blocks|key|1483150|text|你可以像下面这样做：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1483151|for+i,+row+in+df.iterrows():
++++nans+=+row[row.isna()].index
++++for+n+in+nans:
++++++++print('row:+%25s,+col:+%25s'+%25+(i,+n))|code-block|syntax|javascript|1483152|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

You could do something like the below:
<pre class="lang-py prettyprint-override"><code>for i, row in df.iterrows():
 nans = row[row.isna()].index
 for n in nans:
 print('row: %s, col: %s' % (i, n))
</code></pre>

blocks|key|5547892|text|由于NaN的原因，我不得不对df做了一些修改。替换为np.nan|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|5547893|import+numpy+as+np
import+pandas+as+pd
raw_data+=+{'first_name':+[np.nan,+'Molly',+'Tina',+'Jake',+'Amy'],+
++++++++'last_name':+['Miller',+'Jacobson',+'Ali',+'Milner',+'Cooze'],+
++++++++'age':+[42,+52,+np.nan,+24,+73],+
++++++++'preTestScore':+[4,+24,+31,+33,+3],
++++++++'postTestScore':+[25,+94,+57,+62,+70]}|code-block|syntax|javascript|5547894|你可以做到的|5547895|dfs+=+df.stack(dropna+=+False)
[f'There+is+a+missing+value+in+row+{i[0]}+for+{i[1]}'+for+i+in+dfs[dfs.isna()].index]|5547896|打印列表|5547897|['There+is+a+missing+value+in+row+0+for+first_name',
+'There+is+a+missing+value+in+row+2+for+age']|5547898|entityMap^0|2|3|Q|6|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@$9|V|A|W|B|C]|$9|X|A|Y|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|Z|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|10|8|@]|D|@]|E|$]]|$1|M|3|N|5|H|7|11|8|@]|D|@]|E|$I|J]]|$1|O|3|P|5|6|7|12|8|@]|D|@]|E|$]]|$1|Q|3|R|5|H|7|13|8|@]|D|@]|E|$I|J]]|$1|S|3|-4|5|6|7|14|8|@]|D|@]|E|$]]]|T|$]]

had to change the df a bit because of <code>NaN</code>. Replaced with <code>np.nan</code>
<pre><code>import numpy as np
import pandas as pd
raw_data = {'first_name': [np.nan, 'Molly', 'Tina', 'Jake', 'Amy'], 
 'last_name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze'], 
 'age': [42, 52, np.nan, 24, 73], 
 'preTestScore': [4, 24, 31, 33, 3],
 'postTestScore': [25, 94, 57, 62, 70]}
</code></pre>
you can do this
<pre><code>dfs = df.stack(dropna = False)
[f'There is a missing value in row {i[0]} for {i[1]}' for i in dfs[dfs.isna()].index]
</code></pre>
prints a list
<pre><code>['There is a missing value in row 0 for first_name',
 'There is a missing value in row 2 for age']
</code></pre>

blocks|key|5547929|text|您可以只创建一个变量NaN+=+"null“来指示空单元格|type|unstyled|depth|inlineStyleRanges|entityRanges|data|5547930|import+pandas+as++pd

NaN+=+"null"
raw_data+=+{'first_name':+[NaN,+'Molly',+'Tina',+'Jake',+'Amy'],+
++++++++'last_name':+['Miller',+'Jacobson',+'Ali',+'Milner',+'Cooze'],+
++++++++'age':+[42,+52,+NaN,+24,+73],+
++++++++'preTestScore':+[4,+24,+31,+33,+3],
++++++++'postTestScore':+[25,+94,+57,+62,+70]}

df+=+pd.DataFrame(raw_data,+columns+=+['first_name',+'last_name',+'age',+
'preTestScore','postTestScore'])
print(df)|code-block|syntax|javascript|5547931|输出：|5547932|first_name+last_name+++age++preTestScore++postTestScore
0+++++++null++++Miller++++42+++++++++++++4+++++++++++++25
1++++++Molly++Jacobson++++52++++++++++++24+++++++++++++94
2+++++++Tina+++++++Ali++null++++++++++++31+++++++++++++57
3+++++++Jake++++Milner++++24++++++++++++33+++++++++++++62
4++++++++Amy+++++Cooze++++73+++++++++++++3+++++++++++++70|5547933|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

you can just create a variable
NaN = &quot;null&quot;
to indicate empty cell
<pre><code>
import pandas as pd

NaN = &quot;null&quot;
raw_data = {'first_name': [NaN, 'Molly', 'Tina', 'Jake', 'Amy'], 
 'last_name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze'], 
 'age': [42, 52, NaN, 24, 73], 
 'preTestScore': [4, 24, 31, 33, 3],
 'postTestScore': [25, 94, 57, 62, 70]}

df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 
'preTestScore','postTestScore'])
print(df)

