From e7088cf4539235df8b77ddaa04d3cee830737349 Mon Sep 17 00:00:00 2001 From: Leandro de Oliveira Date: Wed, 12 Feb 2020 00:55:42 -0300 Subject: [PATCH] Update Ranking_ITTF_TOP100.ipynb --- Ranking_ITTF_TOP100.ipynb | 1135 ++++++++++++++++++++++++++++++++++--- 1 file changed, 1047 insertions(+), 88 deletions(-) diff --git a/Ranking_ITTF_TOP100.ipynb b/Ranking_ITTF_TOP100.ipynb index f81bc4e..8a03a6c 100644 --- a/Ranking_ITTF_TOP100.ipynb +++ b/Ranking_ITTF_TOP100.ipynb @@ -2,11 +2,11 @@ "cells": [ { "cell_type": "code", - "execution_count": 99, + "execution_count": 272, "metadata": { "ExecuteTime": { - "end_time": "2020-02-11T21:55:21.274449Z", - "start_time": "2020-02-11T21:55:21.266448Z" + "end_time": "2020-02-12T02:39:17.586903Z", + "start_time": "2020-02-12T02:39:17.565902Z" } }, "outputs": [], @@ -14,16 +14,99 @@ "import bs4 as bs\n", "import urllib.request\n", "import json\n", - "import pandas as pd" + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "#REFERENCIAS\n", + "#https://stackoverflow.com/questions/21104592/json-to-pandas-dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 273, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-12T02:39:26.434978Z", + "start_time": "2020-02-12T02:39:17.593903Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "html_mulheres = urllib.request.urlopen('https://ranking.ittf.com/public/s/ranking/list?category=SEN&typeGender=W%3BSINGLES&year=2020&week=6&offset=0&size=100').read()\n", + "html_homens = urllib.request.urlopen('https://ranking.ittf.com/public/s/ranking/list?category=SEN&typeGender=M%3BSINGLES&year=2020&week=6&offset=0&size=100').read()\n", + "\n", + "def ranking(html, top):\n", + " soup = bs.BeautifulSoup(html, 'lxml') # pega so o html\n", + " soup = soup.find(\"p\").get_text('') # pega o json dentro e tira as aspas ->

'json'

\n", + " json_top100 = json.loads(soup)\n", + "\n", + " nome, pais, pontos, rank_atual, rank_anterior, player_id = [], [], [], [], [], []\n", + "\n", + " for elemento in json_top100['ranks']:\n", + " rank_anterior.append(elemento['PrevRk'])\n", + " rank_atual.append(elemento['Rk'])\n", + " pontos.append(elemento['Points'])\n", + " pais.append(elemento['Country']['desc'])\n", + " nome.append(elemento['Player']['Name'])\n", + " player_id.append(elemento['Player']['ittfId'])\n", + "\n", + " df = pd.DataFrame([rank_atual, rank_anterior, pontos, pais, nome, player_id]).T\n", + " colunas = ['Ranking Atual','Ranking Anterior', 'Pontos', 'País', 'Nome', 'Id']\n", + " df.columns = colunas\n", + " return df.head(top)\n", + "\n", + "\n", + "def progresso(ittf_id, sexo): #singles\n", + " \n", + " url = 'https://ranking.ittf.com/public/s/ranking/progress/'+str(ittf_id)+'?category=SEN&typeGender='+str(sexo)+'%3BSINGLES'\n", + " html = urllib.request.urlopen(url).read()\n", + " soup = bs.BeautifulSoup(html, 'lxml') # pega so o html\n", + " soup = soup.find(\"p\").get_text() # pega o json dentro e tira as aspas ->

'json'

