In [1]:

    

import pandas as pd


    

In [2]:

    

df = pd.DataFrame({'col_1': range(11),
                   'col_2': [i ** 2 for i in range(11)],
                   'col_3': list('abcdefghijk')})


    

In [3]:

    

print(df)


    

    




    col_1  col_2 col_3
0       0      0     a
1       1      1     b
2       2      4     c
3       3      9     d
4       4     16     e
5       5     25     f
6       6     36     g
7       7     49     h
8       8     64     i
9       9     81     j
10     10    100     k

In [4]:

    

print(df.quantile())


    

    




col_1     5.0
col_2    25.0
Name: 0.5, dtype: float64

In [5]:

    

print(type(df.quantile()))


    

    




<class 'pandas.core.series.Series'>

In [6]:

    

print(df['col_1'].quantile())


    

    




5.0

In [7]:

    

print(type(df['col_1'].quantile()))


    

    




<class 'float'>

In [8]:

    

print(df.quantile(0.2))


    

    




col_1    2.0
col_2    4.0
Name: 0.2, dtype: float64

In [9]:

    

print(df.quantile([0, 0.25, 0.5, 0.75, 1.0]))


    

    




      col_1  col_2
0.00    0.0    0.0
0.25    2.5    6.5
0.50    5.0   25.0
0.75    7.5   56.5
1.00   10.0  100.0

In [10]:

    

print(type(df.quantile([0, 0.25, 0.5, 0.75, 1.0])))


    

    




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

In [11]:

    

print(df['col_1'].quantile([0, 0.25, 0.5, 0.75, 1.0]))


    

    




0.00     0.0
0.25     2.5
0.50     5.0
0.75     7.5
1.00    10.0
Name: col_1, dtype: float64

In [12]:

    

print(type(df['col_1'].quantile([0, 0.25, 0.5, 0.75, 1.0])))


    

    




<class 'pandas.core.series.Series'>

In [13]:

    

print(df.quantile(0.21))


    

    




col_1    2.1
col_2    4.5
Name: 0.21, dtype: float64

In [14]:

    

print(df.quantile(0.21, interpolation='linear'))


    

    




col_1    2.1
col_2    4.5
Name: 0.21, dtype: float64

In [15]:

    

print(df.quantile(0.21, interpolation='lower'))


    

    




col_1    2
col_2    4
Name: 0.21, dtype: int64

In [16]:

    

print(df.quantile(0.21, interpolation='higher'))


    

    




col_1    3
col_2    9
Name: 0.21, dtype: int64

In [17]:

    

print(df.quantile(0.21, interpolation='nearest'))


    

    




col_1    2
col_2    4
Name: 0.21, dtype: int64

In [18]:

    

print(df.quantile(0.21, interpolation='midpoint'))


    

    




col_1    2.5
col_2    6.5
Name: 0.21, dtype: float64

In [19]:

    

print(df.quantile(0.2))


    

    




col_1    2.0
col_2    4.0
Name: 0.2, dtype: float64

In [20]:

    

print(df.quantile(0.2, interpolation='lower'))


    

    




col_1    2
col_2    4
Name: 0.2, dtype: int64

In [21]:

    

print(df.quantile(axis=1))


    

    




0      0.0
1      1.0
2      3.0
3      6.0
4     10.0
5     15.0
6     21.0
7     28.0
8     36.0
9     45.0
10    55.0
Name: 0.5, dtype: float64

In [22]:

    

# print(df.quantile(numeric_only=False))
# TypeError: can't multiply sequence by non-int of type 'float'


    

In [23]:

    

print(df.quantile(numeric_only=False, interpolation='lower'))


    

    




col_1     5
col_2    25
col_3     f
Name: 0.5, dtype: object

In [24]:

    

print(df.quantile(0.25, numeric_only=False, interpolation='lower'))


    

    




col_1    2
col_2    4
col_3    c
Name: 0.25, dtype: object

In [25]:

    

print(df.quantile(0.25, numeric_only=False, interpolation='higher'))


    

    




col_1    3
col_2    9
col_3    d
Name: 0.25, dtype: object

In [26]:

    

df['col_3'] = pd.date_range('2019-01-01', '2019-01-11')


    

In [27]:

    

print(df)


    

    




    col_1  col_2      col_3
0       0      0 2019-01-01
1       1      1 2019-01-02
2       2      4 2019-01-03
3       3      9 2019-01-04
4       4     16 2019-01-05
5       5     25 2019-01-06
6       6     36 2019-01-07
7       7     49 2019-01-08
8       8     64 2019-01-09
9       9     81 2019-01-10
10     10    100 2019-01-11

In [28]:

    

print(df.dtypes)


    

    




col_1             int64
col_2             int64
col_3    datetime64[ns]
dtype: object

In [29]:

    

print(df.quantile())


    

    




col_1     5.0
col_2    25.0
Name: 0.5, dtype: float64

In [30]:

    

print(df.quantile(numeric_only=False))


    

    




col_1                      5
col_2                     25
col_3    2019-01-06 00:00:00
Name: 0.5, dtype: object

In [31]:

    

print(df.quantile(0.25, numeric_only=False))


    

    




col_1                    2.5
col_2                    6.5
col_3    2019-01-03 12:00:00
Name: 0.25, dtype: object

In [32]:

    

print(df.quantile(0.25, numeric_only=False, interpolation='lower'))


    

    




col_1                      2
col_2                      4
col_3    2019-01-03 00:00:00
Name: 0.25, dtype: object