回答編集履歴
2
test
CHANGED
|
@@ -4,6 +4,7 @@
|
|
|
4
4
|
from datetime import timedelta
|
|
5
5
|
|
|
6
6
|
df = pd.read_csv('data.csv', parse_dates=['Time'])
|
|
7
|
+
cols = df.columns
|
|
7
8
|
|
|
8
9
|
# 1/13 0:00:00～1/15 23:55:00 と 2/13 0:00:00～2/15 23:55:00 のインデックス(5分毎)を作成
|
|
9
10
|
first_day, last_day = df['Time'].min().date(), df['Time'].max().date() + timedelta(days=1)
|
|
@@ -11,14 +12,13 @@
|
|
|
11
12
|
time_idx = time_idx.union(pd.date_range('2023-02-15', last_day, freq='5min', inclusive='left'))
|
|
12
13
|
|
|
13
14
|
# reset datetime index
|
|
14
|
-
dfx = df.set_index('Time').reindex(time_idx).rename_axis('Time').reset_index()
|
|
15
|
+
dfx = df.set_index('Time').reindex(time_idx).rename_axis('Time').reset_index()[cols]
|
|
15
16
|
|
|
16
17
|
# fullfill NaN
|
|
17
18
|
dfx['A'] = dfx['A'].fillna('A').map('"{}"'.format)
|
|
18
19
|
dfx['num'] = dfx['num'].fillna(0, downcast='infer')
|
|
19
20
|
|
|
20
21
|
# save to CSV file
|
|
21
|
-
dfx = dfx[['A', 'Time', 'num']]
|
|
22
22
|
dfx.to_csv('filled_data.csv', index=False, quoting=3)
|
|
23
23
|
```
|
|
24
24
|
|
1
test
CHANGED
|
@@ -1,35 +1,24 @@
|
|
|
1
|
-
|
|
1
|
+
> 1/13の0:00:00～1/15の23:55:00・2/13の0:00:00～2/15の23:55:00と、この期間内であれば全ての時間帯での5分おきのデータを作成したい
|
|
2
2
|
```python
|
|
3
3
|
import pandas as pd
|
|
4
|
+
from datetime import timedelta
|
|
4
5
|
|
|
5
6
|
df = pd.read_csv('data.csv', parse_dates=['Time'])
|
|
6
7
|
|
|
8
|
+
# 1/13 0:00:00～1/15 23:55:00 と 2/13 0:00:00～2/15 23:55:00 のインデックス(5分毎)を作成
|
|
7
|
-
d
|
|
9
|
+
first_day, last_day = df['Time'].min().date(), df['Time'].max().date() + timedelta(days=1)
|
|
8
|
-
d
|
|
10
|
+
time_idx = pd.date_range(first_day, '2023-01-16', freq='5min', inclusive='left')
|
|
11
|
+
time_idx = time_idx.union(pd.date_range('2023-02-15', last_day, freq='5min', inclusive='left'))
|
|
9
12
|
|
|
13
|
+
# reset datetime index
|
|
14
|
+
dfx = df.set_index('Time').reindex(time_idx).rename_axis('Time').reset_index()
|
|
15
|
+
|
|
16
|
+
# fullfill NaN
|
|
10
|
-
dfx['A'] = dfx['A'].f
|
|
17
|
+
dfx['A'] = dfx['A'].fillna('A').map('"{}"'.format)
|
|
11
18
|
dfx['num'] = dfx['num'].fillna(0, downcast='infer')
|
|
19
|
+
|
|
20
|
+
# save to CSV file
|
|
12
21
|
dfx = dfx[['A', 'Time', 'num']]
|
|
13
22
|
dfx.to_csv('filled_data.csv', index=False, quoting=3)
|
|
14
23
|
```
|
|
15
24
|
|
|
16
|
-
**filled_data.csv**
|
|
17
|
-
```csv
|
|
18
|
-
A,Time,num
|
|
19
|
-
"A",2023-02-15 23:55:00,1
|
|
20
|
-
"A",2023-02-15 23:50:00,2
|
|
21
|
-
"A",2023-02-15 23:45:00,0
|
|
22
|
-
"A",2023-02-15 23:40:00,3
|
|
23
|
-
"A",2023-02-15 23:35:00,0
|
|
24
|
-
"A",2023-02-15 23:30:00,2
|
|
25
|
-
"A",2023-02-15 23:25:00,1
|
|
26
|
-
"A",2023-02-15 23:20:00,0
|
|
27
|
-
"A",2023-02-15 23:15:00,0
|
|
28
|
-
"A",2023-02-15 23:10:00,1
|
|
29
|
-
"A",2023-02-15 23:05:00,0
|
|
30
|
-
"A",2023-02-15 23:00:00,4
|
|
31
|
-
"A",2023-02-15 22:55:00,2
|
|
32
|
-
"A",2023-02-15 22:50:00,0
|
|
33
|
-
"A",2023-02-15 22:45:00,1
|
|
34
|
-
```
|
|
35
|
-
|