Question

我已将以下for循环编码。主要思想是，在“ A_D”列中每次出现“ D”时，它都会寻找所有可能发生某些特定条件的情况。验证所有条件后，会将一个值添加到列表中。

a = []
for i in df.index:
    if df['A_D'][i] == 'D':
         if df['TROUND_ID'][i] == '        ':
             vb = df[(df['O_D'] == df['O_D'][i])
             & (df['A_D'] == 'A' )
             & (df['Terminal'] == df['Terminal'][i])
             & (df['Operator'] == df['Operator'][i])]

            number = df['number_ac'][i]
            try: ## if all the conditions above are verified a value is added to a list
                x = df.START[i] - pd.Timedelta(int(number), unit='m')
                value = vb.loc[(vb.START-x).abs().idxmin()].FlightID
            except: ## if are not verified, several strings are added to the list
                value = 'No_link_found'
        else:
            value = 'Has_link'
    else:
        value = 'IsArrival'
a.append(value)

我的主要问题是df有数百万行，因此此for循环太浪费时间了。是否有任何我不需要使用for循环的矢量化解决方案？

Answer 1

最初的改进：使用apply而不是循环；在df["A_D"] == "A"所在行的开头创建第二个数据框；并将值x向量化。

arr = df[df["A_D"] == "A"]
# if the next line is slow, apply it only to those rows where x is needed
df["x"] = df.START - pd.Timedelta(int(df["number_ac"]), unit='m')

def link_func(row):
    if row["A_D"] != "D":
        return "IsArrival"
    if row["TROUND_ID"] != "        ":
        return "Has_link"
    vb = arr[arr["O_D"] == row["O_D"]
             & arr["Terminal"] == row["Terminal"]
             & arr["Operator"] == row["Operator"]]
    try:
        return vb.loc[(vb.START - row["x"]).abs().idxmin()].FlightID
    except:
        return "No_link_found"            

df["a"] = df.apply(link_func, axis=1)

使用apply是apparently more efficient，但不会自动向量化计算。但是，基于arr的每一行在df中查找值本质上是耗时的，但是实现起来却很有效。考虑是否可以通过某种方式将原始数据帧的两个部分（分别为df["A_D"] == "A"和df["A_D"] == "D"重构为宽格式。

编辑：您可以通过将查询字符串存储在arr中来加快df的查询，如下所示：

df["query_string"] = ('O_D == "' + df["O_D"] 
                    + '" & Terminal == "' + df["Terminal"] 
                    + '" & Operator == "' + df["Operator"] + '"')
def link_func(row):
    vb = arr.query(row["query_string"])
    try:
        row["a"] = vb.loc[(vb.START - row["x"]).abs().idxmin()].FlightID
    except:
        row["a"] = "No_link_found"

df.query('(A_D == "D") & (TROUND_ID == "        ")').apply(link_func, axis=1)

对于循环熊猫和numpy：性能

1 个答案: