CODEWITHSUNDEEP
pythonpandasdataframe
cryptoking
cryptoking

Reputation: 13

Multiple if conditions pandas

Looking to write an if statement which does a calculation based on if 3 conditions across other columns in a dataframe are true. I have tried the below code which seems to have worked for others on stackoverflow but kicks up an error for me. Note the 'check', 'sqm' and 'sqft' columns are in float64 format.

if ((merge['check'] == 1) & (merge['sqft'] > 0) & (merge['sqm'] == 0)):
         merge['checksqm'] == merge['sqft']/10.7639

#Error below:


alueError                                Traceback (most recent call last)
<ipython-input-383-e84717fde2c0> in <module>
----> 1 if ((merge['check'] == 1) & (merge['sqft'] > 0) & (merge['sqm'] == 0)):
      2          merge['checksqm'] == merge['sqft']/10.7639

~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/generic.py in __nonzero__(self)
   1327 
   1328     def __nonzero__(self):
-> 1329         raise ValueError(
   1330             f"The truth value of a {type(self).__name__} is ambiguous. "
   1331             "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().

Upvotes: 0

Views: 153

Answers (2)

Alexander Volkovsky
Alexander Volkovsky

Reputation: 2918

You are trying to use pd.Series as the condition inside the if clause. This series is a mask of True, False values. You need to cast the series to bool using series.any() or series.all().

Upvotes: 0

SeaBean
SeaBean

Reputation: 23207

Each condition you code evaluates into a series of multiple boolean values. The combined result of the 3 conditions also become a boolean series. Python if statement cannot handle such Pandas series with evaluating each element in the series and feed to the statement following it one by one. Hence, the error ValueError: The truth value of a Series is ambiguous.

To solve the problem, you have to code it using Pandas syntax, like the following:

mask = (merge['check'] == 1) & (merge['sqft'] > 0) & (merge['sqm'] == 0)
merge.loc[mask, 'checksqm'] = merge['sqft']/10.7639

or, combine in one statement, as follows:

merge.loc[(merge['check'] == 1) & (merge['sqft'] > 0) & (merge['sqm'] == 0), 'checksqm'] = merge['sqft']/10.7639

In this way, Pandas can evaluate the boolean series and work on the rows corresponding to True values of the combined 3 conditions and process each row one by one taking corresponding values from each row for processing. This kind of vectorized operation under the scene is not supported by ordinary Python statement such as if statement.

Upvotes: 1

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