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Python Pandas ValueError :The truth value of a Series is ambiguous


By : Paul Suetrong
Date : October 16 2020, 08:10 PM
To fix this issue You could just use select_dtypes:
code :
for col in data1.select_dtypes('object'):
    print(f'feature {col} has {data1[col].nunique()} unique categories')


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Adjust Pandas Series Using np.where (Avoid ValueError: The truth value of a Series is ambiguous.)


By : Ilya Vasylevsky
Date : March 29 2020, 07:55 AM
I hope this helps . I have the following pandas series that I'm trying to adjust in an if/else manner depending on if the value in the series equals 'Unspecified'. I have been reviewing as much material on this common error in pandas but it doesn't seem to be helping. Can someone please help me have my pandas Series adjust the last state names to uppercase unless the value equals 'Unspecified'. This was my best guess test_series.where('Unspecified', test_series.str[:-2] + test_series.str[-2:].str.upper()) !! , I think you want:
code :
test_series.where(test_series == 'Unspecified', 
                  test_series.str[:-2] + test_series.str[-2:].str.upper())
0         Asheville, NC
1           Cowpens, NC
2    Hendersonville, NC
3             Tryon, NC
4          Fletcher, NC
5          Franklin, NC
6           Unspecified
7        Burnsville, NC
8         Flat rock, NC
9          Fairview, NC
dtype: object
%timeit pd.Series([i if i == 'Unspecified' else i[:-2] + i[-2:].upper() for i in test_series])
1000 loops, best of 3: 342 µs per loop
%%timeit 
test_series.where(test_series == 'Unspecified', 
                      test_series.str[:-2] + test_series.str[-2:].str.upper())

100 loops, best of 3: 2.84 ms per loop

ValueError: The truth value of a Series is ambiguous - check a value in pandas column


By : Didik Prasetyo
Date : March 29 2020, 07:55 AM
around this issue IIUC, you are checking if my_col contains "my_value" at any point in the column. In that case, use .any: if not df['my_col'].str.contains('my_value').any():.
Or, you could use: if 'my_value' not in df['my_col'].values, which might be faster than the str accessor
code :
>>> df
     my_col my_col2
0  my_value       x
1         x       x
2         x       x
3  my_value       x

if not df['my_col'].str.contains('my_value').any():
    print('not contained')
else:
    print('contained')
if not df['my_col2'].str.contains('my_value').any():
    print('not contained')
else:
    print('contained')
if 'my_value' not in df['my_col'].values:
    print('not contained')
else:
    print('contained')

Pandas ValueError: The truth value of a Series is ambiguous, when indexing dataframe


By : Garima
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , For Boolean indexing with Pandas series, you need to use bitwise & / | operators for "and" / "or" conditions respectively. For readability, you can also split your mask into components:
code :
m1 = month[['pickup_longitude', 'pickup_latitude']].ne(0).all(1)
m2 = month['pickup_longitude'].le(-74.15)
m3 = month['pickup_latitude'].le(40.5774)
m4 = month['pickup_longitude'].ge(-73.7004)
m5 = month['pickup_latitude'].ge(40.9176)

outlier_locations = month[m1 & (m2 | m3 | m4 | m5)]

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


By : jsoncoder
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , I am trying to replace my values in the title column of my dataframe. However. Its throwing error , try this create a dictionary of names substitutions like this :
code :
names ={'Mr':0, 'Miss':1, 'Master':2, 'Mrs':3} 
df['Title'] = df.Title.replace(names).replace({'': 4})
def change_names(x):
    if x == 'Mr':
        return 0
    elif x =='Miss':
        return 1
    elif x == 'Master':
        return 2
    elif x =='Mrs':
        return 3
    else:
        return 4

df['Title'] = df['Title'].apply(change_names)

ValueError: The truth value of an array with more than one element is ambiguous. Pandas Series


By : Chase Swanstrom
Date : March 29 2020, 07:55 AM
wish of those help Use np.where()
code :
df2['Vpip'] = np.where((df2['Call_Pre'] == '[]') & (df2['Raise_Pre'] == '[]'),'No','Yes')
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