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{
"cells": [
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"cric = pd.read_html('http://stats.espncricinfo.com/ci/engine/player/348144.html?class=3;template=results;type=batting;view=innings', match='innings', na_values='-')\n",
"df = cric[0]"
]
},
{
"cell_type": "code",
"execution_count": 44,
"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>Runs</th>\n",
" <th>Mins</th>\n",
" <th>BF</th>\n",
" <th>4s</th>\n",
" <th>6s</th>\n",
" <th>SR</th>\n",
" <th>Pos</th>\n",
" <th>Dismissal</th>\n",
" <th>Inns</th>\n",
" <th>Unnamed: 9</th>\n",
" <th>Opposition</th>\n",
" <th>Ground</th>\n",
" <th>Start Date</th>\n",
" <th>Unnamed: 13</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>15*</td>\n",
" <td>13.0</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>136.36</td>\n",
" <td>3</td>\n",
" <td>not out</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>v England</td>\n",
" <td>Manchester</td>\n",
" <td>7 Sep 2016</td>\n",
" <td>T20I # 566</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>55*</td>\n",
" <td>49.0</td>\n",
" <td>37</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>148.64</td>\n",
" <td>3</td>\n",
" <td>not out</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>v West Indies</td>\n",
" <td>Dubai (DSC)</td>\n",
" <td>23 Sep 2016</td>\n",
" <td>T20I # 568</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>19</td>\n",
" <td>28.0</td>\n",
" <td>18</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>105.55</td>\n",
" <td>3</td>\n",
" <td>caught</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>v West Indies</td>\n",
" <td>Dubai (DSC)</td>\n",
" <td>24 Sep 2016</td>\n",
" <td>T20I # 569</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>27*</td>\n",
" <td>42.0</td>\n",
" <td>24</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>112.50</td>\n",
" <td>3</td>\n",
" <td>not out</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>v West Indies</td>\n",
" <td>Abu Dhabi</td>\n",
" <td>27 Sep 2016</td>\n",
" <td>T20I # 570</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>29</td>\n",
" <td>NaN</td>\n",
" <td>30</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>96.66</td>\n",
" <td>3</td>\n",
" <td>caught</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>v West Indies</td>\n",
" <td>Bridgetown</td>\n",
" <td>26 Mar 2017</td>\n",
" <td>T20I # 602</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>27</td>\n",
" <td>NaN</td>\n",
" <td>28</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>96.42</td>\n",
" <td>3</td>\n",
" <td>caught</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>v West Indies</td>\n",
" <td>Port of Spain</td>\n",
" <td>30 Mar 2017</td>\n",
" <td>T20I # 603</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>43</td>\n",
" <td>NaN</td>\n",
" <td>38</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>113.15</td>\n",
" <td>4</td>\n",
" <td>bowled</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>v West Indies</td>\n",
" <td>Port of Spain</td>\n",
" <td>1 Apr 2017</td>\n",
" <td>T20I # 604</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>38</td>\n",
" <td>NaN</td>\n",
" <td>36</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>105.55</td>\n",
" <td>3</td>\n",
" <td>caught</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>v West Indies</td>\n",
" <td>Port of Spain</td>\n",
" <td>2 Apr 2017</td>\n",
" <td>T20I # 605</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>86</td>\n",
" <td>NaN</td>\n",
" <td>52</td>\n",
" <td>10</td>\n",
" <td>2</td>\n",
" <td>165.38</td>\n",
" <td>3</td>\n",
" <td>caught</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>v World-XI</td>\n",
" <td>Lahore</td>\n",
" <td>12 Sep 2017</td>\n",
" <td>T20I # 619</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>45</td>\n",
" <td>NaN</td>\n",
" <td>38</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>118.42</td>\n",
" <td>3</td>\n",
" <td>caught</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>v World-XI</td>\n",
" <td>Lahore</td>\n",
" <td>13 Sep 2017</td>\n",
" <td>T20I # 620</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Runs Mins BF 4s 6s SR Pos Dismissal Inns Unnamed: 9 \\\n",
"0 15* 13.0 11 2 0 136.36 3 not out 2 NaN \n",
