Error: The truth value of a Series is ambiguous – Python pandas

I know this question has been asked before, however, when I am trying to do an if statement and I am getting an error. I looked at this link , but did not help much in my case. My dfs is a list of DataFrames.

I am trying the following,

for i in dfs:
    if (i['var1'] < 3.000):
       print(i)

Gives the following error:

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

AND I tried the following and getting the same error.

for i,j in enumerate(dfs):
    if (j['var1'] < 3.000):
       print(i)

My var1 data type is float32. I am not using any other logical operators and & or |. In the above link it seemed to be because of using logical operators. Why do I get ValueError?

Here is Solutions:

We have many solutions to this problem, But we recommend you to use the first solution because it is tested & true solution that will 100% work for you.

Solution 1

Here is a small demo, which shows why this is happenning:

In [131]: df = pd.DataFrame(np.random.randint(0,20,(5,2)), columns=list('AB'))

In [132]: df
Out[132]:
    A   B
0   3  11
1   0  16
2  16   1
3   2  11
4  18  15

In [133]: res = df['A'] > 10

In [134]: res
Out[134]:
0    False
1    False
2     True
3    False
4     True
Name: A, dtype: bool

when we try to check whether such Series is True – Pandas doesn’t know what to do:

In [135]: if res:
     ...:     print(df)
     ...:
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
...
skipped
...
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Workarounds:

we can decide how to treat Series of boolean values – for example if should return True if all values are True:

In [136]: res.all()
Out[136]: False

or when at least one value is True:

In [137]: res.any()
Out[137]: True

In [138]: if res.any():
     ...:     print(df)
     ...:
    A   B
0   3  11
1   0  16
2  16   1
3   2  11
4  18  15

Solution 2

Currently, you’re selecting the entire series for comparison. To get an individual value from the series, you’ll want to use something along the lines of:

for i in dfs:
if (i['var1'].iloc[0] < 3.000):
   print(i)

To compare each of the individual elements you can use series.iteritems (documentation is sparse on this one) like so:

for i in dfs:
    for _, v in i['var1'].iteritems():
        if v < 3.000:
            print(v)

The better solution here for most cases is to select a subset of the dataframe to use for whatever you need, like so:

for i in dfs:
    subset = i[i['var1'] < 3.000]
    # do something with the subset

Performance in pandas is much faster on large dataframes when using series operations instead of iterating over individual values. For more detail, you can check out the pandas documentation on selection.

Solution 3

the comparison returns a range of values, you need to limit it either by any() or all(), for example,

     if((df[col] == ' this is any string or list').any()):
       return(df.loc[df[col] == temp].index.values.astype(int)[0])

Note: Use and implement solution 1 because this method fully tested our system.
Thank you 🙂

All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0

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