I have Data Frame in Python Pandas like below:
Input data: (columns survey and event are object date type)
data = [
(1, 2, 1, 0.33, '2023-10-10', '2023-09-25', 1, 20, 11),
(1, 2, 1, 0.33, '2023-10-10', '2023-10-04', 0, 10, 10),
(1, 5, 0, 0.58, '2023-05-05', '2023-05-01', 0, 10, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-02-10', 0, 25, 10),
(2, 8, 1, 0.45, '2023-02-13', '2023-02-02', None, 12, None)
]
df = pd.DataFrame(data, columns=['id', 'nps', 'target', 'score', 'survey', 'event', 'col1', 'col2', 'col3'])
df
Requirements:
And I need to create new rows for each combination of values in columns: id, survey. New rows have to:
- For each combination of values in: id, survey create dates in the event column that do not exist for the given combination of values in: id, survey going back 31 days from date in survey column, for example if the combination id = 1 and survey = '2023-05-05' I need to have date rows in the event column from 2023-05-04 to 2023-04-04, i.e. 31 days backwards for that combination of: id, survey
- each new row need to has NaN values in columns: col1, col2, col3
- each new row need to has same values in columns: nps, target, score for each combination of values in: id, survey
Example output:
So, as a result I need to have something like below:
data = [
(1, 2, 1, 0.33, '2023-10-10', '2023-09-25', 1, 20, 11),
(1, 2, 1, 0.33, '2023-10-10', '2023-10-04', 0, 10, 10),
(1, 2, 1, 0.33, '2023-10-10', '2023-10-09', None, None, None),
(1, 2, 1, 0.33, '2023-10-10', '2023-10-08', None, None, None),
(1, 2, 1, 0.33, '2023-10-10', '2023-10-07', None, None, None),
(1, 2, 1, 0.33, '2023-10-10', '2023-10-06', None, None, None),
(1, 2, 1, 0.33, '2023-10-10', '2023-10-05', None, None, None),
(1, 2, 1, 0.33, '2023-10-10', '2023-10-03', None, None, None),
(1, 2, 1, 0.33, '2023-10-10', '2023-10-02', None, None, None),
(1, 2, 1, 0.33, '2023-10-10', '2023-10-01', None, None, None),
(1, 2, 1, 0.33, '2023-10-10', '2023-09-30', None, None, None),
(1, 2, 1, 0.33, '2023-10-10', '2023-09-29', None, None, None),
(1, 2, 1, 0.33, '2023-10-10', '2023-09-28', None, None, None),
(1, 2, 1, 0.33, '2023-10-10', '2023-09-27', None, None, None),
(1, 2, 1, 0.33, '2023-10-10', '2023-09-26', None, None, None),
(1, 2, 1, 0.33, '2023-10-10', '2023-09-24', None, None, None),
(1, 2, 1, 0.33, '2023-10-10', '2023-09-23', None, None, None),
(1, 2, 1, 0.33, '2023-10-10', '2023-09-22', None, None, None),
(1, 2, 1, 0.33, '2023-10-10', '2023-09-21', None, None, None),
(1, 2, 1, 0.33, '2023-10-10', '2023-09-20', None, None, None),
(1, 2, 1, 0.33, '2023-10-10', '2023-09-19', None, None, None),
(1, 2, 1, 0.33, '2023-10-10', '2023-09-18', None, None, None),
(1, 2, 1, 0.33, '2023-10-10', '2023-09-17', None, None, None),
(1, 2, 1, 0.33, '2023-10-10', '2023-09-16', None, None, None),
(1, 2, 1, 0.33, '2023-10-10', '2023-09-15', None, None, None),
(1, 2, 1, 0.33, '2023-10-10', '2023-09-14', None, None, None),
(1, 2, 1, 0.33, '2023-10-10', '2023-09-13', None, None, None),
(1, 2, 1, 0.33, '2023-10-10', '2023-09-12', None, None, None),
(1, 2, 1, 0.33, '2023-10-10', '2023-09-11', None, None, None),
(1, 2, 1, 0.33, '2023-10-10', '2023-09-10', None, None, None),
(1, 2, 1, 0.33, '2023-10-10', '2023-09-09', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-05-01', 0, 10, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-05-04', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-05-03', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-05-02', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-04-30', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-04-29', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-04-28', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-04-27', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-04-26', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-04-25', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-04-24', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-04-23', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-04-22', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-04-21', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-04-20', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-04-19', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-04-18', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-04-17', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-04-16', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-04-15', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-04-14', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-04-13', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-04-12', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-04-11', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-04-10', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-04-09', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-04-08', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-04-07', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-04-06', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-04-05', None, None, None),
(1, 5, 0, 0.58, '2023-05-05', '2023-04-04', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-02-10', 0, 25, 10),
(2, 8, 1, 0.45, '2023-02-13', '2023-02-02', None, 12, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-02-12', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-02-11', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-02-09', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-02-08', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-02-07', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-02-06', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-02-05', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-02-04', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-02-03', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-02-01', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-01-31', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-01-30', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-01-29', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-01-28', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-01-27', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-01-26', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-01-25', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-01-24', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-01-23', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-01-22', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-01-21', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-01-20', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-01-19', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-01-18', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-01-17', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-01-16', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-01-15', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-01-14', None, None, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-01-13', None, None, None),
]
df = pd.DataFrame(data, columns=['id', 'nps', 'target', 'score', 'survey', 'event', 'col1', 'col2', 'col3'])
df
How can I do that in Python Pandas ?
CodePudding user response：
Try:
df["event"] = pd.to_datetime(df["event"])
df["survey"] = pd.to_datetime(df["survey"])
def fn(g):
g = g.set_index("event")
d = g["survey"].min()
g = (
g.reindex(pd.date_range(d - pd.Timedelta(days=31), d))
.reset_index()
.rename(columns={"index": "event"})
)
g[["id", "nps", "target", "score", "survey"]] = (
g[["id", "nps", "target", "score", "survey"]].ffill().bfill()
)
return g
df = df.groupby(["id", "survey"], group_keys=False).apply(fn)
print(df)
Prints:
event id nps target score survey col1 col2 col3
0 2023-04-04 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
1 2023-04-05 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
2 2023-04-06 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
3 2023-04-07 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
4 2023-04-08 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
5 2023-04-09 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
6 2023-04-10 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
7 2023-04-11 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
8 2023-04-12 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
9 2023-04-13 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
10 2023-04-14 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
11 2023-04-15 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
12 2023-04-16 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
13 2023-04-17 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
14 2023-04-18 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
15 2023-04-19 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
16 2023-04-20 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
17 2023-04-21 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
18 2023-04-22 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
19 2023-04-23 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
20 2023-04-24 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
21 2023-04-25 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
22 2023-04-26 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
23 2023-04-27 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
24 2023-04-28 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
25 2023-04-29 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
26 2023-04-30 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
27 2023-05-01 1.0 5.0 0.0 0.58 2023-05-05 0.0 10.0 NaN
28 2023-05-02 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
29 2023-05-03 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
30 2023-05-04 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
31 2023-05-05 1.0 5.0 0.0 0.58 2023-05-05 NaN NaN NaN
0 2023-09-09 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
1 2023-09-10 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
2 2023-09-11 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
3 2023-09-12 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
4 2023-09-13 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
5 2023-09-14 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
6 2023-09-15 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
7 2023-09-16 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
8 2023-09-17 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
9 2023-09-18 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
10 2023-09-19 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
11 2023-09-20 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
12 2023-09-21 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
13 2023-09-22 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
14 2023-09-23 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
15 2023-09-24 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
16 2023-09-25 1.0 2.0 1.0 0.33 2023-10-10 1.0 20.0 11.0
17 2023-09-26 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
18 2023-09-27 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
19 2023-09-28 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
20 2023-09-29 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
21 2023-09-30 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
22 2023-10-01 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
23 2023-10-02 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
24 2023-10-03 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
25 2023-10-04 1.0 2.0 1.0 0.33 2023-10-10 0.0 10.0 10.0
26 2023-10-05 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
27 2023-10-06 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
28 2023-10-07 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
29 2023-10-08 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
30 2023-10-09 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
31 2023-10-10 1.0 2.0 1.0 0.33 2023-10-10 NaN NaN NaN
0 2023-01-13 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
1 2023-01-14 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
2 2023-01-15 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
3 2023-01-16 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
4 2023-01-17 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
5 2023-01-18 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
6 2023-01-19 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
7 2023-01-20 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
8 2023-01-21 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
9 2023-01-22 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
10 2023-01-23 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
11 2023-01-24 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
12 2023-01-25 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
13 2023-01-26 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
14 2023-01-27 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
15 2023-01-28 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
16 2023-01-29 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
17 2023-01-30 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
18 2023-01-31 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
19 2023-02-01 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
20 2023-02-02 2.0 8.0 1.0 0.45 2023-02-13 NaN 12.0 NaN
21 2023-02-03 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
22 2023-02-04 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
23 2023-02-05 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
24 2023-02-06 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
25 2023-02-07 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
26 2023-02-08 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
27 2023-02-09 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
28 2023-02-10 2.0 8.0 1.0 0.45 2023-02-13 0.0 25.0 10.0
29 2023-02-11 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
30 2023-02-12 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
31 2023-02-13 2.0 8.0 1.0 0.45 2023-02-13 NaN NaN NaN
CodePudding user response：
Creating those new rows in your Pandas DataFrame based on the given requirements can be done with Python. You can achieve this using the pandas library and some date calculations.
# import Libraries
import pandas as pd
import numpy as np
from datetime import timedelta
# create the dataframe using your data
data = [
(1, 2, 1, 0.33, '2023-10-10', '2023-09-25', 1, 20, 11),
(1, 2, 1, 0.33, '2023-10-10', '2023-10-04', 0, 10, 10),
(1, 5, 0, 0.58, '2023-05-05', '2023-05-01', 0, 10, None),
(2, 8, 1, 0.45, '2023-02-13', '2023-02-10', 0, 25, 10),
(2, 8, 1, 0.45, '2023-02-13', '2023-02-02', None, 12, None)
]
df = pd.DataFrame(data, columns=['id', 'nps', 'target', 'score', 'survey', 'event', 'col1', 'col2', 'col3'])
df
# convert 'event' and 'survey' columns to datetime type
df['survey'] = pd.to_datetime(df['survey'])
df['event'] = pd.to_datetime(df['event'])
# Generate the date range list
date_range_list = [pd.date_range(end=survey_date, periods=32, freq='D')[::-1][1:] for survey_date in df['survey']]
# Create a new DataFrame with the desired values
new_data = []
for i, survey_date in enumerate(df['survey']):
for event_date in date_range_list[i]:
if pd.to_datetime(df.at[i, 'event']) == event_date:
new_data.append((df.at[i, 'id'], df.at[i, 'nps'], df.at[i, 'target'], df.at[i, 'score'], survey_date, event_date, df.at[i, 'col1'], df.at[i, 'col2'], df.at[i, 'col3']))
else:
new_data.append((df.at[i, 'id'], df.at[i, 'nps'], df.at[i, 'target'], df.at[i, 'score'], survey_date, event_date, np.NaN, np.NaN, np.NaN))
# Create a new DataFrame with the desired values
new_data = []
for i, survey_date in enumerate(df['survey']):
for event_date in date_range_list[i]:
if pd.to_datetime(df.at[i, 'event']) == event_date:
new_data.append((df.at[i, 'id'], df.at[i, 'nps'], df.at[i, 'target'], df.at[i, 'score'], survey_date, event_date, df.at[i, 'col1'], df.at[i, 'col2'], df.at[i, 'col3']))
else:
new_data.append((df.at[i, 'id'], df.at[i, 'nps'], df.at[i, 'target'], df.at[i, 'score'], survey_date, event_date, np.NaN, np.NaN, np.NaN))
# Create a new DataFrame from the new_data list
new_df = pd.DataFrame(new_data, columns=['id', 'nps', 'target', 'score', 'survey', 'event', 'col1', 'col2', 'col3'])
# Now, new_df contains the original data plus new rows with 'event' dates from the date range list
new_df
Here's the print result of the new_df:
id nps target score survey event col1 col2 col3
0 1 2 1 0.33 2023-10-10 2023-10-09 NaN NaN NaN
1 1 2 1 0.33 2023-10-10 2023-10-08 NaN NaN NaN
2 1 2 1 0.33 2023-10-10 2023-10-07 NaN NaN NaN
3 1 2 1 0.33 2023-10-10 2023-10-06 NaN NaN NaN
4 1 2 1 0.33 2023-10-10 2023-10-05 NaN NaN NaN
.. .. ... ... ... ... ... ... ... ...
150 2 8 1 0.45 2023-02-13 2023-01-17 NaN NaN NaN
151 2 8 1 0.45 2023-02-13 2023-01-16 NaN NaN NaN
152 2 8 1 0.45 2023-02-13 2023-01-15 NaN NaN NaN
153 2 8 1 0.45 2023-02-13 2023-01-14 NaN NaN NaN
154 2 8 1 0.45 2023-02-13 2023-01-13 NaN NaN NaN
[155 rows x 9 columns]
After this you can alse sort the dataFrame by the 'id' and 'survey' columns:
new_df = new_df.sort_values(by=['id', 'survey'])
print(new_df)
Print result:
id nps target score survey event col1 col2 col3
62 1 5 0 0.58 2023-05-05 2023-05-04 NaN NaN NaN
63 1 5 0 0.58 2023-05-05 2023-05-03 NaN NaN NaN
64 1 5 0 0.58 2023-05-05 2023-05-02 NaN NaN NaN
65 1 5 0 0.58 2023-05-05 2023-05-01 0.0 10.0 NaN
66 1 5 0 0.58 2023-05-05 2023-04-30 NaN NaN NaN
.. .. ... ... ... ... ... ... ... ...
150 2 8 1 0.45 2023-02-13 2023-01-17 NaN NaN NaN
151 2 8 1 0.45 2023-02-13 2023-01-16 NaN NaN NaN
152 2 8 1 0.45 2023-02-13 2023-01-15 NaN NaN NaN
153 2 8 1 0.45 2023-02-13 2023-01-14 NaN NaN NaN
154 2 8 1 0.45 2023-02-13 2023-01-13 NaN NaN NaN
[155 rows x 9 columns]
I hope this helps, Good luck with your project!