What is the difference between NaN and None?
Question:
I am reading two columns of a csv file using pandas readcsv() and then assigning the values to a dictionary. The columns contain strings of numbers and letters. Occasionally there are cases where a cell is empty. In my opinion, the value read to that dictionary entry should be None but instead nan is assigned. Surely None is more descriptive of an empty cell as it has a null value, whereas nan just says that the value read is not a number.
Is my understanding correct, what IS the difference between None and nan? Why is nan assigned instead of None?
Also, my dictionary check for any empty cells has been using numpy.isnan():
for k, v in my_dict.iteritems():
if np.isnan(v):
But this gives me an error saying that I cannot use this check for v. I guess it is because an integer or float variable, not a string is meant to be used. If this is true, how can I check v for an “empty cell”/nan case?
Answers:
The function isnan() checks to see if something is “Not A Number” and will return whether or not a variable is a number, for example isnan(2) would return false
The conditional myVar is not None returns whether or not the variable is defined
Your numpy array uses isnan() because it is intended to be an array of numbers and it initializes all elements of the array to NaN these elements are considered “empty”
NaN can be used as a numerical value on mathematical operations, while None cannot (or at least shouldn’t).
NaN is a numeric value, as defined in IEEE 754 floating-point standard.
None is an internal Python type (NoneType) and would be more like “inexistent” or “empty” than “numerically invalid” in this context.
The main “symptom” of that is that, if you perform, say, an average or a sum on an array containing NaN, even a single one, you get NaN as a result…
In the other hand, you cannot perform mathematical operations using None as operand.
So, depending on the case, you could use None as a way to tell your algorithm not to consider invalid or inexistent values on computations. That would mean the algorithm should test each value to see if it is None.
Numpy has some functions to avoid NaN values to contaminate your results, such as nansum and nan_to_num for example.
NaN is used as a placeholder for missing data consistently in pandas, consistency is good. I usually read/translate NaN as “missing”. Also see the ‘working with missing data’ section in the docs.
Wes writes in the docs ‘choice of NA-representation’:
After years of production use [NaN] has proven, at least in my opinion, to be the best decision given the state of affairs in NumPy and Python in general. The special value NaN (Not-A-Number) is used everywhere as the NA value, and there are API functions isnull and notnull which can be used across the dtypes to detect NA values.
…
Thus, I have chosen the Pythonic “practicality beats purity” approach and traded integer NA capability for a much simpler approach of using a special value in float and object arrays to denote NA, and promoting integer arrays to floating when NAs must be introduced.
Note: the “gotcha” that integer Series containing missing data are upcast to floats.
In my opinion the main reason to use NaN (over None) is that it can be stored with numpy’s float64 dtype, rather than the less efficient object dtype, see NA type promotions.
# without forcing dtype it changes None to NaN!
s_bad = pd.Series([1, None], dtype=object)
s_good = pd.Series([1, np.nan])
In [13]: s_bad.dtype
Out[13]: dtype('O')
In [14]: s_good.dtype
Out[14]: dtype('float64')
Jeff comments (below) on this:
np.nan allows for vectorized operations; its a float value, while None, by definition, forces object type, which basically disables all efficiency in numpy.
So repeat 3 times fast: object==bad, float==good
Saying that, many operations may still work just as well with None vs NaN (but perhaps are not supported i.e. they may sometimes give surprising results):
In [15]: s_bad.sum()
Out[15]: 1
In [16]: s_good.sum()
Out[16]: 1.0
To answer the second question:
You should be using pd.isnull and pd.notnull to test for missing data (NaN).
Below are the differences:
nan belongs to the class floatNone belongs to the class NoneType
I found the below article very helpful:
https://medium.com/analytics-vidhya/dealing-with-missing-values-nan-and-none-in-python-6fc9b8fb4f31
I am reading two columns of a csv file using pandas readcsv() and then assigning the values to a dictionary. The columns contain strings of numbers and letters. Occasionally there are cases where a cell is empty. In my opinion, the value read to that dictionary entry should be None but instead nan is assigned. Surely None is more descriptive of an empty cell as it has a null value, whereas nan just says that the value read is not a number.
Is my understanding correct, what IS the difference between None and nan? Why is nan assigned instead of None?
Also, my dictionary check for any empty cells has been using numpy.isnan():
for k, v in my_dict.iteritems():
if np.isnan(v):
But this gives me an error saying that I cannot use this check for v. I guess it is because an integer or float variable, not a string is meant to be used. If this is true, how can I check v for an “empty cell”/nan case?
The function isnan() checks to see if something is “Not A Number” and will return whether or not a variable is a number, for example isnan(2) would return false
The conditional myVar is not None returns whether or not the variable is defined
Your numpy array uses isnan() because it is intended to be an array of numbers and it initializes all elements of the array to NaN these elements are considered “empty”
NaN can be used as a numerical value on mathematical operations, while None cannot (or at least shouldn’t).
NaN is a numeric value, as defined in IEEE 754 floating-point standard.
None is an internal Python type (NoneType) and would be more like “inexistent” or “empty” than “numerically invalid” in this context.
The main “symptom” of that is that, if you perform, say, an average or a sum on an array containing NaN, even a single one, you get NaN as a result…
In the other hand, you cannot perform mathematical operations using None as operand.
So, depending on the case, you could use None as a way to tell your algorithm not to consider invalid or inexistent values on computations. That would mean the algorithm should test each value to see if it is None.
Numpy has some functions to avoid NaN values to contaminate your results, such as nansum and nan_to_num for example.
NaN is used as a placeholder for missing data consistently in pandas, consistency is good. I usually read/translate NaN as “missing”. Also see the ‘working with missing data’ section in the docs.
Wes writes in the docs ‘choice of NA-representation’:
After years of production use [NaN] has proven, at least in my opinion, to be the best decision given the state of affairs in NumPy and Python in general. The special value NaN (Not-A-Number) is used everywhere as the NA value, and there are API functions
isnullandnotnullwhich can be used across the dtypes to detect NA values.
…
Thus, I have chosen the Pythonic “practicality beats purity” approach and traded integer NA capability for a much simpler approach of using a special value in float and object arrays to denote NA, and promoting integer arrays to floating when NAs must be introduced.
Note: the “gotcha” that integer Series containing missing data are upcast to floats.
In my opinion the main reason to use NaN (over None) is that it can be stored with numpy’s float64 dtype, rather than the less efficient object dtype, see NA type promotions.
# without forcing dtype it changes None to NaN!
s_bad = pd.Series([1, None], dtype=object)
s_good = pd.Series([1, np.nan])
In [13]: s_bad.dtype
Out[13]: dtype('O')
In [14]: s_good.dtype
Out[14]: dtype('float64')
Jeff comments (below) on this:
np.nanallows for vectorized operations; its a float value, whileNone, by definition, forces object type, which basically disables all efficiency in numpy.So repeat 3 times fast: object==bad, float==good
Saying that, many operations may still work just as well with None vs NaN (but perhaps are not supported i.e. they may sometimes give surprising results):
In [15]: s_bad.sum()
Out[15]: 1
In [16]: s_good.sum()
Out[16]: 1.0
To answer the second question:
You should be using pd.isnull and pd.notnull to test for missing data (NaN).
Below are the differences:
nanbelongs to the classfloatNonebelongs to the classNoneType
I found the below article very helpful:
https://medium.com/analytics-vidhya/dealing-with-missing-values-nan-and-none-in-python-6fc9b8fb4f31