\n", + " json_progresso = json.loads(soup)\n", + "\n", + " data, rank = [], []\n", + " for elemento in json_progresso['ranks']:\n", + " data.append(elemento['Date'])\n", + " rank.append(elemento['Rk'])\n", + "\n", + " df = pd.DataFrame([data, rank]).T\n", + " colunas = ['Data', 'Ranking']\n", + " df.columns = colunas\n", + " x = df['Data']\n", + " y = df['Ranking']\n", + " fig, ax = plt.subplots(figsize=(30, 10))\n", + " plt.gca().invert_yaxis()\n", + " plt.xticks(rotation=90)\n", + " ax.plot(x, y)\n", + " ax.set_xlabel('Data')\n", + " ax.set_ylabel('Ranking')\n", + " ax.set_title('Progresso do jogador ao longo do tempo')\n", + " ax.grid(True)\n", + " return plt.show()\n", + "\n", + "progresso('112019', 'W')\n" ] }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 274, "metadata": { "ExecuteTime": { - "end_time": "2020-02-11T21:54:48.852406Z", - "start_time": "2020-02-11T21:54:47.322367Z" + "end_time": "2020-02-12T02:39:26.570983Z", + "start_time": "2020-02-12T02:39:26.434978Z" } }, "outputs": [ @@ -51,8 +134,9 @@ " Ranking Atual\n", " Ranking Anterior\n", " Pontos\n", - " País\n", + " País\n", " Nome\n", + " Id\n", " \n", " \n", " \n", @@ -63,6 +147,7 @@ " 17015\n", " China\n", " CHEN Meng\n", + " 112019\n", " \n", " \n", " 1\n", @@ -71,6 +156,7 @@ " 15460\n", " China\n", " SUN Yingsha\n", + " 131163\n", " \n", " \n", " 2\n", @@ -79,6 +165,7 @@ " 14720\n", " Japan\n", " ITO Mima\n", + " 117821\n", " \n", " \n", " 3\n", @@ -87,6 +174,7 @@ " 14425\n", " China\n", " WANG Manyu\n", + " 121411\n", " \n", " \n", " 4\n", @@ -95,6 +183,7 @@ " 14400\n", " China\n", " LIU Shiwen\n", + " 105482\n", " \n", " \n", " 5\n", @@ -103,6 +192,7 @@ " 13450\n", " China\n", " DING Ning\n", + " 102265\n", " \n", " \n", " 6\n", @@ -111,6 +201,7 @@ " 13015\n", " China\n", " ZHU Yuling\n", + " 117332\n", " \n", " \n", " 7\n", @@ -119,6 +210,7 @@ " 11100\n", " Singapore\n", " FENG Tianwei\n", + " 102712\n", " \n", " \n", " 8\n", @@ -127,6 +219,7 @@ " 10950\n", " Japan\n", " ISHIKAWA Kasumi\n", + " 110752\n", " \n", " \n", " 9\n", @@ -135,60 +228,494 @@ " 10915\n", " Chinese Taipei\n", " CHENG I-Ching\n", + " 110797\n", + " \n", + " \n", + " 10\n", + " 11\n", + " 11\n", + " 10815\n", + " Japan\n", + " HIRANO Miu\n", + " 117820\n", + " \n", + " \n", + " 11\n", + " 12\n", + " 13\n", + " 10435\n", + " China\n", + " WANG Yidi\n", + " 124110\n", + " \n", + " \n", + " 12\n", + " 13\n", + " 12\n", + " 10145\n", + " China\n", + " CHEN Xingtong\n", + " 121403\n", + " \n", + " \n", + " 13\n", + " 14\n", + " 14\n", + " 9095\n", + " Austria\n", + " POLCANOVA Sofia\n", + " 107477\n", + " \n", + " \n", + " 14\n", + " 15\n", + " 15\n", + " 9060\n", + " Hong Kong, China\n", + " DOO Hoi Kem\n", + " 115543\n", + " \n", + " \n", + " 15\n", + " 16\n", + " 16\n", + " 9020\n", + " Republic of Korea\n", + " JEON Jihee\n", + " 118994\n", + " \n", + " \n", + " 16\n", + " 17\n", + " 18\n", + " 8660\n", + " China\n", + " HE Zhuojia\n", + " 119730\n", + " \n", + " \n", + " 17\n", + " 18\n", + " 17\n", + " 8550\n", + " Japan\n", + " SATO Hitomi\n", + " 122261\n", + " \n", + " \n", + " 18\n", + " 19\n", + " 20\n", + " 8065\n", + " Germany\n", + " SOLJA Petrissa\n", + " 111065\n", + " \n", + " \n", + " 19\n", + " 20\n", + " 21\n", + " 7820\n", + " Puerto Rico\n", + " DIAZ Adriana\n", + " 115009\n", + " \n", + " \n", + " 20\n", + " 21\n", + " 23\n", + " 7745\n", + " Japan\n", + " HAYATA Hina\n", + " 123672\n", + " \n", + " \n", + " 21\n", + " 22\n", + " 19\n", + " 7710\n", + " Romania\n", + " SZOCS Bernadette\n", + " 111012\n", + " \n", + " \n", + " 22\n", + " 23\n", + " 22\n", + " 7585\n", + " Japan\n", + " KATO Miyu\n", + " 117822\n", + " \n", + " \n", + " 23\n", + " 24\n", + " 30\n", + " 7300\n", + " China\n", + " QIAN Tianyi\n", + " 119797\n", + " \n", + " \n", + " 24\n", + " 25\n", + " 24\n", + " 7265\n", + " Republic of Korea\n", + " SUH Hyowon\n", + " 108470\n", + " \n", + " \n", + " 25\n", + " 26\n", + " 25\n", + " 7015\n", + " Chinese Taipei\n", + " CHEN Szu-Yu\n", + " 114105\n", + " \n", + " \n", + " 26\n", + " 27\n", + " 29\n", + " 6905\n", + " Germany\n", + " HAN Ying\n", + " 118762\n", + " \n", + " \n", + " 27\n", + " 28\n", + " 26\n", + " 6890\n", + " United States\n", + " ZHANG Lily\n", + " 112221\n", + " \n", + " \n", + " 28\n", + " 29\n", + " 28\n", + " 6750\n", + " Hong Kong, China\n", + " SOO Wai Yam Minnie\n", + " 115037\n", + " \n", + " \n", + " 29\n", + " 30\n", + " 33\n", + " 6590\n", + " United States\n", + " WU Yue\n", + " 110227\n", + " \n", + " \n", + " 30\n", + " 31\n", + " 32\n", + " 6580\n", + " Egypt\n", + " MESHREF Dina\n", + " 111656\n", + " \n", + " \n", + " 31\n", + " 32\n", + " 27\n", + " 6565\n", + " Canada\n", + " ZHANG Mo\n", + " 110560\n", + " \n", + " \n", + " 32\n", + " 33\n", + " 34\n", + " 6480\n", + " Poland\n", + " LI Qian\n", + " 111882\n", + " \n", + " \n", + " 33\n", + " 34\n", + " 31\n", + " 6375\n", + " Romania\n", + " SAMARA Elizabeta\n", + " 108226\n", + " \n", + " \n", + " 34\n", + " 35\n", + " 35\n", + " 6220\n", + " Sweden\n", + " EKHOLM Matilda\n", + " 102472\n", + " \n", + " \n", + " 35\n", + " 36\n", + " 37\n", + " 6210\n", + " China\n", + " GU Yuting\n", + " 115760\n", + " \n", + " \n", + " 36\n", + " 37\n", + " 36\n", + " 6020\n", + " Ukraine\n", + " PESOTSKA Margaryta\n", + " 107321\n", + " \n", + " \n", + " 37\n", + " 38\n", + " 43\n", + " 5945\n", + " Japan\n", + " SHIBATA Saki\n", + " 123848\n", + " \n", + " \n", + " 38\n", + " 39\n", + " 46\n", + " 5885\n", + " Japan\n", + " HASHIMOTO Honoka\n", + " 133000\n", + " \n", + " \n", + " 39\n", + " 40\n", + " 39\n", + " 5865\n", + " Germany\n", + " MITTELHAM Nina\n", + " 114726\n", + " \n", + " \n", + " 40\n", + " 41\n", + " 40\n", + " 5835\n", + " Hong Kong, China\n", + " LEE Ho Ching\n", + " 105159\n", + " \n", + " \n", + " 41\n", + " 42\n", + " 42\n", + " 5785\n", + " Thailand\n", + " SAWETTABUT Suthasini\n", + " 111833\n", + " \n", + " \n", + " 42\n", + " 43\n", + " 47\n", + " 5700\n", + " Russian Federation\n", + " MIKHAILOVA Polina\n", + " 106114\n", + " \n", + " \n", + " 43\n", + " 44\n", + " 38\n", + " 5690\n", + " Hungary\n", + " POTA Georgina\n", + " 107525\n", + " \n", + " \n", + " 44\n", + " 45\n", + " 49\n", + " 5680\n", + " Brazil\n", + " TAKAHASHI Bruna\n", + " 121623\n", + " \n", + " \n", + " 45\n", + " 46\n", + " 44\n", + " 5650\n", + " Luxembourg\n", + " NI Xia Lian\n", + " 106706\n", + " \n", + " \n", + " 46\n", + " 47\n", + " 45\n", + " 5555\n", + " Netherlands\n", + " EERLAND Britt\n", + " 113701\n", + " \n", + " \n", + " 47\n", + " 48\n", + " 50\n", + " 5420\n", + " DPR Korea\n", + " KIM Song I\n", + " 115781\n", + " \n", + " \n", + " 48\n", + " 49\n", + " 51\n", + " 5395\n", + " Japan\n", + " KIHARA Miyuu\n", + " 131036\n", + " \n", + " \n", + " 49\n", + " 50\n", + " 48\n", + " 5360\n", + " Czech Republic\n", + " MATELOVA Hana\n", + " 105913\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Ranking Atual Ranking Anterior Pontos País Nome\n", - "0 1 1 17015 China CHEN Meng\n", - "1 2 2 15460 China SUN Yingsha\n", - "2 3 3 14720 Japan ITO Mima\n", - "3 4 5 14425 China WANG Manyu\n", - "4 5 4 14400 China LIU Shiwen\n", - "5 6 7 13450 China DING Ning\n", - "6 7 6 13015 China ZHU Yuling\n", - "7 8 8 11100 Singapore FENG Tianwei\n", - "8 9 9 10950 Japan ISHIKAWA Kasumi\n", - "9 10 10 10915 Chinese Taipei CHENG I-Ching" + " Ranking Atual Ranking Anterior Pontos País \\\n", + "0 1 1 17015 China \n", + "1 2 2 15460 China \n", + "2 3 3 14720 Japan \n", + "3 4 5 14425 China \n", + "4 5 4 14400 China \n", + "5 6 7 13450 China \n", + "6 7 6 13015 China \n", + "7 8 8 11100 Singapore \n", + "8 9 9 10950 Japan \n", + "9 10 10 10915 Chinese Taipei \n", + "10 11 11 10815 Japan \n", + "11 12 13 10435 China \n", + "12 13 12 10145 China \n", + "13 14 14 9095 Austria \n", + "14 15 15 9060 Hong Kong, China \n", + "15 16 16 9020 Republic of Korea \n", + "16 17 18 8660 China \n", + "17 18 17 8550 Japan \n", + "18 19 20 8065 Germany \n", + "19 20 21 7820 Puerto Rico \n", + "20 21 23 7745 Japan \n", + "21 22 19 7710 Romania \n", + "22 23 22 7585 Japan \n", + "23 24 30 7300 China \n", + "24 25 24 7265 Republic of Korea \n", + "25 26 25 7015 Chinese Taipei \n", + "26 27 29 6905 Germany \n", + "27 28 26 6890 United States \n", + "28 29 28 6750 Hong Kong, China \n", + "29 30 33 6590 United States \n", + "30 31 32 6580 Egypt \n", + "31 32 27 6565 Canada \n", + "32 33 34 6480 Poland \n", + "33 34 31 6375 Romania \n", + "34 35 35 6220 Sweden \n", + "35 36 37 6210 China \n", + "36 37 36 6020 Ukraine \n", + "37 38 43 5945 Japan \n", + "38 39 46 5885 Japan \n", + "39 40 39 5865 Germany \n", + "40 41 40 5835 Hong Kong, China \n", + "41 42 42 5785 Thailand \n", + "42 43 47 5700 Russian Federation \n", + "43 44 38 5690 Hungary \n", + "44 45 49 5680 Brazil \n", + "45 46 44 5650 Luxembourg \n", + "46 47 45 5555 Netherlands \n", + "47 48 50 5420 DPR Korea \n", + "48 49 51 5395 Japan \n", + "49 50 48 5360 Czech Republic \n", + "\n", + " Nome Id \n", + "0 CHEN Meng 112019 \n", + "1 SUN Yingsha 131163 \n", + "2 ITO Mima 117821 \n", + "3 WANG Manyu 121411 \n", + "4 LIU Shiwen 105482 \n", + "5 DING Ning 102265 \n", + "6 ZHU Yuling 117332 \n", + "7 FENG Tianwei 102712 \n", + "8 ISHIKAWA Kasumi 110752 \n", + "9 CHENG I-Ching 110797 \n", + "10 HIRANO Miu 117820 \n", + "11 WANG Yidi 124110 \n", + "12 CHEN Xingtong 121403 \n", + "13 POLCANOVA Sofia 107477 \n", + "14 DOO Hoi Kem 115543 \n", + "15 JEON Jihee 118994 \n", + "16 HE Zhuojia 119730 \n", + "17 SATO Hitomi 122261 \n", + "18 SOLJA Petrissa 111065 \n", + "19 DIAZ Adriana 115009 \n", + "20 HAYATA Hina 123672 \n", + "21 SZOCS Bernadette 111012 \n", + "22 KATO Miyu 117822 \n", + "23 QIAN Tianyi 119797 \n", + "24 SUH Hyowon 108470 \n", + "25 CHEN Szu-Yu 114105 \n", + "26 HAN Ying 118762 \n", + "27 ZHANG Lily 112221 \n", + "28 SOO Wai Yam Minnie 115037 \n", + "29 WU Yue 110227 \n", + "30 MESHREF Dina 111656 \n", + "31 ZHANG Mo 110560 \n", + "32 LI Qian 111882 \n", + "33 SAMARA Elizabeta 108226 \n", + "34 EKHOLM Matilda 102472 \n", + "35 GU Yuting 115760 \n", + "36 PESOTSKA Margaryta 107321 \n", + "37 SHIBATA Saki 123848 \n", + "38 HASHIMOTO Honoka 133000 \n", + "39 MITTELHAM Nina 114726 \n", + "40 LEE Ho Ching 105159 \n", + "41 SAWETTABUT Suthasini 111833 \n", + "42 MIKHAILOVA Polina 106114 \n", + "43 POTA Georgina 107525 \n", + "44 TAKAHASHI Bruna 121623 \n", + "45 NI Xia Lian 106706 \n", + "46 EERLAND Britt 113701 \n", + "47 KIM Song I 115781 \n", + "48 KIHARA Miyuu 131036 \n", + "49 MATELOVA Hana 105913 " ] }, - "execution_count": 97, + "execution_count": 274, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "html_mulheres = urllib.request.urlopen('https://ranking.ittf.com/public/s/ranking/list?category=SEN&typeGender=W%3BSINGLES&year=2020&week=6&offset=0&size=100').read()\n", - "\n", - "soup = bs.BeautifulSoup(html_mulheres, 'lxml') # pega so o html\n", - "soup = soup.find(\"p\").get_text('') # pega o json dentro e tira as aspas ->

'json'

\n", - "json_top100 = json.loads(soup)\n", - "json_top100 = json_top100['ranks']\n", - "\n", - "nome, pais, pontos, rank_atual, rank_anterior = [], [], [], [], []\n", - "\n", - "for elemento in json_top100:\n", - " rank_anterior.append(elemento['PrevRk'])\n", - " rank_atual.append(elemento['Rk'])\n", - " pontos.append(elemento['Points'])\n", - " pais.append(elemento['Country']['desc'])\n", - " nome.append(elemento['Player']['Name'])\n", - " \n", - "df = pd.DataFrame([rank_atual, rank_anterior, pontos, pais, nome]).T\n", - "colunas = ['Ranking Atual','Ranking Anterior', 'Pontos', 'País', 'Nome']\n", - "df.columns = colunas\n", - "df.head(10)" + "ranking(html_mulheres, 50)" ] }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 275, "metadata": { "ExecuteTime": { - "end_time": "2020-02-11T21:55:37.481985Z", - "start_time": "2020-02-11T21:55:35.920914Z" + "end_time": "2020-02-12T02:39:26.850999Z", + "start_time": "2020-02-12T02:39:26.576983Z" } }, "outputs": [ @@ -216,8 +743,9 @@ " Ranking Atual\n", " Ranking Anterior\n", " Pontos\n", - " País\n", + " País\n", " Nome\n", + " Id\n", " \n", " \n", " \n", @@ -228,6 +756,7 @@ " 17260\n", " China\n", " XU Xin\n", + " 110267\n", " \n", " \n", " 1\n", @@ -236,6 +765,7 @@ " 16915\n", " China\n", " FAN Zhendong\n", + " 121404\n", " \n", " \n", " 2\n", @@ -244,6 +774,7 @@ " 16335\n", " China\n", " MA Long\n", + " 105649\n", " \n", " \n", " 3\n", @@ -252,6 +783,7 @@ " 13915\n", " China\n", " LIN Gaoyuan\n", + " 115910\n", " \n", " \n", " 4\n", @@ -260,6 +792,7 @@ " 12615\n", " Japan\n", " HARIMOTO Tomokazu\n", + " 123980\n", " \n", " \n", " 5\n", @@ -268,6 +801,7 @@ " 12585\n", " Chinese Taipei\n", " LIN Yun-Ju\n", + " 121582\n", " \n", " \n", " 6\n", @@ -276,6 +810,7 @@ " 12315\n", " Brazil\n", " CALDERANO Hugo\n", + " 115641\n", " \n", " \n", " 7\n", @@ -284,6 +819,7 @@ " 11630\n", " Sweden\n", " FALCK Mattias\n", + " 112074\n", " \n", " \n", " 8\n", @@ -292,6 +828,7 @@ " 11205\n", " China\n", " LIANG Jingkun\n", + " 119588\n", " \n", " \n", " 9\n", @@ -300,51 +837,485 @@ " 11075\n", " Germany\n", " OVTCHAROV Dimitrij\n", + " 107028\n", + " \n", + " \n", + " 10\n", + " 11\n", + " 10\n", + " 10910\n", + " Germany\n", + " BOLL Timo\n", + " 101222\n", + " \n", + " \n", + " 11\n", + " 12\n", + " 15\n", + " 9570\n", + " Japan\n", + " NIWA Koki\n", + " 110729\n", + " \n", + " \n", + " 12\n", + " 13\n", + " 13\n", + " 9555\n", + " Republic of Korea\n", + " JEOUNG Youngsik\n", + " 104257\n", + " \n", + " \n", + " 13\n", + " 14\n", + " 14\n", + " 9455\n", + " Germany\n", + " FRANZISKA Patrick\n", + " 102832\n", + " \n", + " \n", + " 14\n", + " 15\n", + " 16\n", + " 9270\n", + " Japan\n", + " MIZUTANI Jun\n", + " 106195\n", + " \n", + " \n", + " 15\n", + " 16\n", + " 17\n", + " 9015\n", + " Republic of Korea\n", + " JANG Woojin\n", + " 114936\n", + " \n", + " \n", + " 16\n", + " 17\n", + " 12\n", + " 8770\n", + " China\n", + " WANG Chuqin\n", + " 121558\n", + " \n", + " \n", + " 17\n", + " 18\n", + " 18\n", + " 8710\n", + " Nigeria\n", + " ARUNA Quadri\n", + " 112092\n", + " \n", + " \n", + " 18\n", + " 19\n", + " 19\n", + " 8695\n", + " Hong Kong, China\n", + " WONG Chun Ting\n", + " 112620\n", + " \n", + " \n", + " 19\n", + " 20\n", + " 20\n", + " 8620\n", + " Republic of Korea\n", + " LEE Sangsu\n", + " 105197\n", + " \n", + " \n", + " 20\n", + " 21\n", + " 21\n", + " 8530\n", + " France\n", + " GAUZY Simon\n", + " 112062\n", + " \n", + " \n", + " 21\n", + " 22\n", + " 24\n", + " 7750\n", + " China\n", + " ZHAO Zihao\n", + " 134380\n", + " \n", + " \n", + " 22\n", + " 23\n", + " 22\n", + " 7565\n", + " England\n", + " PITCHFORD Liam\n", + " 112442\n", + " \n", + " \n", + " 23\n", + " 24\n", + " 22\n", + " 7385\n", + " Belarus\n", + " SAMSONOV Vladimir\n", + " 108246\n", + " \n", + " \n", + " 24\n", + " 25\n", + " 27\n", + " 7190\n", + " United States\n", + " JHA Kanak\n", + " 116021\n", + " \n", + " \n", + " 25\n", + " 26\n", + " 26\n", + " 7010\n", + " Portugal\n", + " FREITAS Marcos\n", + " 102841\n", + " \n", + " \n", + " 26\n", + " 27\n", + " 28\n", + " 6900\n", + " Denmark\n", + " GROTH Jonathan\n", + " 112409\n", + " \n", + " \n", + " 27\n", + " 28\n", + " 25\n", + " 6835\n", + " Sweden\n", + " KARLSSON Kristian\n", + " 104379\n", + " \n", + " \n", + " 28\n", + " 29\n", + " 29\n", + " 6625\n", + " Egypt\n", + " ASSAR Omar\n", + " 100696\n", + " \n", + " \n", + " 29\n", + " 30\n", + " 30\n", + " 6330\n", + " India\n", + " GNANASEKARAN Sathiyan\n", + " 103126\n", + " \n", + " \n", + " 30\n", + " 31\n", + " 31\n", + " 6220\n", + " Austria\n", + " HABESOHN Daniel\n", + " 103345\n", + " \n", + " \n", + " 31\n", + " 32\n", + " 32\n", + " 5980\n", + " Croatia\n", + " PUCAR Tomislav\n", + " 116532\n", + " \n", + " \n", + " 32\n", + " 33\n", + " 34\n", + " 5945\n", + " Chinese Taipei\n", + " CHUANG Chih-Yuan\n", + " 101820\n", + " \n", + " \n", + " 33\n", + " 34\n", + " 33\n", + " 5910\n", + " India\n", + " ACHANTA Sharath Kamal\n", + " 104314\n", + " \n", + " \n", + " 34\n", + " 35\n", + " 39\n", + " 5845\n", + " Republic of Korea\n", + " AN Jaehyun\n", + " 121514\n", + " \n", + " \n", + " 35\n", + " 36\n", + " 35\n", + " 5775\n", + " Austria\n", + " GARDOS Robert\n", + " 102968\n", + " \n", + " \n", + " 36\n", + " 37\n", + " 38\n", + " 5765\n", + " Slovakia\n", + " WANG Yang\n", + " 112735\n", + " \n", + " \n", + " 37\n", + " 38\n", + " 36\n", + " 5635\n", + " Germany\n", + " FILUS