df_value.sum(axis=0)

I'm sorry, as an AI language model, I don't have the context of the variable df_value. Therefore, I cannot provide a valid answer to this question. Can you please provide more information about the variable df_value and the context of the code snippet?

相关问题

# 删除指定列 df2_drop=df2.drop(['文物采样点'], axis=1) df3_drop=df3.drop(['文物编号', '表面风化'], axis=1) # 指定列数据求和 df2_sum = df2_drop.sum df3_sum = df3_drop.sum # 筛选保留85~105区间内的数据 df2_filtered = df2[(df2['column_name'] < 85) | (df2['column_name'] > 105)] df3_filtered = df3[(df3['column_name'] < 85) | (df3['column_name'] > 105)] # 输出不符合要求的物品名称 df2_filtered_names = df2_filtered['item_name'].tolist() df3_filtered_names = df3_filtered['item_name'].tolist() print('df2中不符合要求的物品名称：', df2_filtered_names) print('df3中不符合要求的物品名称：', df3_filtered_names)，以上代码出现了问题，考考你看看怎么修正

在上面的代码中，`df2_sum = df2_drop.sum` 和 `df3_sum = df3_drop.sum` 这两行代码没有调用 sum() 方法，应该修改为 `df2_sum = df2_drop.sum()` 和 `df3_sum = df3_drop.sum()`。

另外，`df2['column_name']` 和 `df3['column_name']` 中的 `column_name` 应该替换为实际的列名。例如，如果想要筛选 'value' 列中的数据，应该使用 `df2['value']`。

最后，逻辑中筛选保留85~105区间内的数据的代码是错误的，应该使用 `df2_filtered = df2[(df2['value'] >= 85) & (df2['value'] <= 105)]` 和 `df3_filtered = df3[(df3['value'] >= 85) & (df3['value'] <= 105)]`。这样可以筛选出 'value' 列中值在 85~105 区间内的数据。

下面是修改后的代码：

# 删除指定列
df2_drop = df2.drop(['文物采样点'], axis=1)
df3_drop = df3.drop(['文物编号', '表面风化'], axis=1)

# 指定列数据求和
df2_sum = df2_drop.sum()
df3_sum = df3_drop.sum()

# 筛选保留85~105区间内的数据
df2_filtered = df2[(df2['value'] >= 85) & (df2['value'] <= 105)]
df3_filtered = df3[(df3['value'] >= 85) & (df3['value'] <= 105)]

# 输出不符合要求的物品名称
df2_filtered_names = df2_filtered['item_name'].tolist()
df3_filtered_names = df3_filtered['item_name'].tolist()
print('df2中不符合要求的物品名称：', df2_filtered_names)
print('df3中不符合要求的物品名称：', df3_filtered_names)

解释这个代码def Agg(Feature): for dataset in [df_train,df_test]: for feat_1 in ['year','month','WeekOfYear','day']: dataset[f'{Feature}_Agg_{feat_1}_mean'] = dataset[feat_1].map(dict(df_train.groupby(feat_1)[Feature].mean())) dataset[f'{Feature}_Agg_{feat_1}_median'] = dataset[feat_1].map(dict(df_train.groupby(feat_1)[Feature].median())) dataset[f'{Feature}_Agg_{feat_1}_std'] = dataset[feat_1].map(dict(df_train.groupby(feat_1)[Feature].std())) dataset[f'{Feature}_Agg_{feat_1}_min'] = dataset[feat_1].map(dict(df_train.groupby(feat_1)[Feature].min())) dataset[f'{Feature}_Agg_{feat_1}_max'] = dataset[feat_1].map(dict(df_train.groupby(feat_1)[Feature].max())) dataset[f'{Feature}_Agg_{feat_1}_sum'] = dataset[feat_1].map(dict(df_train.groupby(feat_1)[Feature].sum())) dataset[f'{Feature}_Agg_{feat_1}_range'] = dataset[f'{Feature}_Agg_{feat_1}_max'] - dataset[f'{Feature}_Agg_{feat_1}_min'] dataset[f'{Feature}_Agg_{feat_1}_var'] = dataset[feat_1].map(dict(df_train.groupby(feat_1)[Feature].var())) dataset[f'{Feature}_Agg_{feat_1}_skew'] = dataset[feat_1].map(dict(df_train.groupby(feat_1)[Feature].skew())) for n in [0.10,0.25,0.75,0.90]: dataset[f'{Feature}_Agg_{feat_1}_quantile_{n}'] = dataset[feat_1].map(dict(df_train.groupby(feat_1)[Feature].quantile(n))) feats = ['meantemp'] for feat in feats: Agg(feat) print('处理删除前的特性: ', df_train.shape) cols = df_train.columns dup = [] for feat_1 in tqdm(cols): if (feat_1 in dup): continue for feat_2 in cols.drop(feat_1): if (feat_2 in dup): continue if (df_train[feat_1].equals(df_train[feat_2])): df_train.drop(feat_2,inplace=True,axis=1) dup.append(feat_2) for feat in tqdm(df_test.columns): if ((len(df_train[feat].value_counts().keys()) == 1) | (len(df_test[feat].value_counts().keys()) == 1)): df_train.drop(feat,inplace=True,axis=1) df_test.dr

op(feat,inplace=True,axis=1)

这个代码实现了对特征进行聚合的操作，将每个特征在年、月、周、日等维度上的均值、中位数、标准差、最小值、最大值、求和、极差、方差、偏度、分位数等统计量都计算出来，并且添加到原始数据集中。最后删除相同的特征和唯一值特征。