In [1]:

    

import pandas as pd


    

In [2]:

    

df = pd.read_csv('data/src/sample_pandas_normal_nan.csv')
print(df)


    

    




      name   age state  point  other
0    Alice  24.0    NY    NaN    NaN
1      NaN   NaN   NaN    NaN    NaN
2  Charlie   NaN    CA    NaN    NaN
3     Dave  68.0    TX   70.0    NaN
4    Ellen   NaN    CA   88.0    NaN
5    Frank  30.0   NaN    NaN    NaN

In [3]:

    

print(df.isnull())


    

    




    name    age  state  point  other
0  False  False  False   True   True
1   True   True   True   True   True
2  False   True  False   True   True
3  False  False  False  False   True
4  False   True  False  False   True
5  False  False   True   True   True

In [4]:

    

print(df.isnull().all())


    

    




name     False
age      False
state    False
point    False
other     True
dtype: bool

In [5]:

    

print(df.isnull().all(axis=1))


    

    




0    False
1     True
2    False
3    False
4    False
5    False
dtype: bool

In [6]:

    

print(df.isnull().any())


    

    




name     True
age      True
state    True
point    True
other    True
dtype: bool

In [7]:

    

print(df.isnull().any(axis=1))


    

    




0    True
1    True
2    True
3    True
4    True
5    True
dtype: bool

In [8]:

    

print(df.isnull().sum())


    

    




name     1
age      3
state    2
point    4
other    6
dtype: int64

In [9]:

    

print(df.isnull().sum(axis=1))


    

    




0    2
1    5
2    3
3    1
4    2
5    3
dtype: int64

In [10]:

    

print(df.count())


    

    




name     5
age      3
state    4
point    2
other    0
dtype: int64

In [11]:

    

print(df.count(axis=1))


    

    




0    3
1    0
2    2
3    4
4    3
5    2
dtype: int64

In [12]:

    

print(df.isnull().values)


    

    




[[False False False  True  True]
 [ True  True  True  True  True]
 [False  True False  True  True]
 [False False False False  True]
 [False  True False False  True]
 [False False  True  True  True]]

In [13]:

    

print(type(df.isnull().values))


    

    




<class 'numpy.ndarray'>

In [14]:

    

print(df.isnull().values.sum())


    

    




16

In [15]:

    

print(df.count().sum())


    

    




14

In [16]:

    

print(df.isnull().values.sum() != 0)


    

    




True

In [17]:

    

print(df.size)


    

    




30

In [18]:

    

print(df.isnull().values.sum() == df.size)


    

    




False

In [19]:

    

s = df['state']
print(s)


    

    




0     NY
1    NaN
2     CA
3     TX
4     CA
5    NaN
Name: state, dtype: object

In [20]:

    

print(s.isnull())


    

    




0    False
1     True
2    False
3    False
4    False
5     True
Name: state, dtype: bool

In [21]:

    

print(s.isnull().any())


    

    




True

In [22]:

    

print(s.isnull().all())


    

    




False

In [23]:

    

print(s.isnull().sum())


    

    




2

In [24]:

    

print(s.count())


    

    




4