Ruwen\n", + " 102761\n", + " \n", + " \n", + " 38\n", + " 39\n", + " 40\n", + " 5605\n", + " Slovenia\n", + " JORGIC Darko\n", + " 118927\n", + " \n", + " \n", + " 39\n", + " 40\n", + " 40\n", + " 5380\n", + " Brazil\n", + " TSUBOI Gustavo\n", + " 109581\n", + " \n", + " \n", + " 40\n", + " 41\n", + " 37\n", + " 5310\n", + " France\n", + " LEBESSON Emmanuel\n", + " 105136\n", + " \n", + " \n", + " 41\n", + " 42\n", + " 48\n", + " 5285\n", + " Germany\n", + " DUDA Benedikt\n", + " 116620\n", + " \n", + " \n", + " 42\n", + " 43\n", + " 42\n", + " 5195\n", + " Japan\n", + " MORIZONO Masataka\n", + " 112580\n", + " \n", + " \n", + " 43\n", + " 44\n", + " 53\n", + " 5110\n", + " Croatia\n", + " GACINA Andrej\n", + " 102891\n", + " \n", + " \n", + " 44\n", + " 45\n", + " 45\n", + " 5080\n", + " Czech Republic\n", + " SIRUCEK Pavel\n", + " 113243\n", + " \n", + " \n", + " 45\n", + " 46\n", + " 44\n", + " 5055\n", + " Japan\n", + " JIN Takuya\n", + " 117336\n", + " \n", + " \n", + " 46\n", + " 47\n", + " 46\n", + " 4950\n", + " Belgium\n", + " NUYTINCK Cedric\n", + " 106814\n", + " \n", + " \n", + " 47\n", + " 48\n", + " 46\n", + " 4860\n", + " Greece\n", + " GIONIS Panagiotis\n", + " 103107\n", + " \n", + " \n", + " 48\n", + " 49\n", + " 43\n", + " 4840\n", + " Republic of Korea\n", + " LIM Jonghoon\n", + " 117357\n", + " \n", + " \n", + " 49\n", + " 50\n", + " 50\n", + " 4830\n", + " Japan\n", + " YOSHIMURA Kazuhiro\n", + " 123962\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Ranking Atual Ranking Anterior Pontos País Nome\n", - "0 1 2 17260 China XU Xin\n", - "1 2 1 16915 China FAN Zhendong\n", - "2 3 3 16335 China MA Long\n", - "3 4 4 13915 China LIN Gaoyuan\n", - "4 5 5 12615 Japan HARIMOTO Tomokazu\n", - "5 6 6 12585 Chinese Taipei LIN Yun-Ju\n", - "6 7 7 12315 Brazil CALDERANO Hugo\n", - "7 8 8 11630 Sweden FALCK Mattias\n", - "8 9 9 11205 China LIANG Jingkun\n", - "9 10 11 11075 Germany OVTCHAROV Dimitrij" + " Ranking Atual Ranking Anterior Pontos País \\\n", + "0 1 2 17260 China \n", + "1 2 1 16915 China \n", + "2 3 3 16335 China \n", + "3 4 4 13915 China \n", + "4 5 5 12615 Japan \n", + "5 6 6 12585 Chinese Taipei \n", + "6 7 7 12315 Brazil \n", + "7 8 8 11630 Sweden \n", + "8 9 9 11205 China \n", + "9 10 11 11075 Germany \n", + "10 11 10 10910 Germany \n", + "11 12 15 9570 Japan \n", + "12 13 13 9555 Republic of Korea \n", + "13 14 14 9455 Germany \n", + "14 15 16 9270 Japan \n", + "15 16 17 9015 Republic of Korea \n", + "16 17 12 8770 China \n", + "17 18 18 8710 Nigeria \n", + "18 19 19 8695 Hong Kong, China \n", + "19 20 20 8620 Republic of Korea \n", + "20 21 21 8530 France \n", + "21 22 24 7750 China \n", + "22 23 22 7565 England \n", + "23 24 22 7385 Belarus \n", + "24 25 27 7190 United States \n", + "25 26 26 7010 Portugal \n", + "26 27 28 6900 Denmark \n", + "27 28 25 6835 Sweden \n", + "28 29 29 6625 Egypt \n", + "29 30 30 6330 India \n", + "30 31 31 6220 Austria \n", + "31 32 32 5980 Croatia \n", + "32 33 34 5945 Chinese Taipei \n", + "33 34 33 5910 India \n", + "34 35 39 5845 Republic of Korea \n", + "35 36 35 5775 Austria \n", + "36 37 38 5765 Slovakia \n", + "37 38 36 5635 Germany \n", + "38 39 40 5605 Slovenia \n", + "39 40 40 