机器学习-数值特征

离散值处理

import pandas as pd
import numpy as np

vg_df = pd.read_csv('datasets/vgsales.csv', encoding = "ISO-8859-1")
vg_df[['Name', 'Platform', 'Year', 'Genre', 'Publisher']].iloc[1:7]

	Name	Platform	Year	Genre	Publisher
1	Super Mario Bros.	NES	1985.0	Platform	Nintendo
2	Mario Kart Wii	Wii	2008.0	Racing	Nintendo
3	Wii Sports Resort	Wii	2009.0	Sports	Nintendo
4	Pokemon Red/Pokemon Blue	GB	1996.0	Role-Playing	Nintendo
5	Tetris	GB	1989.0	Puzzle	Nintendo
6	New Super Mario Bros.	DS	2006.0	Platform	Nintendo

genres = np.unique(vg_df['Genre'])
genres

array(['Action', 'Adventure', 'Fighting', 'Misc', 'Platform', 'Puzzle','Racing', 'Role-Playing', 'Shooter', 'Simulation', 'Sports','Strategy'], dtype=object)

LabelEncoder

from sklearn.preprocessing import LabelEncodergle = LabelEncoder()
genre_labels = gle.fit_transform(vg_df['Genre'])
genre_mappings = {index: label for index, label in enumerate(gle.classes_)}
genre_mappings

{0: 'Action',1: 'Adventure',2: 'Fighting',3: 'Misc',4: 'Platform',5: 'Puzzle',6: 'Racing',7: 'Role-Playing',8: 'Shooter',9: 'Simulation',10: 'Sports',11: 'Strategy'}

vg_df['GenreLabel'] = genre_labels
vg_df[['Name', 'Platform', 'Year', 'Genre', 'GenreLabel']].iloc[1:7]

	Name	Platform	Year	Genre	GenreLabel
1	Super Mario Bros.	NES	1985.0	Platform	4
2	Mario Kart Wii	Wii	2008.0	Racing	6
3	Wii Sports Resort	Wii	2009.0	Sports	10
4	Pokemon Red/Pokemon Blue	GB	1996.0	Role-Playing	7
5	Tetris	GB	1989.0	Puzzle	5
6	New Super Mario Bros.	DS	2006.0	Platform	4

Map

poke_df = pd.read_csv('datasets/Pokemon.csv', encoding='utf-8')
poke_df = poke_df.sample(random_state=1, frac=1).reset_index(drop=True)np.unique(poke_df['Generation'])

array(['Gen 1', 'Gen 2', 'Gen 3', 'Gen 4', 'Gen 5', 'Gen 6'], dtype=object)

gen_ord_map = {'Gen 1': 1, 'Gen 2': 2, 'Gen 3': 3, 'Gen 4': 4, 'Gen 5': 5, 'Gen 6': 6}poke_df['GenerationLabel'] = poke_df['Generation'].map(gen_ord_map)
poke_df[['Name', 'Generation', 'GenerationLabel']].iloc[4:10]

	Name	Generation	GenerationLabel
4	Octillery	Gen 2	2
5	Helioptile	Gen 6	6
6	Dialga	Gen 4	4
7	DeoxysDefense Forme	Gen 3	3
8	Rapidash	Gen 1	1
9	Swanna	Gen 5	5

One-hot Encoding

poke_df[['Name', 'Generation', 'Legendary']].iloc[4:10]

	Name	Generation	Legendary
4	Octillery	Gen 2	False
5	Helioptile	Gen 6	False
6	Dialga	Gen 4	True
7	DeoxysDefense Forme	Gen 3	True
8	Rapidash	Gen 1	False
9	Swanna	Gen 5	False

from sklearn.preprocessing import OneHotEncoder, LabelEncoder# transform and map pokemon generations
gen_le = LabelEncoder()
gen_labels = gen_le.fit_transform(poke_df['Generation'])
poke_df['Gen_Label'] = gen_labels# transform and map pokemon legendary status
leg_le = LabelEncoder()
leg_labels = leg_le.fit_transform(poke_df['Legendary'])
poke_df['Lgnd_Label'] = leg_labelspoke_df_sub = poke_df[['Name', 'Generation', 'Gen_Label', 'Legendary', 'Lgnd_Label']]
poke_df_sub.iloc[4:10]

	Name	Generation	Gen_Label	Legendary	Lgnd_Label
4	Octillery	Gen 2	1	False	0
5	Helioptile	Gen 6	5	False	0
6	Dialga	Gen 4	3	True	1
7	DeoxysDefense Forme	Gen 3	2	True	1
8	Rapidash	Gen 1	0	False	0
9	Swanna	Gen 5	4	False	0

# encode generation labels using one-hot encoding scheme
gen_ohe = OneHotEncoder()
gen_feature_arr = gen_ohe.fit_transform(poke_df[['Gen_Label']]).toarray()
gen_feature_labels = list(gen_le.classes_)
print (gen_feature_labels)
gen_features = pd.DataFrame(gen_feature_arr, columns=gen_feature_labels)# encode legendary status labels using one-hot encoding scheme
leg_ohe = OneHotEncoder()
leg_feature_arr = leg_ohe.fit_transform(poke_df[['Lgnd_Label']]).toarray()
leg_feature_labels = ['Legendary_'+str(cls_label) for cls_label in leg_le.classes_]
print (leg_feature_labels)
leg_features = pd.DataFrame(leg_feature_arr, columns=leg_feature_labels)

['Gen 1', 'Gen 2', 'Gen 3', 'Gen 4', 'Gen 5', 'Gen 6']
['Legendary_False', 'Legendary_True']

poke_df_ohe = pd.concat([poke_df_sub, gen_features, leg_features], axis=1)
columns = sum([['Name', 'Generation', 'Gen_Label'],gen_feature_labels,['Legendary', 'Lgnd_Label'],leg_feature_labels], [])
poke_df_ohe[columns].iloc[4:10]

	Name	Generation	Gen_Label	Gen 1	Gen 2	Gen 3	Gen 4	Gen 5	Gen 6	Legendary	Lgnd_Label	Legendary_False	Legendary_True
4	Octillery	Gen 2	1	0.0	1.0	0.0	0.0	0.0	0.0	False	0	1.0	0.0
5	Helioptile	Gen 6	5	0.0	0.0	0.0	0.0	0.0	1.0	False	0	1.0	0.0
6	Dialga	Gen 4	3	0.0	0.0	0.0	1.0	0.0	0.0	True	1	0.0	1.0
7	DeoxysDefense Forme	Gen 3	2	0.0	0.0	1.0	0.0	0.0	0.0	True	1	0.0	1.0
8	Rapidash	Gen 1	0	1.0	0.0	0.0	0.0	0.0	0.0	False	0	1.0	0.0
9	Swanna	Gen 5	4	0.0	0.0	0.0	0.0	1.0	0.0	False	0	1.0	0.0

Get Dummy

gen_dummy_features = pd.get_dummies(poke_df['Generation'], drop_first=True)
pd.concat([poke_df[['Name', 'Generation']], gen_dummy_features], axis=1).iloc[4:10]

	Name	Generation	Gen 2	Gen 3	Gen 4	Gen 5	Gen 6
4	Octillery	Gen 2	1	0	0	0	0
5	Helioptile	Gen 6	0	0	0	0	1
6	Dialga	Gen 4	0	0	1	0	0
7	DeoxysDefense Forme	Gen 3	0	1	0	0	0
8	Rapidash	Gen 1	0	0	0	0	0
9	Swanna	Gen 5	0	0	0	1	0

gen_onehot_features = pd.get_dummies(poke_df['Generation'])
pd.concat([poke_df[['Name', 'Generation']], gen_onehot_features], axis=1).iloc[4:10]

	Name	Generation	Gen 1	Gen 2	Gen 3	Gen 4	Gen 5	Gen 6
4	Octillery	Gen 2	0	1	0	0	0	0
5	Helioptile	Gen 6	0	0	0	0	0	1
6	Dialga	Gen 4	0	0	0	1	0	0
7	DeoxysDefense Forme	Gen 3	0	0	1	0	0	0
8	Rapidash	Gen 1	1	0	0	0	0	0
9	Swanna	Gen 5	0	0	0	0	1	0

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
import scipy.stats as spstats%matplotlib inline
mpl.style.reload_library()
mpl.style.use('classic')
mpl.rcParams['figure.facecolor'] = (1, 1, 1, 0)
mpl.rcParams['figure.figsize'] = [6.0, 4.0]
mpl.rcParams['figure.dpi'] = 100

poke_df = pd.read_csv('datasets/Pokemon.csv', encoding='utf-8')
poke_df.head()

	#	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
0	1	Bulbasaur	Grass	Poison	318	45	49	49	65	65	45	Gen 1	False
1	2	Ivysaur	Grass	Poison	405	60	62	63	80	80	60	Gen 1	False
2	3	Venusaur	Grass	Poison	525	80	82	83	100	100	80	Gen 1	False
3	3	VenusaurMega Venusaur	Grass	Poison	625	80	100	123	122	120	80	Gen 1	False
4	4	Charmander	Fire	NaN	309	39	52	43	60	50	65	Gen 1	False

poke_df[['HP', 'Attack', 'Defense']].head()

	HP	Attack	Defense
0	45	49	49
1	60	62	63
2	80	82	83
3	80	100	123
4	39	52	43

poke_df[['HP', 'Attack', 'Defense']].describe()

	HP	Attack	Defense
count	800.000000	800.000000	800.000000
mean	69.258750	79.001250	73.842500
std	25.534669	32.457366	31.183501
min	1.000000	5.000000	5.000000
25%	50.000000	55.000000	50.000000
50%	65.000000	75.000000	70.000000
75%	80.000000	100.000000	90.000000
max	255.000000	190.000000	230.000000

popsong_df = pd.read_csv('datasets/song_views.csv', encoding='utf-8')
popsong_df.head(10)

	user_id	song_id	title	listen_count
0	b6b799f34a204bd928ea014c243ddad6d0be4f8f	SOBONKR12A58A7A7E0	You're The One	2
1	b41ead730ac14f6b6717b9cf8859d5579f3f8d4d	SOBONKR12A58A7A7E0	You're The One	0
2	4c84359a164b161496d05282707cecbd50adbfc4	SOBONKR12A58A7A7E0	You're The One	0
3	779b5908593756abb6ff7586177c966022668b06	SOBONKR12A58A7A7E0	You're The One	0
4	dd88ea94f605a63d9fc37a214127e3f00e85e42d	SOBONKR12A58A7A7E0	You're The One	0
5	68f0359a2f1cedb0d15c98d88017281db79f9bc6	SOBONKR12A58A7A7E0	You're The One	0
6	116a4c95d63623a967edf2f3456c90ebbf964e6f	SOBONKR12A58A7A7E0	You're The One	17
7	45544491ccfcdc0b0803c34f201a6287ed4e30f8	SOBONKR12A58A7A7E0	You're The One	0
8	e701a24d9b6c59f5ac37ab28462ca82470e27cfb	SOBONKR12A58A7A7E0	You're The One	68
9	edc8b7b1fd592a3b69c3d823a742e1a064abec95	SOBONKR12A58A7A7E0	You're The One	0

二值特征

watched = np.array(popsong_df['listen_count']) 
watched[watched >= 1] = 1
popsong_df['watched'] = watched
popsong_df.head(10)

	user_id	song_id	title	listen_count	watched
0	b6b799f34a204bd928ea014c243ddad6d0be4f8f	SOBONKR12A58A7A7E0	You're The One	2	1
1	b41ead730ac14f6b6717b9cf8859d5579f3f8d4d	SOBONKR12A58A7A7E0	You're The One	0	0
2	4c84359a164b161496d05282707cecbd50adbfc4	SOBONKR12A58A7A7E0	You're The One	0	0
3	779b5908593756abb6ff7586177c966022668b06	SOBONKR12A58A7A7E0	You're The One	0	0
4	dd88ea94f605a63d9fc37a214127e3f00e85e42d	SOBONKR12A58A7A7E0	You're The One	0	0
5	68f0359a2f1cedb0d15c98d88017281db79f9bc6	SOBONKR12A58A7A7E0	You're The One	0	0
6	116a4c95d63623a967edf2f3456c90ebbf964e6f	SOBONKR12A58A7A7E0	You're The One	17	1
7	45544491ccfcdc0b0803c34f201a6287ed4e30f8	SOBONKR12A58A7A7E0	You're The One	0	0
8	e701a24d9b6c59f5ac37ab28462ca82470e27cfb	SOBONKR12A58A7A7E0	You're The One	68	1
9	edc8b7b1fd592a3b69c3d823a742e1a064abec95	SOBONKR12A58A7A7E0	You're The One	0	0

from sklearn.preprocessing import Binarizerbn = Binarizer(threshold=0.9)
pd_watched = bn.transform([popsong_df['listen_count']])[0]
popsong_df['pd_watched'] = pd_watched
popsong_df.head(11)

	user_id	song_id	title	listen_count	watched	pd_watched
0	b6b799f34a204bd928ea014c243ddad6d0be4f8f	SOBONKR12A58A7A7E0	You're The One	2	1	1
1	b41ead730ac14f6b6717b9cf8859d5579f3f8d4d	SOBONKR12A58A7A7E0	You're The One	0	0	0
2	4c84359a164b161496d05282707cecbd50adbfc4	SOBONKR12A58A7A7E0	You're The One	0	0	0
3	779b5908593756abb6ff7586177c966022668b06	SOBONKR12A58A7A7E0	You're The One	0	0	0
4	dd88ea94f605a63d9fc37a214127e3f00e85e42d	SOBONKR12A58A7A7E0	You're The One	0	0	0
5	68f0359a2f1cedb0d15c98d88017281db79f9bc6	SOBONKR12A58A7A7E0	You're The One	0	0	0
6	116a4c95d63623a967edf2f3456c90ebbf964e6f	SOBONKR12A58A7A7E0	You're The One	17	1	1
7	45544491ccfcdc0b0803c34f201a6287ed4e30f8	SOBONKR12A58A7A7E0	You're The One	0	0	0
8	e701a24d9b6c59f5ac37ab28462ca82470e27cfb	SOBONKR12A58A7A7E0	You're The One	68	1	1
9	edc8b7b1fd592a3b69c3d823a742e1a064abec95	SOBONKR12A58A7A7E0	You're The One	0	0	0
10	fb41d1c374d093ab643ef3bcd70eeb258d479076	SOBONKR12A58A7A7E0	You're The One	1	1	1

多项式特征

atk_def = poke_df[['Attack', 'Defense']]
atk_def.head()

	Attack	Defense
0	49	49
1	62	63
2	82	83
3	100	123
4	52	43

from sklearn.preprocessing import PolynomialFeaturespf = PolynomialFeatures(degree=2, interaction_only=False, include_bias=False)
res = pf.fit_transform(atk_def)
res

array([[    49.,     49.,   2401.,   2401.,   2401.],[    62.,     63.,   3844.,   3906.,   3969.],[    82.,     83.,   6724.,   6806.,   6889.],..., [   110.,     60.,  12100.,   6600.,   3600.],[   160.,     60.,  25600.,   9600.,   3600.],[   110.,    120.,  12100.,  13200.,  14400.]])

intr_features = pd.DataFrame(res, columns=['Attack', 'Defense', 'Attack^2', 'Attack x Defense', 'Defense^2'])
intr_features.head(5)

	Attack	Defense	Attack^2	Attack x Defense	Defense^2
0	49.0	49.0	2401.0	2401.0	2401.0
1	62.0	63.0	3844.0	3906.0	3969.0
2	82.0	83.0	6724.0	6806.0	6889.0
3	100.0	123.0	10000.0	12300.0	15129.0
4	52.0	43.0	2704.0	2236.0	1849.0

binning特征

fcc_survey_df = pd.read_csv('datasets/fcc_2016_coder_survey_subset.csv', encoding='utf-8')
fcc_survey_df[['ID.x', 'EmploymentField', 'Age', 'Income']].head()

	ID.x	EmploymentField	Age	Income
0	cef35615d61b202f1dc794ef2746df14	office and administrative support	28.0	32000.0
1	323e5a113644d18185c743c241407754	food and beverage	22.0	15000.0
2	b29a1027e5cd062e654a63764157461d	finance	19.0	48000.0
3	04a11e4bcb573a1261eb0d9948d32637	arts, entertainment, sports, or media	26.0	43000.0
4	9368291c93d5d5f5c8cdb1a575e18bec	education	20.0	6000.0

fig, ax = plt.subplots()
fcc_survey_df['Age'].hist(color='#A9C5D3')
ax.set_title('Developer Age Histogram', fontsize=12)
ax.set_xlabel('Age', fontsize=12)
ax.set_ylabel('Frequency', fontsize=12)

Text(0,0.5,'Frequency')

Binning based on rounding

Age Range: Bin
---------------0 -  9  : 0
10 - 19  : 1
20 - 29  : 2
30 - 39  : 3
40 - 49  : 4
50 - 59  : 5
60 - 69  : 6... and so on

fcc_survey_df['Age_bin_round'] = np.array(np.floor(np.array(fcc_survey_df['Age']) / 10.))
fcc_survey_df[['ID.x', 'Age', 'Age_bin_round']].iloc[1071:1076]

	ID.x	Age	Age_bin_round
1071	6a02aa4618c99fdb3e24de522a099431	17.0	1.0
1072	f0e5e47278c5f248fe861c5f7214c07a	38.0	3.0
1073	6e14f6d0779b7e424fa3fdd9e4bd3bf9	21.0	2.0
1074	c2654c07dc929cdf3dad4d1aec4ffbb3	53.0	5.0
1075	f07449fc9339b2e57703ec7886232523	35.0	3.0

分位数切分

fcc_survey_df[['ID.x', 'Age', 'Income']].iloc[4:9]

	ID.x	Age	Income
4	9368291c93d5d5f5c8cdb1a575e18bec	20.0	6000.0
5	dd0e77eab9270e4b67c19b0d6bbf621b	34.0	40000.0
6	7599c0aa0419b59fd11ffede98a3665d	23.0	32000.0
7	6dff182db452487f07a47596f314bddc	35.0	40000.0
8	9dc233f8ed1c6eb2432672ab4bb39249	33.0	80000.0

fig, ax = plt.subplots()
fcc_survey_df['Income'].hist(bins=30, color='#A9C5D3')
ax.set_title('Developer Income Histogram', fontsize=12)
ax.set_xlabel('Developer Income', fontsize=12)
ax.set_ylabel('Frequency', fontsize=12)

Text(0,0.5,'Frequency')

quantile_list = [0, .25, .5, .75, 1.]
quantiles = fcc_survey_df['Income'].quantile(quantile_list)
quantiles

0.00      6000.0
0.25     20000.0
0.50     37000.0
0.75     60000.0
1.00    200000.0
Name: Income, dtype: float64

fig, ax = plt.subplots()
fcc_survey_df['Income'].hist(bins=30, color='#A9C5D3')for quantile in quantiles:qvl = plt.axvline(quantile, color='r')
ax.legend([qvl], ['Quantiles'], fontsize=10)ax.set_title('Developer Income Histogram with Quantiles', fontsize=12)
ax.set_xlabel('Developer Income', fontsize=12)
ax.set_ylabel('Frequency', fontsize=12)

Text(0,0.5,'Frequency')

quantile_labels = ['0-25Q', '25-50Q', '50-75Q', '75-100Q']
fcc_survey_df['Income_quantile_range'] = pd.qcut(fcc_survey_df['Income'], q=quantile_list)
fcc_survey_df['Income_quantile_label'] = pd.qcut(fcc_survey_df['Income'], q=quantile_list, labels=quantile_labels)
fcc_survey_df[['ID.x', 'Age', 'Income', 'Income_quantile_range', 'Income_quantile_label']].iloc[4:9]

	ID.x	Age	Income	Income_quantile_range	Income_quantile_label
4	9368291c93d5d5f5c8cdb1a575e18bec	20.0	6000.0	(5999.999, 20000.0]	0-25Q
5	dd0e77eab9270e4b67c19b0d6bbf621b	34.0	40000.0	(37000.0, 60000.0]	50-75Q
6	7599c0aa0419b59fd11ffede98a3665d	23.0	32000.0	(20000.0, 37000.0]	25-50Q
7	6dff182db452487f07a47596f314bddc	35.0	40000.0	(37000.0, 60000.0]	50-75Q
8	9dc233f8ed1c6eb2432672ab4bb39249	33.0	80000.0	(60000.0, 200000.0]	75-100Q

对数变换 COX-BOX

fcc_survey_df['Income_log'] = np.log((1+ fcc_survey_df['Income']))
fcc_survey_df[['ID.x', 'Age', 'Income', 'Income_log']].iloc[4:9]

	ID.x	Age	Income	Income_log
4	9368291c93d5d5f5c8cdb1a575e18bec	20.0	6000.0	8.699681
5	dd0e77eab9270e4b67c19b0d6bbf621b	34.0	40000.0	10.596660
6	7599c0aa0419b59fd11ffede98a3665d	23.0	32000.0	10.373522
7	6dff182db452487f07a47596f314bddc	35.0	40000.0	10.596660
8	9dc233f8ed1c6eb2432672ab4bb39249	33.0	80000.0	11.289794

income_log_mean = np.round(np.mean(fcc_survey_df['Income_log']), 2)fig, ax = plt.subplots()
fcc_survey_df['Income_log'].hist(bins=30, color='#A9C5D3')
plt.axvline(income_log_mean, color='r')
ax.set_title('Developer Income Histogram after Log Transform', fontsize=12)
ax.set_xlabel('Developer Income (log scale)', fontsize=12)
ax.set_ylabel('Frequency', fontsize=12)
ax.text(11.5, 450, r'$\mu$='+str(income_log_mean), fontsize=10)

Text(11.5,450,'$\\mu$=10.43')

日期相关特征

import datetime
import numpy as np
import pandas as pd
from dateutil.parser import parse
import pytz

time_stamps = ['2015-03-08 10:30:00.360000+00:00', '2017-07-13 15:45:05.755000-07:00','2012-01-20 22:30:00.254000+05:30', '2016-12-25 00:30:00.000000+10:00']
df = pd.DataFrame(time_stamps, columns=['Time'])
df

	Time
0	2015-03-08 10:30:00.360000+00:00
1	2017-07-13 15:45:05.755000-07:00
2	2012-01-20 22:30:00.254000+05:30
3	2016-12-25 00:30:00.000000+10:00

ts_objs = np.array([pd.Timestamp(item) for item in np.array(df.Time)])
df['TS_obj'] = ts_objs
ts_objs

array([Timestamp('2015-03-08 10:30:00.360000+0000', tz='UTC'),Timestamp('2017-07-13 15:45:05.755000-0700', tz='pytz.FixedOffset(-420)'),Timestamp('2012-01-20 22:30:00.254000+0530', tz='pytz.FixedOffset(330)'),Timestamp('2016-12-25 00:30:00+1000', tz='pytz.FixedOffset(600)')], dtype=object)

df['Year'] = df['TS_obj'].apply(lambda d: d.year)
df['Month'] = df['TS_obj'].apply(lambda d: d.month)
df['Day'] = df['TS_obj'].apply(lambda d: d.day)
df['DayOfWeek'] = df['TS_obj'].apply(lambda d: d.dayofweek)
df['DayName'] = df['TS_obj'].apply(lambda d: d.weekday_name)
df['DayOfYear'] = df['TS_obj'].apply(lambda d: d.dayofyear)
df['WeekOfYear'] = df['TS_obj'].apply(lambda d: d.weekofyear)
df['Quarter'] = df['TS_obj'].apply(lambda d: d.quarter)df[['Time', 'Year', 'Month', 'Day', 'Quarter', 'DayOfWeek', 'DayName', 'DayOfYear', 'WeekOfYear']]

	Time	Year	Month	Day	Quarter	DayOfWeek	DayName	DayOfYear	WeekOfYear
0	2015-03-08 10:30:00.360000+00:00	2015	3	8	1	6	Sunday	67	10
1	2017-07-13 15:45:05.755000-07:00	2017	7	13	3	3	Thursday	194	28
2	2012-01-20 22:30:00.254000+05:30	2012	1	20	1	4	Friday	20	3
3	2016-12-25 00:30:00.000000+10:00	2016	12	25	4	6	Saturday	360	51

时间相关特征

df['Hour'] = df['TS_obj'].apply(lambda d: d.hour)
df['Minute'] = df['TS_obj'].apply(lambda d: d.minute)
df['Second'] = df['TS_obj'].apply(lambda d: d.second)
df['MUsecond'] = df['TS_obj'].apply(lambda d: d.microsecond)   #毫秒
df['UTC_offset'] = df['TS_obj'].apply(lambda d: d.utcoffset()) #UTC时间位移df[['Time', 'Hour', 'Minute', 'Second', 'MUsecond', 'UTC_offset']]

	Time	Hour	Minute	Second	MUsecond	UTC_offset
0	2015-03-08 10:30:00.360000+00:00	10	30	0	360000	00:00:00
1	2017-07-13 15:45:05.755000-07:00	15	45	5	755000	-1 days +17:00:00
2	2012-01-20 22:30:00.254000+05:30	22	30	0	254000	05:30:00
3	2016-12-25 00:30:00.000000+10:00	0	30	0	0	10:00:00

按照早晚切分时间

hour_bins = [-1, 5, 11, 16, 21, 23]
bin_names = ['Late Night', 'Morning', 'Afternoon', 'Evening', 'Night']
df['TimeOfDayBin'] = pd.cut(df['Hour'], bins=hour_bins, labels=bin_names)
df[['Time', 'Hour', 'TimeOfDayBin']]

	Time	Hour	TimeOfDayBin
0	2015-03-08 10:30:00.360000+00:00	10	Morning
1	2017-07-13 15:45:05.755000-07:00	15	Afternoon
2	2012-01-20 22:30:00.254000+05:30	22	Night
3	2016-12-25 00:30:00.000000+10:00	0	Late Night