在pandas中比较两个列表会引发TypeError错误：“float”对象不可迭代。

huangapple
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英文:

Comparing two lists in pandas gives TypeError: 'float' object is not iterable

问题

我想比较包含字符串列表的DataFrame中的两列。我输入数据的一部分如下所示：

我尝试了不同的方法，使用迭代，但都引发了TypeError错误。以下是我的代码的前一部分：

  1. df['one_one'] = df.apply(lambda row: set(row['one_one']), axis=1)
  2. df['one_one_back'] = df.apply(lambda row: set(row['one_one_back']), axis=1)
  3. df['check'] = df.apply(lambda row: row['one_one'] in row['one_one_back'], axis=1)

和错误：

  1. Traceback (most recent call last):
  2.   File "/home/emoranska/Pulpit/burak/homozyg_regions/homozyg_bins.py", line 47, in <module>
  3.     df['one_one_back'] = df.apply(lambda row: set(row['one_one_back']), axis=1)
  4.   File "/home/emoranska/miniconda3/envs/burak/lib/python3.7/site-packages/pandas/core/frame.py", line 8740, in apply
  5.     return op.apply()
  6.   File "/home/emoranska/miniconda3/envs/burak/lib/python3.7/site-packages/pandas/core/apply.py", line 688, in apply
  7.     return self.apply_standard()
  8.   File "/home/emoranska/miniconda3/envs/burak/lib/python3.7/site-packages/pandas/core/apply.py", line 812, in apply_standard
  9.     results, res_index = self.apply_series_generator()
  10.   File "/home/emoranska/miniconda3/envs/burak/lib/python3.7/site-packages/pandas/core/apply.py", line 828, in apply_series_generator
  11.     results[i] = self.f(v)
  12.   File "/home/emoranska/Pulpit/burak/homozyg_regions/homozyg_bins.py", line 47, in <lambda>
  13.     df['one_one_back'] = df.apply(lambda row: set(row['one_one_back']), axis=1)
  14. TypeError: 'float' object is not iterable

代码的第二部分使用itertuples函数也不起作用：

  1. for x in df.itertuples():
  2.     if x.one_one in x.one_one_back:
  3.         df['start_end'] = 'start'
  4.     else:
  5.         df['start_end'] = 0

并引发错误：

  1. Traceback (most recent call last):
  2.   File "/home/emoranska/Pulpit/burak/homozyg_regions/homozyg_bins.py", line 55, in <module>
  3.     if x.one_one in x.one_one_back:
  4. TypeError: argument of type 'float' is not iterable

我已经检查了数据类型：

  1. print(df.loc[0, 'one_one'], type(df.loc[0, 'one_one']))
  2. print(df.loc[1, 'one_one_back'], type(df.loc[1, 'one_one_back']))
  4. for x in df.loc[1, 'one_one_back']:
  5.     print(x, type(x))

并且可以确定这些都是字符串列表：

  1. ['P1-88', 'P1-89', 'P1-26', 'P1-12'] <class 'list'>
  2. ['P1-88', 'P1-89', 'P1-26', 'P1-12'] <class 'list'>
  3. P1-88 <class 'str'>
  4. P1-89 <class 'str'>
  5. P1-26 <class 'str'>
  6. P1-12 <class 'str'>

那么为什么会出现浮点数，以及为什么会出现TypeError错误？请帮助我解决这个问题，因为我感到非常困惑...

英文:

I want to compare two columns in the dataframe containing lists of strings. A part of my input data looks like that:

  1.           CHROM      POS ID REF ALT QUAL FILTER INFO FORMAT P1-25 P1-93 P1-88 P1-6 P1-89 P1-26 P1-12 P1-92 P1-22 P1-90 P1-28 P1-95                                                  one_one                                         zero_zero                                       one_one_back                             zero_zero_next
  2. 0   NC_064017.1   153210  .   T   C    .      .    .     GT   0/0   0/0   1/1  0/0   1/1   1/1   1/1   0/0   0/0   0/1   0/0   0/0                             [P1-88, P1-89, P1-26, P1-12]  [P1-25, P1-93, P1-6, P1-92, P1-22, P1-28, P1-95]                                                NaN                      [P1-25, P1-12, P1-22]
  3. 1   NC_064017.1   965007  .   A   G    .      .    .     GT   0/0   1/1     .  0/1   1/1     .   0/0   1/1   0/0   0/1     .   0/1                                    [P1-93, P1-89, P1-92]                             [P1-25, P1-12, P1-22]                       [P1-88, P1-89, P1-26, P1-12]        [P1-25, P1-88, P1-12, P1-22, P1-28]
  4. 2   NC_064017.1   965038  .   C   T    .      .    .     GT   0/0   1/1   0/0  0/1   1/1     .   0/0   1/1   0/0   0/1   0/0   0/1                                    [P1-93, P1-89, P1-92]               [P1-25, P1-88, P1-12, P1-22, P1-28]                              [P1-93, P1-89, P1-92]                      [P1-93, P1-26, P1-92]
  5. 3   NC_064017.1  1084455  .   A   G    .      .    .     GT   1/1   0/0     .  1/1   1/1   0/0   1/1   0/0   0/1   1/1   0/1   1/1                [P1-25, P1-6, P1-89, P1-12, P1-90, P1-95]                             [P1-93, P1-26, P1-92]                              [P1-93, P1-89, P1-92]                       [P1-25, P1-6, P1-28]
  6. 4   NC_064017.1  1117756  .   A   C    .      .    .     GT   0/0   0/1   1/1  0/0     .   1/1   1/1   1/1   1/1   1/1   0/0   1/1        [P1-88, P1-26, P1-12, P1-92, P1-22, P1-90, P1-95]                              [P1-25, P1-6, P1-28]          [P1-25, P1-6, P1-89, P1-12, P1-90, P1-95]                      [P1-22, P1-90, P1-28]
  7. 5   NC_064017.1  1250643  .   T   C    .      .    .     GT   0/1   0/1   0/1  1/1   0/1   1/1   0/1   0/1   0/0   0/0   0/0   1/1                                     [P1-6, P1-26, P1-95]                             [P1-22, P1-90, P1-28]  [P1-88, P1-26, P1-12, P1-92, P1-22, P1-90, P1-95]                      [P1-22, P1-90, P1-28]
  8. 6   NC_064017.1  1250740  .   T   A    .      .    .     GT   0/1   1/1   0/1  1/1   0/1   1/1   0/1   0/1   0/0   0/0   0/0   0/1                                     [P1-93, P1-6, P1-26]                             [P1-22, P1-90, P1-28]                               [P1-6, P1-26, P1-95]  [P1-93, P1-6, P1-89, P1-12, P1-90, P1-95]
  9. 7   NC_064017.1  1372722  .   A   C    .      .    .     GT   1/1   0/0   1/1  0/0   0/0   1/1   0/0   1/1   1/1   0/0   1/1   0/0               [P1-25, P1-88, P1-26, P1-92, P1-22, P1-28]         [P1-93, P1-6, P1-89, P1-12, P1-90, P1-95]                               [P1-93, P1-6, P1-26]                      [P1-93, P1-26, P1-28]
  10. 8   NC_064017.1  1502890  .   G   T    .      .    .     GT     .   0/0   1/1  0/1   0/1   0/0   1/1   1/1   1/1   0/1   0/0   1/1                      [P1-88, P1-12, P1-92, P1-22, P1-95]                             [P1-93, P1-26, P1-28]         [P1-25, P1-88, P1-26, P1-92, P1-22, P1-28]                      [P1-89, P1-26, P1-95]

I've tried different methods using iteration but all raise the TypeError. Here is the first part of my code:

  1. df[&#39;one_one&#39;] = df.apply(lambda row: set(row[&#39;one_one&#39;]), axis=1)
  2. df[&#39;one_one_back&#39;] = df.apply(lambda row: set(row[&#39;one_one_back&#39;]), axis=1)
  3. df[&#39;check&#39;] = df.apply(lambda row: row[&#39;one_one&#39;] in row[&#39;one_one_back&#39;], axis=1)

and the error:

