지방이의 Data Science Lab

from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler(copy=True, feature_range=(0, 1)) temp = label.iloc[:, 1:] scaler.fit_transform(temp.values)from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler(copy=True, feature_range=(0, 1)) temp = label.iloc[:, 1:] temp = pd.DataFrame(scaler.fit_transform(temp.values)) temp.columns = label.iloc[:,1:].columns temp=pd.concat([..

test2_act['week'] = np.floor((test2_act['day'] -1)/ 7 +1 ) temp['dayofweek'] = temp['day'] % 7 + 1

def resumetable(df): print(f"Dataset Shape: {df.shape}") summary = pd.DataFrame(df.dtypes,columns=['dtypes']) summary = summary.reset_index() summary['Name'] = summary['index'] summary = summary[['Name','dtypes']] summary['Min'] = df.min().values summary['Max'] = df.max().values summary['Missing'] = df.isnull().sum().values summary['Uniques'] = df.nunique().values return summary train_label = pd..

temp = pd.merge(final, train_activity.groupby('acc_id')['npc_kill'].sum().to_frame('is_npc_kill').reset_index(), on ='acc_id', how='left') temp['is_npc_kill'] = np.where(temp['is_npc_kill']==0, 1, 0)

sum(temp,[]) list(set(tempp)