# %load q02_get_unique_values/build.py from greyatomlib.pandas_project.q01_read_csv_data_to_df.build import read_csv_data_to_df # You have been given the dataset already in 'ipl_df'. ipl_df = read_csv_data_to_df('data/ipl_dataset.csv') ipl_df #Solution def get_unique_venues(): a = ipl_df['venue'].unique() return a get_unique_venues()
# Default Imports from greyatomlib.pandas_project.q01_read_csv_data_to_df.build import read_csv_data_to_df # You have been given dataset already in 'ipl_df'. ipl_df = read_csv_data_to_df( "/home/darshind/Workspace/code/pandas_project/data/ipl_dataset.csv") # Solution def get_match_innings_runs(): filtered_data = ipl_df[['match_code', 'inning', 'runs']] return filtered_data.groupby(['match_code', 'inning']).sum() print get_match_innings_runs()
# Default Imports from greyatomlib.pandas_project.q01_read_csv_data_to_df.build import read_csv_data_to_df # You have been given the dataset already in 'ipl_df'. ipl_df = read_csv_data_to_df("./data/ipl_dataset.csv") # Solution def get_run_counts(): return ipl_df['runs'].value_counts()
def create_bowler_filter(bowler): ipl_df = read_csv_data_to_df('./data/ipl_dataset.csv') bowler_df = ipl_df.loc[ipl_df['bowler'] == bowler] bowler_series = pd.Series(bowler_df['bowler']) return bowler_series.count()
def get_unique_venues(): ipl_df = read_csv_data_to_df('data/ipl_dataset.csv') return ipl_df['venue'].unique()
def get_run_counts(): ipl_df = read_csv_data_to_df('./data/ipl_dataset.csv') runs_count = ipl_df['runs'].value_counts() count_series = pd.Series(runs_count) return count_series
def create_bowler_filter(bowler): ipl_df = read_csv_data_to_df('./data/ipl_dataset.csv') return(ipl_df['bowler']==bowler)
def get_run_counts(): ipl_df = read_csv_data_to_df('./data/ipl_dataset.csv') runs_count= ipl_df['runs'].value_counts() return runs_count
def test_get_unique_venues(self): path = "./data/ipl_dataset.csv" ipl_df = read_csv_data_to_df(path) venues = get_unique_venues() self.assertTrue(35 == len(venues))
import pandas as pd from greyatomlib.pandas_project.q01_read_csv_data_to_df.build import read_csv_data_to_df path = "./data/ipl_dataset.csv" ipl_df = read_csv_data_to_df(path) def get_run_counts(): runs_count = pd.Series.value_counts(ipl_df['runs']) return runs_count
def get_match_specific_df(mt_code): ipl_df = read_csv_data_to_df("./data/ipl_dataset.csv") return ipl_df[ipl_df['match_code'] == mt_code]
# %load q07_get_run_counts_by_match/build.py # Default Imports from greyatomlib.pandas_project.q01_read_csv_data_to_df.build import read_csv_data_to_df import pandas as pd import numpy as np # You have been give the dataset already in 'ipl_df'. ipl_df = pd.DataFrame(read_csv_data_to_df('./data/ipl_dataset.csv')) # Solution def get_runs_counts_by_match(): a = pd.DataFrame( ipl_df.pivot_table(index='match_code', columns='runs', aggfunc='count')) return (a['batsman']) print(get_runs_counts_by_match())
def get_match_specific_df(match_code): ipl_df = read_csv_data_to_df('./data/ipl_dataset.csv') match_data = ipl_df.loc[ipl_df['match_code'] == match_code] return match_data
def get_runs_counts_by_match(): ipl_df = read_csv_data_to_df("./data/ipl_dataset.csv") return ipl_df.pivot_table(values='total', index='match_code', columns=['runs'], aggfunc='count')
def get_match_innings_runs(): ipl_df = read_csv_data_to_df('data/ipl_dataset.csv') df = ipl_df.groupby('match_code').sum() return df[['runs']]
def get_match_innings_runs(): ipl_df = read_csv_data_to_df("data/ipl_dataset.csv") return ipl_df.groupby(['match_code', 'inning'])['runs'].sum()