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

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

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Hello Guys, How are you all? Hope You all Are Fine. Today I am trying to use single conditions dataframe to get exact value But I am facing following error ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all() in python. So Here I am Explain to you all the possible solutions here.

Without wasting your time, Let’s start This Article to Solve This Error.

Contents
  1. How ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all() Error Occurs?
  2. How To Solve ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all() Error ?
  3. Solution 1: pass statement into dataframe brackets
  4. Solution 2: Use any()
  5. Solution 3: use .all()
  6. Solution 4: use & instead of and
  7. Solution 5: use | instead of or
  8. Summary

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

I am trying to use single conditions dataframe to get exact value. Here is My Code That I am trying to run. This is my dataframe.

import pandas as pd

df = pd.DataFrame.from_dict({
    'manufacturer': ['HP', 'DELL', 'LENOVO', 'Mi'], 
    'processor_i': ['3', '5', '7', '5'],
    'price': [30000, 45000, 80000, 55000],
})

I am trying to compare price.

if df['price'] < 50000:
    print(df)

But I am facing following error.


~\AppData\Local\Programs\Python\Python38\lib\site-packages\pandas\core\generic.py in __nonzero__(self)
   1476 
   1477     def __nonzero__(self):
-> 1478         raise ValueError(
   1479             f"The truth value of a {type(self).__name__} is ambiguous. "
   1480             "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

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

  1. How To Solve ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all() Error ?

    To Solve ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all() Error Just need to pass if statement into dataframe brackets and your error will be solved. Just like this df[df[‘price’] < 50000]. Here use any() to obtain a single truth value based on a mask. For Example .any() method will return true value. Just like below code (df[‘price’] < 50000).any(). Here use all() to obtain a single truth value based on a mask. For Example .all() method will return true value.

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

    To Solve ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all() Error Just need to pass if statement into dataframe brackets and your error will be solved. Just like this df[df[‘price’] < 50000]. Here use any() to obtain a single truth value based on a mask. For Example .any() method will return true value. Just like below code (df[‘price’] < 50000).any(). Here use all() to obtain a single truth value based on a mask. For Example .all() method will return true value.

Solution 1: pass statement into dataframe brackets

Just need to pass if statement into dataframe brackets and your error will be solved. Just like this.

df[df['price'] < 50000]

Solution 2: Use any()

Here use any() to obtain a single truth value based on a mask. For Example .any() method will return true value. Just like below code.

(df['price'] < 50000).any()

Solution 3: use .all()

Here use all() to obtain a single truth value based on a mask. For Example .all() method will return true value. Just like below code. 

(df['price'] < 50000).all()

Solution 4: use & instead of and

If You are comparing multiple conditions then you can face above error. So That you just need to use & instead of and word. Same as below example.

df[(df['price'] < 50000) & (df['processor_i'] < 7)]

Solution 5: use | instead of or

And Also Use | for OR condition. For Example.

df[(df['price'] < 50000) | (df['processor_i'] < 7)]

Summary

It’s all About this issue. Hope all solution helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which solution worked for you?

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