In [1]:

    

import pandas as pd


    

In [2]:

    

s = pd.Series(['X', 'X', 'Y', 'X'])
print(s)


    

    




0    X
1    X
2    Y
3    X
dtype: object

In [3]:

    

print(s.mode())


    

    




0    X
dtype: object

In [4]:

    

print(type(s.mode()))


    

    




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

In [5]:

    

mode_value = s.mode()[0]
print(mode_value)


    

    




X

In [6]:

    

print(type(mode_value))


    

    




<class 'str'>

In [7]:

    

s_same = pd.Series(['X', 'Y', 'Y', 'X'])
print(s_same)


    

    




0    X
1    Y
2    Y
3    X
dtype: object

In [8]:

    

print(s_same.mode())


    

    




0    X
1    Y
dtype: object

In [9]:

    

print(s_same.mode()[0])


    

    




X

In [10]:

    

l_modes = s_same.mode().tolist()
print(l_modes)


    

    




['X', 'Y']

In [11]:

    

print(type(l_modes))


    

    




<class 'list'>

In [12]:

    

df = pd.DataFrame({'col1': ['X', 'X', 'Y', 'X'],
                   'col2': ['X', 'Y', 'Y', 'X']},
                  index=['row1', 'row2', 'row3', 'row4'])
print(df)


    

    




     col1 col2
row1    X    X
row2    X    Y
row3    Y    Y
row4    X    X

In [13]:

    

print(df.mode())


    

    




  col1 col2
0    X    X
1  NaN    Y

In [14]:

    

print(type(df.mode()))


    

    




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

In [15]:

    

print(df.mode().count())


    

    




col1    1
col2    2
dtype: int64

In [16]:

    

print(df.mode().iloc[0])


    

    




col1    X
col2    X
Name: 0, dtype: object

In [17]:

    

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


    

    




0      X
1    NaN
Name: col1, dtype: object

In [18]:

    

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


    

    




0    X
dtype: object

In [19]:

    

l_mode = df.apply(lambda x: x.mode().tolist())
print(l_mode)


    

    




col1       [X]
col2    [X, Y]
dtype: object

In [20]:

    

print(l_mode.iat[1])


    

    




['X', 'Y']

In [21]:

    

print(type(l_mode.iat[1]))


    

    




<class 'list'>

In [22]:

    

print(l_mode.iat[1][1])


    

    




Y

In [23]:

    

print(type(l_mode.iat[1][1]))


    

    




<class 'str'>

In [24]:

    

print(df.mode(axis=1))


    

    




      0    1
row1  X  NaN
row2  X    Y
row3  Y  NaN
row4  X  NaN

In [25]:

    

print(df.mode(axis=1).count(axis=1))


    

    




row1    1
row2    2
row3    1
row4    1
dtype: int64

In [26]:

    

df_t = df.T
print(df_t)


    

    




     row1 row2 row3 row4
col1    X    X    Y    X
col2    X    Y    Y    X

In [27]:

    

print(df_t.mode())


    

    




  row1 row2 row3 row4
0    X    X    Y    X
1  NaN    Y  NaN  NaN

In [28]:

    

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


    

    




X    3
Y    1
Name: col1, dtype: int64

In [29]:

    

print(df['col1'].value_counts().iat[0])


    

    




3

In [30]:

    

print(df.apply(lambda x: x.value_counts().iat[0]))


    

    




col1    3
col2    2
dtype: int64

In [31]:

    

print(df.describe())


    

    




       col1 col2
count     4    4
unique    2    2
top       X    Y
freq      3    2

In [32]:

    

print(df.describe().loc['freq'])


    

    




col1    3
col2    2
Name: freq, dtype: object

In [33]:

    

print(df.describe().at['freq', 'col2'])


    

    




2

In [34]:

    

print(df.T.describe())


    

    




       row1 row2 row3 row4
count     2    2    2    2
unique    1    2    1    1
top       X    Y    Y    X
freq      2    1    2    2