15</td>\n",
111 |        "      <td>23</td>\n",
112 |        "      <td>137</td>\n",
113 |        "      <td>93</td>\n",
114 |        "      <td>44</td>\n",
115 |        "      <td>144</td>\n",
116 |        "    </tr>\n",
117 |        "    <tr>\n",
118 |        "      <th>4</th>\n",
119 |        "      <td>5</td>\n",
120 |        "      <td>France</td>\n",
121 |        "      <td>15</td>\n",
122 |        "      <td>2</td>\n",
123 |        "      <td>66</td>\n",
124 |        "      <td>34</td>\n",
125 |        "      <td>13</td>\n",
126 |        "      <td>19</td>\n",
127 |        "      <td>120</td>\n",
128 |        "      <td>77</td>\n",
129 |        "      <td>43</td>\n",
130 |        "      <td>115</td>\n",
131 |        "    </tr>\n",
132 |        "    <tr>\n",
133 |        "      <th>5</th>\n",
134 |        "      <td>6</td>\n",
135 |        "      <td>England</td>\n",
136 |        "      <td>15</td>\n",
137 |        "      <td>1</td>\n",
138 |        "      <td>69</td>\n",
139 |        "      <td>29</td>\n",
140 |        "      <td>21</td>\n",
141 |        "      <td>19</td>\n",
142 |        "      <td>91</td>\n",
143 |        "      <td>64</td>\n",
144 |        "      <td>27</td>\n",
145 |        "      <td>108</td>\n",
146 |        "    </tr>\n",
147 |        "    <tr>\n",
148 |        "      <th>6</th>\n",
149 |        "      <td>7</td>\n",
150 |        "      <td>Spain</td>\n",
151 |        "      <td>15</td>\n",
152 |        "      <td>1</td>\n",
153 |        "      <td>63</td>\n",
154 |        "      <td>30</td>\n",
155 |        "      <td>15</td>\n",
156 |        "      <td>18</td>\n",
157 |        "      <td>99</td>\n",
158 |        "      <td>72</td>\n",
159 |        "      <td>27</td>\n",
160 |        "      <td>105</td>\n",
161 |        "    </tr>\n",
162 |        "    <tr>\n",
163 |        "      <th>7</th>\n",
164 |        "      <td>8</td>\n",
165 |        "      <td>Uruguay</td>\n",
166 |        "      <td>13</td>\n",
167 |        "      <td>2</td>\n",
168 |        "      <td>56</td>\n",
169 |        "      <td>24</td>\n",
170 |        "      <td>12</td>\n",
171 |        "      <td>20</td>\n",
172 |        "      <td>87</td>\n",
173 |        "      <td>74</td>\n",
174 |        "      <td>13</td>\n",
175 |        "      <td>84</td>\n",
176 |        "    </tr>\n",
177 |        "  </tbody>\n",
178 |        "</table>\n",
179 |        "</div>"
180 |       ],
181 |       "text/plain": [
182 |        "   Rank       Team  Participations  Titles  Pld   W   D   L   GF   GA   GD  \\\n",
183 |        "0     1     Brazil              21       5  109  73  18  18  229  105  124   \n",
184 |        "1     2    Germany              19       4  109  67  20  22  226  125  101   \n",
185 |        "2     3      Italy              18       4   83  45  21  17  128   77   51   \n",
186 |        "3     4  Argentina              17       2   81  43  15  23  137   93   44   \n",
187 |        "4     5     France              15       2   66  34  13  19  120   77   43   \n",
188 |        "5     6    England              15       1   69  29  21  19   91   64   27   \n",
189 |        "6     7      Spain              15       1   63  30  15  18   99   72   27   \n",
190 |        "7     8    Uruguay              13       2   56  24  12  20   87   74   13   \n",
191 |        "\n",
192 |        "   Pts  \n",
193 |        "0  237  \n",
194 |        "1  221  \n",
195 |        "2  156  \n",
196 |        "3  144  \n",
197 |        "4  115  \n",
198 |        "5  108  \n",
199 |        "6  105  \n",
200 |        "7   84  "
201 |       ]
202 |      },
203 |      "execution_count": 28,
204 |      "metadata": {},
205 |      "output_type": "execute_result"
206 |     }
207 |    ],