"1 55* 49.0 37 6 2 148.64 3 not out 2 NaN \n",
"2 19 28.0 18 2 0 105.55 3 caught 1 NaN \n",
"3 27* 42.0 24 1 0 112.50 3 not out 2 NaN \n",
"4 29 NaN 30 3 0 96.66 3 caught 2 NaN \n",
"5 27 NaN 28 4 0 96.42 3 caught 1 NaN \n",
"6 43 NaN 38 3 1 113.15 4 bowled 1 NaN \n",
"7 38 NaN 36 1 1 105.55 3 caught 2 NaN \n",
"8 86 NaN 52 10 2 165.38 3 caught 1 NaN \n",
"9 45 NaN 38 5 0 118.42 3 caught 1 NaN \n",
"\n",
" Opposition Ground Start Date Unnamed: 13 \n",
"0 v England Manchester 7 Sep 2016 T20I # 566 \n",
"1 v West Indies Dubai (DSC) 23 Sep 2016 T20I # 568 \n",
"2 v West Indies Dubai (DSC) 24 Sep 2016 T20I # 569 \n",
"3 v West Indies Abu Dhabi 27 Sep 2016 T20I # 570 \n",
"4 v West Indies Bridgetown 26 Mar 2017 T20I # 602 \n",
"5 v West Indies Port of Spain 30 Mar 2017 T20I # 603 \n",
"6 v West Indies Port of Spain 1 Apr 2017 T20I # 604 \n",
"7 v West Indies Port of Spain 2 Apr 2017 T20I # 605 \n",
"8 v World-XI Lahore 12 Sep 2017 T20I # 619 \n",
"9 v World-XI Lahore 13 Sep 2017 T20I # 620 "
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"# Clean up example,eg remove *\n",
"df['Start Date'] = pd.to_datetime(df['Start Date'])\n",
"df['Runs'] = df['Runs'].str.extract('(\\d+)').astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Runs int32\n",
"Mins float64\n",
"BF int64\n",
"4s int64\n",
"6s int64\n",
"SR float64\n",
"Pos int64\n",
"Dismissal object\n",
"Inns int64\n",
"Unnamed: 9 float64\n",
"Opposition object\n",
"Ground object\n",
"Start Date datetime64[ns]\n",
"Unnamed: 13 object\n",
"dtype: object"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"# Examle take out columns\n",
"col_choice = df.loc[:, 'Runs':'6s'].head(10)\n",
"#col_choice.to_csv('out.csv')"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" 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>Runs</th>\n",
" <th>Mins</th>\n",
" <th>BF</th>\n",
" <th>4s</th>\n",
" <th>6s</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>15</td>\n",
" <td>13.0</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>55</td>\n",
" <td>49.0</td>\n",
" <td>37</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>19</td>\n",
" <td>28.0</td>\n",
" <td>18</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>27</td>\n",
" <td>42.0</td>\n",
" <td>24</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>29</td>\n",
" <td>NaN</td>\n",
" <td>30</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>27</td>\n",
" <td>NaN</td>\n",
" <td>28</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>43</td>\n",
" <td>NaN</td>\n",
" <td>38</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>38</td>\n",
" <td>NaN</td>\n",
" <td>36</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>86</td>\n",
" <td>NaN</td>\n",
" <td>52</td>\n",
" <td>10</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>45</td>\n",
" <td>NaN</td>\n",
" <td>38</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Runs Mins BF 4s 6s\n",
"0 15 13.0 11 2 0\n",
"1 55 49.0 37 6 2\n",
"2 19 28.0 18 2 0\n",
"3 27 42.0 24 1 0\n",
"4 29 NaN 30 3 0\n",
"5 27 NaN 28 4 0\n",
"6 43 NaN 38 3 1\n",
"7 38 NaN 36 1 1\n",
"8 86 NaN 52 10 2\n",
"9 45 NaN 38 5 0"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"col_choice.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"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>Runs</th>\n",
" <th>Mins</th>\n",
" <th>BF</th>\n",
" <th>4s</th>\n",
" <th>6s</th>\n",
" <th>SR</th>\n",
" <th>Pos</th>\n",
" <th>Dismissal</th>\n",
" <th>Inns</th>\n",
" <th>Unnamed: 9</th>\n",
" <th>Opposition</th>\n",
" <th>Ground</th>\n",
" <th>Start Date</th>\n",
" <th>Unnamed: 13</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>15</td>\n",
" <td>13.0</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>136.36</td>\n",
" <td>3</td>\n",
" <td>not out</td>\n",
" <td>2</td>\n",
" <td>0.0</td>\n",
" <td>v England</td>\n",
" <td>Manchester</td>\n",
" <td>2016-09-07</td>\n",
" <td>T20I # 566</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>55</td>\n",
" <td>49.0</td>\n",
" <td>37</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>148.64</td>\n",
" <td>3</td>\n",
" <td>not out</td>\n",
" <td>2</td>\n",
" <td>0.0</td>\n",
" <td>v West Indies</td>\n",
" <td>Dubai (DSC)</td>\n",
" <td>2016-09-23</td>\n",
