ValueError: Use a.empty, a.bool(), a.item(), a.any() or a.all() & AttributeError: 'bool' o-CodePudding

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?

CodePudding user response：

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

CodePudding user response：

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]

CodePudding user response：

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)