pandas 数据框上的多个条件 [英] Multiple conditions on pandas dataframe

问题描述

我有一个要在数据集上运行的条件列表，以对大量数据进行排序.

I have a list of conditions to be run on the dataset to sort huge data.

df = 一个巨大的数据帧.例如.

  Index D1  D2  D3   D5      D6
    0   8   5   0  False   True
    1  45  35   0   True  False
    2  35  10   1  False   True
    3  40   5   2   True  False
    4  12  10   5  False  False
    5  18  15  13  False   True
    6  25  15   5   True  False
    7  35  10  11  False   True
    8  95  50   0  False  False

我必须根据给定的订单在 df 以上排序:

I have to sort above df based on given orders:

orders = [[A, B],[D, ~E, B], [~C, ~A], [~C, A]...] 
#(where A, B, C , D, E are the conditions) 

例如

A = df['D1'].le(50)
B = df['D2'].ge(5)
C = df['D3'].ne(0)
D = df['D1'].ne(False)
E = df['D1'].ne(True)
# In the real scenario, I have 64 such conditions to be run on 5 million records. 

例如.我必须运行所有这些条件才能获得结果输出.

eg. I have to run all these conditions to get the resultant output.

实现以下任务的最简单方法是什么，使用 for loop 或 map 或 .apply 对它们进行排序?

What is the easiest way to achieve the following task, to order them using for loop or map or .apply?

  df = df.loc[A & B]
  df = df.loc[D & ~E & B]
  df = df.loc[~C & ~A]
  df = df.loc[~C & A]

结果 df 将是我预期的输出.

Resultant df would be my expected output.

在这里，我更想知道，如果我想运行存储在列表中的 multiple conditions，您将如何使用循环或映射或 .apply.不是结果输出.

Here I am more interested in knowing, how would you use loop or map or .apply, If I want to run multiple conditions which are stored in a list. Not the resultant output.

例如:

for i in orders:
   df = df[all(i)] # I am not able to implement this logic for each order

推荐答案

您正在寻找 bitwise and orders 中的所有元素.在这种情况下:

You are looking for bitwise and all the elements inside orders. In which case:

df = df[np.concatenate(orders).all(0)]

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