208 |    "source": [
209 |     "df=pd.read_csv('champions.csv')\n",
210 |     "df\n"
211 |    ]
212 |   },
213 |   {
214 |    "cell_type": "code",
215 |    "execution_count": 29,
216 |    "id": "9d5d9f4f",
217 |    "metadata": {
218 |     "collapsed": true
219 |    },
220 |    "outputs": [
221 |     {
222 |      "data": {
223 |       "text/html": [
224 |        "<div>\n",
225 |        "<style scoped>\n",
226 |        "    .dataframe tbody tr th:only-of-type {\n",
227 |        "        vertical-align: middle;\n",
228 |        "    }\n",
229 |        "\n",
230 |        "    .dataframe tbody tr th {\n",
231 |        "        vertical-align: top;\n",
232 |        "    }\n",
233 |        "\n",
234 |        "    .dataframe thead th {\n",
235 |        "        text-align: right;\n",
236 |        "    }\n",
237 |        "</style>\n",
238 |        "<table border=\"1\" class=\"dataframe\">\n",
239 |        "  <thead>\n",
240 |        "    <tr style=\"text-align: right;\">\n",
241 |        "      <th></th>\n",
242 |        "      <th>Rank</th>\n",
243 |        "      <th>Participations</th>\n",
244 |        "      <th>Titles</th>\n",
245 |        "      <th>Pld</th>\n",
246 |        "      <th>W</th>\n",
247 |        "      <th>D</th>\n",
248 |        "      <th>L</th>\n",
249 |        "      <th>GF</th>\n",
250 |        "      <th>GA</th>\n",
251 |        "      <th>GD</th>\n",
252 |        "      <th>Pts</th>\n",
253 |        "    </tr>\n",
254 |        "  </thead>\n",
255 |        "  <tbody>\n",
256 |        "    <tr>\n",
257 |        "      <th>count</th>\n",
258 |        "      <td>8.00000</td>\n",
259 |        "      <td>8.000000</td>\n",
260 |        "      <td>8.000000</td>\n",
261 |        "      <td>8.000000</td>\n",
262 |        "      <td>8.000000</td>\n",
263 |        "      <td>8.000000</td>\n",
264 |        "      <td>8.000000</td>\n",
265 |        "      <td>8.000000</td>\n",
266 |        "      <td>8.000000</td>\n",
267 |        "      <td>8.000000</td>\n",
268 |        "      <td>8.000000</td>\n",
269 |        "    </tr>\n",
270 |        "    <tr>\n",
271 |        "      <th>mean</th>\n",
272 |        "      <td>4.50000</td>\n",
273 |        "      <td>16.625000</td>\n",
274 |        "      <td>2.625000</td>\n",
275 |        "      <td>79.500000</td>\n",
276 |        "      <td>43.125000</td>\n",
277 |        "      <td>16.875000</td>\n",
278 |        "      <td>19.500000</td>\n",
279 |        "      <td>139.625000</td>\n",
280 |        "      <td>85.875000</td>\n",
281 |        "      <td>53.750000</td>\n",
282 |        "      <td>146.250000</td>\n",
283 |        "    </tr>\n",
284 |        "    <tr>\n",
285 |        "      <th>std</th>\n",
286 |        "      <td>2.44949</td>\n",
287 |        "      <td>2.615203</td>\n",
288 |        "      <td>1.505941</td>\n",
289 |        "      <td>20.255511</td>\n",
290 |        "      <td>18.074746</td>\n",
291 |        "      <td>3.603074</td>\n",
292 |        "      <td>2.070197</td>\n",
293 |        "      <td>57.021143</td>\n",
294 |        "      <td>20.413144</td>\n",
295 |        "      <td>38.688315</td>\n",
296 |        "      <td>55.984054</td>\n",
297 |        "    </tr>\n",
298 |        "    <tr>\n",
299 |        "      <th>min</th>\n",
300 |        "      <td>1.00000</td>\n",
301 |        "      <td>13.000000</td>\n",
302 |        "      <td>1.000000</td>\n",
303 |        "      <td>56.000000</td>\n",
304 |        "      <td>24.000000</td>\n",
305 |        "      <td>12.000000</td>\n",
306 |        "      <td>17.000000</td>\n",
307 |        "      <td>87.000000</td>\n",
308 |        "      <td>64.000000</td>\n",
309 |        "      <td>13.000000</td>\n",
310 |        "      <td>84.000000</td>\n",
