长熊猫逻辑运算(Long pandas logic operation)

 有没有更好的方法来编写这个长pandas逻辑运算？ 我只列出了下面的几个例子，但实际上有更多的检查。 这样做的工作，但我觉得可能有一种更清洁的方式来表达这个操作。 如果您对这些数据感到好奇，那就是纽约出租车数据。任何建议都表示赞赏。 
 
mask = (df.Fare_amount >= 2.5) & \
       (df.Total_amount >= 2.5) & \
       (df.Tip_amount >= 0) & \
       (df.Tolls_amount >= 0) & \
       (df.Extra >= 0) & \
       (df.Trip_distance > 0) & \
       (df.Passenger_count.between(1, 5))

# I actually have some more checks here like these above....

new_df = df[mask].copy()

Is there a better way to write this long pandas logic operation? I only listed a few examples below but actually have a lot more checks. This does the job but I feel like there might be a cleaner way to express this operation. In case if you are curious about this data, it is NYC taxi data Any advice is appreciated.
 
mask = (df.Fare_amount >= 2.5) & \
       (df.Total_amount >= 2.5) & \
       (df.Tip_amount >= 0) & \
       (df.Tolls_amount >= 0) & \
       (df.Extra >= 0) & \
       (df.Trip_distance > 0) & \
       (df.Passenger_count.between(1, 5))

# I actually have some more checks here like these above....

new_df = df[mask].copy()

原文：https://stackoverflow.com/questions/43312532