熊猫：如何根据先前的值有效地更新行？

Question

我有以下代码根据前一行的状态更新当前行：

prev_status = 0
for idx, row in df.iterrows():
    if prev_status in [1, 2] and row[column_a] != 0:
        row[column_b] += row[column_a]
        row[column_c] = 0
        row[column_d] = 0
        row[column_a] = 0
    prev_status = row[status]
    df.loc[idx] = row

但是，当运行1GB数据时，这非常慢。有什么方法可以对此进行优化？

Answer 1

例如，使用shift

df["new_column"] = df["column_name"].shift(x)

这将创建一列，其中值是另一列的值，该列的值偏移了x行数。这样一来，与对DataFrame中的每一行应用函数相比，对一列进行矢量化计算就更快了。

Answer 2

尝试一下：

df['previous_status'] = df['status'].shift(1)
df.loc[df['previous_status'] in [1, 2] & df['column_a'] != 0, 'column_b'] += df['column_a']
df.loc[df['previous_status'] in [1, 2] & df['column_a'] != 0, 'column_c']  = 0
df.loc[df['previous_status'] in [1, 2] & df['column_a'] != 0, 'column_d']  = 0
df.loc[df['previous_status'] in [1, 2] & df['column_a'] != 0, 'column_a']  = 0