Chiều rộng bằng nhau binning gấu trúc Python

bạn có thể làm một cái gì đó như

# create a random df
df = pd.DataFrame[np.random.randn[10, 10], columns=list['ABCDEFGHIJ']]

# sort valeus
df = df.sort_values[by = ['A'],  ascending=True]

# use your code but on a transposed dataframe
new = np.array_split[df.T, 5] # split columns into 5 bins

# list comprehension to transposed dataframes
dfs = [new[i].T for i in range[len[new]]]

# random df
df = pd.DataFrame[np.random.randn[1000, 5], columns=list['ABCDE']]

# sort on A
df.sort_values['A', inplace=True]

# create bins
df['bin'] = pd.cut[df['A'], 20, include_lowest = True]

# group on bin
group = df.groupby['bin']

# list comprehension to split groups into list of dataframes 
dfs = [group.get_group[x] for x in group.groups]


[            A         B         C         D         E               bin
 218 -2.716093  0.833726 -0.771400  0.691251  0.162448  [-2.723, -2.413]
 207 -2.581388 -2.318333 -0.001467  0.035277  1.219666  [-2.723, -2.413]
 380 -2.499710  1.946709 -0.519070  1.653383  0.309689  [-2.723, -2.413]
 866 -2.492050  0.246500 -0.596392  0.872888  2.371652  [-2.723, -2.413]
 876 -2.469238 -0.156470 -0.841065 -1.248793 -0.489665  [-2.723, -2.413]
 314 -2.456308  0.630691 -0.072146  1.139697  0.663674  [-2.723, -2.413]
 310 -2.455353  0.075842  0.589515 -0.427233  1.207979  [-2.723, -2.413]
 660 -2.427255  0.890125 -0.042716 -1.038401  0.651324  [-2.723, -2.413],
             A         B         C         D         E              bin
 571 -2.355430  0.383794 -1.266575 -1.214833 -0.862611  [-2.413, -2.11]
 977 -2.354416 -1.964189  0.440376  0.028032 -0.181360  [-2.413, -2.11]
 83  -2.276908  0.288462  0.370555 -0.546359 -2.033892  [-2.413, -2.11]
 196 -2.213729 -1.087783 -0.592884  1.233886  1.051164  [-2.413, -2.11]
 227 -2.146631  0.365183 -0.095293 -0.882414  0.385117  [-2.413, -2.11]
 39  -2.136800 -1.150065  0.180182 -0.424071  0.040370  [-2.413, -2.11],
             A         B         C         D         E              bin
 104 -2.108961 -0.396602 -1.014224 -1.277124  0.001030  [-2.11, -1.806]
 360 -2.098928  1.093483  1.438421 -0.980215  0.010359  [-2.11, -1.806]
 530 -2.088592  1.043201 -0.522468  0.482176 -0.680166  [-2.11, -1.806]
 158 -2.062759  2.070387  2.124621 -2.751532  0.674055  [-2.11, -1.806]
 971 -2.053039  0.347577 -0.498513  1.917305 -1.746493  [-2.11, -1.806]
 658 -2.002482 -1.222292 -0.398816  0.279228 -1.485782  [-2.11, -1.806]
 90  -1.985261  3.499251 -2.089028  1.238524 -1.781089  [-2.11, -1.806]
 466 -1.973640 -1.609920 -1.029454  0.809143 -0.228893  [-2.11, -1.806]
 40  -1.966016 -1.479240 -1.564966 -0.310133  1.338023  [-2.11, -1.806]
 279 -1.943666  0.762493  0.060038  0.449159  0.244411  [-2.11, -1.806]
 204 -1.940045  0.844901 -0.343691 -1.144836  1.385915  [-2.11, -1.806]
 780 -1.918548  0.212452  0.225789  0.216110  1.710532  [-2.11, -1.806]
 289 -1.897438  0.847664  0.689778 -0.454152 -0.747836  [-2.11, -1.806]
 159 -1.848425  0.477726  0.391384 -0.477804  0.168160  [-2.11, -1.806],
. . . 

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

6

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

9

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

32

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

1

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

2

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

35

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

36

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

37

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

38

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

39

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

90

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

36

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

92

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

93

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

94

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

95

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

5

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

97

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

98

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

99

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

93______27

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

5

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

7

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

38

# random df
df = pd.DataFrame[np.random.randn[1000, 5], columns=list['ABCDE']]

# sort on A
df.sort_values['A', inplace=True]

# create bins
df['bin'] = pd.cut[df['A'], 20, include_lowest = True]

# group on bin
group = df.groupby['bin']

# list comprehension to split groups into list of dataframes 
dfs = [group.get_group[x] for x in group.groups]


[            A         B         C         D         E               bin
 218 -2.716093  0.833726 -0.771400  0.691251  0.162448  [-2.723, -2.413]
 207 -2.581388 -2.318333 -0.001467  0.035277  1.219666  [-2.723, -2.413]
 380 -2.499710  1.946709 -0.519070  1.653383  0.309689  [-2.723, -2.413]
 866 -2.492050  0.246500 -0.596392  0.872888  2.371652  [-2.723, -2.413]
 876 -2.469238 -0.156470 -0.841065 -1.248793 -0.489665  [-2.723, -2.413]
 314 -2.456308  0.630691 -0.072146  1.139697  0.663674  [-2.723, -2.413]
 310 -2.455353  0.075842  0.589515 -0.427233  1.207979  [-2.723, -2.413]
 660 -2.427255  0.890125 -0.042716 -1.038401  0.651324  [-2.723, -2.413],
             A         B         C         D         E              bin
 571 -2.355430  0.383794 -1.266575 -1.214833 -0.862611  [-2.413, -2.11]
 977 -2.354416 -1.964189  0.440376  0.028032 -0.181360  [-2.413, -2.11]
 83  -2.276908  0.288462  0.370555 -0.546359 -2.033892  [-2.413, -2.11]
 196 -2.213729 -1.087783 -0.592884  1.233886  1.051164  [-2.413, -2.11]
 227 -2.146631  0.365183 -0.095293 -0.882414  0.385117  [-2.413, -2.11]
 39  -2.136800 -1.150065  0.180182 -0.424071  0.040370  [-2.413, -2.11],
             A         B         C         D         E              bin
 104 -2.108961 -0.396602 -1.014224 -1.277124  0.001030  [-2.11, -1.806]
 360 -2.098928  1.093483  1.438421 -0.980215  0.010359  [-2.11, -1.806]
 530 -2.088592  1.043201 -0.522468  0.482176 -0.680166  [-2.11, -1.806]
 158 -2.062759  2.070387  2.124621 -2.751532  0.674055  [-2.11, -1.806]
 971 -2.053039  0.347577 -0.498513  1.917305 -1.746493  [-2.11, -1.806]
 658 -2.002482 -1.222292 -0.398816  0.279228 -1.485782  [-2.11, -1.806]
 90  -1.985261  3.499251 -2.089028  1.238524 -1.781089  [-2.11, -1.806]
 466 -1.973640 -1.609920 -1.029454  0.809143 -0.228893  [-2.11, -1.806]
 40  -1.966016 -1.479240 -1.564966 -0.310133  1.338023  [-2.11, -1.806]
 279 -1.943666  0.762493  0.060038  0.449159  0.244411  [-2.11, -1.806]
 204 -1.940045  0.844901 -0.343691 -1.144836  1.385915  [-2.11, -1.806]
 780 -1.918548  0.212452  0.225789  0.216110  1.710532  [-2.11, -1.806]
 289 -1.897438  0.847664  0.689778 -0.454152 -0.747836  [-2.11, -1.806]
 159 -1.848425  0.477726  0.391384 -0.477804  0.168160  [-2.11, -1.806],
. . . 

025

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

6

# random df
df = pd.DataFrame[np.random.randn[1000, 5], columns=list['ABCDE']]

# sort on A
df.sort_values['A', inplace=True]

# create bins
df['bin'] = pd.cut[df['A'], 20, include_lowest = True]

# group on bin
group = df.groupby['bin']

# list comprehension to split groups into list of dataframes 
dfs = [group.get_group[x] for x in group.groups]


[            A         B         C         D         E               bin
 218 -2.716093  0.833726 -0.771400  0.691251  0.162448  [-2.723, -2.413]
 207 -2.581388 -2.318333 -0.001467  0.035277  1.219666  [-2.723, -2.413]
 380 -2.499710  1.946709 -0.519070  1.653383  0.309689  [-2.723, -2.413]
 866 -2.492050  0.246500 -0.596392  0.872888  2.371652  [-2.723, -2.413]
 876 -2.469238 -0.156470 -0.841065 -1.248793 -0.489665  [-2.723, -2.413]
 314 -2.456308  0.630691 -0.072146  1.139697  0.663674  [-2.723, -2.413]
 310 -2.455353  0.075842  0.589515 -0.427233  1.207979  [-2.723, -2.413]
 660 -2.427255  0.890125 -0.042716 -1.038401  0.651324  [-2.723, -2.413],
             A         B         C         D         E              bin
 571 -2.355430  0.383794 -1.266575 -1.214833 -0.862611  [-2.413, -2.11]
 977 -2.354416 -1.964189  0.440376  0.028032 -0.181360  [-2.413, -2.11]
 83  -2.276908  0.288462  0.370555 -0.546359 -2.033892  [-2.413, -2.11]
 196 -2.213729 -1.087783 -0.592884  1.233886  1.051164  [-2.413, -2.11]
 227 -2.146631  0.365183 -0.095293 -0.882414  0.385117  [-2.413, -2.11]
 39  -2.136800 -1.150065  0.180182 -0.424071  0.040370  [-2.413, -2.11],
             A         B         C         D         E              bin
 104 -2.108961 -0.396602 -1.014224 -1.277124  0.001030  [-2.11, -1.806]
 360 -2.098928  1.093483  1.438421 -0.980215  0.010359  [-2.11, -1.806]
 530 -2.088592  1.043201 -0.522468  0.482176 -0.680166  [-2.11, -1.806]
 158 -2.062759  2.070387  2.124621 -2.751532  0.674055  [-2.11, -1.806]
 971 -2.053039  0.347577 -0.498513  1.917305 -1.746493  [-2.11, -1.806]
 658 -2.002482 -1.222292 -0.398816  0.279228 -1.485782  [-2.11, -1.806]
 90  -1.985261  3.499251 -2.089028  1.238524 -1.781089  [-2.11, -1.806]
 466 -1.973640 -1.609920 -1.029454  0.809143 -0.228893  [-2.11, -1.806]
 40  -1.966016 -1.479240 -1.564966 -0.310133  1.338023  [-2.11, -1.806]
 279 -1.943666  0.762493  0.060038  0.449159  0.244411  [-2.11, -1.806]
 204 -1.940045  0.844901 -0.343691 -1.144836  1.385915  [-2.11, -1.806]
 780 -1.918548  0.212452  0.225789  0.216110  1.710532  [-2.11, -1.806]
 289 -1.897438  0.847664  0.689778 -0.454152 -0.747836  [-2.11, -1.806]
 159 -1.848425  0.477726  0.391384 -0.477804  0.168160  [-2.11, -1.806],
. . . 

027____1028

# random df
df = pd.DataFrame[np.random.randn[1000, 5], columns=list['ABCDE']]

# sort on A
df.sort_values['A', inplace=True]

# create bins
df['bin'] = pd.cut[df['A'], 20, include_lowest = True]

# group on bin
group = df.groupby['bin']

# list comprehension to split groups into list of dataframes 
dfs = [group.get_group[x] for x in group.groups]


[            A         B         C         D         E               bin
 218 -2.716093  0.833726 -0.771400  0.691251  0.162448  [-2.723, -2.413]
 207 -2.581388 -2.318333 -0.001467  0.035277  1.219666  [-2.723, -2.413]
 380 -2.499710  1.946709 -0.519070  1.653383  0.309689  [-2.723, -2.413]
 866 -2.492050  0.246500 -0.596392  0.872888  2.371652  [-2.723, -2.413]
 876 -2.469238 -0.156470 -0.841065 -1.248793 -0.489665  [-2.723, -2.413]
 314 -2.456308  0.630691 -0.072146  1.139697  0.663674  [-2.723, -2.413]
 310 -2.455353  0.075842  0.589515 -0.427233  1.207979  [-2.723, -2.413]
 660 -2.427255  0.890125 -0.042716 -1.038401  0.651324  [-2.723, -2.413],
             A         B         C         D         E              bin
 571 -2.355430  0.383794 -1.266575 -1.214833 -0.862611  [-2.413, -2.11]
 977 -2.354416 -1.964189  0.440376  0.028032 -0.181360  [-2.413, -2.11]
 83  -2.276908  0.288462  0.370555 -0.546359 -2.033892  [-2.413, -2.11]
 196 -2.213729 -1.087783 -0.592884  1.233886  1.051164  [-2.413, -2.11]
 227 -2.146631  0.365183 -0.095293 -0.882414  0.385117  [-2.413, -2.11]
 39  -2.136800 -1.150065  0.180182 -0.424071  0.040370  [-2.413, -2.11],
             A         B         C         D         E              bin
 104 -2.108961 -0.396602 -1.014224 -1.277124  0.001030  [-2.11, -1.806]
 360 -2.098928  1.093483  1.438421 -0.980215  0.010359  [-2.11, -1.806]
 530 -2.088592  1.043201 -0.522468  0.482176 -0.680166  [-2.11, -1.806]
 158 -2.062759  2.070387  2.124621 -2.751532  0.674055  [-2.11, -1.806]
 971 -2.053039  0.347577 -0.498513  1.917305 -1.746493  [-2.11, -1.806]
 658 -2.002482 -1.222292 -0.398816  0.279228 -1.485782  [-2.11, -1.806]
 90  -1.985261  3.499251 -2.089028  1.238524 -1.781089  [-2.11, -1.806]
 466 -1.973640 -1.609920 -1.029454  0.809143 -0.228893  [-2.11, -1.806]
 40  -1.966016 -1.479240 -1.564966 -0.310133  1.338023  [-2.11, -1.806]
 279 -1.943666  0.762493  0.060038  0.449159  0.244411  [-2.11, -1.806]
 204 -1.940045  0.844901 -0.343691 -1.144836  1.385915  [-2.11, -1.806]
 780 -1.918548  0.212452  0.225789  0.216110  1.710532  [-2.11, -1.806]
 289 -1.897438  0.847664  0.689778 -0.454152 -0.747836  [-2.11, -1.806]
 159 -1.848425  0.477726  0.391384 -0.477804  0.168160  [-2.11, -1.806],
. . . 

029

# random df
df = pd.DataFrame[np.random.randn[1000, 5], columns=list['ABCDE']]

# sort on A
df.sort_values['A', inplace=True]

# create bins
df['bin'] = pd.cut[df['A'], 20, include_lowest = True]

# group on bin
group = df.groupby['bin']

# list comprehension to split groups into list of dataframes 
dfs = [group.get_group[x] for x in group.groups]


[            A         B         C         D         E               bin
 218 -2.716093  0.833726 -0.771400  0.691251  0.162448  [-2.723, -2.413]
 207 -2.581388 -2.318333 -0.001467  0.035277  1.219666  [-2.723, -2.413]
 380 -2.499710  1.946709 -0.519070  1.653383  0.309689  [-2.723, -2.413]
 866 -2.492050  0.246500 -0.596392  0.872888  2.371652  [-2.723, -2.413]
 876 -2.469238 -0.156470 -0.841065 -1.248793 -0.489665  [-2.723, -2.413]
 314 -2.456308  0.630691 -0.072146  1.139697  0.663674  [-2.723, -2.413]
 310 -2.455353  0.075842  0.589515 -0.427233  1.207979  [-2.723, -2.413]
 660 -2.427255  0.890125 -0.042716 -1.038401  0.651324  [-2.723, -2.413],
             A         B         C         D         E              bin
 571 -2.355430  0.383794 -1.266575 -1.214833 -0.862611  [-2.413, -2.11]
 977 -2.354416 -1.964189  0.440376  0.028032 -0.181360  [-2.413, -2.11]
 83  -2.276908  0.288462  0.370555 -0.546359 -2.033892  [-2.413, -2.11]
 196 -2.213729 -1.087783 -0.592884  1.233886  1.051164  [-2.413, -2.11]
 227 -2.146631  0.365183 -0.095293 -0.882414  0.385117  [-2.413, -2.11]
 39  -2.136800 -1.150065  0.180182 -0.424071  0.040370  [-2.413, -2.11],
             A         B         C         D         E              bin
 104 -2.108961 -0.396602 -1.014224 -1.277124  0.001030  [-2.11, -1.806]
 360 -2.098928  1.093483  1.438421 -0.980215  0.010359  [-2.11, -1.806]
 530 -2.088592  1.043201 -0.522468  0.482176 -0.680166  [-2.11, -1.806]
 158 -2.062759  2.070387  2.124621 -2.751532  0.674055  [-2.11, -1.806]
 971 -2.053039  0.347577 -0.498513  1.917305 -1.746493  [-2.11, -1.806]
 658 -2.002482 -1.222292 -0.398816  0.279228 -1.485782  [-2.11, -1.806]
 90  -1.985261  3.499251 -2.089028  1.238524 -1.781089  [-2.11, -1.806]
 466 -1.973640 -1.609920 -1.029454  0.809143 -0.228893  [-2.11, -1.806]
 40  -1.966016 -1.479240 -1.564966 -0.310133  1.338023  [-2.11, -1.806]
 279 -1.943666  0.762493  0.060038  0.449159  0.244411  [-2.11, -1.806]
 204 -1.940045  0.844901 -0.343691 -1.144836  1.385915  [-2.11, -1.806]
 780 -1.918548  0.212452  0.225789  0.216110  1.710532  [-2.11, -1.806]
 289 -1.897438  0.847664  0.689778 -0.454152 -0.747836  [-2.11, -1.806]
 159 -1.848425  0.477726  0.391384 -0.477804  0.168160  [-2.11, -1.806],
. . . 

060

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

9

# random df
df = pd.DataFrame[np.random.randn[1000, 5], columns=list['ABCDE']]

# sort on A
df.sort_values['A', inplace=True]

# create bins
df['bin'] = pd.cut[df['A'], 20, include_lowest = True]

# group on bin
group = df.groupby['bin']

# list comprehension to split groups into list of dataframes 
dfs = [group.get_group[x] for x in group.groups]


[            A         B         C         D         E               bin
 218 -2.716093  0.833726 -0.771400  0.691251  0.162448  [-2.723, -2.413]
 207 -2.581388 -2.318333 -0.001467  0.035277  1.219666  [-2.723, -2.413]
 380 -2.499710  1.946709 -0.519070  1.653383  0.309689  [-2.723, -2.413]
 866 -2.492050  0.246500 -0.596392  0.872888  2.371652  [-2.723, -2.413]
 876 -2.469238 -0.156470 -0.841065 -1.248793 -0.489665  [-2.723, -2.413]
 314 -2.456308  0.630691 -0.072146  1.139697  0.663674  [-2.723, -2.413]
 310 -2.455353  0.075842  0.589515 -0.427233  1.207979  [-2.723, -2.413]
 660 -2.427255  0.890125 -0.042716 -1.038401  0.651324  [-2.723, -2.413],
             A         B         C         D         E              bin
 571 -2.355430  0.383794 -1.266575 -1.214833 -0.862611  [-2.413, -2.11]
 977 -2.354416 -1.964189  0.440376  0.028032 -0.181360  [-2.413, -2.11]
 83  -2.276908  0.288462  0.370555 -0.546359 -2.033892  [-2.413, -2.11]
 196 -2.213729 -1.087783 -0.592884  1.233886  1.051164  [-2.413, -2.11]
 227 -2.146631  0.365183 -0.095293 -0.882414  0.385117  [-2.413, -2.11]
 39  -2.136800 -1.150065  0.180182 -0.424071  0.040370  [-2.413, -2.11],
             A         B         C         D         E              bin
 104 -2.108961 -0.396602 -1.014224 -1.277124  0.001030  [-2.11, -1.806]
 360 -2.098928  1.093483  1.438421 -0.980215  0.010359  [-2.11, -1.806]
 530 -2.088592  1.043201 -0.522468  0.482176 -0.680166  [-2.11, -1.806]
 158 -2.062759  2.070387  2.124621 -2.751532  0.674055  [-2.11, -1.806]
 971 -2.053039  0.347577 -0.498513  1.917305 -1.746493  [-2.11, -1.806]
 658 -2.002482 -1.222292 -0.398816  0.279228 -1.485782  [-2.11, -1.806]
 90  -1.985261  3.499251 -2.089028  1.238524 -1.781089  [-2.11, -1.806]
 466 -1.973640 -1.609920 -1.029454  0.809143 -0.228893  [-2.11, -1.806]
 40  -1.966016 -1.479240 -1.564966 -0.310133  1.338023  [-2.11, -1.806]
 279 -1.943666  0.762493  0.060038  0.449159  0.244411  [-2.11, -1.806]
 204 -1.940045  0.844901 -0.343691 -1.144836  1.385915  [-2.11, -1.806]
 780 -1.918548  0.212452  0.225789  0.216110  1.710532  [-2.11, -1.806]
 289 -1.897438  0.847664  0.689778 -0.454152 -0.747836  [-2.11, -1.806]
 159 -1.848425  0.477726  0.391384 -0.477804  0.168160  [-2.11, -1.806],
. . . 

062

# random df
df = pd.DataFrame[np.random.randn[1000, 5], columns=list['ABCDE']]

# sort on A
df.sort_values['A', inplace=True]

# create bins
df['bin'] = pd.cut[df['A'], 20, include_lowest = True]

# group on bin
group = df.groupby['bin']

# list comprehension to split groups into list of dataframes 
dfs = [group.get_group[x] for x in group.groups]


[            A         B         C         D         E               bin
 218 -2.716093  0.833726 -0.771400  0.691251  0.162448  [-2.723, -2.413]
 207 -2.581388 -2.318333 -0.001467  0.035277  1.219666  [-2.723, -2.413]
 380 -2.499710  1.946709 -0.519070  1.653383  0.309689  [-2.723, -2.413]
 866 -2.492050  0.246500 -0.596392  0.872888  2.371652  [-2.723, -2.413]
 876 -2.469238 -0.156470 -0.841065 -1.248793 -0.489665  [-2.723, -2.413]
 314 -2.456308  0.630691 -0.072146  1.139697  0.663674  [-2.723, -2.413]
 310 -2.455353  0.075842  0.589515 -0.427233  1.207979  [-2.723, -2.413]
 660 -2.427255  0.890125 -0.042716 -1.038401  0.651324  [-2.723, -2.413],
             A         B         C         D         E              bin
 571 -2.355430  0.383794 -1.266575 -1.214833 -0.862611  [-2.413, -2.11]
 977 -2.354416 -1.964189  0.440376  0.028032 -0.181360  [-2.413, -2.11]
 83  -2.276908  0.288462  0.370555 -0.546359 -2.033892  [-2.413, -2.11]
 196 -2.213729 -1.087783 -0.592884  1.233886  1.051164  [-2.413, -2.11]
 227 -2.146631  0.365183 -0.095293 -0.882414  0.385117  [-2.413, -2.11]
 39  -2.136800 -1.150065  0.180182 -0.424071  0.040370  [-2.413, -2.11],
             A         B         C         D         E              bin
 104 -2.108961 -0.396602 -1.014224 -1.277124  0.001030  [-2.11, -1.806]
 360 -2.098928  1.093483  1.438421 -0.980215  0.010359  [-2.11, -1.806]
 530 -2.088592  1.043201 -0.522468  0.482176 -0.680166  [-2.11, -1.806]
 158 -2.062759  2.070387  2.124621 -2.751532  0.674055  [-2.11, -1.806]
 971 -2.053039  0.347577 -0.498513  1.917305 -1.746493  [-2.11, -1.806]
 658 -2.002482 -1.222292 -0.398816  0.279228 -1.485782  [-2.11, -1.806]
 90  -1.985261  3.499251 -2.089028  1.238524 -1.781089  [-2.11, -1.806]
 466 -1.973640 -1.609920 -1.029454  0.809143 -0.228893  [-2.11, -1.806]
 40  -1.966016 -1.479240 -1.564966 -0.310133  1.338023  [-2.11, -1.806]
 279 -1.943666  0.762493  0.060038  0.449159  0.244411  [-2.11, -1.806]
 204 -1.940045  0.844901 -0.343691 -1.144836  1.385915  [-2.11, -1.806]
 780 -1.918548  0.212452  0.225789  0.216110  1.710532  [-2.11, -1.806]
 289 -1.897438  0.847664  0.689778 -0.454152 -0.747836  [-2.11, -1.806]
 159 -1.848425  0.477726  0.391384 -0.477804  0.168160  [-2.11, -1.806],
. . . 

06

# random df
df = pd.DataFrame[np.random.randn[1000, 5], columns=list['ABCDE']]

# sort on A
df.sort_values['A', inplace=True]

# create bins
df['bin'] = pd.cut[df['A'], 20, include_lowest = True]

# group on bin
group = df.groupby['bin']

# list comprehension to split groups into list of dataframes 
dfs = [group.get_group[x] for x in group.groups]


[            A         B         C         D         E               bin
 218 -2.716093  0.833726 -0.771400  0.691251  0.162448  [-2.723, -2.413]
 207 -2.581388 -2.318333 -0.001467  0.035277  1.219666  [-2.723, -2.413]
 380 -2.499710  1.946709 -0.519070  1.653383  0.309689  [-2.723, -2.413]
 866 -2.492050  0.246500 -0.596392  0.872888  2.371652  [-2.723, -2.413]
 876 -2.469238 -0.156470 -0.841065 -1.248793 -0.489665  [-2.723, -2.413]
 314 -2.456308  0.630691 -0.072146  1.139697  0.663674  [-2.723, -2.413]
 310 -2.455353  0.075842  0.589515 -0.427233  1.207979  [-2.723, -2.413]
 660 -2.427255  0.890125 -0.042716 -1.038401  0.651324  [-2.723, -2.413],
             A         B         C         D         E              bin
 571 -2.355430  0.383794 -1.266575 -1.214833 -0.862611  [-2.413, -2.11]
 977 -2.354416 -1.964189  0.440376  0.028032 -0.181360  [-2.413, -2.11]
 83  -2.276908  0.288462  0.370555 -0.546359 -2.033892  [-2.413, -2.11]
 196 -2.213729 -1.087783 -0.592884  1.233886  1.051164  [-2.413, -2.11]
 227 -2.146631  0.365183 -0.095293 -0.882414  0.385117  [-2.413, -2.11]
 39  -2.136800 -1.150065  0.180182 -0.424071  0.040370  [-2.413, -2.11],
             A         B         C         D         E              bin
 104 -2.108961 -0.396602 -1.014224 -1.277124  0.001030  [-2.11, -1.806]
 360 -2.098928  1.093483  1.438421 -0.980215  0.010359  [-2.11, -1.806]
 530 -2.088592  1.043201 -0.522468  0.482176 -0.680166  [-2.11, -1.806]
 158 -2.062759  2.070387  2.124621 -2.751532  0.674055  [-2.11, -1.806]
 971 -2.053039  0.347577 -0.498513  1.917305 -1.746493  [-2.11, -1.806]
 658 -2.002482 -1.222292 -0.398816  0.279228 -1.485782  [-2.11, -1.806]
 90  -1.985261  3.499251 -2.089028  1.238524 -1.781089  [-2.11, -1.806]
 466 -1.973640 -1.609920 -1.029454  0.809143 -0.228893  [-2.11, -1.806]
 40  -1.966016 -1.479240 -1.564966 -0.310133  1.338023  [-2.11, -1.806]
 279 -1.943666  0.762493  0.060038  0.449159  0.244411  [-2.11, -1.806]
 204 -1.940045  0.844901 -0.343691 -1.144836  1.385915  [-2.11, -1.806]
 780 -1.918548  0.212452  0.225789  0.216110  1.710532  [-2.11, -1.806]
 289 -1.897438  0.847664  0.689778 -0.454152 -0.747836  [-2.11, -1.806]
 159 -1.848425  0.477726  0.391384 -0.477804  0.168160  [-2.11, -1.806],
. . . 

10

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

5

# random df
df = pd.DataFrame[np.random.randn[1000, 5], columns=list['ABCDE']]

# sort on A
df.sort_values['A', inplace=True]

# create bins
df['bin'] = pd.cut[df['A'], 20, include_lowest = True]

# group on bin
group = df.groupby['bin']

# list comprehension to split groups into list of dataframes 
dfs = [group.get_group[x] for x in group.groups]


[            A         B         C         D         E               bin
 218 -2.716093  0.833726 -0.771400  0.691251  0.162448  [-2.723, -2.413]
 207 -2.581388 -2.318333 -0.001467  0.035277  1.219666  [-2.723, -2.413]
 380 -2.499710  1.946709 -0.519070  1.653383  0.309689  [-2.723, -2.413]
 866 -2.492050  0.246500 -0.596392  0.872888  2.371652  [-2.723, -2.413]
 876 -2.469238 -0.156470 -0.841065 -1.248793 -0.489665  [-2.723, -2.413]
 314 -2.456308  0.630691 -0.072146  1.139697  0.663674  [-2.723, -2.413]
 310 -2.455353  0.075842  0.589515 -0.427233  1.207979  [-2.723, -2.413]
 660 -2.427255  0.890125 -0.042716 -1.038401  0.651324  [-2.723, -2.413],
             A         B         C         D         E              bin
 571 -2.355430  0.383794 -1.266575 -1.214833 -0.862611  [-2.413, -2.11]
 977 -2.354416 -1.964189  0.440376  0.028032 -0.181360  [-2.413, -2.11]
 83  -2.276908  0.288462  0.370555 -0.546359 -2.033892  [-2.413, -2.11]
 196 -2.213729 -1.087783 -0.592884  1.233886  1.051164  [-2.413, -2.11]
 227 -2.146631  0.365183 -0.095293 -0.882414  0.385117  [-2.413, -2.11]
 39  -2.136800 -1.150065  0.180182 -0.424071  0.040370  [-2.413, -2.11],
             A         B         C         D         E              bin
 104 -2.108961 -0.396602 -1.014224 -1.277124  0.001030  [-2.11, -1.806]
 360 -2.098928  1.093483  1.438421 -0.980215  0.010359  [-2.11, -1.806]
 530 -2.088592  1.043201 -0.522468  0.482176 -0.680166  [-2.11, -1.806]
 158 -2.062759  2.070387  2.124621 -2.751532  0.674055  [-2.11, -1.806]
 971 -2.053039  0.347577 -0.498513  1.917305 -1.746493  [-2.11, -1.806]
 658 -2.002482 -1.222292 -0.398816  0.279228 -1.485782  [-2.11, -1.806]
 90  -1.985261  3.499251 -2.089028  1.238524 -1.781089  [-2.11, -1.806]
 466 -1.973640 -1.609920 -1.029454  0.809143 -0.228893  [-2.11, -1.806]
 40  -1.966016 -1.479240 -1.564966 -0.310133  1.338023  [-2.11, -1.806]
 279 -1.943666  0.762493  0.060038  0.449159  0.244411  [-2.11, -1.806]
 204 -1.940045  0.844901 -0.343691 -1.144836  1.385915  [-2.11, -1.806]
 780 -1.918548  0.212452  0.225789  0.216110  1.710532  [-2.11, -1.806]
 289 -1.897438  0.847664  0.689778 -0.454152 -0.747836  [-2.11, -1.806]
 159 -1.848425  0.477726  0.391384 -0.477804  0.168160  [-2.11, -1.806],
. . . 

12

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

0

# random df
df = pd.DataFrame[np.random.randn[1000, 5], columns=list['ABCDE']]

# sort on A
df.sort_values['A', inplace=True]

# create bins
df['bin'] = pd.cut[df['A'], 20, include_lowest = True]

# group on bin
group = df.groupby['bin']

# list comprehension to split groups into list of dataframes 
dfs = [group.get_group[x] for x in group.groups]


[            A         B         C         D         E               bin
 218 -2.716093  0.833726 -0.771400  0.691251  0.162448  [-2.723, -2.413]
 207 -2.581388 -2.318333 -0.001467  0.035277  1.219666  [-2.723, -2.413]
 380 -2.499710  1.946709 -0.519070  1.653383  0.309689  [-2.723, -2.413]
 866 -2.492050  0.246500 -0.596392  0.872888  2.371652  [-2.723, -2.413]
 876 -2.469238 -0.156470 -0.841065 -1.248793 -0.489665  [-2.723, -2.413]
 314 -2.456308  0.630691 -0.072146  1.139697  0.663674  [-2.723, -2.413]
 310 -2.455353  0.075842  0.589515 -0.427233  1.207979  [-2.723, -2.413]
 660 -2.427255  0.890125 -0.042716 -1.038401  0.651324  [-2.723, -2.413],
             A         B         C         D         E              bin
 571 -2.355430  0.383794 -1.266575 -1.214833 -0.862611  [-2.413, -2.11]
 977 -2.354416 -1.964189  0.440376  0.028032 -0.181360  [-2.413, -2.11]
 83  -2.276908  0.288462  0.370555 -0.546359 -2.033892  [-2.413, -2.11]
 196 -2.213729 -1.087783 -0.592884  1.233886  1.051164  [-2.413, -2.11]
 227 -2.146631  0.365183 -0.095293 -0.882414  0.385117  [-2.413, -2.11]
 39  -2.136800 -1.150065  0.180182 -0.424071  0.040370  [-2.413, -2.11],
             A         B         C         D         E              bin
 104 -2.108961 -0.396602 -1.014224 -1.277124  0.001030  [-2.11, -1.806]
 360 -2.098928  1.093483  1.438421 -0.980215  0.010359  [-2.11, -1.806]
 530 -2.088592  1.043201 -0.522468  0.482176 -0.680166  [-2.11, -1.806]
 158 -2.062759  2.070387  2.124621 -2.751532  0.674055  [-2.11, -1.806]
 971 -2.053039  0.347577 -0.498513  1.917305 -1.746493  [-2.11, -1.806]
 658 -2.002482 -1.222292 -0.398816  0.279228 -1.485782  [-2.11, -1.806]
 90  -1.985261  3.499251 -2.089028  1.238524 -1.781089  [-2.11, -1.806]
 466 -1.973640 -1.609920 -1.029454  0.809143 -0.228893  [-2.11, -1.806]
 40  -1.966016 -1.479240 -1.564966 -0.310133  1.338023  [-2.11, -1.806]
 279 -1.943666  0.762493  0.060038  0.449159  0.244411  [-2.11, -1.806]
 204 -1.940045  0.844901 -0.343691 -1.144836  1.385915  [-2.11, -1.806]
 780 -1.918548  0.212452  0.225789  0.216110  1.710532  [-2.11, -1.806]
 289 -1.897438  0.847664  0.689778 -0.454152 -0.747836  [-2.11, -1.806]
 159 -1.848425  0.477726  0.391384 -0.477804  0.168160  [-2.11, -1.806],
. . . 

06

# random df
df = pd.DataFrame[np.random.randn[1000, 5], columns=list['ABCDE']]

# sort on A
df.sort_values['A', inplace=True]

# create bins
df['bin'] = pd.cut[df['A'], 20, include_lowest = True]

# group on bin
group = df.groupby['bin']

# list comprehension to split groups into list of dataframes 
dfs = [group.get_group[x] for x in group.groups]


[            A         B         C         D         E               bin
 218 -2.716093  0.833726 -0.771400  0.691251  0.162448  [-2.723, -2.413]
 207 -2.581388 -2.318333 -0.001467  0.035277  1.219666  [-2.723, -2.413]
 380 -2.499710  1.946709 -0.519070  1.653383  0.309689  [-2.723, -2.413]
 866 -2.492050  0.246500 -0.596392  0.872888  2.371652  [-2.723, -2.413]
 876 -2.469238 -0.156470 -0.841065 -1.248793 -0.489665  [-2.723, -2.413]
 314 -2.456308  0.630691 -0.072146  1.139697  0.663674  [-2.723, -2.413]
 310 -2.455353  0.075842  0.589515 -0.427233  1.207979  [-2.723, -2.413]
 660 -2.427255  0.890125 -0.042716 -1.038401  0.651324  [-2.723, -2.413],
             A         B         C         D         E              bin
 571 -2.355430  0.383794 -1.266575 -1.214833 -0.862611  [-2.413, -2.11]
 977 -2.354416 -1.964189  0.440376  0.028032 -0.181360  [-2.413, -2.11]
 83  -2.276908  0.288462  0.370555 -0.546359 -2.033892  [-2.413, -2.11]
 196 -2.213729 -1.087783 -0.592884  1.233886  1.051164  [-2.413, -2.11]
 227 -2.146631  0.365183 -0.095293 -0.882414  0.385117  [-2.413, -2.11]
 39  -2.136800 -1.150065  0.180182 -0.424071  0.040370  [-2.413, -2.11],
             A         B         C         D         E              bin
 104 -2.108961 -0.396602 -1.014224 -1.277124  0.001030  [-2.11, -1.806]
 360 -2.098928  1.093483  1.438421 -0.980215  0.010359  [-2.11, -1.806]
 530 -2.088592  1.043201 -0.522468  0.482176 -0.680166  [-2.11, -1.806]
 158 -2.062759  2.070387  2.124621 -2.751532  0.674055  [-2.11, -1.806]
 971 -2.053039  0.347577 -0.498513  1.917305 -1.746493  [-2.11, -1.806]
 658 -2.002482 -1.222292 -0.398816  0.279228 -1.485782  [-2.11, -1.806]
 90  -1.985261  3.499251 -2.089028  1.238524 -1.781089  [-2.11, -1.806]
 466 -1.973640 -1.609920 -1.029454  0.809143 -0.228893  [-2.11, -1.806]
 40  -1.966016 -1.479240 -1.564966 -0.310133  1.338023  [-2.11, -1.806]
 279 -1.943666  0.762493  0.060038  0.449159  0.244411  [-2.11, -1.806]
 204 -1.940045  0.844901 -0.343691 -1.144836  1.385915  [-2.11, -1.806]
 780 -1.918548  0.212452  0.225789  0.216110  1.710532  [-2.11, -1.806]
 289 -1.897438  0.847664  0.689778 -0.454152 -0.747836  [-2.11, -1.806]
 159 -1.848425  0.477726  0.391384 -0.477804  0.168160  [-2.11, -1.806],
. . . 

069_______95

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

4

# random df
df = pd.DataFrame[np.random.randn[1000, 5], columns=list['ABCDE']]

# sort on A
df.sort_values['A', inplace=True]

# create bins
df['bin'] = pd.cut[df['A'], 20, include_lowest = True]

# group on bin
group = df.groupby['bin']

# list comprehension to split groups into list of dataframes 
dfs = [group.get_group[x] for x in group.groups]


[            A         B         C         D         E               bin
 218 -2.716093  0.833726 -0.771400  0.691251  0.162448  [-2.723, -2.413]
 207 -2.581388 -2.318333 -0.001467  0.035277  1.219666  [-2.723, -2.413]
 380 -2.499710  1.946709 -0.519070  1.653383  0.309689  [-2.723, -2.413]
 866 -2.492050  0.246500 -0.596392  0.872888  2.371652  [-2.723, -2.413]
 876 -2.469238 -0.156470 -0.841065 -1.248793 -0.489665  [-2.723, -2.413]
 314 -2.456308  0.630691 -0.072146  1.139697  0.663674  [-2.723, -2.413]
 310 -2.455353  0.075842  0.589515 -0.427233  1.207979  [-2.723, -2.413]
 660 -2.427255  0.890125 -0.042716 -1.038401  0.651324  [-2.723, -2.413],
             A         B         C         D         E              bin
 571 -2.355430  0.383794 -1.266575 -1.214833 -0.862611  [-2.413, -2.11]
 977 -2.354416 -1.964189  0.440376  0.028032 -0.181360  [-2.413, -2.11]
 83  -2.276908  0.288462  0.370555 -0.546359 -2.033892  [-2.413, -2.11]
 196 -2.213729 -1.087783 -0.592884  1.233886  1.051164  [-2.413, -2.11]
 227 -2.146631  0.365183 -0.095293 -0.882414  0.385117  [-2.413, -2.11]
 39  -2.136800 -1.150065  0.180182 -0.424071  0.040370  [-2.413, -2.11],
             A         B         C         D         E              bin
 104 -2.108961 -0.396602 -1.014224 -1.277124  0.001030  [-2.11, -1.806]
 360 -2.098928  1.093483  1.438421 -0.980215  0.010359  [-2.11, -1.806]
 530 -2.088592  1.043201 -0.522468  0.482176 -0.680166  [-2.11, -1.806]
 158 -2.062759  2.070387  2.124621 -2.751532  0.674055  [-2.11, -1.806]
 971 -2.053039  0.347577 -0.498513  1.917305 -1.746493  [-2.11, -1.806]
 658 -2.002482 -1.222292 -0.398816  0.279228 -1.485782  [-2.11, -1.806]
 90  -1.985261  3.499251 -2.089028  1.238524 -1.781089  [-2.11, -1.806]
 466 -1.973640 -1.609920 -1.029454  0.809143 -0.228893  [-2.11, -1.806]
 40  -1.966016 -1.479240 -1.564966 -0.310133  1.338023  [-2.11, -1.806]
 279 -1.943666  0.762493  0.060038  0.449159  0.244411  [-2.11, -1.806]
 204 -1.940045  0.844901 -0.343691 -1.144836  1.385915  [-2.11, -1.806]
 780 -1.918548  0.212452  0.225789  0.216110  1.710532  [-2.11, -1.806]
 289 -1.897438  0.847664  0.689778 -0.454152 -0.747836  [-2.11, -1.806]
 159 -1.848425  0.477726  0.391384 -0.477804  0.168160  [-2.11, -1.806],
. . . 

102

# random df
df = pd.DataFrame[np.random.randn[1000, 5], columns=list['ABCDE']]

# sort on A
df.sort_values['A', inplace=True]

# create bins
df['bin'] = pd.cut[df['A'], 20, include_lowest = True]

# group on bin
group = df.groupby['bin']

# list comprehension to split groups into list of dataframes 
dfs = [group.get_group[x] for x in group.groups]


[            A         B         C         D         E               bin
 218 -2.716093  0.833726 -0.771400  0.691251  0.162448  [-2.723, -2.413]
 207 -2.581388 -2.318333 -0.001467  0.035277  1.219666  [-2.723, -2.413]
 380 -2.499710  1.946709 -0.519070  1.653383  0.309689  [-2.723, -2.413]
 866 -2.492050  0.246500 -0.596392  0.872888  2.371652  [-2.723, -2.413]
 876 -2.469238 -0.156470 -0.841065 -1.248793 -0.489665  [-2.723, -2.413]
 314 -2.456308  0.630691 -0.072146  1.139697  0.663674  [-2.723, -2.413]
 310 -2.455353  0.075842  0.589515 -0.427233  1.207979  [-2.723, -2.413]
 660 -2.427255  0.890125 -0.042716 -1.038401  0.651324  [-2.723, -2.413],
             A         B         C         D         E              bin
 571 -2.355430  0.383794 -1.266575 -1.214833 -0.862611  [-2.413, -2.11]
 977 -2.354416 -1.964189  0.440376  0.028032 -0.181360  [-2.413, -2.11]
 83  -2.276908  0.288462  0.370555 -0.546359 -2.033892  [-2.413, -2.11]
 196 -2.213729 -1.087783 -0.592884  1.233886  1.051164  [-2.413, -2.11]
 227 -2.146631  0.365183 -0.095293 -0.882414  0.385117  [-2.413, -2.11]
 39  -2.136800 -1.150065  0.180182 -0.424071  0.040370  [-2.413, -2.11],
             A         B         C         D         E              bin
 104 -2.108961 -0.396602 -1.014224 -1.277124  0.001030  [-2.11, -1.806]
 360 -2.098928  1.093483  1.438421 -0.980215  0.010359  [-2.11, -1.806]
 530 -2.088592  1.043201 -0.522468  0.482176 -0.680166  [-2.11, -1.806]
 158 -2.062759  2.070387  2.124621 -2.751532  0.674055  [-2.11, -1.806]
 971 -2.053039  0.347577 -0.498513  1.917305 -1.746493  [-2.11, -1.806]
 658 -2.002482 -1.222292 -0.398816  0.279228 -1.485782  [-2.11, -1.806]
 90  -1.985261  3.499251 -2.089028  1.238524 -1.781089  [-2.11, -1.806]
 466 -1.973640 -1.609920 -1.029454  0.809143 -0.228893  [-2.11, -1.806]
 40  -1.966016 -1.479240 -1.564966 -0.310133  1.338023  [-2.11, -1.806]
 279 -1.943666  0.762493  0.060038  0.449159  0.244411  [-2.11, -1.806]
 204 -1.940045  0.844901 -0.343691 -1.144836  1.385915  [-2.11, -1.806]
 780 -1.918548  0.212452  0.225789  0.216110  1.710532  [-2.11, -1.806]
 289 -1.897438  0.847664  0.689778 -0.454152 -0.747836  [-2.11, -1.806]
 159 -1.848425  0.477726  0.391384 -0.477804  0.168160  [-2.11, -1.806],
. . . 

103____30

# random df
df = pd.DataFrame[np.random.randn[1000, 5], columns=list['ABCDE']]

# sort on A
df.sort_values['A', inplace=True]

# create bins
df['bin'] = pd.cut[df['A'], 20, include_lowest = True]

# group on bin
group = df.groupby['bin']

# list comprehension to split groups into list of dataframes 
dfs = [group.get_group[x] for x in group.groups]


[            A         B         C         D         E               bin
 218 -2.716093  0.833726 -0.771400  0.691251  0.162448  [-2.723, -2.413]
 207 -2.581388 -2.318333 -0.001467  0.035277  1.219666  [-2.723, -2.413]
 380 -2.499710  1.946709 -0.519070  1.653383  0.309689  [-2.723, -2.413]
 866 -2.492050  0.246500 -0.596392  0.872888  2.371652  [-2.723, -2.413]
 876 -2.469238 -0.156470 -0.841065 -1.248793 -0.489665  [-2.723, -2.413]
 314 -2.456308  0.630691 -0.072146  1.139697  0.663674  [-2.723, -2.413]
 310 -2.455353  0.075842  0.589515 -0.427233  1.207979  [-2.723, -2.413]
 660 -2.427255  0.890125 -0.042716 -1.038401  0.651324  [-2.723, -2.413],
             A         B         C         D         E              bin
 571 -2.355430  0.383794 -1.266575 -1.214833 -0.862611  [-2.413, -2.11]
 977 -2.354416 -1.964189  0.440376  0.028032 -0.181360  [-2.413, -2.11]
 83  -2.276908  0.288462  0.370555 -0.546359 -2.033892  [-2.413, -2.11]
 196 -2.213729 -1.087783 -0.592884  1.233886  1.051164  [-2.413, -2.11]
 227 -2.146631  0.365183 -0.095293 -0.882414  0.385117  [-2.413, -2.11]
 39  -2.136800 -1.150065  0.180182 -0.424071  0.040370  [-2.413, -2.11],
             A         B         C         D         E              bin
 104 -2.108961 -0.396602 -1.014224 -1.277124  0.001030  [-2.11, -1.806]
 360 -2.098928  1.093483  1.438421 -0.980215  0.010359  [-2.11, -1.806]
 530 -2.088592  1.043201 -0.522468  0.482176 -0.680166  [-2.11, -1.806]
 158 -2.062759  2.070387  2.124621 -2.751532  0.674055  [-2.11, -1.806]
 971 -2.053039  0.347577 -0.498513  1.917305 -1.746493  [-2.11, -1.806]
 658 -2.002482 -1.222292 -0.398816  0.279228 -1.485782  [-2.11, -1.806]
 90  -1.985261  3.499251 -2.089028  1.238524 -1.781089  [-2.11, -1.806]
 466 -1.973640 -1.609920 -1.029454  0.809143 -0.228893  [-2.11, -1.806]
 40  -1.966016 -1.479240 -1.564966 -0.310133  1.338023  [-2.11, -1.806]
 279 -1.943666  0.762493  0.060038  0.449159  0.244411  [-2.11, -1.806]
 204 -1.940045  0.844901 -0.343691 -1.144836  1.385915  [-2.11, -1.806]
 780 -1.918548  0.212452  0.225789  0.216110  1.710532  [-2.11, -1.806]
 289 -1.897438  0.847664  0.689778 -0.454152 -0.747836  [-2.11, -1.806]
 159 -1.848425  0.477726  0.391384 -0.477804  0.168160  [-2.11, -1.806],
. . . 

105

# random df
df = pd.DataFrame[np.random.randn[1000, 5], columns=list['ABCDE']]

# sort on A
df.sort_values['A', inplace=True]

# create bins
df['bin'] = pd.cut[df['A'], 20, include_lowest = True]

# group on bin
group = df.groupby['bin']

# list comprehension to split groups into list of dataframes 
dfs = [group.get_group[x] for x in group.groups]


[            A         B         C         D         E               bin
 218 -2.716093  0.833726 -0.771400  0.691251  0.162448  [-2.723, -2.413]
 207 -2.581388 -2.318333 -0.001467  0.035277  1.219666  [-2.723, -2.413]
 380 -2.499710  1.946709 -0.519070  1.653383  0.309689  [-2.723, -2.413]
 866 -2.492050  0.246500 -0.596392  0.872888  2.371652  [-2.723, -2.413]
 876 -2.469238 -0.156470 -0.841065 -1.248793 -0.489665  [-2.723, -2.413]
 314 -2.456308  0.630691 -0.072146  1.139697  0.663674  [-2.723, -2.413]
 310 -2.455353  0.075842  0.589515 -0.427233  1.207979  [-2.723, -2.413]
 660 -2.427255  0.890125 -0.042716 -1.038401  0.651324  [-2.723, -2.413],
             A         B         C         D         E              bin
 571 -2.355430  0.383794 -1.266575 -1.214833 -0.862611  [-2.413, -2.11]
 977 -2.354416 -1.964189  0.440376  0.028032 -0.181360  [-2.413, -2.11]
 83  -2.276908  0.288462  0.370555 -0.546359 -2.033892  [-2.413, -2.11]
 196 -2.213729 -1.087783 -0.592884  1.233886  1.051164  [-2.413, -2.11]
 227 -2.146631  0.365183 -0.095293 -0.882414  0.385117  [-2.413, -2.11]
 39  -2.136800 -1.150065  0.180182 -0.424071  0.040370  [-2.413, -2.11],
             A         B         C         D         E              bin
 104 -2.108961 -0.396602 -1.014224 -1.277124  0.001030  [-2.11, -1.806]
 360 -2.098928  1.093483  1.438421 -0.980215  0.010359  [-2.11, -1.806]
 530 -2.088592  1.043201 -0.522468  0.482176 -0.680166  [-2.11, -1.806]
 158 -2.062759  2.070387  2.124621 -2.751532  0.674055  [-2.11, -1.806]
 971 -2.053039  0.347577 -0.498513  1.917305 -1.746493  [-2.11, -1.806]
 658 -2.002482 -1.222292 -0.398816  0.279228 -1.485782  [-2.11, -1.806]
 90  -1.985261  3.499251 -2.089028  1.238524 -1.781089  [-2.11, -1.806]
 466 -1.973640 -1.609920 -1.029454  0.809143 -0.228893  [-2.11, -1.806]
 40  -1.966016 -1.479240 -1.564966 -0.310133  1.338023  [-2.11, -1.806]
 279 -1.943666  0.762493  0.060038  0.449159  0.244411  [-2.11, -1.806]
 204 -1.940045  0.844901 -0.343691 -1.144836  1.385915  [-2.11, -1.806]
 780 -1.918548  0.212452  0.225789  0.216110  1.710532  [-2.11, -1.806]
 289 -1.897438  0.847664  0.689778 -0.454152 -0.747836  [-2.11, -1.806]
 159 -1.848425  0.477726  0.391384 -0.477804  0.168160  [-2.11, -1.806],
. . . 

106

# random df
df = pd.DataFrame[np.random.randn[1000, 5], columns=list['ABCDE']]

# sort on A
df.sort_values['A', inplace=True]

# create bins
df['bin'] = pd.cut[df['A'], 20, include_lowest = True]

# group on bin
group = df.groupby['bin']

# list comprehension to split groups into list of dataframes 
dfs = [group.get_group[x] for x in group.groups]


[            A         B         C         D         E               bin
 218 -2.716093  0.833726 -0.771400  0.691251  0.162448  [-2.723, -2.413]
 207 -2.581388 -2.318333 -0.001467  0.035277  1.219666  [-2.723, -2.413]
 380 -2.499710  1.946709 -0.519070  1.653383  0.309689  [-2.723, -2.413]
 866 -2.492050  0.246500 -0.596392  0.872888  2.371652  [-2.723, -2.413]
 876 -2.469238 -0.156470 -0.841065 -1.248793 -0.489665  [-2.723, -2.413]
 314 -2.456308  0.630691 -0.072146  1.139697  0.663674  [-2.723, -2.413]
 310 -2.455353  0.075842  0.589515 -0.427233  1.207979  [-2.723, -2.413]
 660 -2.427255  0.890125 -0.042716 -1.038401  0.651324  [-2.723, -2.413],
             A         B         C         D         E              bin
 571 -2.355430  0.383794 -1.266575 -1.214833 -0.862611  [-2.413, -2.11]
 977 -2.354416 -1.964189  0.440376  0.028032 -0.181360  [-2.413, -2.11]
 83  -2.276908  0.288462  0.370555 -0.546359 -2.033892  [-2.413, -2.11]
 196 -2.213729 -1.087783 -0.592884  1.233886  1.051164  [-2.413, -2.11]
 227 -2.146631  0.365183 -0.095293 -0.882414  0.385117  [-2.413, -2.11]
 39  -2.136800 -1.150065  0.180182 -0.424071  0.040370  [-2.413, -2.11],
             A         B         C         D         E              bin
 104 -2.108961 -0.396602 -1.014224 -1.277124  0.001030  [-2.11, -1.806]
 360 -2.098928  1.093483  1.438421 -0.980215  0.010359  [-2.11, -1.806]
 530 -2.088592  1.043201 -0.522468  0.482176 -0.680166  [-2.11, -1.806]
 158 -2.062759  2.070387  2.124621 -2.751532  0.674055  [-2.11, -1.806]
 971 -2.053039  0.347577 -0.498513  1.917305 -1.746493  [-2.11, -1.806]
 658 -2.002482 -1.222292 -0.398816  0.279228 -1.485782  [-2.11, -1.806]
 90  -1.985261  3.499251 -2.089028  1.238524 -1.781089  [-2.11, -1.806]
 466 -1.973640 -1.609920 -1.029454  0.809143 -0.228893  [-2.11, -1.806]
 40  -1.966016 -1.479240 -1.564966 -0.310133  1.338023  [-2.11, -1.806]
 279 -1.943666  0.762493  0.060038  0.449159  0.244411  [-2.11, -1.806]
 204 -1.940045  0.844901 -0.343691 -1.144836  1.385915  [-2.11, -1.806]
 780 -1.918548  0.212452  0.225789  0.216110  1.710532  [-2.11, -1.806]
 289 -1.897438  0.847664  0.689778 -0.454152 -0.747836  [-2.11, -1.806]
 159 -1.848425  0.477726  0.391384 -0.477804  0.168160  [-2.11, -1.806],
. . . 

107

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

6

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

7

# random df
df = pd.DataFrame[np.random.randn[1000, 5], columns=list['ABCDE']]

# sort on A
df.sort_values['A', inplace=True]

# create bins
df['bin'] = pd.cut[df['A'], 20, include_lowest = True]

# group on bin
group = df.groupby['bin']

# list comprehension to split groups into list of dataframes 
dfs = [group.get_group[x] for x in group.groups]


[            A         B         C         D         E               bin
 218 -2.716093  0.833726 -0.771400  0.691251  0.162448  [-2.723, -2.413]
 207 -2.581388 -2.318333 -0.001467  0.035277  1.219666  [-2.723, -2.413]
 380 -2.499710  1.946709 -0.519070  1.653383  0.309689  [-2.723, -2.413]
 866 -2.492050  0.246500 -0.596392  0.872888  2.371652  [-2.723, -2.413]
 876 -2.469238 -0.156470 -0.841065 -1.248793 -0.489665  [-2.723, -2.413]
 314 -2.456308  0.630691 -0.072146  1.139697  0.663674  [-2.723, -2.413]
 310 -2.455353  0.075842  0.589515 -0.427233  1.207979  [-2.723, -2.413]
 660 -2.427255  0.890125 -0.042716 -1.038401  0.651324  [-2.723, -2.413],
             A         B         C         D         E              bin
 571 -2.355430  0.383794 -1.266575 -1.214833 -0.862611  [-2.413, -2.11]
 977 -2.354416 -1.964189  0.440376  0.028032 -0.181360  [-2.413, -2.11]
 83  -2.276908  0.288462  0.370555 -0.546359 -2.033892  [-2.413, -2.11]
 196 -2.213729 -1.087783 -0.592884  1.233886  1.051164  [-2.413, -2.11]
 227 -2.146631  0.365183 -0.095293 -0.882414  0.385117  [-2.413, -2.11]
 39  -2.136800 -1.150065  0.180182 -0.424071  0.040370  [-2.413, -2.11],
             A         B         C         D         E              bin
 104 -2.108961 -0.396602 -1.014224 -1.277124  0.001030  [-2.11, -1.806]
 360 -2.098928  1.093483  1.438421 -0.980215  0.010359  [-2.11, -1.806]
 530 -2.088592  1.043201 -0.522468  0.482176 -0.680166  [-2.11, -1.806]
 158 -2.062759  2.070387  2.124621 -2.751532  0.674055  [-2.11, -1.806]
 971 -2.053039  0.347577 -0.498513  1.917305 -1.746493  [-2.11, -1.806]
 658 -2.002482 -1.222292 -0.398816  0.279228 -1.485782  [-2.11, -1.806]
 90  -1.985261  3.499251 -2.089028  1.238524 -1.781089  [-2.11, -1.806]
 466 -1.973640 -1.609920 -1.029454  0.809143 -0.228893  [-2.11, -1.806]
 40  -1.966016 -1.479240 -1.564966 -0.310133  1.338023  [-2.11, -1.806]
 279 -1.943666  0.762493  0.060038  0.449159  0.244411  [-2.11, -1.806]
 204 -1.940045  0.844901 -0.343691 -1.144836  1.385915  [-2.11, -1.806]
 780 -1.918548  0.212452  0.225789  0.216110  1.710532  [-2.11, -1.806]
 289 -1.897438  0.847664  0.689778 -0.454152 -0.747836  [-2.11, -1.806]
 159 -1.848425  0.477726  0.391384 -0.477804  0.168160  [-2.11, -1.806],
. . . 

06

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

51

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

5

# random df
df = pd.DataFrame[np.random.randn[1000, 5], columns=list['ABCDE']]

# sort on A
df.sort_values['A', inplace=True]

# create bins
df['bin'] = pd.cut[df['A'], 20, include_lowest = True]

# group on bin
group = df.groupby['bin']

# list comprehension to split groups into list of dataframes 
dfs = [group.get_group[x] for x in group.groups]


[            A         B         C         D         E               bin
 218 -2.716093  0.833726 -0.771400  0.691251  0.162448  [-2.723, -2.413]
 207 -2.581388 -2.318333 -0.001467  0.035277  1.219666  [-2.723, -2.413]
 380 -2.499710  1.946709 -0.519070  1.653383  0.309689  [-2.723, -2.413]
 866 -2.492050  0.246500 -0.596392  0.872888  2.371652  [-2.723, -2.413]
 876 -2.469238 -0.156470 -0.841065 -1.248793 -0.489665  [-2.723, -2.413]
 314 -2.456308  0.630691 -0.072146  1.139697  0.663674  [-2.723, -2.413]
 310 -2.455353  0.075842  0.589515 -0.427233  1.207979  [-2.723, -2.413]
 660 -2.427255  0.890125 -0.042716 -1.038401  0.651324  [-2.723, -2.413],
             A         B         C         D         E              bin
 571 -2.355430  0.383794 -1.266575 -1.214833 -0.862611  [-2.413, -2.11]
 977 -2.354416 -1.964189  0.440376  0.028032 -0.181360  [-2.413, -2.11]
 83  -2.276908  0.288462  0.370555 -0.546359 -2.033892  [-2.413, -2.11]
 196 -2.213729 -1.087783 -0.592884  1.233886  1.051164  [-2.413, -2.11]
 227 -2.146631  0.365183 -0.095293 -0.882414  0.385117  [-2.413, -2.11]
 39  -2.136800 -1.150065  0.180182 -0.424071  0.040370  [-2.413, -2.11],
             A         B         C         D         E              bin
 104 -2.108961 -0.396602 -1.014224 -1.277124  0.001030  [-2.11, -1.806]
 360 -2.098928  1.093483  1.438421 -0.980215  0.010359  [-2.11, -1.806]
 530 -2.088592  1.043201 -0.522468  0.482176 -0.680166  [-2.11, -1.806]
 158 -2.062759  2.070387  2.124621 -2.751532  0.674055  [-2.11, -1.806]
 971 -2.053039  0.347577 -0.498513  1.917305 -1.746493  [-2.11, -1.806]
 658 -2.002482 -1.222292 -0.398816  0.279228 -1.485782  [-2.11, -1.806]
 90  -1.985261  3.499251 -2.089028  1.238524 -1.781089  [-2.11, -1.806]
 466 -1.973640 -1.609920 -1.029454  0.809143 -0.228893  [-2.11, -1.806]
 40  -1.966016 -1.479240 -1.564966 -0.310133  1.338023  [-2.11, -1.806]
 279 -1.943666  0.762493  0.060038  0.449159  0.244411  [-2.11, -1.806]
 204 -1.940045  0.844901 -0.343691 -1.144836  1.385915  [-2.11, -1.806]
 780 -1.918548  0.212452  0.225789  0.216110  1.710532  [-2.11, -1.806]
 289 -1.897438  0.847664  0.689778 -0.454152 -0.747836  [-2.11, -1.806]
 159 -1.848425  0.477726  0.391384 -0.477804  0.168160  [-2.11, -1.806],
. . . 

106

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

0

# random df
df = pd.DataFrame[np.random.randn[1000, 5], columns=list['ABCDE']]

# sort on A
df.sort_values['A', inplace=True]

# create bins
df['bin'] = pd.cut[df['A'], 20, include_lowest = True]

# group on bin
group = df.groupby['bin']

# list comprehension to split groups into list of dataframes 
dfs = [group.get_group[x] for x in group.groups]


[            A         B         C         D         E               bin
 218 -2.716093  0.833726 -0.771400  0.691251  0.162448  [-2.723, -2.413]
 207 -2.581388 -2.318333 -0.001467  0.035277  1.219666  [-2.723, -2.413]
 380 -2.499710  1.946709 -0.519070  1.653383  0.309689  [-2.723, -2.413]
 866 -2.492050  0.246500 -0.596392  0.872888  2.371652  [-2.723, -2.413]
 876 -2.469238 -0.156470 -0.841065 -1.248793 -0.489665  [-2.723, -2.413]
 314 -2.456308  0.630691 -0.072146  1.139697  0.663674  [-2.723, -2.413]
 310 -2.455353  0.075842  0.589515 -0.427233  1.207979  [-2.723, -2.413]
 660 -2.427255  0.890125 -0.042716 -1.038401  0.651324  [-2.723, -2.413],
             A         B         C         D         E              bin
 571 -2.355430  0.383794 -1.266575 -1.214833 -0.862611  [-2.413, -2.11]
 977 -2.354416 -1.964189  0.440376  0.028032 -0.181360  [-2.413, -2.11]
 83  -2.276908  0.288462  0.370555 -0.546359 -2.033892  [-2.413, -2.11]
 196 -2.213729 -1.087783 -0.592884  1.233886  1.051164  [-2.413, -2.11]
 227 -2.146631  0.365183 -0.095293 -0.882414  0.385117  [-2.413, -2.11]
 39  -2.136800 -1.150065  0.180182 -0.424071  0.040370  [-2.413, -2.11],
             A         B         C         D         E              bin
 104 -2.108961 -0.396602 -1.014224 -1.277124  0.001030  [-2.11, -1.806]
 360 -2.098928  1.093483  1.438421 -0.980215  0.010359  [-2.11, -1.806]
 530 -2.088592  1.043201 -0.522468  0.482176 -0.680166  [-2.11, -1.806]
 158 -2.062759  2.070387  2.124621 -2.751532  0.674055  [-2.11, -1.806]
 971 -2.053039  0.347577 -0.498513  1.917305 -1.746493  [-2.11, -1.806]
 658 -2.002482 -1.222292 -0.398816  0.279228 -1.485782  [-2.11, -1.806]
 90  -1.985261  3.499251 -2.089028  1.238524 -1.781089  [-2.11, -1.806]
 466 -1.973640 -1.609920 -1.029454  0.809143 -0.228893  [-2.11, -1.806]
 40  -1.966016 -1.479240 -1.564966 -0.310133  1.338023  [-2.11, -1.806]
 279 -1.943666  0.762493  0.060038  0.449159  0.244411  [-2.11, -1.806]
 204 -1.940045  0.844901 -0.343691 -1.144836  1.385915  [-2.11, -1.806]
 780 -1.918548  0.212452  0.225789  0.216110  1.710532  [-2.11, -1.806]
 289 -1.897438  0.847664  0.689778 -0.454152 -0.747836  [-2.11, -1.806]
 159 -1.848425  0.477726  0.391384 -0.477804  0.168160  [-2.11, -1.806],
. . . 

06

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

7____95

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

9

# random df
df = pd.DataFrame[np.random.randn[1000, 5], columns=list['ABCDE']]

# sort on A
df.sort_values['A', inplace=True]

# create bins
df['bin'] = pd.cut[df['A'], 20, include_lowest = True]

# group on bin
group = df.groupby['bin']

# list comprehension to split groups into list of dataframes 
dfs = [group.get_group[x] for x in group.groups]


[            A         B         C         D         E               bin
 218 -2.716093  0.833726 -0.771400  0.691251  0.162448  [-2.723, -2.413]
 207 -2.581388 -2.318333 -0.001467  0.035277  1.219666  [-2.723, -2.413]
 380 -2.499710  1.946709 -0.519070  1.653383  0.309689  [-2.723, -2.413]
 866 -2.492050  0.246500 -0.596392  0.872888  2.371652  [-2.723, -2.413]
 876 -2.469238 -0.156470 -0.841065 -1.248793 -0.489665  [-2.723, -2.413]
 314 -2.456308  0.630691 -0.072146  1.139697  0.663674  [-2.723, -2.413]
 310 -2.455353  0.075842  0.589515 -0.427233  1.207979  [-2.723, -2.413]
 660 -2.427255  0.890125 -0.042716 -1.038401  0.651324  [-2.723, -2.413],
             A         B         C         D         E              bin
 571 -2.355430  0.383794 -1.266575 -1.214833 -0.862611  [-2.413, -2.11]
 977 -2.354416 -1.964189  0.440376  0.028032 -0.181360  [-2.413, -2.11]
 83  -2.276908  0.288462  0.370555 -0.546359 -2.033892  [-2.413, -2.11]
 196 -2.213729 -1.087783 -0.592884  1.233886  1.051164  [-2.413, -2.11]
 227 -2.146631  0.365183 -0.095293 -0.882414  0.385117  [-2.413, -2.11]
 39  -2.136800 -1.150065  0.180182 -0.424071  0.040370  [-2.413, -2.11],
             A         B         C         D         E              bin
 104 -2.108961 -0.396602 -1.014224 -1.277124  0.001030  [-2.11, -1.806]
 360 -2.098928  1.093483  1.438421 -0.980215  0.010359  [-2.11, -1.806]
 530 -2.088592  1.043201 -0.522468  0.482176 -0.680166  [-2.11, -1.806]
 158 -2.062759  2.070387  2.124621 -2.751532  0.674055  [-2.11, -1.806]
 971 -2.053039  0.347577 -0.498513  1.917305 -1.746493  [-2.11, -1.806]
 658 -2.002482 -1.222292 -0.398816  0.279228 -1.485782  [-2.11, -1.806]
 90  -1.985261  3.499251 -2.089028  1.238524 -1.781089  [-2.11, -1.806]
 466 -1.973640 -1.609920 -1.029454  0.809143 -0.228893  [-2.11, -1.806]
 40  -1.966016 -1.479240 -1.564966 -0.310133  1.338023  [-2.11, -1.806]
 279 -1.943666  0.762493  0.060038  0.449159  0.244411  [-2.11, -1.806]
 204 -1.940045  0.844901 -0.343691 -1.144836  1.385915  [-2.11, -1.806]
 780 -1.918548  0.212452  0.225789  0.216110  1.710532  [-2.11, -1.806]
 289 -1.897438  0.847664  0.689778 -0.454152 -0.747836  [-2.11, -1.806]
 159 -1.848425  0.477726  0.391384 -0.477804  0.168160  [-2.11, -1.806],
. . . 

06

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

9

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

0

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

1

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

2

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

3

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

4

# random df
df = pd.DataFrame[np.random.randn[1000, 5], columns=list['ABCDE']]

# sort on A
df.sort_values['A', inplace=True]

# create bins
df['bin'] = pd.cut[df['A'], 20, include_lowest = True]

# group on bin
group = df.groupby['bin']

# list comprehension to split groups into list of dataframes 
dfs = [group.get_group[x] for x in group.groups]


[            A         B         C         D         E               bin
 218 -2.716093  0.833726 -0.771400  0.691251  0.162448  [-2.723, -2.413]
 207 -2.581388 -2.318333 -0.001467  0.035277  1.219666  [-2.723, -2.413]
 380 -2.499710  1.946709 -0.519070  1.653383  0.309689  [-2.723, -2.413]
 866 -2.492050  0.246500 -0.596392  0.872888  2.371652  [-2.723, -2.413]
 876 -2.469238 -0.156470 -0.841065 -1.248793 -0.489665  [-2.723, -2.413]
 314 -2.456308  0.630691 -0.072146  1.139697  0.663674  [-2.723, -2.413]
 310 -2.455353  0.075842  0.589515 -0.427233  1.207979  [-2.723, -2.413]
 660 -2.427255  0.890125 -0.042716 -1.038401  0.651324  [-2.723, -2.413],
             A         B         C         D         E              bin
 571 -2.355430  0.383794 -1.266575 -1.214833 -0.862611  [-2.413, -2.11]
 977 -2.354416 -1.964189  0.440376  0.028032 -0.181360  [-2.413, -2.11]
 83  -2.276908  0.288462  0.370555 -0.546359 -2.033892  [-2.413, -2.11]
 196 -2.213729 -1.087783 -0.592884  1.233886  1.051164  [-2.413, -2.11]
 227 -2.146631  0.365183 -0.095293 -0.882414  0.385117  [-2.413, -2.11]
 39  -2.136800 -1.150065  0.180182 -0.424071  0.040370  [-2.413, -2.11],
             A         B         C         D         E              bin
 104 -2.108961 -0.396602 -1.014224 -1.277124  0.001030  [-2.11, -1.806]
 360 -2.098928  1.093483  1.438421 -0.980215  0.010359  [-2.11, -1.806]
 530 -2.088592  1.043201 -0.522468  0.482176 -0.680166  [-2.11, -1.806]
 158 -2.062759  2.070387  2.124621 -2.751532  0.674055  [-2.11, -1.806]
 971 -2.053039  0.347577 -0.498513  1.917305 -1.746493  [-2.11, -1.806]
 658 -2.002482 -1.222292 -0.398816  0.279228 -1.485782  [-2.11, -1.806]
 90  -1.985261  3.499251 -2.089028  1.238524 -1.781089  [-2.11, -1.806]
 466 -1.973640 -1.609920 -1.029454  0.809143 -0.228893  [-2.11, -1.806]
 40  -1.966016 -1.479240 -1.564966 -0.310133  1.338023  [-2.11, -1.806]
 279 -1.943666  0.762493  0.060038  0.449159  0.244411  [-2.11, -1.806]
 204 -1.940045  0.844901 -0.343691 -1.144836  1.385915  [-2.11, -1.806]
 780 -1.918548  0.212452  0.225789  0.216110  1.710532  [-2.11, -1.806]
 289 -1.897438  0.847664  0.689778 -0.454152 -0.747836  [-2.11, -1.806]
 159 -1.848425  0.477726  0.391384 -0.477804  0.168160  [-2.11, -1.806],
. . . 

