把数据集随机切分为训练集和测试集
method 1:
df = pd.read_csv(\'data/tgnb_merge.csv\', encoding=\'utf-8\') df.drop_duplicates(keep=\'first\', inplace=True) # 去重，只保留第一次出现的样本 df_test = df.sample(frac=0.1) df_train = pd.concat([df, df_test], axis=0) # 拼接 df_train.drop_duplicates(keep=False, inplace=True) # 去除所有重复的样本 print df.shape, df_test.shape, df_train.shape # (3045, 12) (305, 12) (2740, 12)
method 2（推荐）:
df = pd.read_csv(\'data/tgnb_merge.csv\', encoding=\'utf-8\') # df.drop_duplicates(keep=\'first\', inplace=True) # 去重，只保留第一次出现的样本 df = df.sample(frac=1.0) # 全部打乱 cut_idx = int(round(0.1 * df.shape[0])) df_test, df_train = df.iloc[:cut_idx], df.iloc[cut_idx:] print df.shape, df_test.shape, df_train.shape # (3184, 12) (318, 12) (2866, 12)