srijansrj5901/ipl-data-analysis

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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

Out[11]:

'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 [16]:

matches_raw_df.winner.value_counts()

Out[16]:

Mumbai Indians                 109
Chennai Super Kings            100
Kolkata Knight Riders           92
Royal Challengers Bangalore     84
Kings XI Punjab                 82
Rajasthan Royals                75
Delhi Daredevils                67
Sunrisers Hyderabad             58
Deccan Chargers                 29
Gujarat Lions                   13
Pune Warriors                   12
Rising Pune Supergiant          10
Delhi Capitals                  10
Kochi Tuskers Kerala             6
Rising Pune Supergiants          5
Name: winner, dtype: int64

In [ ]:

jovian.commit()

[jovian] Attempting to save notebook..

In [ ]: