Add the time remaining until the end of the year to today's date

I want to filter a data frame using a date column. The code yould return only the rows with a date less than today’s date + time until the year’s end. I tried the following: df2=df[df[‘date’] < dt.datetime.today().strftime(‘%Y-%m-%d’)+pd.tseries.offsets.YearEnd] >Solution : You shouldn’t be turning your datetimes into strings. Also, you forgot to instantiate YearEnd. The… Read More Add the time remaining until the end of the year to today's date

How to convert string date column to timestamp in a new column in Python Pandas

I have the following example dataframe: d = {‘col1’: ["2022-05-16T12:31:00Z", "2021-01-11T11:32:00Z"]} df = pd.DataFrame(data=d) df col1 0 2022-05-16T12:31:00Z 1 2021-01-11T11:32:00Z I need a second column (say col2) which will have the corresponding timestamp value for each col1 date string value from col1. How can I do that without using a for loop? >Solution : Maybe… Read More How to convert string date column to timestamp in a new column in Python Pandas

Perform a merge by date field without creating an auxiliary column in the DataFrame

Be the following DataFrames in python pandas: | date | counter | |—————————–|——————| | 2022-01-01 10:00:02+00:00 | 34 | | 2022-01-03 11:03:02+00:00 | 23 | | 2022-02-01 12:00:05+00:00 | 12 | | 2022-03-01 21:04:02+00:00 | 7 | | date | holiday | |—————————–|——————| | 2022-01-01 | True | | 2022-01-02 | False | | 2022-01-03… Read More Perform a merge by date field without creating an auxiliary column in the DataFrame

Datetime + String from a Pandas table into a new table. One line of code

I’m a beginner at python. I’m moving specific cells/scalars from one Dataframe to another. I’m trying to work out why my first block of code didn’t work but my expanded code does. Why does concat give an error? My Initial Dataframes: df1_Data and df2_Data and code date_string=df1_Data.iat[0,2] date_string.strftime("%Y-%m-%d, %H:%M:%S") df2_Data.iat[0,0] = pd.concat([date_string,df1_Data.iat[2,2]]) Gives this error:… Read More Datetime + String from a Pandas table into a new table. One line of code

Pandas: groupby by date then return first valid value with matching datetime

With: df = pd.DataFrame({‘datetime’: pd.date_range(‘2022-05-01 10:00:00′, periods=10, freq=’10H’), ‘value’: [np.nan, np.nan, np.nan, -0.61, np.nan, 0.55, 0.63, np.nan, 0.15, np.nan]}) df datetime value 0 2022-05-01 10:00:00 NaN 1 2022-05-01 20:00:00 NaN 2 2022-05-02 06:00:00 NaN 3 2022-05-02 16:00:00 -0.61 4 2022-05-03 02:00:00 NaN 5 2022-05-03 12:00:00 0.55 6 2022-05-03 22:00:00 0.63 7 2022-05-04 08:00:00 NaN 8… Read More Pandas: groupby by date then return first valid value with matching datetime

Format specific datetime string to datetime datatype in SQL Server

I have a string which stands for a specific format of datetime. Format: yyyymmddhhmmss For example: 20220504111621 I want it to convert to a SQL Server datetime datatype: yyyy-mm-dd hh:mm:ss (Format 120) Is there a way to do that? I tried something like: select [SYNC_TIME] replace(convert(varchar, [SYNC_TIME],101),’/’,”) + replace(convert(varchar, [SYNC_TIME],108),’:’,”) FROM [DB].[dbo].[DATABASENAME] >Solution : Lots… Read More Format specific datetime string to datetime datatype in SQL Server

Pandas: Find closest date – without set_index – multiple conditions

We have the following Pandas Dataframe: # Stackoverflow question data = {‘category’:[1, 2, 3, 1, 2, 3, 1, 2, 3], ‘date’:[‘2000-01-01’, ‘2000-01-01’, ‘2000-01-01’, ‘2000-01-02’, ‘2000-01-02’, ‘2000-01-02’, ‘2000-01-03’, ‘2000-01-03’, ‘2000-01-03’]} df = pd.DataFrame(data=data) df[‘date’] = pd.to_datetime(df[‘date’]) df category date 0 1 2000-01-01 1 2 2000-01-01 2 3 2000-01-01 3 1 2000-01-02 4 2 2000-01-02 5 3… Read More Pandas: Find closest date – without set_index – multiple conditions