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

Question

I would like to create a function which could convert some concentrations into nanomolar concentration. So I wrote this function:

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

And I started it like this:

standard_units = convert_molars(IC50_nonan_units, 'Standard Value', 'Standard Units') standard_units.to_csv("standard_units.csv") standard_units.head()

But I got this error:

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

I tried one more option like this:

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

But I got this:

AttributeError: 'bool' object has no attribute 'bool'

Could someone please explain me that I did wrong?

Praneeth Jain · Accepted Answer · 2022-06-23 12:30:35Z

the == operator returns a bool value. In your if and elif conditions you are trying to run the bool() method on a bool.

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

This should work

This won't work. df[field_units].str returns a pandas.core.strings.accessor.StringMethods object which is never going to be equal to 'uM'. The only thing this will do is multiply 1000000 onto every row since both conditionals will always be False.

Pulkit Gupta · Accepted Answer · 2022-06-23 12:41:36Z

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]

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Axe319 · Accepted Answer · 2022-06-23 12:55:46Z

Take a look at what df[field_units].str.contains('uM') gives you. It should be a pandas Series of bools.

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

Even if this wouldn't raise an error, df[field_values] *= 1000 would multiply 1000 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 DataFrame as a whole.

For this you could use a for loop or better, df.apply().

df.apply takes a function to apply to each column or row.

For example:

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

Or, if you want to keep the same function signature you were using :

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)

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