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In [9]:
!pip install jovian --upgrade --quiet
In [2]:
!pip install pandas --upgrade
Collecting pandas Downloading pandas-1.1.2-cp37-cp37m-manylinux1_x86_64.whl (10.5 MB) |████████████████████████████████| 10.5 MB 4.3 MB/s eta 0:00:01 |█████████ | 3.0 MB 4.3 MB/s eta 0:00:02 Collecting numpy>=1.15.4 Downloading numpy-1.19.2-cp37-cp37m-manylinux2010_x86_64.whl (14.5 MB) |████████████████████████████████| 14.5 MB 48.5 MB/s eta 0:00:01 Collecting pytz>=2017.2 Downloading pytz-2020.1-py2.py3-none-any.whl (510 kB) |████████████████████████████████| 510 kB 30.0 MB/s eta 0:00:01 Requirement already satisfied, skipping upgrade: python-dateutil>=2.7.3 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from pandas) (2.8.1) Requirement already satisfied, skipping upgrade: six>=1.5 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas) (1.15.0) Installing collected packages: numpy, pytz, pandas Successfully installed numpy-1.19.2 pandas-1.1.2 pytz-2020.1
In [3]:
import pandas as pd
In [4]:
matches_raw_df = pd.read_csv('matches.csv')
In [5]:
matches_raw_df
Out[5]:
In [27]:
matches_raw_df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 756 entries, 0 to 755 Data columns (total 18 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 756 non-null int64 1 season 756 non-null int64 2 city 749 non-null object 3 date 756 non-null object 4 team1 756 non-null object 5 team2 756 non-null object 6 toss_winner 756 non-null object 7 toss_decision 756 non-null object 8 result 756 non-null object 9 dl_applied 756 non-null int64 10 winner 752 non-null object 11 win_by_runs 756 non-null int64 12 win_by_wickets 756 non-null int64 13 player_of_match 752 non-null object 14 venue 756 non-null object 15 umpire1 754 non-null object 16 umpire2 754 non-null object 17 umpire3 119 non-null object dtypes: int64(5), object(13) memory usage: 106.4+ KB
In [10]:
import jovian
In [11]:
jovian.commit(files = ['matches.csv'])
[jovian] Attempting to save notebook.. [jovian] Please enter your API key ( from https://jovian.ml/ ): API KEY: ········ [jovian] Updating notebook "srijansrj5901/ipl-data-anaysis-78bb0" on https://jovian.ml/ [jovian] Uploading notebook.. [jovian] Capturing environment.. [jovian] Uploading additional files... [jovian] Committed successfully! https://jovian.ml/srijansrj5901/ipl-data-anaysis-78bb0
In [54]:
matches_raw_df[(matches_raw_df.toss_winner == matches_raw_df.winner) & (matches_raw_df.toss_decision == 'field')].groupby('season').winner.count() / 
Out[54]:
season
2008    19
2009    14
2010    10
2011    27
2012    18
2013    15
2014    24
2015    14
2016    32
2017    28
2018    27
2019    31
Name: winner, dtype: int64
In [52]:
matches_raw_df.groupby('season').toss_decision.value_counts()
Out[52]:
season  toss_decision
2008    field            32
        bat              26
2009    bat              35
        field            22
2010    bat              39
        field            21
2011    field            48
        bat              25
2012    bat              37
        field            37
2013    bat              45
        field            31
2014    field            41
        bat              19
2015    field            34
        bat              25
2016    field            49
        bat              11
2017    field            48
        bat              11
2018    field            50
        bat              10
2019    field            50
        bat              10
Name: toss_decision, dtype: int64
In [70]:
(matches_raw_df.team2.value_counts() + matches_raw_df.team1.value_counts())

Out[70]:
Chennai Super Kings            164
Deccan Chargers                 75
Delhi Capitals                  16
Delhi Daredevils               161
Gujarat Lions                   30
Kings XI Punjab                176
Kochi Tuskers Kerala            14
Kolkata Knight Riders          178
Mumbai Indians                 187
Pune Warriors                   46
Rajasthan Royals               147
Rising Pune Supergiant          16
Rising Pune Supergiants         14
Royal Challengers Bangalore    180
Sunrisers Hyderabad            108
dtype: int64
In [71]:
matches_raw_df.winner.value_counts() / (matches_raw_df.team2.value_counts() + matches_raw_df.team1.value_counts())
Out[71]:
Chennai Super Kings            0.609756
Deccan Chargers                0.386667
Delhi Capitals                 0.625000
Delhi Daredevils               0.416149
Gujarat Lions                  0.433333
Kings XI Punjab                0.465909
Kochi Tuskers Kerala           0.428571
Kolkata Knight Riders          0.516854
Mumbai Indians                 0.582888
Pune Warriors                  0.260870
Rajasthan Royals               0.510204
Rising Pune Supergiant         0.625000
Rising Pune Supergiants        0.357143
Royal Challengers Bangalore    0.466667
Sunrisers Hyderabad            0.537037
dtype: float64
In [ ]:
jovian.commit()
[jovian] Attempting to save notebook..
In [ ]: