blocks|key|2542467|text|df.loc[df[field_units].str.conatins('uM'),+field_values]+=+1000*df.loc[df[field_units].str.conatins('uM'),+field_values]

df.loc[df[field_units].str.conatins('M'),+field_values]+=+1000000000*df.loc[df[field_units].str.conatins('M'),+field_values]

df.loc[(~df[field_units].str.conatins('M'))+&+(~df[field_units].str.conatins('uM')),+field_values]+=+1000000*df.loc[(~df[field_units].str.conatins('M'))+&+(~df[field_units].str.conatins('uM')),+field_values]|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|2542468|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>df.loc[df[field_units].str.conatins('uM'), field_values] = 1000*df.loc[df[field_units].str.conatins('uM'), field_values]

df.loc[df[field_units].str.conatins('M'), field_values] = 1000000000*df.loc[df[field_units].str.conatins('M'), field_values]

df.loc[(~df[field_units].str.conatins('M')) &amp; (~df[field_units].str.conatins('uM')), field_values] = 1000000*df.loc[(~df[field_units].str.conatins('M')) &amp; (~df[field_units].str.conatins('uM')), field_values]
</code></pre>

blocks|key|2008080|text|==操作符返回bool值。在if和elif条件下，您试图在bool上运行bool()方法。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|2008081|def+convert_molars_bool(df,+field_values,+field_units):

++++if+(df[field_units].str+==+'uM'):+#+1+uM+=+1000+nM
++++++++df[field_values]+*=+1000
++++
++++elif+(df[field_units].str+==+'M'):+#+1+M+=+1000000000+nM
++++++++df[field_values]+*=+1000000000
++++
++++else:+#+1+mM+=+1000000+nM
++++++++df[field_values]+*=+1000000
++++
++++return+df|code-block|syntax|javascript|2008082|这应该能行|2008083|entityMap^0|0|2|E|2|H|4|10|6|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@$9|P|A|Q|B|C]|$9|R|A|S|B|C]|$9|T|A|U|B|C]|$9|V|A|W|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|X|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|Y|8|@]|D|@]|E|$]]|$1|M|3|-4|5|6|7|Z|8|@]|D|@]|E|$]]]|N|$]]

the <code>==</code> operator returns a bool value. In your <code>if</code> and <code>elif</code> conditions you are trying to run the <code>bool()</code> method on a bool.
<pre class="lang-py prettyprint-override"><code>def convert_molars_bool(df, field_values, field_units):

 if (df[field_units].str == 'uM'): # 1 uM = 1000 nM
 df[field_values] *= 1000
 
 elif (df[field_units].str == 'M'): # 1 M = 1000000000 nM
 df[field_values] *= 1000000000
 
 else: # 1 mM = 1000000 nM
 df[field_values] *= 1000000
 
 return df
</code></pre>
This should work

blocks|key|2008114|text|看看df[field_units].str.contains('uM')给您带来了什么。它应该是pandas+Series+of+bools。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|2008115|import+pandas+as+pd
dct+=+{
++++'Standard+Units':+['uM',+'M',+'D'],+
++++'Standard+Value':+[23.34,+245.6,+102]
}
df+=+pd.DataFrame(dct)
print(df['Standard+Units'].str.contains('uM'))
#0+++++True
#1++++False
#2++++False
#Name:+Standard+Units,+dtype:+bool|code-block|syntax|javascript|2008116|即使这不会引起错误，df[field_values]+*=+1000也会将1000乘以每一行。它对前一行代码中的条件一无所知。|2008117|您要做的是将此逻辑单独应用于每一行，而不是将其作为一个整体应用于DataFrame。|2008118|为此，您可以使用for循环或更好的df.apply()。|2008119|df.apply接受一个函数来应用于每一列或每一行。|2008120|例如：|2008121|import+pandas+as+pd
dct+=+{
++++'Standard+Units':+['uM',+'M',+'D'],+
++++'Standard+Value':+[23.34,+245.6,+102]
}
df+=+pd.DataFrame(dct)

def+func(row):
++++if+'uM'+in+row['Standard+Units']:
++++++++return+row['Standard+Value']+*+1000
++++elif+'M'+in+row['Standard+Units']:
++++++++return+row['Standard+Value']+*+1000000000
++++else:
++++++++return+row['Standard+Value']+*+1000000

df['Standard+Value']+=+df.apply(func,+axis=1)

print(df)
#++Standard+Units++Standard+Value
#0+++++++++++++uM++++2.334000e%2B04
#1++++++++++++++M++++2.456000e%2B11
#2++++++++++++++D++++1.020000e%2B08|2008122|或者，如果您希望保留您使用的相同的函数签名：|2008123|import+pandas+as+pd
dct+=+{
++++'Standard+Units':+['uM',+'M',+'D'],+
++++'Standard+Value':+[23.34,+245.6,+102]
}
df+=+pd.DataFrame(dct)

def+convert_molars(df,+field_values,+field_units):
++++#+create+a+function+to+apply
++++def+func(row):
++++++++if+'uM'+in+row[field_units]:
++++++++++++return+row[field_values]+*+1000
++++++++elif+'M'+in+row[field_units]:
++++++++++++return+row[field_values]+*+1000000000
++++++++else:
++++++++++++return+row[field_values]+*+1000000
++++#+apply+the+function+that+was+just+created
++++df[field_values]+=+df.apply(func,+axis=1)
++++#+return+the+DataFrame
++++return+df

df+=+convert_molars(df,+'Standard+Value',+'Standard+Units')

print(df)|2008124|entityMap^0|2|Y|1C|6|1J|6|1T|4|0|0|A|O|11|4|0|W|9|0|8|3|H|A|0|0|8|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|12|8|@$9|13|A|14|B|C]|$9|15|A|16|B|C]|$9|17|A|18|B|C]|$9|19|A|1A|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|1B|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|1C|8|@$9|1D|A|1E|B|C]|$9|1F|A|1G|B|C]]|D|@]|E|$]]|$1|M|3|N|5|6|7|1H|8|@$9|1I|A|1J|B|C]]|D|@]|E|$]]|$1|O|3|P|5|6|7|1K|8|@$9|1L|A|1M|B|C]|$9|1N|A|1O|B|C]]|D|@]|E|$]]|$1|Q|3|R|5|6|7|1P|8|@$9|1Q|A|1R|B|C]]|D|@]|E|$]]|$1|S|3|T|5|6|7|1S|8|@]|D|@]|E|$]]|$1|U|3|V|5|H|7|1T|8|@]|D|@]|E|$I|J]]|$1|W|3|X|5|6|7|1U|8|@]|D|@]|E|$]]|$1|Y|3|Z|5|H|7|1V|8|@]|D|@]|E|$I|J]]|$1|10|3|-4|5|6|7|1W|8|@]|D|@]|E|$]]]|11|$]]

Take a look at what <code>df[field_units].str.contains('uM')</code> gives you.
It should be a <code>pandas</code> <code>Series</code> of <code>bool</code>s.
<pre><code>import pandas as pd
dct = {
 'Standard Units': ['uM', 'M', 'D'], 
 'Standard Value': [23.34, 245.6, 102]
}
df = pd.DataFrame(dct)
print(df['Standard Units'].str.contains('uM'))
#0 True
#1 False
#2 False
#Name: Standard Units, dtype: bool
</code></pre>
Even if this wouldn't raise an error, <code>df[field_values] *= 1000</code> would multiply <code>1000</code> onto EVERY row. It doesn't know anything about the conditional in the previous line of code.
What you are trying to do is apply this logic to each row individually rather than the <code>DataFrame</code> as a whole.
For this you could use a <code>for</code> loop or better, <code>df.apply()</code>.
<code>df.apply</code> takes a function to apply to each column or row.
For example:
<pre><code>import pandas as pd
dct = {
 'Standard Units': ['uM', 'M', 'D'], 
 'Standard Value': [23.34, 245.6, 102]
}
df = pd.DataFrame(dct)

def func(row):
 if 'uM' in row['Standard Units']:
 return row['Standard Value'] * 1000
 elif 'M' in row['Standard Units']:
 return row['Standard Value'] * 1000000000
 else:
 return row['Standard Value'] * 1000000

df['Standard Value'] = df.apply(func, axis=1)

print(df)
# Standard Units Standard Value
#0 uM 2.334000e+04
#1 M 2.456000e+11
#2 D 1.020000e+08
</code></pre>
Or, if you want to keep the same function signature you were using :
<pre><code>import pandas as pd
dct = {
 'Standard Units': ['uM', 'M', 'D'], 
 'Standard Value': [23.34, 245.6, 102]
}
df = pd.DataFrame(dct)

def convert_molars(df, field_values, field_units):
 # create a function to apply
 def func(row):
 if 'uM' in row[field_units]:
 return row[field_values] * 1000
 elif 'M' in row[field_units]:
 return row[field_values] * 1000000000
 else:
 return row[field_values] * 1000000
 # apply the function that was just created
 df[field_values] = df.apply(func, axis=1)
 # return the DataFrame
 return df

df = convert_molars(df, 'Standard Value', 'Standard Units')

print(df)
</code></pre>

I would like to create a function which could convert some concentrations into nanomolar concentration. So I wrote this function:
<pre><code>def convert_molars(df, field_values, field_units):

 if (df[field_units].str.contains('uM')): # 1 uM = 1000 nM
 df[field_values] *= 1000
 
 elif (df[field_units].str.contains('M')): # 1 M = 1000000000 nM
 df[field_values] *= 1000000000
 
 else: # 1 mM = 1000000 nM
 df[field_values] *= 1000000
 
 return df
</code></pre>
And I started it like this:
<pre><code>standard_units = convert_molars(IC50_nonan_units, 'Standard Value', 'Standard Units')
standard_units.to_csv(&quot;standard_units.csv&quot;)
standard_units.head()
</code></pre>
But I got this error:
<pre><code>ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
</code></pre>
I tried one more option like this:
<pre><code>def convert_molars_bool(df, field_values, field_units):

 if (df[field_units].str == 'uM').bool(): # 1 uM = 1000 nM
 df[field_values] *= 1000
 
 elif (df[field_units].str == 'M').bool(): # 1 M = 1000000000 nM
 df[field_values] *= 1000000000
 
 else: # 1 mM = 1000000 nM
 df[field_values] *= 1000000
 
 return df
</code></pre>
But I got this:
<pre><code>AttributeError: 'bool' object has no attribute 'bool'
</code></pre>
Could someone please explain me that I did wrong?

ValueError: Use a.empty, a.bool(), a.item(), a.any() or a.all() & AttributeError: 'bool' object has no attribute 'bool'

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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我想要创建一个功能，可以将某些浓度转化为纳米摩尔浓度。所以我写了这个函数：def convert_molars(df, field_values, field_units):    if (df[field_units].str.contains('uM')): # 1 uM = 1000 nM        df[f...

问ValueError:使用a.empty、a.bool()、a.item()、a.any()或a.all() & AttributeError：'bool‘对象没有属性'bool’
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问ValueError:使用a.empty、a.bool()、a.item()、a.any()或a.all() & AttributeError：'bool‘对象没有属性'bool’
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