  1. Traceback (most recent call last):
  2.   File &quot;/home/emoranska/Pulpit/burak/homozyg_regions/homozyg_bins.py&quot;, line 47, in &lt;module&gt;
  3.     df[&#39;one_one_back&#39;] = df.apply(lambda row: set(row[&#39;one_one_back&#39;]), axis=1)
  4.   File &quot;/home/emoranska/miniconda3/envs/burak/lib/python3.7/site-packages/pandas/core/frame.py&quot;, line 8740, in apply
  5.     return op.apply()
  6.   File &quot;/home/emoranska/miniconda3/envs/burak/lib/python3.7/site-packages/pandas/core/apply.py&quot;, line 688, in apply
  7.     return self.apply_standard()
  8.   File &quot;/home/emoranska/miniconda3/envs/burak/lib/python3.7/site-packages/pandas/core/apply.py&quot;, line 812, in apply_standard
  9.     results, res_index = self.apply_series_generator()
  10.   File &quot;/home/emoranska/miniconda3/envs/burak/lib/python3.7/site-packages/pandas/core/apply.py&quot;, line 828, in apply_series_generator
  11.     results[i] = self.f(v)
  12.   File &quot;/home/emoranska/Pulpit/burak/homozyg_regions/homozyg_bins.py&quot;, line 47, in &lt;lambda&gt;
  13.     df[&#39;one_one_back&#39;] = df.apply(lambda row: set(row[&#39;one_one_back&#39;]), axis=1)
  14. TypeError: &#39;float&#39; object is not iterable

The second part of the code with itertuples function doesn't work too:

  1. for x in df.itertuples():
  2.     if x.one_one in x.one_one_back:
  3.         df[&#39;start_end&#39;] = &#39;start&#39;
  4.     else:
  5.         df[&#39;start_end&#39;] = 0

and raises the error:

  1. Traceback (most recent call last):
  2.   File &quot;/home/emoranska/Pulpit/burak/homozyg_regions/homozyg_bins.py&quot;, line 55, in &lt;module&gt;
  3.     if x.one_one in x.one_one_back:
  4. TypeError: argument of type &#39;float&#39; is not iterable

I've checked the data types:

  1. print(df.loc[0, &#39;one_one&#39;], type(df.loc[0, &#39;one_one&#39;]))
  2. print(df.loc[1, &#39;one_one_back&#39;], type(df.loc[1, &#39;one_one_back&#39;]))
  4. for x in df.loc[1, &#39;one_one_back&#39;]:
  5.     print(x, type(x))

and for sure there are lists of strings:

  1. [&#39;P1-88&#39;, &#39;P1-89&#39;, &#39;P1-26&#39;, &#39;P1-12&#39;] &lt;class &#39;list&#39;&gt;
  2. [&#39;P1-88&#39;, &#39;P1-89&#39;, &#39;P1-26&#39;, &#39;P1-12&#39;] &lt;class &#39;list&#39;&gt;
  3. P1-88 &lt;class &#39;str&#39;&gt;
  4. P1-89 &lt;class &#39;str&#39;&gt;
  5. P1-26 &lt;class &#39;str&#39;&gt;
  6. P1-12 &lt;class &#39;str&#39;&gt;

So where is the float and why do I see the TypeError? Please, help because I'm totally confused...

答案1

得分: 1

你可以使用列表推导式循环遍历你的一对一配对，并使用布尔索引仅考虑具有非NaN值的行：

  1. # 哪些行在两列中都有非NaN值？
  2. m = df[['one_one', 'one_one_back']].notna().all(axis=1)
  4. # 对于那些行，检查第一个列表是否是第二个列表的子集
  5. df.loc[m, 'check'] = [set(a) <= set(b) for a, b in
  6.                       zip(df.loc[m, 'one_one'], df.loc[m, 'one_one_back'])]

