Error: The truth value of a Series is ambiguous - Python pandas
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.
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])
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