In [1]:

    

import pandas as pd


    

In [2]:

    

df = pd.read_csv('data/src/sample_pandas_normal.csv')
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 [3]:

    

df = df.append({'name': 'Dave', 'age': 68, 'state': 'TX', 'point': 70}, ignore_index=True)
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
6     Dave   68    TX     70

In [4]:

    

print(df.duplicated())


    

    




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

In [5]:

    

print(df[df.duplicated()])


    

    




   name  age state  point
6  Dave   68    TX     70

In [6]:

    

print(df.duplicated(keep='last'))


    

    




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

In [7]:

    

print(df.duplicated(keep=False))


    

    




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

In [8]:

    

print(df.duplicated(subset='state'))


    

    




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

In [9]:

    

print(df.duplicated(subset=['state', 'point']))


    

    




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

In [10]:

    

print(df.duplicated().value_counts())


    

    




False    6
True     1
dtype: int64

In [11]:

    

print(df.duplicated().value_counts()[True])


    

    




1

In [12]:

    

print(df.duplicated(keep=False).value_counts()[True])


    

    




2

In [13]:

    

print(df[~df.duplicated()])


    

    




      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 [14]:

    

print(df.drop_duplicates())


    

    




      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 [15]:

    

print(df.drop_duplicates(keep=False))


    

    




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

In [16]:

    

print(df.drop_duplicates(subset='state'))


    

    




    name  age state  point
0  Alice   24    NY     64
1    Bob   42    CA     92
3   Dave   68    TX     70

In [17]:

    

df.drop_duplicates(subset='state', keep='last', inplace=True)
print(df)


    

    




    name  age state  point
4  Ellen   24    CA     88
5  Frank   30    NY     57
6   Dave   68    TX     70

In [18]:

    

df = pd.read_csv('data/src/sample_pandas_normal.csv')
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 [19]:

    

print(df.groupby('state').mean())


    

    




        age      point
state                 
CA     28.0  83.333333
NY     27.0  60.500000
TX     68.0  70.000000

In [20]:

    

print(df.groupby('state').agg(
    {'name': lambda x: ','.join(x),
     'age': 'mean',
     'point': 'mean'}))


    

    




                    name  age      point
state                                   
CA     Bob,Charlie,Ellen   28  83.333333
NY           Alice,Frank   27  60.500000
TX                  Dave   68  70.000000

In [21]:

    

print(df.groupby('state').agg(
    {'name': list,
     'age': 'mean',
     'point': 'mean'}))


    

    




                        name  age      point
state                                       
CA     [Bob, Charlie, Ellen]   28  83.333333
NY            [Alice, Frank]   27  60.500000
TX                    [Dave]   68  70.000000