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Iterating Date in python Dataframe

I realize this is probably a very trivial question but I have a dataframe of 1000+ rows and I want to create a new column "Date" but for a single date "2018-01-31". I tried the code below but python just returns "Length of values (1) does not match length of index"
I would really appreciate any help!
Date = ['2018-01-31']
for i in range(len(Output)):
Output['Date']= Date

Assuming Output is the name of your pandas dataframe with 1000+ rows you can do:
Output['Date'] = "2018-01-31"
or using the datetime library you could do:
from datetime import date
Output["Date"] = date(2018, 1, 31)
to format it as a date object rather than a string. You also do not need to iterate over each row if you are wanting the same value for each row. Simply adding a new column with the value will set the value of the new column to the assigned value for each row.

How to make Date a column in dataframe

I have the below dataframe and i am trying to display how many rides per day.
But i can see only 1 column "near_penn" is considered as a column but "Date" is not.
c = df[['start day','near_penn','Date']]
c=c.loc[c['near_penn']==1]
pre_pandemic_df_new=pd.DataFrame()
pre_pandemic_df_new=c.groupby('Date').agg({'near_penn':'sum'})
print(pre_pandemic_df_new)
print(pre_pandemic_df_new.columns)
Why doesn't it consider "Date" as a column?
How can i make Date as a column of "pre_pandemic_df_new"?

Feel you can use to to_datetime method.
import pandas as pd
pre_pandemic_df_new["Date"]= pd.to_datetime(pre_pandemic_df_new["Date"])
Hope this works

Why doesn't it consider "Date" as a column?
Because the date is an index for your Dataframe.
How can I make Date as a column of "pre_pandemic_df_new"?
you can try this:
pre_pandemic_df_new.reset_index(level=['Date'])

df[['Date','near_penn']] = df[['Date_new','near_penn_new']]
Once you created your dataframe you can try this to add new columns to the end of the dataframe to test if it works before you make adjustments
OR
You can check for a value for the first row corresponding to the first "date" row.
These are the first things that came to my mind hope it helps

Creating Datetime index in python

I am trying to create datetime index in python. I have an existing dataframe with date column (CrimeDate), here is a snapshot of it:
The date is not in datetime format though.
I intent to have an output similar to the below format, but with my existing dataframe's date column-
The Crimedate column has approx. 334192 rows and start date from 2021-04-24 to 1963-10-30 (all are in sequence of months and year)

First you'll need to convert the date column to datetime:
df['CrimeDate'] = pd.to_datetime(df['CrimeDate'])
And after that set that column as the index:
df.set_index(['CrimeDate'], inplace=True)
Once set, you can access the datetime index directly:
df.index

Populate dates in dataframe

I have a dataframe, which contains following records:
I need to fill this dataframe with rows with dates which are not present in it.
After inserting new dates the timestamp column should be in range df.timestamp.iloc[0] and df.timestamp.iloc[0]

You can use relativedelta() along with split() from the datetime library

Pandas, select dates using input from list

here is my input df:
df:
date , name
1990-12-21, adam1
1990-12-22, adam2
1990-12-23, adam3
1990-12-24, adam4
1990-12-25, adam5
I want to select all dates above given date from list (always on fist place)
list = ['1990-12-23','name','22']
df = pd.to_datetime(df['date'))
df = df[df.date > list[0]]
And its working.
My question is, why its working without converting this first element of a list to datetime format?

Pandas has flexible Partial String Indexing. This allows dates and times that can be automatically parsed into a datetime or timestamp to be used as strings without first converting them.