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I have a dataframe with a date column in the following format: 20130526T150000. How do you change this column to datetime 2013-05-26 15:00:00? [duplicate]

This question already has answers here:
How to define format when using pandas to_datetime?
(2 answers)
Convert Pandas Column to DateTime
(8 answers)
Closed 5 months ago.
Does anyone know how to change 20130526T150000 to datetime format?

One note: the 'T' is usefull. Use pd.to_datetime() directly, the T is actually usefull as it denotes the iso format and will help not confuse for some locales (some countries have the month first, then the day, others the oposite - iso goes from most significant to less: year, month, day)...
pd.to_datetime("20130526T150000")
Timestamp('2013-05-26 15:00:00')
If you want to be more explicit, specify the format:
pd.to_datetime("20130526T150000", format=...)
However, this might be a duplicate: How to define format when using pandas to_datetime? ... For best results, if you are doing a conversion of a column, use Convert Pandas Column to DateTime

Add a row of totals to the table [duplicate]

This question already has answers here:
Panda pivot table margins only on row [closed]
(1 answer)
Pandas: add crosstab totals
(3 answers)
Pandas dataframe total row
(13 answers)
Closed 6 months ago.
This post was edited and submitted for review 6 months ago and failed to reopen the post:
Original close reason(s) were not resolved
I have the following code for a table:
n_area = pd.crosstab(index=airbnb["neighbourhood"], columns=airbnb["neighbourhood_group"])
and I would like to add a row of totals (count) in the end of each column with a single function, completing what I have already written
this all comes from the dataset here
https://www.kaggle.com/code/dgomonov/data-exploration-on-nyc-airbnb/data
A screenshot of the data is as follows

Python - valuecounts() method - display all results [duplicate]

This question already has answers here:
How do I expand the output display to see more columns of a Pandas DataFrame?
(22 answers)
Closed 1 year ago.
I am a complete novice when it comes to Python so this might be badly explained.
I have a pandas dataframe with 2485 entries for years from 1960-2020. I want to know how many entries there are for each year, which I can easily get with the .value_counts() method. My issue is that when I print this, the output only shows me the top 5 and bottom 5 entries, rather than the number for every year. Is there a way to display all the value counts for all the years in the DataFrame?

Use pd.set_options and set display.max_rows to None:
>>> pd.set_option("display.max_rows", None)
Now you can display all rows of your dataframe.
Options and settings
pandas.set_option

If suppose the name of dataframe is 'df' then use
counts = df.year.value_counts()
counts.to_csv('name.csv',index=false)
As our terminal can't display entire columns they just display the top and bottom by collapsing the remaining values so try saving in a csv and see the records

How to convert this into datetime [duplicate]

This question already has answers here:
Convert string "Jun 1 2005 1:33PM" into datetime
(26 answers)
Closed 3 years ago.
I'm currently getting a panda data frame from alpaca - a stock trading platform. The data I'm getting is for minute data and is giving me a timestamp that cant be converted to DateTime to make my functions easier.
Heres an output for AMZN minute data
open high low
timestamp
2019-08-08 14:12:00-04:00 1826.8600 1826.9649 1826.1026
2019-08-08 14:13:00-04:00 1826.6500 1827.6520 1826.6500
Heres the data type:
print(type(AMZN.index[0]))
<class'pandas._libs.tslibs.timestamps.Timestamp'>
I'm trying to split this index into two columns where the day is the index and the time(minutes and hours) is the next column. Or just getting it into all into datetime format together would work fine too.
I've tried finding a similar problem and using _datetime() but it doesn't seem to work. I have the datetime library but it doesn't seem to register when I use the code as in other posts.
Overall Im just trying to find an easy way of iterating over a certain time on any given day and this format is giving me trouble. If someone has a better way of dealing with time series of this type please feel free to share.

Actually Timestamp is one of the easiest data type to manipulate in pandas:
df['Date'] = df.index.date
df['Time'] = df.index.time
df = df.set_index(df['Date])
Result:
open high low Time
Date
2019-08-08 1826.86 1826.9649 1826.1026 14:12:00
2019-08-08 1826.65 1827.6520 1826.6500 14:13:00

Changing Date/Time in python pandas [duplicate]

This question already has answers here:
Convert hh:mm:ss to minutes using python pandas
(3 answers)
Closed 5 years ago.
So I have Date/Time string in a pandas dataframe that looks like this:
2016-10-13 02:33:40
And I need to cut out the year/month/day completely, and convert the time to minutes. So that time/date above needs to be converted into just:
153
^^2 hours and 33 minutes = 153 minutes
I am basically trying to sift the data by the amount of time in between each entry and converting it all to minutes (since the amount of time that passes will not go past a day per session) seemed to make the most sense to me. But, I am open to any other suggestions!
Thanks for the help

lets say the date and time is in a column called DateTime.
df["Datetime"] = pd.to_datetime(df["Datetime"])
df["Minutes"] = 60*df["Datetime"].dt.hour +df["Datetime"].dt.minute
There once the datetime column is in the right format, you can access a lot of properties. See here for more https://pandas.pydata.org/pandas-docs/stable/api.html#datetimelike-properties