311 |        "    </tr>\n",
312 |        "    <tr>\n",
313 |        "      <th>25%</th>\n",
314 |        "      <td>2.75000</td>\n",
315 |        "      <td>15.000000</td>\n",
316 |        "      <td>1.750000</td>\n",
317 |        "      <td>65.250000</td>\n",
318 |        "      <td>29.750000</td>\n",
319 |        "      <td>14.500000</td>\n",
320 |        "      <td>18.000000</td>\n",
321 |        "      <td>97.000000</td>\n",
322 |        "      <td>73.500000</td>\n",
323 |        "      <td>27.000000</td>\n",
324 |        "      <td>107.250000</td>\n",
325 |        "    </tr>\n",
326 |        "    <tr>\n",
327 |        "      <th>50%</th>\n",
328 |        "      <td>4.50000</td>\n",
329 |        "      <td>16.000000</td>\n",
330 |        "      <td>2.000000</td>\n",
331 |        "      <td>75.000000</td>\n",
332 |        "      <td>38.500000</td>\n",
333 |        "      <td>16.500000</td>\n",
334 |        "      <td>19.000000</td>\n",
335 |        "      <td>124.000000</td>\n",
336 |        "      <td>77.000000</td>\n",
337 |        "      <td>43.500000</td>\n",
338 |        "      <td>129.500000</td>\n",
339 |        "    </tr>\n",
340 |        "    <tr>\n",
341 |        "      <th>75%</th>\n",
342 |        "      <td>6.25000</td>\n",
343 |        "      <td>18.250000</td>\n",
344 |        "      <td>4.000000</td>\n",
345 |        "      <td>89.500000</td>\n",
346 |        "      <td>50.500000</td>\n",
347 |        "      <td>20.250000</td>\n",
348 |        "      <td>20.500000</td>\n",
349 |        "      <td>159.250000</td>\n",
350 |        "      <td>96.000000</td>\n",
351 |        "      <td>63.500000</td>\n",
352 |        "      <td>172.250000</td>\n",
353 |        "    </tr>\n",
354 |        "    <tr>\n",
355 |        "      <th>max</th>\n",
356 |        "      <td>8.00000</td>\n",
357 |        "      <td>21.000000</td>\n",
358 |        "      <td>5.000000</td>\n",
359 |        "      <td>109.000000</td>\n",
360 |        "      <td>73.000000</td>\n",
361 |        "      <td>21.000000</td>\n",
362 |        "      <td>23.000000</td>\n",
363 |        "      <td>229.000000</td>\n",
364 |        "      <td>125.000000</td>\n",
365 |        "      <td>124.000000</td>\n",
366 |        "      <td>237.000000</td>\n",
367 |        "    </tr>\n",
368 |        "  </tbody>\n",
369 |        "</table>\n",
370 |        "</div>"
371 |       ],
372 |       "text/plain": [
373 |        "          Rank  Participations    Titles         Pld          W          D  \\\n",
374 |        "count  8.00000        8.000000  8.000000    8.000000   8.000000   8.000000   \n",
375 |        "mean   4.50000       16.625000  2.625000   79.500000  43.125000  16.875000   \n",
376 |        "std    2.44949        2.615203  1.505941   20.255511  18.074746   3.603074   \n",
377 |        "min    1.00000       13.000000  1.000000   56.000000  24.000000  12.000000   \n",
378 |        "25%    2.75000       15.000000  1.750000   65.250000  29.750000  14.500000   \n",
379 |        "50%    4.50000       16.000000  2.000000   75.000000  38.500000  16.500000   \n",
380 |        "75%    6.25000       18.250000  4.000000   89.500000  50.500000  20.250000   \n",
381 |        "max    8.00000       21.000000  5.000000  109.000000  73.000000  21.000000   \n",
382 |        "\n",
383 |        "               L          GF          GA          GD         Pts  \n",
384 |        "count   8.000000    8.000000    8.000000    8.000000    8.000000  \n",
385 |        "mean   19.500000  139.625000   85.875000   53.750000  146.250000  \n",
386 |        "std     2.070197   57.021143   20.413144   38.688315   55.984054  \n",
387 |        "min    17.000000   87.000000   64.000000   13.000000   84.000000  \n",
