如何去除csv文件中的NaN值？Python

Question

首先，我知道有关于这件事的答案，但直到现在他们都没有为我工作。无论如何，我想知道你的答案，虽然我已经使用了这个解决方案。

我有一个名为mbti_datasets.csv的csv文件。第一列的标签是type，第二列称为description。每一行代表一种新的个性类型(及其各自的类型和描述)。

TYPE        | DESCRIPTION
 a          | This personality likes to eat apples...\nThey look like monkeys...\nIn fact, are strong people...
 b          | b.description
 c          | c.description
 d          | d.description
...16 types | ...

在下面的代码中，当描述有\n时，我尝试复制每个个性类型。

代码：

import pandas as pd

# Reading the file
path_root = 'gdrive/My Drive/Colab Notebooks/MBTI/mbti_datasets.csv'
root_fn = path_rooth + 'mbti_datasets.csv'
df = pd.read_csv(path_root, sep = ',', quotechar = '"', usecols = [0, 1])

# split the column where there are new lines and turn it into a series
serie = df['description'].str.split('\n').apply(pd.Series, 1).stack()

# remove the second index for the DataFrame and the series to share indexes
serie.index = serie.index.droplevel(1)

# give it a name to join it to the DataFrame
serie.name = 'description'

# remove original column
del df['description']

# join the series with the DataFrame, based on the shared index
df = df.join(serie)

# New file name and writing the new csv file
root_new_fn = path_root + 'mbti_new.csv'

df.to_csv(root_new_fn, sep = ',', quotechar = '"', encoding = 'utf-8', index = False)
new_df = pd.read_csv(root_new_fn)

print(new_df)

预期输出：

TYPE | DESCRIPTION
 a   | This personality likes to eat apples... 
 a   | They look like monkeys...
 a   | In fact, are strong people...
 b   | b.description
 b   | b.description
 c   | c.description
...  | ...

电流输出：

TYPE | DESCRIPTION
 a   | This personality likes to eat apples...
 a   | They look like monkeys...NaN
 a   | NaN
 a   | In fact, are strong people...NaN
 b   | b.description...NaN
 b   | NaN
 b   | b.description
 c   | c.description
...  | ...

我不是百分之百确定，但我认为NaN值是\r。

应请求上载到github的文件： CSV文件

使用@YOLO解决方案的： CSV YOLO文件，例如哪里失败了：

2 INTJ  Existe soledad en la cima y-- siendo # adds -- in blank random blank spaces
3 INTJ  -- y las mujeres # adds -- in the beginning
3 INTJ  (...) el 0--8-- de la poblaci # doesnt end the word 'población'
10 INTJ icos-- un conflicto que parecer--a imposible. # starts letters randomly
12 INTJ c #adds just 1 letter

为充分理解而翻译：

2 INTJ There is loneliness at the top and-- being # adds -- in blank spaces
3 INTJ -- and women # adds - in the beginning
3 INTJ (...) on 0--8-- of the popula-- # doesnt end the word 'population'
10 INTJ icos-- a conflict that seems--to impossible. # starts letters randomly
12 INTJ c #adds just 1 letter

当我显示是否有NaN值和哪种类型：

print(new_df['descripcion'].isnull())

<class 'float'>
0     False
1     False
2     False
3     False
4     False
5     False
6     False
7      True
8     False
9      True
10    False
11     True
continue...

Answer 1

这个问题可以归因于描述单元格，因为有两个新的连续行的部分，它们之间没有任何内容。

我只是使用.dropna()读取新创建的csv，并在没有NaN值的情况下重写它。无论如何，我认为重复这个过程并不是最好的方法，但它是一个直接的解决方案。

df.to_csv(root_new_fn, sep = ',', quotechar = '"', encoding = 'utf-8', index = False)
new_df = pd.read_csv(root_new_fn).dropna()

new_df.to_csv(root_new_fn, sep = ',', quotechar = '"', encoding = 'utf-8', index = False)
new_df = pd.read_csv(root_new_fn)

print(type(new_df.iloc[7, 1]))# where was a NaN value
print(new_df['descripcion'].isnull())

<class 'str'>
0     False
1     False
2     False
3     False
4     False
5     False
6     False
7     False
8     False
and continues...

Answer 2

这里有一种方法，我必须找到一个替代\n字符的解决办法，不知怎么的，它不是以直接的方式工作的：

df['DESCRIPTION'] = df['DESCRIPTION'].str.replace('[^a-zA-Z0-9\s.]','--').str.split('--n')

df = df.explode('DESCRIPTION')

print(df)

           TYPE                               DESCRIPTION
0   a             This personality likes to eat apples...
0   a                           They look like monkeys...
0   a                      In fact-- are strong people...
1   b                                       b.description
2   c                                       c.description
3   d                                       d.description