[英]How to build nested conditional logic in Pandas DataFrame?
我有一个数据框,其中每一行都包含一个字符串列表。 我编写了一个函数,对每个字符串执行伯努利型试验,如果试验成功,则每个单词都有一定的概率(此处为 0.5)被删除。 见下文:
import numpy as np
import pandas as pd
def bernoulli_trial (sublist, prob = 0.5):
# create mask of trial outcomes per each object in sublist
mask = np.random.binomial(n=1, p=prob, size=len(sublist))
# perform transformation on bernoulli successes
transformed_sublist = [token for delete, token in zip(mask, sublist) if not delete]
return transformed_sublist
当我传递数据帧的每一行时,这按预期工作,如下所示:
df = pd.DataFrame(data={'store': [1,2,3], 'colours': [['red','blue','yellow','green','brown','pink'],
['black','white'],
['purple','orange','cyan','mauve']]})
df['colours'] = df['colours'].apply(bernoulli_trial)
Out:
0 [red, green]
1 [black]
2 [orange, cyan]
Name: colours, dtype: object
但是,我现在要做的不是在每个子列表和每个字符串上统一应用该函数,而是应用条件(a)是否将给定的子列表传递给函数(是/否),以及(b)哪个将应用该子列表中的字符串(即通过指定我只想测试某些颜色)。
) way to do this.我想我对 (a) 部分有一个可行的解决方案 - 通过将 Bernoulli 函数包装在一个函数中,该函数检查是否满足给定条件(即子列表的长度是否大于 2 个对象?) - 这有效(见下文)但我不确定是否有更有效的(阅读 )方法来做到这一点。
def sublist_condition_check(sublist):
if len(sublist) > 2:
sublist = bernoulli_trial(sublist)
else:
sublist = sublist
return sublist
请注意,任何不满足条件的子列表都应保持不变。
df['colours'].apply(sublist_condition_check)
Out:
0 [red, brown]
1 [black, white] # this sublist had only two elements so remains unchanged
2 [mauve]
Name: colours, dtype: object
但是,我完全被困在如何对每个单词应用条件逻辑上。 举例来说,我只想将试验应用于预先指定的颜色列表 ['red','mauve','black'] - 前提是它通过了子列表条件检查 - 我该怎么做?
我希望实现的伪代码如下所示:
for sublist in df:
if len(sublist) > 2: # check if sublist contains more than two objects
for colour in sublist: # cycle through each colour within the sublist
if colour in ['red','mauve','black']:
colour = bernoulli_trial (colour) # only run bernoulli if colour in list
else:
colour = colour # if colour not in list, colour remains unchanged
else:
sublist = sublist # if sublist <= 2, sublist remains unchanged
我知道对此的字面解释是行不通的,因为最初的 bernoulli_trial 函数接收的是一个列表而不是单个字符串。 但希望它描述了我想要实现的目标。
不确定关于回答我自己的问题的礼仪,但我想我会提供一些我已经确定的工作解决方案的细节,以防万一有人遇到类似的情况。
我已经扩展了初始 bernoulli 函数以包含一个额外的 if 语句,该语句基于每个字符串是否满足包含标准。
# internal function - bernoulli trial for each string in sublist
def bernoulli_trial (sublist, prob = 0.50):
# set token criteria for performing bernoulli trial
token_criteria = ['red','black','purple'] # perform trial only on these strings
# create mask of trial outcomes per each word in sublist
mask = np.random.binomial(n=1, p=prob, size=len(turn))
# perform transformation (deletion) on bernoulli successes
transformed_turn = []
for token, delete in zip(turn, mask):
if token not in token_criteria:
transformed_turn.append(token)
else:
if delete == 0: # retain only those strings not marked for deletion
transformed_turn.append(token)
return transformed_sublist
结合问题中描述的sublist_condition_check
函数,现在按预期执行
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