126

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

38

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

39

# random df
df = pd.DataFrame[np.random.randn[1000, 5], columns=list['ABCDE']]

# sort on A
df.sort_values['A', inplace=True]

# create bins
df['bin'] = pd.cut[df['A'], 20, include_lowest = True]

# group on bin
group = df.groupby['bin']

# list comprehension to split groups into list of dataframes 
dfs = [group.get_group[x] for x in group.groups]


[            A         B         C         D         E               bin
 218 -2.716093  0.833726 -0.771400  0.691251  0.162448  [-2.723, -2.413]
 207 -2.581388 -2.318333 -0.001467  0.035277  1.219666  [-2.723, -2.413]
 380 -2.499710  1.946709 -0.519070  1.653383  0.309689  [-2.723, -2.413]
 866 -2.492050  0.246500 -0.596392  0.872888  2.371652  [-2.723, -2.413]
 876 -2.469238 -0.156470 -0.841065 -1.248793 -0.489665  [-2.723, -2.413]
 314 -2.456308  0.630691 -0.072146  1.139697  0.663674  [-2.723, -2.413]
 310 -2.455353  0.075842  0.589515 -0.427233  1.207979  [-2.723, -2.413]
 660 -2.427255  0.890125 -0.042716 -1.038401  0.651324  [-2.723, -2.413],
             A         B         C         D         E              bin
 571 -2.355430  0.383794 -1.266575 -1.214833 -0.862611  [-2.413, -2.11]
 977 -2.354416 -1.964189  0.440376  0.028032 -0.181360  [-2.413, -2.11]
 83  -2.276908  0.288462  0.370555 -0.546359 -2.033892  [-2.413, -2.11]
 196 -2.213729 -1.087783 -0.592884  1.233886  1.051164  [-2.413, -2.11]
 227 -2.146631  0.365183 -0.095293 -0.882414  0.385117  [-2.413, -2.11]
 39  -2.136800 -1.150065  0.180182 -0.424071  0.040370  [-2.413, -2.11],
             A         B         C         D         E              bin
 104 -2.108961 -0.396602 -1.014224 -1.277124  0.001030  [-2.11, -1.806]
 360 -2.098928  1.093483  1.438421 -0.980215  0.010359  [-2.11, -1.806]
 530 -2.088592  1.043201 -0.522468  0.482176 -0.680166  [-2.11, -1.806]
 158 -2.062759  2.070387  2.124621 -2.751532  0.674055  [-2.11, -1.806]
 971 -2.053039  0.347577 -0.498513  1.917305 -1.746493  [-2.11, -1.806]
 658 -2.002482 -1.222292 -0.398816  0.279228 -1.485782  [-2.11, -1.806]
 90  -1.985261  3.499251 -2.089028  1.238524 -1.781089  [-2.11, -1.806]
 466 -1.973640 -1.609920 -1.029454  0.809143 -0.228893  [-2.11, -1.806]
 40  -1.966016 -1.479240 -1.564966 -0.310133  1.338023  [-2.11, -1.806]
 279 -1.943666  0.762493  0.060038  0.449159  0.244411  [-2.11, -1.806]
 204 -1.940045  0.844901 -0.343691 -1.144836  1.385915  [-2.11, -1.806]
 780 -1.918548  0.212452  0.225789  0.216110  1.710532  [-2.11, -1.806]
 289 -1.897438  0.847664  0.689778 -0.454152 -0.747836  [-2.11, -1.806]
 159 -1.848425  0.477726  0.391384 -0.477804  0.168160  [-2.11, -1.806],
. . . 

129

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

6

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

7

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

5

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

7

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

38

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

05

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

38

# random df
df = pd.DataFrame[np.random.randn[1000, 5], columns=list['ABCDE']]

# sort on A
df.sort_values['A', inplace=True]

# create bins
df['bin'] = pd.cut[df['A'], 20, include_lowest = True]

# group on bin
group = df.groupby['bin']

# list comprehension to split groups into list of dataframes 
dfs = [group.get_group[x] for x in group.groups]


[            A         B         C         D         E               bin
 218 -2.716093  0.833726 -0.771400  0.691251  0.162448  [-2.723, -2.413]
 207 -2.581388 -2.318333 -0.001467  0.035277  1.219666  [-2.723, -2.413]
 380 -2.499710  1.946709 -0.519070  1.653383  0.309689  [-2.723, -2.413]
 866 -2.492050  0.246500 -0.596392  0.872888  2.371652  [-2.723, -2.413]
 876 -2.469238 -0.156470 -0.841065 -1.248793 -0.489665  [-2.723, -2.413]
 314 -2.456308  0.630691 -0.072146  1.139697  0.663674  [-2.723, -2.413]
 310 -2.455353  0.075842  0.589515 -0.427233  1.207979  [-2.723, -2.413]
 660 -2.427255  0.890125 -0.042716 -1.038401  0.651324  [-2.723, -2.413],
             A         B         C         D         E              bin
 571 -2.355430  0.383794 -1.266575 -1.214833 -0.862611  [-2.413, -2.11]
 977 -2.354416 -1.964189  0.440376  0.028032 -0.181360  [-2.413, -2.11]
 83  -2.276908  0.288462  0.370555 -0.546359 -2.033892  [-2.413, -2.11]
 196 -2.213729 -1.087783 -0.592884  1.233886  1.051164  [-2.413, -2.11]
 227 -2.146631  0.365183 -0.095293 -0.882414  0.385117  [-2.413, -2.11]
 39  -2.136800 -1.150065  0.180182 -0.424071  0.040370  [-2.413, -2.11],
             A         B         C         D         E              bin
 104 -2.108961 -0.396602 -1.014224 -1.277124  0.001030  [-2.11, -1.806]
 360 -2.098928  1.093483  1.438421 -0.980215  0.010359  [-2.11, -1.806]
 530 -2.088592  1.043201 -0.522468  0.482176 -0.680166  [-2.11, -1.806]
 158 -2.062759  2.070387  2.124621 -2.751532  0.674055  [-2.11, -1.806]
 971 -2.053039  0.347577 -0.498513  1.917305 -1.746493  [-2.11, -1.806]
 658 -2.002482 -1.222292 -0.398816  0.279228 -1.485782  [-2.11, -1.806]
 90  -1.985261  3.499251 -2.089028  1.238524 -1.781089  [-2.11, -1.806]
 466 -1.973640 -1.609920 -1.029454  0.809143 -0.228893  [-2.11, -1.806]
 40  -1.966016 -1.479240 -1.564966 -0.310133  1.338023  [-2.11, -1.806]
 279 -1.943666  0.762493  0.060038  0.449159  0.244411  [-2.11, -1.806]
 204 -1.940045  0.844901 -0.343691 -1.144836  1.385915  [-2.11, -1.806]
 780 -1.918548  0.212452  0.225789  0.216110  1.710532  [-2.11, -1.806]
 289 -1.897438  0.847664  0.689778 -0.454152 -0.747836  [-2.11, -1.806]
 159 -1.848425  0.477726  0.391384 -0.477804  0.168160  [-2.11, -1.806],
. . . 

069

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

36

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

09

# random df
df = pd.DataFrame[np.random.randn[1000, 5], columns=list['ABCDE']]

# sort on A
df.sort_values['A', inplace=True]

# create bins
df['bin'] = pd.cut[df['A'], 20, include_lowest = True]

# group on bin
group = df.groupby['bin']

# list comprehension to split groups into list of dataframes 
dfs = [group.get_group[x] for x in group.groups]


[            A         B         C         D         E               bin
 218 -2.716093  0.833726 -0.771400  0.691251  0.162448  [-2.723, -2.413]
 207 -2.581388 -2.318333 -0.001467  0.035277  1.219666  [-2.723, -2.413]
 380 -2.499710  1.946709 -0.519070  1.653383  0.309689  [-2.723, -2.413]
 866 -2.492050  0.246500 -0.596392  0.872888  2.371652  [-2.723, -2.413]
 876 -2.469238 -0.156470 -0.841065 -1.248793 -0.489665  [-2.723, -2.413]
 314 -2.456308  0.630691 -0.072146  1.139697  0.663674  [-2.723, -2.413]
 310 -2.455353  0.075842  0.589515 -0.427233  1.207979  [-2.723, -2.413]
 660 -2.427255  0.890125 -0.042716 -1.038401  0.651324  [-2.723, -2.413],
             A         B         C         D         E              bin
 571 -2.355430  0.383794 -1.266575 -1.214833 -0.862611  [-2.413, -2.11]
 977 -2.354416 -1.964189  0.440376  0.028032 -0.181360  [-2.413, -2.11]
 83  -2.276908  0.288462  0.370555 -0.546359 -2.033892  [-2.413, -2.11]
 196 -2.213729 -1.087783 -0.592884  1.233886  1.051164  [-2.413, -2.11]
 227 -2.146631  0.365183 -0.095293 -0.882414  0.385117  [-2.413, -2.11]
 39  -2.136800 -1.150065  0.180182 -0.424071  0.040370  [-2.413, -2.11],
             A         B         C         D         E              bin
 104 -2.108961 -0.396602 -1.014224 -1.277124  0.001030  [-2.11, -1.806]
 360 -2.098928  1.093483  1.438421 -0.980215  0.010359  [-2.11, -1.806]
 530 -2.088592  1.043201 -0.522468  0.482176 -0.680166  [-2.11, -1.806]
 158 -2.062759  2.070387  2.124621 -2.751532  0.674055  [-2.11, -1.806]
 971 -2.053039  0.347577 -0.498513  1.917305 -1.746493  [-2.11, -1.806]
 658 -2.002482 -1.222292 -0.398816  0.279228 -1.485782  [-2.11, -1.806]
 90  -1.985261  3.499251 -2.089028  1.238524 -1.781089  [-2.11, -1.806]
 466 -1.973640 -1.609920 -1.029454  0.809143 -0.228893  [-2.11, -1.806]
 40  -1.966016 -1.479240 -1.564966 -0.310133  1.338023  [-2.11, -1.806]
 279 -1.943666  0.762493  0.060038  0.449159  0.244411  [-2.11, -1.806]
 204 -1.940045  0.844901 -0.343691 -1.144836  1.385915  [-2.11, -1.806]
 780 -1.918548  0.212452  0.225789  0.216110  1.710532  [-2.11, -1.806]
 289 -1.897438  0.847664  0.689778 -0.454152 -0.747836  [-2.11, -1.806]
 159 -1.848425  0.477726  0.391384 -0.477804  0.168160  [-2.11, -1.806],
. . . 

06

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

11____95

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

9

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

14

# random df
df = pd.DataFrame[np.random.randn[1000, 5], columns=list['ABCDE']]

# sort on A
df.sort_values['A', inplace=True]

# create bins
df['bin'] = pd.cut[df['A'], 20, include_lowest = True]

# group on bin
group = df.groupby['bin']

# list comprehension to split groups into list of dataframes 
dfs = [group.get_group[x] for x in group.groups]


[            A         B         C         D         E               bin
 218 -2.716093  0.833726 -0.771400  0.691251  0.162448  [-2.723, -2.413]
 207 -2.581388 -2.318333 -0.001467  0.035277  1.219666  [-2.723, -2.413]
 380 -2.499710  1.946709 -0.519070  1.653383  0.309689  [-2.723, -2.413]
 866 -2.492050  0.246500 -0.596392  0.872888  2.371652  [-2.723, -2.413]
 876 -2.469238 -0.156470 -0.841065 -1.248793 -0.489665  [-2.723, -2.413]
 314 -2.456308  0.630691 -0.072146  1.139697  0.663674  [-2.723, -2.413]
 310 -2.455353  0.075842  0.589515 -0.427233  1.207979  [-2.723, -2.413]
 660 -2.427255  0.890125 -0.042716 -1.038401  0.651324  [-2.723, -2.413],
             A         B         C         D         E              bin
 571 -2.355430  0.383794 -1.266575 -1.214833 -0.862611  [-2.413, -2.11]
 977 -2.354416 -1.964189  0.440376  0.028032 -0.181360  [-2.413, -2.11]
 83  -2.276908  0.288462  0.370555 -0.546359 -2.033892  [-2.413, -2.11]
 196 -2.213729 -1.087783 -0.592884  1.233886  1.051164  [-2.413, -2.11]
 227 -2.146631  0.365183 -0.095293 -0.882414  0.385117  [-2.413, -2.11]
 39  -2.136800 -1.150065  0.180182 -0.424071  0.040370  [-2.413, -2.11],
             A         B         C         D         E              bin
 104 -2.108961 -0.396602 -1.014224 -1.277124  0.001030  [-2.11, -1.806]
 360 -2.098928  1.093483  1.438421 -0.980215  0.010359  [-2.11, -1.806]
 530 -2.088592  1.043201 -0.522468  0.482176 -0.680166  [-2.11, -1.806]
 158 -2.062759  2.070387  2.124621 -2.751532  0.674055  [-2.11, -1.806]
 971 -2.053039  0.347577 -0.498513  1.917305 -1.746493  [-2.11, -1.806]
 658 -2.002482 -1.222292 -0.398816  0.279228 -1.485782  [-2.11, -1.806]
 90  -1.985261  3.499251 -2.089028  1.238524 -1.781089  [-2.11, -1.806]
 466 -1.973640 -1.609920 -1.029454  0.809143 -0.228893  [-2.11, -1.806]
 40  -1.966016 -1.479240 -1.564966 -0.310133  1.338023  [-2.11, -1.806]
 279 -1.943666  0.762493  0.060038  0.449159  0.244411  [-2.11, -1.806]
 204 -1.940045  0.844901 -0.343691 -1.144836  1.385915  [-2.11, -1.806]
 780 -1.918548  0.212452  0.225789  0.216110  1.710532  [-2.11, -1.806]
 289 -1.897438  0.847664  0.689778 -0.454152 -0.747836  [-2.11, -1.806]
 159 -1.848425  0.477726  0.391384 -0.477804  0.168160  [-2.11, -1.806],
. . . 