388 |        "25%    18.000000   97.000000   73.500000   27.000000  107.250000  \n",
389 |        "50%    19.000000  124.000000   77.000000   43.500000  129.500000  \n",
390 |        "75%    20.500000  159.250000   96.000000   63.500000  172.250000  \n",
391 |        "max    23.000000  229.000000  125.000000  124.000000  237.000000  "
392 |       ]
393 |      },
394 |      "execution_count": 29,
395 |      "metadata": {},
396 |      "output_type": "execute_result"
397 |     }
398 |    ],
399 |    "source": [
400 |     "df.describe()"
401 |    ]
402 |   },
403 |   {
404 |    "cell_type": "markdown",
405 |    "id": "4d5babca",
406 |    "metadata": {},
407 |    "source": [
408 |     "### selecting only team and titles \n"
409 |    ]
410 |   },
411 |   {
412 |    "cell_type": "code",
413 |    "execution_count": 30,
414 |    "id": "ce358583",
415 |    "metadata": {},
416 |    "outputs": [
417 |     {
418 |      "data": {
419 |       "text/html": [
420 |        "<div>\n",
421 |        "<style scoped>\n",
422 |        "    .dataframe tbody tr th:only-of-type {\n",
423 |        "        vertical-align: middle;\n",
424 |        "    }\n",
425 |        "\n",
426 |        "    .dataframe tbody tr th {\n",
427 |        "        vertical-align: top;\n",
428 |        "    }\n",
429 |        "\n",
430 |        "    .dataframe thead th {\n",
431 |        "        text-align: right;\n",
432 |        "    }\n",
433 |        "</style>\n",
434 |        "<table border=\"1\" class=\"dataframe\">\n",
435 |        "  <thead>\n",
436 |        "    <tr style=\"text-align: right;\">\n",
437 |        "      <th></th>\n",
438 |        "      <th>Team</th>\n",
439 |        "      <th>Titles</th>\n",
440 |        "    </tr>\n",
441 |        "  </thead>\n",
442 |        "  <tbody>\n",
443 |        "    <tr>\n",
444 |        "      <th>0</th>\n",
445 |        "      <td>Brazil</td>\n",
446 |        "      <td>5</td>\n",
447 |        "    </tr>\n",
448 |        "    <tr>\n",
449 |        "      <th>1</th>\n",
450 |        "      <td>Germany</td>\n",
451 |        "      <td>4</td>\n",
452 |        "    </tr>\n",
453 |        "    <tr>\n",
454 |        "      <th>2</th>\n",
455 |        "      <td>Italy</td>\n",
456 |        "      <td>4</td>\n",
457 |        "    </tr>\n",
458 |        "    <tr>\n",
459 |        "      <th>3</th>\n",
460 |        "      <td>Argentina</td>\n",
461 |        "      <td>2</td>\n",
462 |        "    </tr>\n",
463 |        "    <tr>\n",
464 |        "      <th>4</th>\n",
465 |        "      <td>France</td>\n",
466 |        "      <td>2</td>\n",
467 |        "    </tr>\n",
468 |        "    <tr>\n",
469 |        "      <th>5</th>\n",
470 |        "      <td>England</td>\n",
471 |        "      <td>1</td>\n",
472 |        "    </tr>\n",
473 |        "    <tr>\n",
474 |        "      <th>6</th>\n",
475 |        "      <td>Spain</td>\n",
476 |        "      <td>1</td>\n",
477 |        "    </tr>\n",
478 |        "    <tr>\n",
479 |        "      <th>7</th>\n",
480 |        "      <td>Uruguay</td>\n",
481 |        "      <td>2</td>\n",
482 |        "    </tr>\n",
483 |        "  </tbody>\n",
484 |        "</table>\n",
485 |        "</div>"
486 |       ],
487 |       "text/plain": [
488 |        "        Team  Titles\n",
489 |        "0     Brazil       5\n",
490 |        "1    Germany       4\n",
491 |        "2      Italy       4\n",
492 |        "3  Argentina       2\n",
493 |        "4     France       2\n",
494 |        "5    England       1\n",
495 |        "6      Spain       1\n",
496 |        "7    Uruguay       2"
497 |       ]
498 |      },
499 |      "execution_count": 30,
500 |      "metadata": {},
501 |      "output_type": "execute_result"
502 |     }
503 |    ],
