首先,我知道这件事有答案,但到目前为止,没有一个是为我工作的。不管怎样,我想知道你的答案,尽管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。第一列的标签为type
,第二列的标签为description
。每一行代表一种新的人格类型(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,我尝试复制每个人格类型
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100%确定,但我认为NaN值是\r
根据请求上传到github的文件:CSV FILES
使用@YOLO解决方案:CSV YOLO FILE 例如,失败的地方:
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
充分理解的翻译:
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
当我显示是否有任何NaN值以及哪种类型时:
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...
问题可归因于描述单元,因为有两个新的连续行的零件,它们之间没有任何内容
我只是使用
.dropna()
读取创建的新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不是最好的方法,但它作为一个解决方案是直接进行的这里有一个方法,我必须找到一个替代
\n
字符的解决方法,但不知怎么的,它没有以直接的方式工作:相关问题 更多 >
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