In [1]:

    

import pandas as pd
import seaborn as sns
import numpy as np


    

In [2]:

    

df = sns.load_dataset('iris')
print(df.shape)


    

    




(150, 5)

In [3]:

    

print(df.head(5))


    

    




   sepal_length  sepal_width  petal_length  petal_width species
0           5.1          3.5           1.4          0.2  setosa
1           4.9          3.0           1.4          0.2  setosa
2           4.7          3.2           1.3          0.2  setosa
3           4.6          3.1           1.5          0.2  setosa
4           5.0          3.6           1.4          0.2  setosa

In [4]:

    

df.columns = ['sl', 'sw', 'pl', 'pw', 'species']
print(df.head(5))


    

    




    sl   sw   pl   pw species
0  5.1  3.5  1.4  0.2  setosa
1  4.9  3.0  1.4  0.2  setosa
2  4.7  3.2  1.3  0.2  setosa
3  4.6  3.1  1.5  0.2  setosa
4  5.0  3.6  1.4  0.2  setosa

In [5]:

    

grouped = df.groupby('species')
print(grouped)


    

    




<pandas.core.groupby.generic.DataFrameGroupBy object at 0x115de1650>

In [6]:

    

print(type(grouped))


    

    




<class 'pandas.core.groupby.generic.DataFrameGroupBy'>

In [7]:

    

print(grouped.size())


    

    




species
setosa        50
versicolor    50
virginica     50
dtype: int64

In [8]:

    

print(grouped.mean())


    

    




               sl     sw     pl     pw
species                               
setosa      5.006  3.428  1.462  0.246
versicolor  5.936  2.770  4.260  1.326
virginica   6.588  2.974  5.552  2.026

In [9]:

    

print(grouped.min())


    

    




             sl   sw   pl   pw
species                       
setosa      4.3  2.3  1.0  0.1
versicolor  4.9  2.0  3.0  1.0
virginica   4.9  2.2  4.5  1.4

In [10]:

    

print(grouped.max())


    

    




             sl   sw   pl   pw
species                       
setosa      5.8  4.4  1.9  0.6
versicolor  7.0  3.4  5.1  1.8
virginica   7.9  3.8  6.9  2.5

In [11]:

    

print(grouped.sum())


    

    




               sl     sw     pl     pw
species                               
setosa      250.3  171.4   73.1   12.3
versicolor  296.8  138.5  213.0   66.3
virginica   329.4  148.7  277.6  101.3

In [12]:

    

print(type(grouped.mean()))


    

    




<class 'pandas.core.frame.DataFrame'>

In [13]:

    

print(grouped.agg('mean'))


    

    




               sl     sw     pl     pw
species                               
setosa      5.006  3.428  1.462  0.246
versicolor  5.936  2.770  4.260  1.326
virginica   6.588  2.974  5.552  2.026

In [14]:

    

print(grouped.agg(max))


    

    




             sl   sw   pl   pw
species                       
setosa      5.8  4.4  1.9  0.6
versicolor  7.0  3.4  5.1  1.8
virginica   7.9  3.8  6.9  2.5

In [15]:

    

print(grouped.agg(np.min))


    

    




             sl   sw   pl   pw
species                       
setosa      4.3  2.3  1.0  0.1
versicolor  4.9  2.0  3.0  1.0
virginica   4.9  2.2  4.5  1.4

In [16]:

    

print(grouped.agg(lambda x: max(x) - min(x)))


    

    




             sl   sw   pl   pw
species                       
setosa      1.5  2.1  0.9  0.5
versicolor  2.1  1.4  2.1  0.8
virginica   3.0  1.6  2.4  1.1

In [17]:

    

print(grouped.agg(lambda x: type(x))['sl'])


    

    




species
setosa        <class 'pandas.core.series.Series'>
versicolor    <class 'pandas.core.series.Series'>
virginica     <class 'pandas.core.series.Series'>
Name: sl, dtype: object

In [18]:

    

# print(grouped.agg(lambda x: x + 1))
# Exception: Must produce aggregated value


    

In [19]:

    

def my_func(x):
    return max(x) - min(x)


    

In [20]:

    

print(grouped.agg(my_func))


    

    




             sl   sw   pl   pw
species                       
setosa      1.5  2.1  0.9  0.5
versicolor  2.1  1.4  2.1  0.8
virginica   3.0  1.6  2.4  1.1

In [21]:

    

print(grouped.agg(['mean', max, np.min]))


    

    




               sl               sw               pl               pw          
             mean  max amin   mean  max amin   mean  max amin   mean  max amin
species                                                                       
setosa      5.006  5.8  4.3  3.428  4.4  2.3  1.462  1.9  1.0  0.246  0.6  0.1
versicolor  5.936  7.0  4.9  2.770  3.4  2.0  4.260  5.1  3.0  1.326  1.8  1.0
virginica   6.588  7.9  4.9  2.974  3.8  2.2  5.552  6.9  4.5  2.026  2.5  1.4

In [22]:

    

print(grouped.agg({'sl': 'mean', 'sw': max, 'pl': np.min, 'pw': my_func}))


    

    




               sl   sw   pl   pw
species                         
setosa      5.006  4.4  1.0  0.5
versicolor  5.936  3.4  3.0  0.8
virginica   6.588  3.8  4.5  1.1

In [23]:

    

print(grouped.describe()['sl'])


    

    




            count   mean       std  min    25%  50%  75%  max
species                                                      
setosa       50.0  5.006  0.352490  4.3  4.800  5.0  5.2  5.8
versicolor   50.0  5.936  0.516171  4.9  5.600  5.9  6.3  7.0
virginica    50.0  6.588  0.635880  4.9  6.225  6.5  6.9  7.9

In [24]:

    

print(type(grouped.max()))


    

    




<class 'pandas.core.frame.DataFrame'>

In [25]:

    

%matplotlib agg


    

In [26]:

    

ax = grouped.max().plot.bar(rot=0)
fig = ax.get_figure()
fig.savefig('data/dst/iris_pandas_groupby_max.jpg')