504 |    "source": [
505 |     "winners=df.loc[:,['Team','Titles']]\n",
506 |     "winners\n"
507 |    ]
508 |   },
509 |   {
510 |    "cell_type": "markdown",
511 |    "id": "d2cc6948",
512 |    "metadata": {},
513 |    "source": [
514 |     "### plotting most no of worldcup winners "
515 |    ]
516 |   },
517 |   {
518 |    "cell_type": "code",
519 |    "execution_count": 31,
520 |    "id": "284101f5",
521 |    "metadata": {},
522 |    "outputs": [
523 |     {
524 |      "data": {
525 |       "text/plain": [
526 |        "<AxesSubplot: xlabel='Team'>"
527 |       ]
528 |      },
529 |      "execution_count": 31,
530 |      "metadata": {},
531 |      "output_type": "execute_result"
532 |     },
533 |     {
534 |      "data": {
535 |       "image/png": 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\n",
536 |       "text/plain": [
537 |        "<Figure size 640x480 with 1 Axes>"
538 |       ]
539 |      },
540 |      "metadata": {},
541 |      "output_type": "display_data"
542 |     }
543 |    ],
544 |    "source": [
545 |     "winners.plot(kind='bar',x='Team',y='Titles')"
546 |    ]
547 |   },
548 |   {
549 |    "cell_type": "code",
550 |    "execution_count": 45,
551 |    "id": "c769107e",
552 |    "metadata": {},
553 |    "outputs": [
554 |     {
555 |      "data": {
556 |       "text/html": [
557 |        "<div>\n",
558 |        "<style scoped>\n",
559 |        "    .dataframe tbody tr th:only-of-type {\n",
560 |        "        vertical-align: middle;\n",
561 |        "    }\n",
562 |        "\n",
563 |        "    .dataframe tbody tr th {\n",
564 |        "        vertical-align: top;\n",
565 |        "    }\n",
566 |        "\n",
567 |        "    .dataframe thead th {\n",
568 |        "        text-align: right;\n",
569 |        "    }\n",
570 |        "</style>\n",
571 |        "<table border=\"1\" class=\"dataframe\">\n",
572 |        "  <thead>\n",
573 |        "    <tr style=\"text-align: right;\">\n",
574 |        "      <th></th>\n",
575 |        "      <th>Team</th>\n",
576 |        "      <th>GF</th>\n",
577 |        "      <th>GA</th>\n",
578 |        "    </tr>\n",
579 |        "  </thead>\n",
580 |        "  <tbody>\n",
581 |        "    <tr>\n",
582 |        "      <th>0</th>\n",
583 |        "      <td>Brazil</td>\n",
584 |        "      <td>229</td>\n",
585 |        "      <td>105</td>\n",
586 |        "    </tr>\n",
587 |        "    <tr>\n",
588 |        "      <th>1</th>\n",
589 |        "      <td>Germany</td>\n",
590 |        "      <td>226</td>\n",
591 |        "      <td>125</td>\n",
592 |        "    </tr>\n",
593 |        "    <tr>\n",
594 |        "      <th>2</th>\n",
595 |        "      <td>Italy</td>\n",
596 |        "      <td>128</td>\n",
597 |        "      <td>77</td>\n",
598 |        "    </tr>\n",
599 |        "    <tr>\n",
600 |        "      <th>3</th>\n",
601 |        "      <td>Argentina</td>\n",
602 |        "      <td>137</td>\n",
603 |        "      <td>93</td>\n",
604 |        "    </tr>\n",
605 |        "    <tr>\n",
606 |        "      <th>4</th>\n",
607 |        "      <td>France</td>\n",
608 |        "      <td>120</td>\n",
609 |        "      <td>77</td>\n",
610 |        "    </tr>\n",
611 |        "    <tr>\n",
612 |        "      <th>5</th>\n",
613 |        "      <td>England</td>\n",
614 |        "      <td>91</td>\n",
615 |        "      <td>64</td>\n",
616 |        "    </tr>\n",
617 |        "    <tr>\n",
618 |        "      <th>6</th>\n",
619 |        "      <td>Spain</td>\n",
620 |        "      <td>99</td>\n",
621 |        "      <td>72</td>\n",
622 |        "    </tr>\n",
623 |        "    <tr>\n",
624 |        "      <th>7</th>\n",
625 |        "      <td>Uruguay</td>\n",
626 |        "      <td>87</td>\n",
