I kept getting ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
when trying boolean tests with pandas. Not understanding what it said, I decided to try to figure it out.
However, I am totally confused at this point.
Here I create a dataframe of two variables, with a single data point shared between them (3):
In [75]:
import pandas as pd
df = pd.DataFrame()
df['x'] = [1,2,3]
df['y'] = [3,4,5]
Now I try all(is x less than y), which I translate to "are all the values of x less than y", and I get an answer that doesn't make sense.
In [79]:
if all(df['x'] < df['y']):
print('True')
else:
print('False')
True
Next I try any(is x less than y), which I translate to "is any value of x less than y", and I get another answer that doesn't make sense.
In [77]:
if any(df['x'] < df['y']):
print('True')
else:
print('False')
False
In short: what does any() and all() actually do?
Pandas DataFrame bool() MethodThe bool() method returns a boolean value, True or False, reflecting the value of the DataFrame. This method will only work if the DataFrame has only 1 value, and that value must be either True or False, otherwise the bool() method will return an error.
tolist()[source] Return a list of the values. These are each a scalar type, which is a Python scalar (for str, int, float) or a pandas scalar (for Timestamp/Timedelta/Interval/Period) Returns list.
To select all columns except one column in Pandas DataFrame, we can use df. loc[:, df. columns != <column name>].
Pandas suggests you to use Series methods any()
and all()
, not Python in-build functions.
I don't quite understand the source of the strange output you have (I get True in both cases in Python 2.7 and Pandas 0.17.0). But try the following, it should work. This uses Series.any()
and Series.all()
methods.
import pandas as pd
df = pd.DataFrame()
df['x'] = [1,2,3]
df['y'] = [3,4,5]
print (df['x'] < df['y']).all() # more pythonic way of
print (df['x'] < df['y']).any() # doing the same thing
This should print:
True
True
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