输出：

  1.          CHROM      POS ID REF ALT QUAL FILTER INFO FORMAT P1-25  ... P1-22 P1-90 P1-28 P1-95                                            one_one  \
  2. 0  NC_064017.1   153210  .   T   C    .      .    .     GT   0/0  ...   0/0   0/1   0/0   0/0                       [P1-88, P1-89, P1-26, P1-12]   
  3. 1  NC_064017.1   965007  .   A   G    .      .    .     GT   0/0  ...   0/1     .   0/1   0/1                              [P1-93, P1-89, P1-92]
  4. 2  NC_064017.1   965038  .   C   T    .      .    .     GT   0/0  ...   0/1   0/0   0/1   0/1                              [P1-93, P1-89, P1-92]
  5. 3  NC_064017.1  1084455  .   A   G    .      .    .     GT   1/1  ...   1/1   0/1   1/1   1/1          [P1-25, P1-6, P1-89, P1-12, P1-90, P1-95]
  6. 4  NC_064017.1  1117756  .   A   C    .      .    .     GT   0/0  ...   1/1   1/1   0/1   1/1  [P1-88, P1-26, P1-12, P1-92, P1-22, P1-90, P1-95]
  7. 5  NC_064017.1  1250643  .   T   C    .      .    .     GT   0/1  ...   0/0   0/0   0/0   1/1                               [P1-6, P1-26, P1-95]
  8. 6  NC_064017.1  1250740  .   T   A    .      .    .     GT   0/1  ...   0/0   0/0   0/1   0/1                               [P1-93, P1-6, P1-26]
  9. 7  NC_064017.1  1372722  .   A   C    .      .    .     GT   1/1  ...   1/1   0/0   1/1   0/0         [P1-25, P1-88, P1-26, P1-92, P1-22, P1-28]
  10. 8  NC_064017.1  1502890  .   G   T    .      .    .     GT     .  ...   0/1   0/0   0/1   1/1                [P1-88, P1-12, P1-92, P1-22, P1-95]
  12.                                           zero_zero                                       one_one_back                             zero_zero_next  check
  13. 0  [P1-25, P1-93, P1-6, P1-92, P1-22, P1-28, P1-95]                                                NaN                      [P1-25, P1-12, P1-22]    NaN
  14. 1                             [P1-25, P1-12, P1-22]                       [P1-88, P1-89, P1-26, P1-12]        [P1-25, P1-88, P1-12, P1-22, P1-28]  False
  15. 2               [P1-25, P1-88, P1-12, P1-22, P1-28]                              [P1-93, P1-89, P1-92]                      [P1-93, P1-26, P1-92]   True
  16. 3                             [P1-93, P1-26, P1-92]                              [P1-93, P1-89, P1-92]                       [P1-25, P1-6, P1-28]  False
  17. 4                              [P1-25, P1-6, P1-28]          [P1-25, P1-6, P1-89, P1-12, P1-90, P1-95]                      [P1-22, P1-90, P1-28]  False
  18. 5                             [P1-22, P1-90, P1-28]  [P1-88, P1-26, P1-12, P1-92, P1-22, P1-90, P1-95]                      [P1-22, P1-90, P1-28]  False
  19. 6                             [P1-22, P1-90, P1-28]                               [P1-6, P1-26, P1-95]  [P1-93, P1-6, P1-89, P1-12, P1-90, P1-95]  False
  20. 7         [P1-93, P1-6, P1-89, P1-12, P1-90, P1-95]                               [P1-93, P1-6, P1-26]                      [P1-93, P1-26, P1-28]  False
  21. 8                             [P1-93, P1-26, P1-28]         [P1-25, P1-88, P1-26, P1-92, P1-22, P1-28]                      [P1-89, P1-26, P1-95]  False
英文:

You can use a list comprehension to loop over your pairs, and boolean indexing to only consider the rows with non-NaN values:

  1. # which rows have non-NA values in both columns?
  2. m = df[[&#39;one_one&#39;, &#39;one_one_back&#39;]].notna().all(axis=1)
  3. # for those, check if the first list is a subset of the second one
  4. df.loc[m, &#39;check&#39;] = [set(a)&lt;=set(b) for a, b in
  5. zip(df.loc[m, &#39;one_one&#39;], df.loc[m, &#39;one_one_back&#39;])]

