<jupyter_start><jupyter_text>HIstory<jupyter_code>ipl_hist = pd.read_csv("/home/emkay/PycharmProjects/temp/deliveries.csv")
ipl_hist.columns
MI_TEAM = players['MI'].values.tolist()
print(len(MI_TEAM))
from fuzzywuzzy import fuzz 
from fuzzywuzzy import process 
# PLAYER =[]
# TEAM = []
# for team in players.columns.tolist():
#     for val in players[team].values.tolist():
#         if process.extractOne(val,ipl_hist['batsman'].unique().tolist())[1] > 40:
#             PLAYER.append(process.extractOne(val,ipl_hist['batsman'].unique().tolist())[0])
#             TEAM.append(team)
# df = pd.DataFrame({"players":PLAYER,"team":TEAM})
#current_team = df.drop_duplicates()
#current_team['team'].unique()
current_team = pd.read_csv("team_ipl_2019.csv",index=False)<jupyter_output><empty_output><jupyter_text>CURRENT MATCH BEGINS<jupyter_code># match between srh x11p
team1 = current_team.loc[current_team['team']=='srh']
team2 = current_team.loc[current_team['team']=='kixp']
the_team = pd.concat([team1,team2])
ipl_hist.columns
ipl_hist = ipl_hist[['batsman','non_striker','bowler','batsman_runs','player_dismissed','dismissal_kind','fielder']]
ipl_hist = ipl_hist.loc[ipl_hist['batsman'].isin(the_team['players'])]
ipl_hist = ipl_hist.fillna(0)
ipl_hist<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.17276264591439688, 'fraction_numerical': 0.011673151750972763, 'mean_word_length': 7.516556291390729, 'pattern_counts': {'":': 2, '<': 9, '<?xml version=': 0, '>': 10, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '430346', 'license': 'Apache-2.0', 'n_tokens_mistral': 521, 'n_tokens_neox': 486, 'n_words': 56, 'path': 'ipl_model.ipynb', 'repo_name': '4emkay/IPLyisis', 'size': '1285'}