5380 Brazil \n", + "40 41 37 5310 France \n", + "41 42 48 5285 Germany \n", + "42 43 42 5195 Japan \n", + "43 44 53 5110 Croatia \n", + "44 45 45 5080 Czech Republic \n", + "45 46 44 5055 Japan \n", + "46 47 46 4950 Belgium \n", + "47 48 46 4860 Greece \n", + "48 49 43 4840 Republic of Korea \n", + "49 50 50 4830 Japan \n", + "\n", + " Nome Id \n", + "0 XU Xin 110267 \n", + "1 FAN Zhendong 121404 \n", + "2 MA Long 105649 \n", + "3 LIN Gaoyuan 115910 \n", + "4 HARIMOTO Tomokazu 123980 \n", + "5 LIN Yun-Ju 121582 \n", + "6 CALDERANO Hugo 115641 \n", + "7 FALCK Mattias 112074 \n", + "8 LIANG Jingkun 119588 \n", + "9 OVTCHAROV Dimitrij 107028 \n", + "10 BOLL Timo 101222 \n", + "11 NIWA Koki 110729 \n", + "12 JEOUNG Youngsik 104257 \n", + "13 FRANZISKA Patrick 102832 \n", + "14 MIZUTANI Jun 106195 \n", + "15 JANG Woojin 114936 \n", + "16 WANG Chuqin 121558 \n", + "17 ARUNA Quadri 112092 \n", + "18 WONG Chun Ting 112620 \n", + "19 LEE Sangsu 105197 \n", + "20 GAUZY Simon 112062 \n", + "21 ZHAO Zihao 134380 \n", + "22 PITCHFORD Liam 112442 \n", + "23 SAMSONOV Vladimir 108246 \n", + "24 JHA Kanak 116021 \n", + "25 FREITAS Marcos 102841 \n", + "26 GROTH Jonathan 112409 \n", + "27 KARLSSON Kristian 104379 \n", + "28 ASSAR Omar 100696 \n", + "29 GNANASEKARAN Sathiyan 103126 \n", + "30 HABESOHN Daniel 103345 \n", + "31 PUCAR Tomislav 116532 \n", + "32 CHUANG Chih-Yuan 101820 \n", + "33 ACHANTA Sharath Kamal 104314 \n", + "34 AN Jaehyun 121514 \n", + "35 GARDOS Robert 102968 \n", + "36 WANG Yang 112735 \n", + "37 FILUS Ruwen 102761 \n", + "38 JORGIC Darko 118927 \n", + "39 TSUBOI Gustavo 109581 \n", + "40 LEBESSON Emmanuel 105136 \n", + "41 DUDA Benedikt 116620 \n", + "42 MORIZONO Masataka 112580 \n", + "43 GACINA Andrej 102891 \n", + "44 SIRUCEK Pavel 113243 \n", + "45 JIN Takuya 117336 \n", + "46 NUYTINCK Cedric 106814 \n", + "47 GIONIS Panagiotis 103107 \n", + "48 LIM Jonghoon 117357 \n", + "49 YOSHIMURA Kazuhiro 123962 " ] }, - "execution_count": 100, + "execution_count": 275, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "html_homens = urllib.request.urlopen('https://ranking.ittf.com/public/s/ranking/list?category=SEN&typeGender=M%3BSINGLES&year=2020&week=6&offset=0&size=100').read()\n", - "\n", - "soup = bs.BeautifulSoup(html_homens, 'lxml') # pega so o html\n", - "soup = soup.find(\"p\").get_text('') # pega o json dentro e tira as aspas ->

'json'

\n", - "json_top100 = json.loads(soup)\n", - "json_top100 = json_top100['ranks']\n", - "\n", - "nome, pais, pontos, rank_atual, rank_anterior = [], [], [], [], []\n", - "\n", - "for elemento in json_top100:\n", - " rank_anterior.append(elemento['PrevRk'])\n", - " rank_atual.append(elemento['Rk'])\n", - " pontos.append(elemento['Points'])\n", - " pais.append(elemento['Country']['desc'])\n", - " nome.append(elemento['Player']['Name'])\n", - " \n", - "df = pd.DataFrame([rank_atual, rank_anterior, pontos, pais, nome]).T\n", - "colunas = ['Ranking Atual','Ranking Anterior', 'Pontos', 'País', 'Nome']\n", - "df.columns = colunas\n", - "df.head(10)" + "ranking(html_homens, 50)" ] } ], @@ -354,18 +1325,6 @@ "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": "3.7.0" - }, "toc": { "base_numbering": 1, "nav_menu": {},