Output:

  1.          CHROM      POS ID REF ALT QUAL FILTER INFO FORMAT P1-25  ... P1-92 P1-22 P1-90 P1-28 P1-95                                            one_one  \
  2. 0  NC_064017.1   153210  .   T   C    .      .    .     GT   0/0  ...   0/0   0/0   0/1   0/0   0/0                       [P1-88, P1-89, P1-26, P1-12]   
  3. 1  NC_064017.1   965007  .   A   G    .      .    .     GT   0/0  ...   1/1   0/0   0/1     .   0/1                              [P1-93, P1-89, P1-92]   
  4. 2  NC_064017.1   965038  .   C   T    .      .    .     GT   0/0  ...   1/1   0/0   0/1   0/0   0/1                              [P1-93, P1-89, P1-92]   
  5. 3  NC_064017.1  1084455  .   A   G    .      .    .     GT   1/1  ...   0/0   0/1   1/1   0/1   1/1          [P1-25, P1-6, P1-89, P1-12, P1-90, P1-95]   
  6. 4  NC_064017.1  1117756  .   A   C    .      .    .     GT   0/0  ...   1/1   1/1   1/1   0/0   1/1  [P1-88, P1-26, P1-12, P1-92, P1-22, P1-90, P1-95]   
  7. 5  NC_064017.1  1250643  .   T   C    .      .    .     GT   0/1  ...   0/1   0/0   0/0   0/0   1/1                               [P1-6, P1-26, P1-95]   
  8. 6  NC_064017.1  1250740  .   T   A    .      .    .     GT   0/1  ...   0/1   0/0   0/0   0/0   0/1                               [P1-93, P1-6, P1-26]   
  9. 7  NC_064017.1  1372722  .   A   C    .      .    .     GT   1/1  ...   1/1   1/1   0/0   1/1   0/0         [P1-25, P1-88, P1-26, P1-92, P1-22, P1-28]   
  10. 8  NC_064017.1  1502890  .   G   T    .      .    .     GT     .  ...   1/1   1/1   0/1   0/0   1/1                [P1-88, P1-12, P1-92, P1-22, P1-95]   
  11. zero_zero                                       one_one_back                             zero_zero_next  check  
  12. 0  [P1-25, P1-93, P1-6, P1-92, P1-22, P1-28, P1-95]                                                NaN                      [P1-25, P1-12, P1-22]    NaN  
  13. 1                             [P1-25, P1-12, P1-22]                       [P1-88, P1-89, P1-26, P1-12]        [P1-25, P1-88, P1-12, P1-22, P1-28]  False  
  14. 2               [P1-25, P1-88, P1-12, P1-22, P1-28]                              [P1-93, P1-89, P1-92]                      [P1-93, P1-26, P1-92]   True  
  15. 3                             [P1-93, P1-26, P1-92]                              [P1-93, P1-89, P1-92]                       [P1-25, P1-6, P1-28]  False  
  16. 4                              [P1-25, P1-6, P1-28]          [P1-25, P1-6, P1-89, P1-12, P1-90, P1-95]                      [P1-22, P1-90, P1-28]  False  
  17. 5                             [P1-22, P1-90, P1-28]  [P1-88, P1-26, P1-12, P1-92, P1-22, P1-90, P1-95]                      [P1-22, P1-90, P1-28]  False  
  18. 6                             [P1-22, P1-90, P1-28]                               [P1-6, P1-26, P1-95]  [P1-93, P1-6, P1-89, P1-12, P1-90, P1-95]  False  
  19. 7         [P1-93, P1-6, P1-89, P1-12, P1-90, P1-95]                               [P1-93, P1-6, P1-26]                      [P1-93, P1-26, P1-28]  False  
  20. 8                             [P1-93, P1-26, P1-28]         [P1-25, P1-88, P1-26, P1-92, P1-22, P1-28]                      [P1-89, P1-26, P1-95]  False  
  21. [9 rows x 26 columns]

huangapple
  • 本文由 发表于 2023年6月1日 17:32:26
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  • pandas
  • python
  • typeerror
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