</code></pre>
output:
<pre><code>first_name last_name age preTestScore postTestScore
0 null Miller 42 4 25
1 Molly Jacobson 52 24 94
2 Tina Ali null 31 57
3 Jake Milner 24 33 62
4 Amy Cooze 73 3 70
</code></pre>

blocks|key|5547968|text|就像这样简单：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|5547969|np.where(df.isnull())|code-block|syntax|javascript|5547970|它分别返回一个带有行索引的元组和带有NAs的列索引。|offset|length|style|CODE|5547971|5547972|示例：|5547973|na_idx+=+np.where(df.isnull())
for+i,j+in+zip(*na_idx):
++++print(f'Row+{i}+and+column+{j}+({df.columns[j]})+is+NA.')|5547974|entityMap^0|0|0|I|3|0|0|0|0^^$0|@$1|2|3|4|5|6|7|T|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|U|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|V|8|@$I|W|J|X|K|L]]|9|@]|A|$]]|$1|M|3|-4|5|6|7|Y|8|@]|9|@]|A|$]]|$1|N|3|O|5|6|7|Z|8|@]|9|@]|A|$]]|$1|P|3|Q|5|D|7|10|8|@]|9|@]|A|$E|F]]|$1|R|3|-4|5|6|7|11|8|@]|9|@]|A|$]]]|S|$]]

As simple as:
<pre><code>np.where(df.isnull())
</code></pre>
It returns a tuple with the row indexes, and column indexes with <code>NAs</code>, respectively.
<hr />
Example:
<pre><code>na_idx = np.where(df.isnull())
for i,j in zip(*na_idx):
 print(f'Row {i} and column {j} ({df.columns[j]}) is NA.')
</code></pre>

blocks|key|4079880|text|我认为熔化是一条可行的道路。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4079881|我首先创建一个包含以下列的数据帧:+index、column_name、value。然后根据not+null筛选列value。并将结果转储到dict。|offset|length|style|CODE|4079882|df+=+pd.melt(df.reset_index(),+id_vars=['index'],+value_vars=df.columns)
selected+=+df[df['value'].isnull()].drop('value',+axis=1).set_index('index')
resp+=+selected.T.to_dict(orient='records')[0]
s+=+"There+is+a+missing+value+in+row+{idx}+for+{col_name}."

for+record+in+resp.items():
++++idx,+col_name+=+record
++++print(s.format(idx=idx,+col_name=col_name))|code-block|syntax|javascript|4079883|entityMap^0|0|1L|5|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|P|8|@$D|Q|E|R|F|G]]|9|@]|A|$]]|$1|H|3|I|5|J|7|S|8|@]|9|@]|A|$K|L]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

I think melting is the way to go.
I'd start by creating a dataframe with columns: index, column_name, value.
Then filter column <code>value</code> by not null.
And dump the result to dict.
<pre><code>df = pd.melt(df.reset_index(), id_vars=['index'], value_vars=df.columns)
selected = df[df['value'].isnull()].drop('value', axis=1).set_index('index')
resp = selected.T.to_dict(orient='records')[0]
s = &quot;There is a missing value in row {idx} for {col_name}.&quot;

for record in resp.items():
 idx, col_name = record
 print(s.format(idx=idx, col_name=col_name))
</code></pre>

I am desperatly trying to figure out how to print out the row index and col name for specific values in my df.
I have the following df:
<pre><code>raw_data = {'first_name': [NaN, 'Molly', 'Tina', 'Jake', 'Amy'], 
 'last_name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze'], 
 'age': [42, 52, NaN, 24, 73], 
 'preTestScore': [4, 24, 31, 33, 3],
 'postTestScore': [25, 94, 57, 62, 70]}

df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 
'preTestScore','postTestScore'])

</code></pre>
I now want to print out the index and column name for the NaN:
<pre><code>There is a missing value in row 0 for first_name.
There is a missing value in row 2 for age.
</code></pre>
I have searched a lot and always found how to do something for one row.
My idea is to first create a df with False and True
<pre><code>na = df.isnull()
</code></pre>
Then I want to apply some function that prints the row number and col_name for every NaN value.
I just cant figure out how to do this.
Thanks in advance for any help!

Print the colname and rowname for values that meet certain condition

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我拼命地试图弄清楚如何打印出我的df中特定值的行索引和列名称。我有以下df：raw_data = {'first_name': [NaN, 'Molly', 'Tina', 'Jake', 'Amy'],         'last_name': ['Miller', 'Jacobson', 'Ali', 'Milne...

问打印满足特定条件的值的colname和rowname
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问打印满足特定条件的值的colname和rownameEN