/
pandas_time_series_datetimeindex.py
172 lines (142 loc) · 4.37 KB
/
pandas_time_series_datetimeindex.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
import pandas as pd
print(pd.__version__)
# 1.2.2
df = pd.read_csv('data/src/sample_date.csv',
index_col='date', parse_dates=True).head(3)
print(df)
# val_1 val_2
# date
# 2017-11-01 65 76
# 2017-11-07 26 66
# 2017-11-18 47 47
print(type(df.index))
# <class 'pandas.core.indexes.datetimes.DatetimeIndex'>
print(df.index[0])
# 2017-11-01 00:00:00
print(type(df.index[0]))
# <class 'pandas._libs.tslibs.timestamps.Timestamp'>
df_default = pd.read_csv('data/src/sample_date.csv', index_col='date').head(3)
print(df_default)
# val_1 val_2
# date
# 2017-11-01 65 76
# 2017-11-07 26 66
# 2017-11-18 47 47
print(type(df_default.index))
# <class 'pandas.core.indexes.base.Index'>
print(df_default.index[0])
# 2017-11-01
print(type(df_default.index[0]))
# <class 'str'>
df_jp_ng = pd.read_csv('data/src/sample_date_jp.csv',
index_col='date', parse_dates=True).head(3)
print(df_jp_ng)
# val_1 val_2
# date
# 2017年11月1日 65 76
# 2017年11月7日 26 66
# 2017年11月18日 47 47
print(type(df_jp_ng.index))
# <class 'pandas.core.indexes.base.Index'>
df_jp = pd.read_csv('data/src/sample_date_jp.csv',
index_col='date', parse_dates=True,
date_parser=lambda x: pd.to_datetime(x, format='%Y年%m月%d日')).head(3)
print(df_jp)
# val_1 val_2
# date
# 2017-11-01 65 76
# 2017-11-07 26 66
# 2017-11-18 47 47
print(type(df_jp.index))
# <class 'pandas.core.indexes.datetimes.DatetimeIndex'>
df = pd.read_csv('data/src/sample_date.csv').head(3)
print(df)
# date val_1 val_2
# 0 2017-11-01 65 76
# 1 2017-11-07 26 66
# 2 2017-11-18 47 47
print(type(df.index))
# <class 'pandas.core.indexes.range.RangeIndex'>
df['date'] = pd.to_datetime(df['date'])
print(df['date'].dtype)
# datetime64[ns]
df.set_index('date', inplace=True)
print(df)
# val_1 val_2
# date
# 2017-11-01 65 76
# 2017-11-07 26 66
# 2017-11-18 47 47
print(type(df.index))
# <class 'pandas.core.indexes.datetimes.DatetimeIndex'>
df = pd.read_csv('data/src/sample_date.csv').head(3)
df.set_axis(pd.to_datetime(df['date']), axis='index', inplace=True)
print(df)
# date val_1 val_2
# date
# 2017-11-01 2017-11-01 65 76
# 2017-11-07 2017-11-07 26 66
# 2017-11-18 2017-11-18 47 47
print(type(df.index))
# <class 'pandas.core.indexes.datetimes.DatetimeIndex'>
df.drop(['date'], axis='columns', inplace=True)
print(df)
# val_1 val_2
# date
# 2017-11-01 65 76
# 2017-11-07 26 66
# 2017-11-18 47 47
df = pd.read_csv('data/src/sample_date.csv').head(3)
df.index = pd.to_datetime(df['date'])
df.drop(['date'], axis='columns', inplace=True)
print(df)
# val_1 val_2
# date
# 2017-11-01 65 76
# 2017-11-07 26 66
# 2017-11-18 47 47
print(type(df.index))
# <class 'pandas.core.indexes.datetimes.DatetimeIndex'>
df = pd.read_csv('data/src/sample_date.csv', index_col='date', parse_dates=True)
print(df)
# val_1 val_2
# date
# 2017-11-01 65 76
# 2017-11-07 26 66
# 2017-11-18 47 47
# 2017-11-27 20 38
# 2017-12-05 65 85
# 2017-12-12 4 29
# 2017-12-22 31 54
# 2017-12-29 21 8
# 2018-01-03 98 76
# 2018-01-08 48 64
# 2018-01-19 18 48
# 2018-01-23 86 70
print(type(df.index))
# <class 'pandas.core.indexes.datetimes.DatetimeIndex'>
print(df.loc['2018'])
# val_1 val_2
# date
# 2018-01-03 98 76
# 2018-01-08 48 64
# 2018-01-19 18 48
# 2018-01-23 86 70
print(df.loc['2017-11'])
# val_1 val_2
# date
# 2017-11-01 65 76
# 2017-11-07 26 66
# 2017-11-18 47 47
# 2017-11-27 20 38
print(df.loc['2017-12-15':'2018-01-15'])
# val_1 val_2
# date
# 2017-12-22 31 54
# 2017-12-29 21 8
# 2018-01-03 98 76
# 2018-01-08 48 64
print(df.at['01/19/2018', 'val_1'])
# 18
print(df.at['20180103', 'val_2'])
# 76