Error when applying a timeframe mask to dataframe

Question

Intro

I am writing a piece of python code that uses pcycopg2, pandas and datetime that retrieves information from a PGAdmin database, which filters the data to the last 14 days of activity.

The columns I am pulling from the database are: id (Unique Number), createdby (Name), created (Date)

I pull all of that information as I thought I could transfer it into a dataframe and then use a mask to get the bits that I want.

Code

import psycopg2
import pandas as pd
import datetime

Today = datetime.datetime.now() #gets todays date
TimeFrameInDays = datetime.timedelta(days = 14) #gets int amount of days
TwoWeeksAgo = Today - TimeFrameInDays #calculates date two weeks ago

Query = 'SELECT createdby, created, id FROM "02 Planning"."fibre"'

print("Robo-Cop Initialised") #DEV

try:
    connection = psycopg2.connect(database = DB_Name,
                            user = DB_User,
                            password = DB_Pass,
                            host = DB_Host,
                            port = DB_Port)

    print("Database connected")

except (Exception, psycopg2.Error) as error: 
    #if error occurs, message is returned
    print("Error Occured Trying to Connect")

finally:
    cursor = connection.cursor() #makes refrencing the cursor easier
    cursor.execute(Query) #executes query
    Data = cursor.fetchall() #saves results
    
    DataFrame = pd.DataFrame(Data) #assembles into dataframe
    DataFrame.rename(columns={0:'Created By', 1:'Created On', 2:'Database Id'}) #renames columns
    #print(DataFrame)
    mask = (DataFrame['Created On'] > TwoWeeksAgo.date) & (DataFrame['Created On'] <= Today.date)
    DataFrame = DataFrame.loc[mask] 
    print(DataFrame)

Error

Traceback (most recent call last):
  File "c:\Users\\Documents\GitHub\\main.py", line 42, in <module>
    mask = (DataFrame['Created On'] > TwoWeeksAgo.date) & (DataFrame['Created On'] <= Today.date)
  File "C:\Users\\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pandas\core\frame.py", line 3805, in __getitem__   
    indexer = self.columns.get_loc(key)
  File "C:\Users\\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pandas\core\indexes\range.py", line 395, in get_loc
    raise KeyError(key)
KeyError: 'Created On'

Why my question is not a duplicate

My question has nothing to do with renaming columns, I am not trying to edit how I rename my columns. What i am trying to do is apply a mask to a dataframe that only shows me dates in the last 14 days.

New Error

With this piece of code

DataFrame = pd.DataFrame(Data, columns=['Created By','Created On','Database Id']),

i get

  File "c:\Users\\Documents\GitHub\\main.py", line 42, in <module>
    mask = (DataFrame['Created On'].dt.date > TwoWeeksAgo.date) & (DataFrame['Created On'].dt.date <= Today.date)
  File "C:\Users\\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pandas\core\generic.py", line 5902, in __getattr__
    return object.__getattribute__(self, name)
  File "C:\Users\\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pandas\core\accessor.py", line 182, in __get__
    accessor_obj = self._accessor(obj)
  File "C:\Users\\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pandas\core\indexes\accessors.py", line 512, in __new__
    raise AttributeError("Can only use .dt accessor with datetimelike values")
AttributeError: Can only use .dt accessor with datetimelike values. Did you mean: 'at'?

Answer 1

Complete solution:

#pass columns names to DataFrame constructor
DataFrame = pd.DataFrame(Data, columns=['Created By','Created On','Database Id'])


Today = datetime.datetime.now() #gets todays date
TimeFrameInDays = datetime.timedelta(days = 14) #gets int amount of days
 #calculates date two weeks ago
TwoWeeksAgo = Today - TimeFrameInDays

#convert column to datetimes
DataFrame['Created On'] = pd.to_datetime(DataFrame['Created On'])

#compare dates
mask = (DataFrame['Created On'].dt.date > TwoWeeksAgo.date()) & 
        (DataFrame['Created On'].dt.date <= Today.date())
DataFrame1 = DataFrame.loc[mask] 
print(DataFrame1)

Or use Timestamp.normalize for datetimes without times (times are 00:00:00), so possible compare datetimes columns:

Today = pd.Timestamp.now().normalize()
TwoWeeksAgo = Today - pd.Timedelta(days = 14)

mask = (DataFrame['Created On'] > TwoWeeksAgo) & (DataFrame['Created On'] <= Today)
DataFrame1 = DataFrame.loc[mask] 
print(DataFrame1)

Or:

Today = pd.Timestamp.now().normalize()
TwoWeeksAgo = Today - pd.Timedelta(days = 14)

mask = DataFrame['Created On'].between(TwoWeeksAgo, Today, inclusive='right')
DataFrame1 = DataFrame.loc[mask]