In [1]:

    

import pandas as pd


    

In [2]:

    

s = pd.Series(data=[x**2 for x in range(11)],
              index=list('abcdefghijk'))


    

In [3]:

    

print(s)


    

    




a      0
b      1
c      4
d      9
e     16
f     25
g     36
h     49
i     64
j     81
k    100
dtype: int64

In [4]:

    

s_cut = pd.cut(s, 4)
print(s_cut)


    

    




a     (-0.1, 25.0]
b     (-0.1, 25.0]
c     (-0.1, 25.0]
d     (-0.1, 25.0]
e     (-0.1, 25.0]
f     (-0.1, 25.0]
g     (25.0, 50.0]
h     (25.0, 50.0]
i     (50.0, 75.0]
j    (75.0, 100.0]
k    (75.0, 100.0]
dtype: category
Categories (4, interval[float64]): [(-0.1, 25.0] < (25.0, 50.0] < (50.0, 75.0] < (75.0, 100.0]]

In [5]:

    

print(type(s_cut))


    

    




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

In [6]:

    

print(pd.cut(s, [0, 10, 50, 100]))


    

    




a          NaN
b      (0, 10]
c      (0, 10]
d      (0, 10]
e     (10, 50]
f     (10, 50]
g     (10, 50]
h     (10, 50]
i    (50, 100]
j    (50, 100]
k    (50, 100]
dtype: category
Categories (3, interval[int64]): [(0, 10] < (10, 50] < (50, 100]]

In [7]:

    

s_cut, bins = pd.cut(s, 4, retbins=True)
print(s_cut)


    

    




a     (-0.1, 25.0]
b     (-0.1, 25.0]
c     (-0.1, 25.0]
d     (-0.1, 25.0]
e     (-0.1, 25.0]
f     (-0.1, 25.0]
g     (25.0, 50.0]
h     (25.0, 50.0]
i     (50.0, 75.0]
j    (75.0, 100.0]
k    (75.0, 100.0]
dtype: category
Categories (4, interval[float64]): [(-0.1, 25.0] < (25.0, 50.0] < (50.0, 75.0] < (75.0, 100.0]]

In [8]:

    

print(bins)
print(type(bins))


    

    




[ -0.1  25.   50.   75.  100. ]
<class 'numpy.ndarray'>

In [9]:

    

print(pd.cut(s, 4, right=False))


    

    




a      [0.0, 25.0)
b      [0.0, 25.0)
c      [0.0, 25.0)
d      [0.0, 25.0)
e      [0.0, 25.0)
f     [25.0, 50.0)
g     [25.0, 50.0)
h     [25.0, 50.0)
i     [50.0, 75.0)
j    [75.0, 100.1)
k    [75.0, 100.1)
dtype: category
Categories (4, interval[float64]): [[0.0, 25.0) < [25.0, 50.0) < [50.0, 75.0) < [75.0, 100.1)]

In [10]:

    

print(pd.cut(s, 4, labels=False))


    

    




a    0
b    0
c    0
d    0
e    0
f    0
g    1
h    1
i    2
j    3
k    3
dtype: int64

In [11]:

    

print(pd.cut(s, 4, labels=['small', 'medium', 'large', 'x-large']))


    

    




a      small
b      small
c      small
d      small
e      small
f      small
g     medium
h     medium
i      large
j    x-large
k    x-large
dtype: category
Categories (4, object): [small < medium < large < x-large]

In [12]:

    

print(pd.cut(s, 3))


    

    




a      (-0.1, 33.333]
b      (-0.1, 33.333]
c      (-0.1, 33.333]
d      (-0.1, 33.333]
e      (-0.1, 33.333]
f      (-0.1, 33.333]
g    (33.333, 66.667]
h    (33.333, 66.667]
i    (33.333, 66.667]
j     (66.667, 100.0]
k     (66.667, 100.0]
dtype: category
Categories (3, interval[float64]): [(-0.1, 33.333] < (33.333, 66.667] < (66.667, 100.0]]

In [13]:

    

print(pd.cut(s, 3, precision=1))


    

    




a     (-0.1, 33.3]
b     (-0.1, 33.3]
c     (-0.1, 33.3]
d     (-0.1, 33.3]
e     (-0.1, 33.3]
f     (-0.1, 33.3]
g     (33.3, 66.7]
h     (33.3, 66.7]
i     (33.3, 66.7]
j    (66.7, 100.0]
k    (66.7, 100.0]
dtype: category
Categories (3, interval[float64]): [(-0.1, 33.3] < (33.3, 66.7] < (66.7, 100.0]]

In [14]:

    

print(pd.qcut(s, 2))


    

    




a    (-0.001, 25.0]
b    (-0.001, 25.0]
c    (-0.001, 25.0]
d    (-0.001, 25.0]
e    (-0.001, 25.0]
f    (-0.001, 25.0]
g     (25.0, 100.0]
h     (25.0, 100.0]
i     (25.0, 100.0]
j     (25.0, 100.0]
k     (25.0, 100.0]
dtype: category
Categories (2, interval[float64]): [(-0.001, 25.0] < (25.0, 100.0]]

In [15]:

    

s_qcut, bins = pd.qcut(s, 4, labels=['Q1', 'Q2', 'Q3', 'Q4'], retbins=True)
print(s_qcut)


    

    




a    Q1
b    Q1
c    Q1
d    Q2
e    Q2
f    Q2
g    Q3
h    Q3
i    Q4
j    Q4
k    Q4
dtype: category
Categories (4, object): [Q1 < Q2 < Q3 < Q4]

In [16]:

    

print(bins)


    

    




[  0.    6.5  25.   56.5 100. ]

In [17]:

    

s_duplicate = pd.Series(data=[0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6],
                        index=list('abcdefghijk'))


    

In [18]:

    

print(s_duplicate)


    

    




a    0
b    0
c    0
d    0
e    0
f    1
g    2
h    3
i    4
j    5
k    6
dtype: int64

In [19]:

    

print(pd.qcut(s_duplicate, 2))


    

    




a    (-0.001, 1.0]
b    (-0.001, 1.0]
c    (-0.001, 1.0]
d    (-0.001, 1.0]
e    (-0.001, 1.0]
f    (-0.001, 1.0]
g       (1.0, 6.0]
h       (1.0, 6.0]
i       (1.0, 6.0]
j       (1.0, 6.0]
k       (1.0, 6.0]
dtype: category
Categories (2, interval[float64]): [(-0.001, 1.0] < (1.0, 6.0]]

In [20]:

    

# print(pd.qcut(s_duplicate, 4))
# ValueError: Bin edges must be unique: array([0. , 0. , 1. , 3.5, 6. ]).
# You can drop duplicate edges by setting the 'duplicates' kwarg


    

In [21]:

    

print(pd.qcut(s_duplicate, 4, duplicates='drop'))


    

    




a    (-0.001, 1.0]
b    (-0.001, 1.0]
c    (-0.001, 1.0]
d    (-0.001, 1.0]
e    (-0.001, 1.0]
f    (-0.001, 1.0]
g       (1.0, 3.5]
h       (1.0, 3.5]
i       (3.5, 6.0]
j       (3.5, 6.0]
k       (3.5, 6.0]
dtype: category
Categories (3, interval[float64]): [(-0.001, 1.0] < (1.0, 3.5] < (3.5, 6.0]]

In [22]:

    

counts = pd.cut(s, 3, labels=['S', 'M', 'L']).value_counts()
print(counts)


    

    




S    6
M    3
L    2
dtype: int64

In [23]:

    

print(type(counts))


    

    




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

In [24]:

    

print(counts['M'])


    

    




3

In [25]:

    

print(pd.value_counts(pd.cut(s, 3, labels=['S', 'M', 'L'])))


    

    




S    6
M    3
L    2
dtype: int64

In [26]:

    

l = [x**2 for x in range(11)]
print(l)


    

    




[0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100]

In [27]:

    

l_cut = pd.cut(l, 3, labels=['S', 'M', 'L'])
print(l_cut)


    

    




[S, S, S, S, S, ..., M, M, M, L, L]
Length: 11
Categories (3, object): [S < M < L]

In [28]:

    

print(type(l_cut))


    

    




<class 'pandas.core.categorical.Categorical'>

In [29]:

    

print(l_cut[0])


    

    




S

In [30]:

    

print(list(l_cut))


    

    




['S', 'S', 'S', 'S', 'S', 'S', 'M', 'M', 'M', 'L', 'L']

In [31]:

    

print(pd.value_counts(l_cut))


    

    




S    6
M    3
L    2
dtype: int64

In [32]:

    

df_titanic = pd.read_csv('data/src/titanic_train.csv').drop(['Name', 'Ticket', 'Cabin', 'Embarked'], axis=1)


    

In [33]:

    

print(df_titanic.head())


    

    




   PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch     Fare
0            1         0       3    male  22.0      1      0   7.2500
1            2         1       1  female  38.0      1      0  71.2833
2            3         1       3  female  26.0      0      0   7.9250
3            4         1       1  female  35.0      1      0  53.1000
4            5         0       3    male  35.0      0      0   8.0500

In [34]:

    

print(df_titanic['Age'].describe())


    

    




count    714.000000
mean      29.699118
std       14.526497
min        0.420000
25%       20.125000
50%       28.000000
75%       38.000000
max       80.000000
Name: Age, dtype: float64

In [35]:

    

print(pd.cut(df_titanic['Age'], 5, precision=0).value_counts(sort=False, dropna=False))


    

    




(0.0, 16.0]     100
(16.0, 32.0]    346
(32.0, 48.0]    188
(48.0, 64.0]     69
(64.0, 80.0]     11
NaN             177
Name: Age, dtype: int64

In [36]:

    

df_titanic['Age_bin'] = pd.cut(df_titanic['Age'], 5, labels=False)


    

In [37]:

    

print(df_titanic.head())


    

    




   PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch     Fare  Age_bin
0            1         0       3    male  22.0      1      0   7.2500      1.0
1            2         1       1  female  38.0      1      0  71.2833      2.0
2            3         1       3  female  26.0      0      0   7.9250      1.0
3            4         1       1  female  35.0      1      0  53.1000      2.0
4            5         0       3    male  35.0      0      0   8.0500      2.0