删除行和ValueError数组的长度不同 [英] remove rows and ValueError Arrays were different lengths

问题描述

我的数据框有一个子类别，在每个类别(cat，dog，bird)下，都会显示统计信息.如果行包含count和freq中的信息，则需要删除这些行，并且仅保留具有sd和mean值的行.某些值为NaN.

My dataframe has subcategory, under each category (cat, dog, bird), stats information is presented. I need to remove the rows if they contain info in count and freq, and only keep rows with sd and mean values. Some values are NaN.

ValueError出现在我的代码中.

df:

 var    stats    A     B     C
 cat     mean    2     3     4
 NaN     sd      2     1     3
 NaN     count   5     2     6
 NaN     freq    3     1     19
 dog     mean    8     1     2
 NaN     sd      2     1     3
 NaN     count   4     6     1
 NaN     freq    3     1     19   
 bird    mean    2     3     4
 NaN     sd      2     1     3
 NaN     count   5     2     6
 NaN     freq    NaN   NaN   NaN 

我的代码:

rows = ['count', 'freq']
df = [df.stats != rows]

预期结果

 var    stats    A     B     C
 cat     mean    2     3     4
 NaN     sd      2     1     3
 dog     mean    8     1     2
 NaN     sd      2     1     3   
 bird    mean    2     3     4
 NaN     sd      2     1     3

错误:

File "pandas/_libs/lib.pyx", line 805, in pandas._libs.lib.vec_compare 
(pandas/_libs/lib.c:14288)
ValueError: Arrays were different lengths: 819 vs 9

我不确定如何检查数组长度，但是在我的Excel电子表格中，所有列和行都具有相同的长度.此错误是由数据中的NaN/空单元格引起的吗?

I am not sure how to check the array length, but in my excel spreadsheet, all columns and rows have the same length. Is this error caused by NaN/empty cell in my data?

谢谢！

推荐答案

!=在这里不起作用.使用pd.Series.isin获取掩码，然后将其用于过滤数据框.

!= will not work here. Use pd.Series.isin to obtain a mask you'll then use to filter your dataframe.

m = ~df.stats.isin(['count', 'freq'])
print(m)
0      True
1      True
2     False
3     False
4      True
5      True
6     False
7     False
8      True
9      True
10    False
11    False
Name: stats, dtype: bool

print(df[m])
    var stats    A    B    C
0   cat  mean  2.0  3.0  4.0
1   NaN    sd  2.0  1.0  3.0
4   dog  mean  8.0  1.0  2.0
5   NaN    sd  2.0  1.0  3.0
8  bird  mean  2.0  3.0  4.0
9   NaN    sd  2.0  1.0  3.0

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