In [1]:

    

import pandas as pd


    

In [2]:

    

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


    

In [3]:

    

print(df)


    

    




      name  age state  point
0    Alice   24    NY     64
1      Bob   42    CA     92
2  Charlie   18    CA     70
3     Dave   68    TX     70
4    Ellen   24    CA     88
5    Frank   30    NY     57

In [4]:

    

df_bool = (df == 'CA')
print(df_bool)


    

    




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

In [5]:

    

print(df_bool.sum())


    

    




name     0
age      0
state    3
point    0
dtype: int64

In [6]:

    

print(df_bool.sum(axis=1))


    

    




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

In [7]:

    

print(df_bool.values)


    

    




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

In [8]:

    

print(type(df_bool.values))


    

    




<class 'numpy.ndarray'>

In [9]:

    

print(df_bool.values.sum())


    

    




3

In [10]:

    

s_bool = df['age'] < 25
print(s_bool)


    

    




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

In [11]:

    

print(s_bool.sum())


    

    




3

In [12]:

    

df_bool_multi = ((df == 'CA') | (df == 70))
print(df_bool_multi)


    

    




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

In [13]:

    

print(df_bool_multi.sum())


    

    




name     0
age      0
state    3
point    2
dtype: int64

In [14]:

    

print(df_bool_multi.sum(axis=1))


    

    




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

In [15]:

    

print(df_bool_multi.values.sum())


    

    




5

In [16]:

    

df_bool_multi_and = ((df['state'] == 'CA') & (df['age'] < 30))
print(df_bool_multi_and)


    

    




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

In [17]:

    

print(df_bool_multi_and.sum())


    

    




2

In [18]:

    

df_bool_multi_or = ((df['state'] == 'CA') | (df['age'] < 30))
print(df_bool_multi_or)


    

    




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

In [19]:

    

print(df_bool_multi_or.sum())


    

    




4

In [20]:

    

df_bool_not = ~(df == 'CA')
print(df_bool_not)


    

    




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

In [21]:

    

print(df_bool_not.sum())


    

    




name     6
age      6
state    3
point    6
dtype: int64

In [22]:

    

print(df_bool_not.sum(axis=1))


    

    




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

In [23]:

    

print(df_bool_not.values.sum())


    

    




21

In [24]:

    

df_num = df[['age', 'point']]
print(df_num)


    

    




   age  point
0   24     64
1   42     92
2   18     70
3   68     70
4   24     88
5   30     57

In [25]:

    

print((df_num <= 70).sum())


    

    




age      6
point    4
dtype: int64

In [26]:

    

print(((df['age'] > 20) & (df['age'] < 40)).sum())


    

    




3

In [27]:

    

print((df_num % 2 == 1).sum())


    

    




age      0
point    1
dtype: int64

In [28]:

    

df_str = df[['name', 'state']]
print(df_str)


    

    




      name state
0    Alice    NY
1      Bob    CA
2  Charlie    CA
3     Dave    TX
4    Ellen    CA
5    Frank    NY

In [29]:

    

print((df_str == 'NY').sum())


    

    




name     0
state    2
dtype: int64

In [30]:

    

print(df_str['name'].str.endswith('e'))


    

    




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

In [31]:

    

print(df_str['name'].str.endswith('e').sum())


    

    




3

In [32]:

    

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


    

In [33]:

    

print(df.head())


    

    




   PassengerId  Survived  Pclass  \
0            1         0       3   
1            2         1       1   
2            3         1       3   
3            4         1       1   
4            5         0       3   

                                                Name     Sex   Age  SibSp  \
0                            Braund, Mr. Owen Harris    male  22.0      1   
1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   
2                             Heikkinen, Miss. Laina  female  26.0      0   
3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   
4                           Allen, Mr. William Henry    male  35.0      0   

   Parch            Ticket     Fare Cabin Embarked  
0      0         A/5 21171   7.2500   NaN        S  
1      0          PC 17599  71.2833   C85        C  
2      0  STON/O2. 3101282   7.9250   NaN        S  
3      0            113803  53.1000  C123        S  
4      0            373450   8.0500   NaN        S  

In [34]:

    

df.info()


    

    




<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
PassengerId    891 non-null int64
Survived       891 non-null int64
Pclass         891 non-null int64
Name           891 non-null object
Sex            891 non-null object
Age            714 non-null float64
SibSp          891 non-null int64
Parch          891 non-null int64
Ticket         891 non-null object
Fare           891 non-null float64
Cabin          204 non-null object
Embarked       889 non-null object
dtypes: float64(2), int64(5), object(5)
memory usage: 83.6+ KB

In [35]:

    

print(df.isnull().head())


    

    




   PassengerId  Survived  Pclass   Name    Sex    Age  SibSp  Parch  Ticket  \
0        False     False   False  False  False  False  False  False   False   
1        False     False   False  False  False  False  False  False   False   
2        False     False   False  False  False  False  False  False   False   
3        False     False   False  False  False  False  False  False   False   
4        False     False   False  False  False  False  False  False   False   

    Fare  Cabin  Embarked  
0  False   True     False  
1  False  False     False  
2  False   True     False  
3  False  False     False  
4  False   True     False  

In [36]:

    

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


    

    




PassengerId      0
Survived         0
Pclass           0
Name             0
Sex              0
Age            177
SibSp            0
Parch            0
Ticket           0
Fare             0
Cabin          687
Embarked         2
dtype: int64

In [37]:

    

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


    

    




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

In [38]:

    

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


    

    




866

In [39]:

    

print(df.count())


    

    




PassengerId    891
Survived       891
Pclass         891
Name           891
Sex            891
Age            714
SibSp          891
Parch          891
Ticket         891
Fare           891
Cabin          204
Embarked       889
dtype: int64

In [40]:

    

print(df.count(axis=1).head())


    

    




0    11
1    12
2    11
3    12
4    11
dtype: int64

In [41]:

    

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


    

    




9826