627 |        "      <td>74</td>\n",
628 |        "    </tr>\n",
629 |        "  </tbody>\n",
630 |        "</table>\n",
631 |        "</div>"
632 |       ],
633 |       "text/plain": [
634 |        "        Team   GF   GA\n",
635 |        "0     Brazil  229  105\n",
636 |        "1    Germany  226  125\n",
637 |        "2      Italy  128   77\n",
638 |        "3  Argentina  137   93\n",
639 |        "4     France  120   77\n",
640 |        "5    England   91   64\n",
641 |        "6      Spain   99   72\n",
642 |        "7    Uruguay   87   74"
643 |       ]
644 |      },
645 |      "execution_count": 45,
646 |      "metadata": {},
647 |      "output_type": "execute_result"
648 |     }
649 |    ],
650 |    "source": [
651 |     "games=df.loc[:,['Team','GF','GA']]\n",
652 |     "games"
653 |    ]
654 |   },
655 |   {
656 |    "cell_type": "markdown",
657 |    "id": "cde6f46a",
658 |    "metadata": {},
659 |    "source": [
660 |     "### Goal For and Goal Against in a bar graph representation by a team\n"
661 |    ]
662 |   },
663 |   {
664 |    "cell_type": "code",
665 |    "execution_count": 49,
666 |    "id": "c74f974f",
667 |    "metadata": {
668 |     "scrolled": false
669 |    },
670 |    "outputs": [
671 |     {
672 |      "data": {
673 |       "text/plain": [
674 |        "<AxesSubplot: xlabel='Team'>"
675 |       ]
676 |      },
677 |      "execution_count": 49,
678 |      "metadata": {},
679 |      "output_type": "execute_result"
680 |     },
681 |     {
682 |      "data": {
683 |       "image/png": 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\n",
684 |       "text/plain": [
685 |        "<Figure size 600x1000 with 1 Axes>"
686 |       ]
687 |      },
688 |      "metadata": {},
689 |      "output_type": "display_data"
690 |     }
691 |    ],
692 |    "source": [
693 |     "games.plot(kind='bar',y=['GF','GA'],x='Team',color=['yellow','green'],figsize=(6,10),)"
694 |    ]
695 |   },
696 |   {
697 |    "cell_type": "code",
698 |    "execution_count": 53,
699 |    "id": "fc00336d",
700 |    "metadata": {},
701 |    "outputs": [
702 |     {
703 |      "data": {
704 |       "text/plain": [
705 |        "(8, 12)"
706 |       ]
707 |      },
708 |      "execution_count": 53,
709 |      "metadata": {},
710 |      "output_type": "execute_result"
711 |     }
712 |    ],
713 |    "source": [
714 |     "df.shape"
715 |    ]
716 |   },
717 |   {
718 |    "cell_type": "code",
719 |    "execution_count": 61,
720 |    "id": "805fd47b",
721 |    "metadata": {},
722 |    "outputs": [
723 |     {
724 |      "data": {
725 |       "text/html": [
726 |        "<div>\n",
727 |        "<style scoped>\n",
728 |        "    .dataframe tbody tr th:only-of-type {\n",
729 |        "        vertical-align: middle;\n",
730 |        "    }\n",
731 |        "\n",
732 |        "    .dataframe tbody tr th {\n",
733 |        "        vertical-align: top;\n",
734 |        "    }\n",
735 |        "\n",
736 |        "    .dataframe thead th {\n",
737 |        "        text-align: right;\n",
738 |        "    }\n",
739 |        "</style>\n",
740 |        "<table border=\"1\" class=\"dataframe\">\n",
741 |        "  <thead>\n",
742 |        "    <tr style=\"text-align: right;\">\n",
743 |        "      <th></th>\n",
744 |        "      <th>Team</th>\n",
745 |        "      <th>Pts</th>\n",
746 |        "    </tr>\n",
747 |        "  </thead>\n",
748 |        "  <tbody>\n",
749 |        "    <tr>\n",
750 |        "      <th>0</th>\n",
751 |        "      <td>Brazil</td>\n",
752 |        "      <td>237</td>\n",
753 |        "    </tr>\n",
754 |        "    <tr>\n",
755 |        "      <th>1</th>\n",
756 |        "      <td>Germany</td>\n",
757 |        "      <td>221</td>\n",