06

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

9

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

0

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

1

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

2

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

3

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

4

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

5

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

6

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

24

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

5

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

9

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

6

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

9

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

32

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

1

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

31

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

93

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

94

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

95

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

5

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

36

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

37

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

38

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

5

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

40

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

38

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

39

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

43

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

98

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

24

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

38

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

5

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

48

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

6

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

11

equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]


equal width binning
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]] 

38

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

5

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

53

# random df
df = pd.DataFrame[np.random.randn[1000, 5], columns=list['ABCDE']]

# sort on A
df.sort_values['A', inplace=True]

# create bins
df['bin'] = pd.cut[df['A'], 20, include_lowest = True]

# group on bin
group = df.groupby['bin']

# list comprehension to split groups into list of dataframes 
dfs = [group.get_group[x] for x in group.groups]


[            A         B         C         D         E               bin
 218 -2.716093  0.833726 -0.771400  0.691251  0.162448  [-2.723, -2.413]
 207 -2.581388 -2.318333 -0.001467  0.035277  1.219666  [-2.723, -2.413]
 380 -2.499710  1.946709 -0.519070  1.653383  0.309689  [-2.723, -2.413]
 866 -2.492050  0.246500 -0.596392  0.872888  2.371652  [-2.723, -2.413]
 876 -2.469238 -0.156470 -0.841065 -1.248793 -0.489665  [-2.723, -2.413]
 314 -2.456308  0.630691 -0.072146  1.139697  0.663674  [-2.723, -2.413]
 310 -2.455353  0.075842  0.589515 -0.427233  1.207979  [-2.723, -2.413]
 660 -2.427255  0.890125 -0.042716 -1.038401  0.651324  [-2.723, -2.413],
             A         B         C         D         E              bin
 571 -2.355430  0.383794 -1.266575 -1.214833 -0.862611  [-2.413, -2.11]
 977 -2.354416 -1.964189  0.440376  0.028032 -0.181360  [-2.413, -2.11]
 83  -2.276908  0.288462  0.370555 -0.546359 -2.033892  [-2.413, -2.11]
 196 -2.213729 -1.087783 -0.592884  1.233886  1.051164  [-2.413, -2.11]
 227 -2.146631  0.365183 -0.095293 -0.882414  0.385117  [-2.413, -2.11]
 39  -2.136800 -1.150065  0.180182 -0.424071  0.040370  [-2.413, -2.11],
             A         B         C         D         E              bin
 104 -2.108961 -0.396602 -1.014224 -1.277124  0.001030  [-2.11, -1.806]
 360 -2.098928  1.093483  1.438421 -0.980215  0.010359  [-2.11, -1.806]
 530 -2.088592  1.043201 -0.522468  0.482176 -0.680166  [-2.11, -1.806]
 158 -2.062759  2.070387  2.124621 -2.751532  0.674055  [-2.11, -1.806]
 971 -2.053039  0.347577 -0.498513  1.917305 -1.746493  [-2.11, -1.806]
 658 -2.002482 -1.222292 -0.398816  0.279228 -1.485782  [-2.11, -1.806]
 90  -1.985261  3.499251 -2.089028  1.238524 -1.781089  [-2.11, -1.806]
 466 -1.973640 -1.609920 -1.029454  0.809143 -0.228893  [-2.11, -1.806]
 40  -1.966016 -1.479240 -1.564966 -0.310133  1.338023  [-2.11, -1.806]
 279 -1.943666  0.762493  0.060038  0.449159  0.244411  [-2.11, -1.806]
 204 -1.940045  0.844901 -0.343691 -1.144836  1.385915  [-2.11, -1.806]
 780 -1.918548  0.212452  0.225789  0.216110  1.710532  [-2.11, -1.806]
 289 -1.897438  0.847664  0.689778 -0.454152 -0.747836  [-2.11, -1.806]
 159 -1.848425  0.477726  0.391384 -0.477804  0.168160  [-2.11, -1.806],
. . . 

06

# random df
df = pd.DataFrame[np.random.randn[1000, 5], columns=list['ABCDE']]

# sort on A
df.sort_values['A', inplace=True]

# create bins
df['bin'] = pd.cut[df['A'], 20, include_lowest = True]

# group on bin
group = df.groupby['bin']

# list comprehension to split groups into list of dataframes 
dfs = [group.get_group[x] for x in group.groups]


[            A         B         C         D         E               bin
 218 -2.716093  0.833726 -0.771400  0.691251  0.162448  [-2.723, -2.413]
 207 -2.581388 -2.318333 -0.001467  0.035277  1.219666  [-2.723, -2.413]
 380 -2.499710  1.946709 -0.519070  1.653383  0.309689  [-2.723, -2.413]
 866 -2.492050  0.246500 -0.596392  0.872888  2.371652  [-2.723, -2.413]
 876 -2.469238 -0.156470 -0.841065 -1.248793 -0.489665  [-2.723, -2.413]
 314 -2.456308  0.630691 -0.072146  1.139697  0.663674  [-2.723, -2.413]
 310 -2.455353  0.075842  0.589515 -0.427233  1.207979  [-2.723, -2.413]
 660 -2.427255  0.890125 -0.042716 -1.038401  0.651324  [-2.723, -2.413],
             A         B         C         D         E              bin
 571 -2.355430  0.383794 -1.266575 -1.214833 -0.862611  [-2.413, -2.11]
 977 -2.354416 -1.964189  0.440376  0.028032 -0.181360  [-2.413, -2.11]
 83  -2.276908  0.288462  0.370555 -0.546359 -2.033892  [-2.413, -2.11]
 196 -2.213729 -1.087783 -0.592884  1.233886  1.051164  [-2.413, -2.11]
 227 -2.146631  0.365183 -0.095293 -0.882414  0.385117  [-2.413, -2.11]
 39  -2.136800 -1.150065  0.180182 -0.424071  0.040370  [-2.413, -2.11],
             A         B         C         D         E              bin
 104 -2.108961 -0.396602 -1.014224 -1.277124  0.001030  [-2.11, -1.806]
 360 -2.098928  1.093483  1.438421 -0.980215  0.010359  [-2.11, -1.806]
 530 -2.088592  1.043201 -0.522468  0.482176 -0.680166  [-2.11, -1.806]
 158 -2.062759  2.070387  2.124621 -2.751532  0.674055  [-2.11, -1.806]
 971 -2.053039  0.347577 -0.498513  1.917305 -1.746493  [-2.11, -1.806]
 658 -2.002482 -1.222292 -0.398816  0.279228 -1.485782  [-2.11, -1.806]
 90  -1.985261  3.499251 -2.089028  1.238524 -1.781089  [-2.11, -1.806]
 466 -1.973640 -1.609920 -1.029454  0.809143 -0.228893  [-2.11, -1.806]
 40  -1.966016 -1.479240 -1.564966 -0.310133  1.338023  [-2.11, -1.806]
 279 -1.943666  0.762493  0.060038  0.449159  0.244411  [-2.11, -1.806]
 204 -1.940045  0.844901 -0.343691 -1.144836  1.385915  [-2.11, -1.806]
 780 -1.918548  0.212452  0.225789  0.216110  1.710532  [-2.11, -1.806]
 289 -1.897438  0.847664  0.689778 -0.454152 -0.747836  [-2.11, -1.806]
 159 -1.848425  0.477726  0.391384 -0.477804  0.168160  [-2.11, -1.806],
. . . 

027______356

# random df
df = pd.DataFrame[np.random.randn[1000, 5], columns=list['ABCDE']]

# sort on A
df.sort_values['A', inplace=True]

# create bins
df['bin'] = pd.cut[df['A'], 20, include_lowest = True]

# group on bin
group = df.groupby['bin']

# list comprehension to split groups into list of dataframes 
dfs = [group.get_group[x] for x in group.groups]


[            A         B         C         D         E               bin
 218 -2.716093  0.833726 -0.771400  0.691251  0.162448  [-2.723, -2.413]
 207 -2.581388 -2.318333 -0.001467  0.035277  1.219666  [-2.723, -2.413]
 380 -2.499710  1.946709 -0.519070  1.653383  0.309689  [-2.723, -2.413]
 866 -2.492050  0.246500 -0.596392  0.872888  2.371652  [-2.723, -2.413]
 876 -2.469238 -0.156470 -0.841065 -1.248793 -0.489665  [-2.723, -2.413]
 314 -2.456308  0.630691 -0.072146  1.139697  0.663674  [-2.723, -2.413]
 310 -2.455353  0.075842  0.589515 -0.427233  1.207979  [-2.723, -2.413]
 660 -2.427255  0.890125 -0.042716 -1.038401  0.651324  [-2.723, -2.413],
             A         B         C         D         E              bin
 571 -2.355430  0.383794 -1.266575 -1.214833 -0.862611  [-2.413, -2.11]
 977 -2.354416 -1.964189  0.440376  0.028032 -0.181360  [-2.413, -2.11]
 83  -2.276908  0.288462  0.370555 -0.546359 -2.033892  [-2.413, -2.11]
 196 -2.213729 -1.087783 -0.592884  1.233886  1.051164  [-2.413, -2.11]
 227 -2.146631  0.365183 -0.095293 -0.882414  0.385117  [-2.413, -2.11]
 39  -2.136800 -1.150065  0.180182 -0.424071  0.040370  [-2.413, -2.11],
             A         B         C         D         E              bin
 104 -2.108961 -0.396602 -1.014224 -1.277124  0.001030  [-2.11, -1.806]
 360 -2.098928  1.093483  1.438421 -0.980215  0.010359  [-2.11, -1.806]
 530 -2.088592  1.043201 -0.522468  0.482176 -0.680166  [-2.11, -1.806]
 158 -2.062759  2.070387  2.124621 -2.751532  0.674055  [-2.11, -1.806]
 971 -2.053039  0.347577 -0.498513  1.917305 -1.746493  [-2.11, -1.806]
 658 -2.002482 -1.222292 -0.398816  0.279228 -1.485782  [-2.11, -1.806]
 90  -1.985261  3.499251 -2.089028  1.238524 -1.781089  [-2.11, -1.806]
 466 -1.973640 -1.609920 -1.029454  0.809143 -0.228893  [-2.11, -1.806]
 40  -1.966016 -1.479240 -1.564966 -0.310133  1.338023  [-2.11, -1.806]
 279 -1.943666  0.762493  0.060038  0.449159  0.244411  [-2.11, -1.806]
 204 -1.940045  0.844901 -0.343691 -1.144836  1.385915  [-2.11, -1.806]
 780 -1.918548  0.212452  0.225789  0.216110  1.710532  [-2.11, -1.806]
 289 -1.897438  0.847664  0.689778 -0.454152 -0.747836  [-2.11, -1.806]
 159 -1.848425  0.477726  0.391384 -0.477804  0.168160  [-2.11, -1.806],
. . . 

029

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

58

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

59

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

5

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

61

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

62

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

63

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

64

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

63

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

66

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

63

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

68

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

63

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

70

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

63

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

72

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

63

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

74

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

63

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

76

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

63

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

78

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

63

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

80

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

63

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

82

Input: [5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]

Output:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

63_____3858

Binning khoảng thời gian bằng nhau là gì?

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