#!/usr/bin/env python
# coding: utf-8

# # Demonstration of some properties related to `NaN`
# 
# * `np.nan != np.nan`, `math.nan != math`.
# * use `np.isnan` or `math.nan`

# In[1]:


import math
import numpy as np


# In[2]:


mt_nan = math.nan


# In[3]:


np_nan = np.nan


# In[4]:


np.isnan(mt_nan)


# In[5]:


np.isnan(np_nan)


# In[6]:


math.isnan(mt_nan)


# In[7]:


math.isnan(np_nan)


# In[8]:


math.isnan == np.isnan


# In[9]:


np_nan == np_nan


# In[10]:


mt_nan == mt_nan


# ## behavior of `pd.DataFrame` wst. `None`

# In[11]:


import pandas as pd


# `None` is converted to `NaN` when the column type is `float`

# In[12]:


df = pd.DataFrame([{'a':1, 'b': None}, {'a': 2, 'b': 1.4}])
display(df)


# In[13]:


display(df.dtypes)


# In[14]:


assert not (df['b'].values[0] is None)


# In[15]:


assert np.isnan(df['b'].values[0])


# `None` is unchanged when the column type is `object`

# In[16]:


df = pd.DataFrame([{'a':1, 'b': None}, {'a': 2, 'b': 's'}])
display(df)
display(df.dtypes)


# In[17]:


assert df['b'].values[0] is None


# In[18]:


import unittest


# np.isnan isn't applicable to `None` type

# In[19]:


try:
    np.isnan(df['b'].values[0])
except TypeError as err:
    print('✓ TypeError is raised!')
    print(err)


# try another case of `object column`

# In[20]:


df = pd.DataFrame([{'a':1, 'b': None}, {'a': 2, 'b': [1, 2]}])
display(df)


# In[21]:


display(df.dtypes)


# In[22]:


assert df['b'].values[0] is None