758 |        "    </tr>\n",
759 |        "    <tr>\n",
760 |        "      <th>2</th>\n",
761 |        "      <td>Italy</td>\n",
762 |        "      <td>156</td>\n",
763 |        "    </tr>\n",
764 |        "    <tr>\n",
765 |        "      <th>3</th>\n",
766 |        "      <td>Argentina</td>\n",
767 |        "      <td>144</td>\n",
768 |        "    </tr>\n",
769 |        "    <tr>\n",
770 |        "      <th>4</th>\n",
771 |        "      <td>France</td>\n",
772 |        "      <td>115</td>\n",
773 |        "    </tr>\n",
774 |        "    <tr>\n",
775 |        "      <th>5</th>\n",
776 |        "      <td>England</td>\n",
777 |        "      <td>108</td>\n",
778 |        "    </tr>\n",
779 |        "    <tr>\n",
780 |        "      <th>6</th>\n",
781 |        "      <td>Spain</td>\n",
782 |        "      <td>105</td>\n",
783 |        "    </tr>\n",
784 |        "    <tr>\n",
785 |        "      <th>7</th>\n",
786 |        "      <td>Uruguay</td>\n",
787 |        "      <td>84</td>\n",
788 |        "    </tr>\n",
789 |        "  </tbody>\n",
790 |        "</table>\n",
791 |        "</div>"
792 |       ],
793 |       "text/plain": [
794 |        "        Team  Pts\n",
795 |        "0     Brazil  237\n",
796 |        "1    Germany  221\n",
797 |        "2      Italy  156\n",
798 |        "3  Argentina  144\n",
799 |        "4     France  115\n",
800 |        "5    England  108\n",
801 |        "6      Spain  105\n",
802 |        "7    Uruguay   84"
803 |       ]
804 |      },
805 |      "execution_count": 61,
806 |      "metadata": {},
807 |      "output_type": "execute_result"
808 |     }
809 |    ],
810 |    "source": [
811 |     "points=df.iloc[:,[1,11]]\n",
812 |     "points"
813 |    ]
814 |   },
815 |   {
816 |    "cell_type": "code",
817 |    "execution_count": 65,
818 |    "id": "d8ae14a0",
819 |    "metadata": {},
820 |    "outputs": [],
821 |    "source": [
822 |     "my_team=df.Team\n",
823 |     "my_pts=df.Pts"
824 |    ]
825 |   },
826 |   {
827 |    "cell_type": "markdown",
828 |    "id": "8b116cdc",
829 |    "metadata": {},
830 |    "source": [
831 |     "### showing total points gained by a team as percentage in the form of pie chart\n"
832 |    ]
833 |   },
834 |   {
835 |    "cell_type": "code",
836 |    "execution_count": 78,
837 |    "id": "08058faf",
838 |    "metadata": {},
839 |    "outputs": [
840 |     {
841 |      "data": {
842 |       "image/png": 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\n",
843 |       "text/plain": [
844 |        "<Figure size 640x480 with 1 Axes>"
845 |       ]
846 |      },
847 |      "metadata": {},
848 |      "output_type": "display_data"
849 |     }
850 |    ],
851 |    "source": [
852 |     "mp.pie(my_pts,labels=my_team,autopct='%1.0f%%')\n",
853 |     "mp.title(\"pie chart to show total points gained\")\n",
854 |     "mp.show()"
855 |    ]
856 |   },
857 |   {
858 |    "cell_type": "code",
859 |    "execution_count": null,
860 |    "id": "aa977979",
861 |    "metadata": {},
862 |    "outputs": [],
863 |    "source": []
864 |   }
865 |  ],
866 |  "metadata": {
867 |   "kernelspec": {
868 |    "display_name": "Python 3 (ipykernel)",
869 |    "language": "python",
870 |    "name": "python3"
871 |   },
872 |   "language_info": {
873 |    "codemirror_mode": {
874 |     "name": "ipython",
875 |     "version": 3
876 |    },
877 |    "file_extension": ".py",
878 |    "mimetype": "text/x-python",
879 |    "name": "python",
880 |    "nbconvert_exporter": "python",
881 |    "pygments_lexer": "ipython3",
882 |    "version": "3.11.0"
883 |   }
884 |  },
885 |  "nbformat": 4,
886 |  "nbformat_minor": 5
887 | }
888 | 


--------------------------------------------------------------------------------