" <td>T20I # 568</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>19</td>\n",
" <td>28.0</td>\n",
" <td>18</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>105.55</td>\n",
" <td>3</td>\n",
" <td>caught</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>v West Indies</td>\n",
" <td>Dubai (DSC)</td>\n",
" <td>2016-09-24</td>\n",
" <td>T20I # 569</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>27</td>\n",
" <td>42.0</td>\n",
" <td>24</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>112.50</td>\n",
" <td>3</td>\n",
" <td>not out</td>\n",
" <td>2</td>\n",
" <td>0.0</td>\n",
" <td>v West Indies</td>\n",
" <td>Abu Dhabi</td>\n",
" <td>2016-09-27</td>\n",
" <td>T20I # 570</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>29</td>\n",
" <td>0.0</td>\n",
" <td>30</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>96.66</td>\n",
" <td>3</td>\n",
" <td>caught</td>\n",
" <td>2</td>\n",
" <td>0.0</td>\n",
" <td>v West Indies</td>\n",
" <td>Bridgetown</td>\n",
" <td>2017-03-26</td>\n",
" <td>T20I # 602</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>27</td>\n",
" <td>0.0</td>\n",
" <td>28</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>96.42</td>\n",
" <td>3</td>\n",
" <td>caught</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>v West Indies</td>\n",
" <td>Port of Spain</td>\n",
" <td>2017-03-30</td>\n",
" <td>T20I # 603</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>43</td>\n",
" <td>0.0</td>\n",
" <td>38</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>113.15</td>\n",
" <td>4</td>\n",
" <td>bowled</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>v West Indies</td>\n",
" <td>Port of Spain</td>\n",
" <td>2017-04-01</td>\n",
" <td>T20I # 604</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>38</td>\n",
" <td>0.0</td>\n",
" <td>36</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>105.55</td>\n",
" <td>3</td>\n",
" <td>caught</td>\n",
" <td>2</td>\n",
" <td>0.0</td>\n",
" <td>v West Indies</td>\n",
" <td>Port of Spain</td>\n",
" <td>2017-04-02</td>\n",
" <td>T20I # 605</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>86</td>\n",
" <td>0.0</td>\n",
" <td>52</td>\n",
" <td>10</td>\n",
" <td>2</td>\n",
" <td>165.38</td>\n",
" <td>3</td>\n",
" <td>caught</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>v World-XI</td>\n",
" <td>Lahore</td>\n",
" <td>2017-09-12</td>\n",
" <td>T20I # 619</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>45</td>\n",
" <td>0.0</td>\n",
" <td>38</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>118.42</td>\n",
" <td>3</td>\n",
" <td>caught</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>v World-XI</td>\n",
" <td>Lahore</td>\n",
" <td>2017-09-13</td>\n",
" <td>T20I # 620</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Runs Mins BF 4s 6s SR Pos Dismissal Inns Unnamed: 9 \\\n",
"0 15 13.0 11 2 0 136.36 3 not out 2 0.0 \n",
"1 55 49.0 37 6 2 148.64 3 not out 2 0.0 \n",
"2 19 28.0 18 2 0 105.55 3 caught 1 0.0 \n",
"3 27 42.0 24 1 0 112.50 3 not out 2 0.0 \n",
"4 29 0.0 30 3 0 96.66 3 caught 2 0.0 \n",
"5 27 0.0 28 4 0 96.42 3 caught 1 0.0 \n",
"6 43 0.0 38 3 1 113.15 4 bowled 1 0.0 \n",
"7 38 0.0 36 1 1 105.55 3 caught 2 0.0 \n",
"8 86 0.0 52 10 2 165.38 3 caught 1 0.0 \n",
"9 45 0.0 38 5 0 118.42 3 caught 1 0.0 \n",
"\n",
" Opposition Ground Start Date Unnamed: 13 \n",
"0 v England Manchester 2016-09-07 T20I # 566 \n",
"1 v West Indies Dubai (DSC) 2016-09-23 T20I # 568 \n",
"2 v West Indies Dubai (DSC) 2016-09-24 T20I # 569 \n",
"3 v West Indies Abu Dhabi 2016-09-27 T20I # 570 \n",
"4 v West Indies Bridgetown 2017-03-26 T20I # 602 \n",
"5 v West Indies Port of Spain 2017-03-30 T20I # 603 \n",
"6 v West Indies Port of Spain 2017-04-01 T20I # 604 \n",
"7 v West Indies Port of Spain 2017-04-02 T20I # 605 \n",
"8 v World-XI Lahore 2017-09-12 T20I # 619 \n",
"9 v World-XI Lahore 2017-09-13 T20I # 620 "
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# If want to fill NaN with 0\n",
"df.fillna(0).head(10)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1fec1cbbf98>"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot(kind='bar', x='Runs', y='BF')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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