In [1]:

    

import pandas as pd


    

In [2]:

    

df = pd.DataFrame({'col1': [0, 3, 2, 3], 'col2': [4, 0, 2, 1]},
                   index=['a', 'b', 'c', 'd'])


    

In [3]:

    

print(df)


    

    




   col1  col2
a     0     4
b     3     0
c     2     2
d     3     1

In [4]:

    

print(df['col1'])


    

    




a    0
b    3
c    2
d    3
Name: col1, dtype: int64

In [5]:

    

print(type(df['col1']))


    

    




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

In [6]:

    

print(df['col1'].max())


    

    




3

In [7]:

    

print(df['col1'].min())


    

    




0

In [8]:

    

print(df.max())


    

    




col1    3
col2    4
dtype: int64

In [9]:

    

print(df.min())


    

    




col1    0
col2    0
dtype: int64

In [10]:

    

print(df.max(axis=1))


    

    




a    4
b    3
c    2
d    3
dtype: int64

In [11]:

    

print(df.min(axis=1))


    

    




a    0
b    0
c    2
d    1
dtype: int64

In [12]:

    

print(type(df.max()))


    

    




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

In [13]:

    

print(df['col1'].idxmax())


    

    




b

In [14]:

    

print(df['col1'].idxmin())


    

    




a

In [15]:

    

print(df['col1'] == df['col1'].max())


    

    




a    False
b     True
c    False
d     True
Name: col1, dtype: bool

In [16]:

    

print(df['col1'][df['col1'] == df['col1'].max()])


    

    




b    3
d    3
Name: col1, dtype: int64

In [17]:

    

print(df['col1'][df['col1'] == df['col1'].max()].index)


    

    




Index(['b', 'd'], dtype='object')

In [18]:

    

print(df['col1'][df['col1'] == df['col1'].max()].index.values)


    

    




['b' 'd']

In [19]:

    

print(type(df['col1'][df['col1'] == df['col1'].max()].index.values))


    

    




<class 'numpy.ndarray'>

In [20]:

    

print(list(df['col1'][df['col1'] == df['col1'].max()].index))


    

    




['b', 'd']

In [21]:

    

print(type(list(df['col1'][df['col1'] == df['col1'].max()].index)))


    

    




<class 'list'>

In [22]:

    

print(df['col1'][df['col1'] == df['col1'].min()].index.values)


    

    




['a']

In [23]:

    

print(df.loc['a'])


    

    




col1    0
col2    4
Name: a, dtype: int64

In [24]:

    

print(df.loc['a'].idxmax())


    

    




col2

In [25]:

    

print(df.loc['a'].idxmin())


    

    




col1

In [26]:

    

print(df.idxmax())


    

    




col1    b
col2    a
dtype: object

In [27]:

    

print(df.idxmin())


    

    




col1    a
col2    b
dtype: object

In [28]:

    

print(df.apply(lambda x: list(x[x == x.max()].index)))


    

    




col1    [b, d]
col2       [a]
dtype: object

In [29]:

    

print(df.apply(lambda x: list(x[x == x.min()].index)))


    

    




col1    [a]
col2    [b]
dtype: object

In [30]:

    

print(df.idxmax(axis=1))


    

    




a    col2
b    col1
c    col1
d    col1
dtype: object

In [31]:

    

print(df.idxmin(axis=1))


    

    




a    col1
b    col2
c    col1
d    col2
dtype: object

In [32]:

    

print(df.apply(lambda x: list(x[x == x.max()].index), axis=1))


    

    




a          [col2]
b          [col1]
c    [col1, col2]
d          [col1]
dtype: object

In [33]:

    

print(df.apply(lambda x: list(x[x == x.min()].index), axis=1))


    

    




a          [col1]
b          [col2]
c    [col1, col2]
d          [col2]
dtype: object

In [34]:

    

df_nan = df.copy()
df_nan.at['b'] = pd.np.nan


    

In [35]:

    

print(df_nan)


    

    




   col1  col2
a   0.0   4.0
b   NaN   NaN
c   2.0   2.0
d   3.0   1.0

In [36]:

    

print(df_nan.idxmax())


    

    




col1    d
col2    a
dtype: object

In [37]:

    

print(df_nan.idxmin())


    

    




col1    a
col2    d
dtype: object

In [38]:

    

print(df_nan.idxmax(axis=1))


    

    




a    col2
b     NaN
c    col1
d    col1
dtype: object

In [39]:

    

print(df_nan.idxmin(axis=1))


    

    




a    col1
b     NaN
c    col1
d    col2
dtype: object

In [40]:

    

print(df_nan.idxmax(skipna=False))


    

    




col1   NaN
col2   NaN
dtype: float64

In [41]:

    

print(df_nan.idxmin(skipna=False))


    

    




col1   NaN
col2   NaN
dtype: float64

In [42]:

    

print(df_nan.idxmax(axis=1, skipna=False))


    

    




a    col2
b     NaN
c    col1
d    col1
dtype: object

In [43]:

    

print(df_nan.idxmin(axis=1, skipna=False))


    

    




a    col1
b     NaN
c    col1
d    col2
dtype: object

In [44]:

    

print(df_nan['col1'].idxmax())


    

    




d

In [45]:

    

print(df_nan['col1'].idxmin())


    

    




a

In [46]:

    

print(df_nan['col1'].idxmax(skipna=False))


    

    




nan

In [47]:

    

print(df_nan['col1'].idxmin(skipna=False))


    

    




nan