pd.to_datetime 不将日期时间转换为 int 以进行 df.rolling 计算

Question

I'm attempting to create a rolling average over 10 minutes on an irregularly time stepped data set. I get the error shown below

Traceback (most recent call last):
  File "asosreaderpandas.py", line 13, in <module>
    df.rolling('10min').mean()
  File "/opt/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py", line 8900, in rolling
    on=on, axis=axis, closed=closed)
  File "/opt/anaconda3/lib/python3.6/site-packages/pandas/core/window.py", line 2469, in rolling
    return Rolling(obj, **kwds)
  File "/opt/anaconda3/lib/python3.6/site-packages/pandas/core/window.py", line 80, in __init__
    self.validate()
  File "/opt/anaconda3/lib/python3.6/site-packages/pandas/core/window.py", line 1478, in validate
    raise ValueError("window must be an integer")
ValueError: window must be an integer

This is my code that I am using to create my rolling average, I would manually input my timestamps, as that has solved my issue in the past, except the .txt file is 98,000 lines long...

import pandas as pd
from datetime import datetime

df = pd.read_csv('KART.txt', header = 0)
#indexing the date format from txt file
pd.to_datetime(df.index, format='%Y-%m-%d %H:%M')
#creating ten minute average
df.rolling('10min').mean()
print(df)

I don't understand the pandas module well, I have tried multiple ways of assigning my datetime differently to no avail am I going about this completely wrong?

Dataset Sample

0,1
2019-01-01 00:00:00,4
2019-01-01 00:05:00,4
2019-01-01 00:10:00,4
2019-01-01 00:15:00,4
2019-01-01 00:25:00,5
2019-01-01 00:30:00,4
2019-01-01 00:35:00,4
2019-01-01 00:40:00,4
2019-01-01 00:45:00,4
2019-01-01 00:50:00,4
2019-01-01 00:55:00,4
2019-01-01 00:56:00,4
2019-01-01 01:00:00,4
...

Answer 1

You have multiple issues in you code:

1. you have an automatic integer index assigned to your dataframe when you load your dataframe without specifying the column index (you later try to convert into datetime which is obviously not what you want)

2. you don't save the index when you convert it to datetime

Here's the fixed version:

import pandas as pd
from datetime import datetime

df = pd.read_csv('KART.txt', header = 0, index_col=0)  # <- specified column index
df.index = pd.to_datetime(df.index, format='%Y-%m-%d %H:%M')  # <- saving index when converting it to datetime
df.rolling('10min').mean()
>                     1
0   
2019-01-01 00:00:00 4.0
2019-01-01 00:05:00 4.0
2019-01-01 00:10:00 4.0
2019-01-01 00:15:00 4.0
2019-01-01 00:25:00 5.0
2019-01-01 00:30:00 4.5
2019-01-01 00:35:00 4.0
2019-01-01 00:40:00 4.0
2019-01-01 00:45:00 4.0
2019-01-01 00:50:00 4.0
2019-01-01 00:55:00 4.0
2019-01-01 00:56:00 4.0
2019-01-01 01:00:00 4.0
...

---

EDIT
Thanks to the comment of Parfait you can be get even a shorter version of a code by parsing dates right in the read_csv method:

import pandas as pd
from datetime import datetime

df = pd.read_csv('KART.txt', 
                 header = 0, 
                 index_col=0,  # <-- specified column index
                 parse_dates=True)  # <-- parsed dates from txt

df.rolling('10min').mean()