Question: This is somewhat of a broad topic, but I will try to pare it to some specific questions. In starting to answer questions on SO, I have found myself sometimes running into a silly error like this when making toy data: In[0]: import pandas as pd df = pd.DataFrame({"values":[1,2,3,4,5,6,7,8,9]}) df[df < 5] = np.nan Out[0]: NameError: name 'np' is not defined  I'm so used to automatically importing numpy with pandas that this doesn't usually occur in real code. However, it did make me wonder why pandas doesn't have it's own value/object for representing null values. I only recently realized that you could just use the Python None instead for a similar situation: import pandas as pd df = pd.DataFrame({"values":[1,2,3,4,5,6,7,8,9]}) df[df < 5] = None  Which works as expected and doesn't produce an error. But I have felt like the convention on SO that I have seen is to use np.nan, and that people are usually referring to np.nan when discussing null values (this is perhaps why I hadn't realized None can be used, but maybe that was my own idiosyncrasy). Briefly looking into this, I have seen now that pandas does have a pandas.NA value since 1.0.0, but I have never seen anyone use it in a post: In[0]: import pandas as pd import numpy as np df = pd.DataFrame({'values':np.random.rand(20,)}) df['above'] = df['values'] df['below'] = df['values'] df['above'][df['values']>0.7] = np.nan df['below'][df['values']<0.3] = pd.NA df['names'] = ['a','b','c','a','b','c','a','b','c','a']*2 df.loc[df['names']=='a','names'] = pd.NA df.loc[df['names']=='b','names'] = np.nan df.loc[df['names']=='c','names'] = None df Out[0]: values above below names 0 0.323531 0.323531 0.323531 <NA> 1 0.690383 0.690383 0.690383 NaN 2 0.692371 0.692371 0.692371 None 3 0.259712 0.259712 NaN <NA> 4 0.473505 0.473505 0.473505 NaN 5 0.907751 NaN 0.907751 None 6 0.642596 0.642596 0.642596 <NA> 7 0.229420 0.229420 NaN NaN 8 0.576324 0.576324 0.576324 None 9 0.823715 NaN 0.823715 <NA> 10 0.210176 0.210176 NaN <NA> 11 0.629563 0.629563 0.629563 NaN 12 0.481969 0.481969 0.481969 None 13 0.400318 0.400318 0.400318 <NA> 14 0.582735 0.582735 0.582735 NaN 15 0.743162 NaN 0.743162 None 16 0.134903 0.134903 NaN <NA> 17 0.386366 0.386366 0.386366 NaN 18 0.313160 0.313160 0.313160 None 19 0.695956 0.695956 0.695956 <NA>  So it seems that for numerical values, the distinction between these different null values doesn't matter, but they are represented differently for strings (and perhaps for other data types?). My questions based on the above:  Is it conventional to use np.nan (rather than None) to represent null values in pandas? Why did pandas not have its own null value for most of its lifetime (until last year)? What was the motivation for adding? In cases where you can have multiple types of missing values in one Series or column, is there any difference between them? Why are they not represented identically (as with numerical data)?  I fully anticipate that I may have a flawed interpretation of things and the distinction between pandas and numpy, so please correct me.

Answer: A main dependency of pandas is numpy, in other words, pandas is built on-top of numpy. Because pandas inherits and uses many of the numpy methods, it makes sense to keep things consistent, that is, missing numeric data are represented with np.NaN. (This choice to build upon numpy has consequences for other things too. For instance date and time operations are built upon the np.timedelta64 and np.datetime64 dtypes, not the standard datetime module.)  One thing you may not have known is that numpy has always been there with pandas import pandas as pd pd.np? pd.np.nan  Though you might think this behavior could be better since you don't import numpy, this is discouraged and in the near future will be deprecated in favor of directly importing numpy  FutureWarning: The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead   Is it conventional to use np.nan (rather than None) to represent null values in pandas? If the data are numeric then yes, you should use np.NaN. None requires the dtype to be Object and with pandas you want numeric data stored in a numeric dtype. pandas will generally coerce to the proper null-type upon creation or import so that it can use the correct dtype pd.Series([1, None]) #0 1.0 #1 NaN <- None became NaN so it can have dtype: float64 #dtype: float64   Why did pandas not have its own null value for most of its lifetime (until last year)? What was the motivation for adding? pandas did not have it's own null value because it got by with np.NaN, which worked for the majority of circumstances. However with pandas it's very common to have missing data, an entire section of the documentation is devoted to this. NaN, being a float, does not fit into an integer container which means that any numeric Series with missing data is upcast to float. This can become problematic because of floating point math, and some integers cannot be represented perfectly by a floating point number. As a result, any joins or merges could fail. # Gets upcast to float pd.Series([1,2,np.NaN]) #0 1.0 #1 2.0 #2 NaN #dtype: float64 # Can safely do merges/joins/math because things are still Int pd.Series([1,2,np.NaN]).astype('Int64') #0 1 #1 2 #2 <NA> #dtype: Int64