Pandas changing dates near each other - python-3.x

Question

I have a pandas dataframe with dates and users which looks like this-
date = ['1/2/2020','1/9/2020','1/10/2020','1/17/2020','1/18/2020','1/24/2020','1/25/2020','5/17/2019','5/18/2019','5/24/2019','5/29/2019']
user =['A','B','C','B','A','A','B','C','A','A','B']
df = pd.DataFrame(data={"Date":date, "User":user})
I am trying to find all dates that are next to each other (Jan-1 and Jan-2) and convert them to a single date so both entries would then become the lower of the two. The number of entries are over a million. This data is created from a scan results that triggers nightly and sometime flows into the other day.
Update-
I wanted to consolidate the date of the scan so that I can show the visualization properly. As right now the results would have more entry on the day the scan starts but very few entries for the day where the scan overflowed. There is a primary date and time stored so I am not loosing the data. The user column is presented as it scans a file with all the usernames and the date stores the date when it was scanned.
So far I was able to read the dataframe and then sort it based on the date to have the entries one after the other.
The output should look like the following -
Is there a pytonic way of doing this?

Answer 1

One issue to consider is the case of multiple consecutive days and how you want to handle these. The following code sets the day to the first of the consecutive days in each block:
import pandas as pd
from datetime import timedelta
# prepend two dates to show multiple consecutive days "use-case"
date = ['12/31/2019','1/1/2020','1/2/2020','1/9/2020','1/10/2020','1/17/2020','1/18/2020','1/24/2020','1/25/2020','5/17/2019','5/18/2019','5/24/2019','5/29/2019']
user = ['Z','Z','A','B','C','B','A','A','B','C','A','A','B']
df = pd.DataFrame(data={"Date":date, "User":user})
# first convert to datetime to allow date operations
df.Date = pd.to_datetime(df.Date)
# check if the the date is one day after the row before (by shifting the Date column)
df['isConsecutive'] = (df.Date == df.Date.shift()+pd.DateOffset(1))
# get number of consecutive days in each block
df['numConsecutive'] = df.isConsecutive.groupby((~df.isConsecutive).cumsum()).cumsum()
# convert to timedelta
df.numConsecutive = df.numConsecutive.apply(lambda x: timedelta(days=x))
# take this as differnce to Date
df['NewDate'] = df.Date - df.numConsecutive
print(df)
This returns:
Date User isConsecutive numConsecutive NewDate
0 2019-12-31 Z False 0 days 2019-12-31
1 2020-01-01 Z True 1 days 2019-12-31
2 2020-01-02 A True 2 days 2019-12-31
3 2020-01-09 B False 0 days 2020-01-09
4 2020-01-10 C True 1 days 2020-01-09
5 2020-01-17 B False 0 days 2020-01-17
6 2020-01-18 A True 1 days 2020-01-17
7 2020-01-24 A False 0 days 2020-01-24
8 2020-01-25 B True 1 days 2020-01-24
9 2019-05-17 C False 0 days 2019-05-17
10 2019-05-18 A True 1 days 2019-05-17
11 2019-05-24 A False 0 days 2019-05-24
12 2019-05-29 B False 0 days 2019-05-29