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 LabelEncoder

gle = 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_labels

poke_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 Binarizer

bn = 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 PolynomialFeatures

pf = 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')

在这里插入图片描述

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')

在这里插入图片描述

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')

在这里插入图片描述

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

在这里插入图片描述

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