In [1]:

    

import pandas as pd


    

In [2]:

    

df = pd.DataFrame({'col1': [50, 80, 100, 80],
                   'col2': [0.3, pd.np.nan, 0.1, pd.np.nan],
                   'col3': ['h', 'j', 'i', 'k']},
                  index=['a', 'b', 'c', 'd'])


    

In [3]:

    

print(df)


    

    




   col1  col2 col3
a    50   0.3    h
b    80   NaN    j
c   100   0.1    i
d    80   NaN    k

In [4]:

    

print(df.rank())


    

    




   col1  col2  col3
a   1.0   2.0   1.0
b   2.5   NaN   3.0
c   4.0   1.0   2.0
d   2.5   NaN   4.0

In [5]:

    

print(df.rank(axis=1))


    

    




   col1  col2
a   2.0   1.0
b   1.0   NaN
c   2.0   1.0
d   1.0   NaN

In [6]:

    

print(df.rank(numeric_only=True))


    

    




   col1  col2
a   1.0   2.0
b   2.5   NaN
c   4.0   1.0
d   2.5   NaN

In [7]:

    

# print(df.rank(axis=1, numeric_only=False))
# TypeError: '<' not supported between instances of 'str' and 'int'


    

In [8]:

    

print(df.rank(ascending=False))


    

    




   col1  col2  col3
a   4.0   1.0   4.0
b   2.5   NaN   2.0
c   1.0   2.0   3.0
d   2.5   NaN   1.0

In [9]:

    

print(df.rank(method='average'))


    

    




   col1  col2  col3
a   1.0   2.0   1.0
b   2.5   NaN   3.0
c   4.0   1.0   2.0
d   2.5   NaN   4.0

In [10]:

    

print(df.rank(method='min'))


    

    




   col1  col2  col3
a   1.0   2.0   1.0
b   2.0   NaN   3.0
c   4.0   1.0   2.0
d   2.0   NaN   4.0

In [11]:

    

print(df.rank(method='max'))


    

    




   col1  col2  col3
a   1.0   2.0   1.0
b   3.0   NaN   3.0
c   4.0   1.0   2.0
d   3.0   NaN   4.0

In [12]:

    

# print(df.rank(method='first'))
# ValueError: first not supported for non-numeric data


    

In [13]:

    

print(df.rank(method='first', numeric_only=True))


    

    




   col1  col2
a   1.0   2.0
b   2.0   NaN
c   4.0   1.0
d   3.0   NaN

In [14]:

    

print(df.rank(method='dense'))


    

    




   col1  col2  col3
a   1.0   2.0   1.0
b   2.0   NaN   3.0
c   3.0   1.0   2.0
d   2.0   NaN   4.0

In [15]:

    

print(df.rank(na_option='keep'))


    

    




   col1  col2  col3
a   1.0   2.0   1.0
b   2.5   NaN   3.0
c   4.0   1.0   2.0
d   2.5   NaN   4.0

In [16]:

    

print(df.rank(na_option='top'))


    

    




   col1  col2  col3
a   1.0   4.0   1.0
b   2.5   1.5   3.0
c   4.0   3.0   2.0
d   2.5   1.5   4.0

In [17]:

    

print(df.rank(na_option='top', method='min'))


    

    




   col1  col2  col3
a   1.0   4.0   1.0
b   2.0   1.0   3.0
c   4.0   3.0   2.0
d   2.0   1.0   4.0

In [18]:

    

print(df.rank(na_option='bottom'))


    

    




   col1  col2  col3
a   1.0   2.0   1.0
b   2.5   3.5   3.0
c   4.0   1.0   2.0
d   2.5   3.5   4.0

In [19]:

    

print(df.rank(na_option='bottom', method='min'))


    

    




   col1  col2  col3
a   1.0   2.0   1.0
b   2.0   3.0   3.0
c   4.0   1.0   2.0
d   2.0   3.0   4.0

In [20]:

    

print(df.rank(pct=True))


    

    




    col1  col2  col3
a  0.250   1.0  0.25
b  0.625   NaN  0.75
c  1.000   0.5  0.50
d  0.625   NaN  1.00

In [21]:

    

print(df.rank(pct=True, method='min', ascending=False, na_option='bottom'))


    

    




   col1  col2  col3
a  1.00  0.25  1.00
b  0.50  0.75  0.50
c  0.25  0.50  0.75
d  0.50  0.75  0.25

In [22]:

    

print(df['col1'].rank(method='min', ascending=False))


    

    




a    4.0
b    2.0
c    1.0
d    2.0
Name: col1, dtype: float64