I have a dataframe called: twitter_df with two columns, tweets and date columns. For every tweet there is a datetime entry like below:
Date
2019-11-29 12:50:54
2019-11-29 12:46:53
2019-11-29 12:46:10
2019-11-29 12:33:36
2019-11-29 12:17:43
I would like the entries for Date column to be this:
Date
2019-11-29
2019-11-29
2019-11-29
2019-11-29
2019-11-29
The reason I want the HH:MM:SS to be removed is because I am going to group it by the date.
I have tried several links but no luck on my end, can anyone assist? Some say I should make use of pd.to_datetime but not sure how i should go about.
This will create a new column, onlyDate, with only the date from the Date column.
If you want to replace the existing column change 'onlyDate' to 'Date' on the left hand side.
twitter_df['onlyDate'] = twitter_df['Date].dt.date
Related
Can someone show me how to extract the year from the String date in databricks SQL.
I am based in the UK and our date format is normally as follows:
dd/mm/yyyy
The field containing the dates is set as StringType()
I am trying to extract the year from the string as follows:
select year(cast(financials_0_accountsDate as Date)) from `financiallimited_csv`
I'm using the following the code to extract the quarter
select quarter(cast(financials_0_accountsDate as Date)) from `financiallimited_csv`
However, both result in NULL values.
Any thoughts on how to extract the year and quarter from dates with StringType() dd/mm/yyyy?
The table looks like the following:
Could you try the to_date function?
select year(to_date(financials_0_accountsDate, 'dd/MM/yyyy')) from `financiallimited_csv`
I have a csv file in which a date column has values like 01080600, basically MM-dd-HH-mm.
I want to add a column in dataframe which shows this in a more readable format.
I do :
spark.sql("SELECT date...")
.withColumn("readable date", to_date(col("date"), "MM:dd HH:mm"))
.show(10)
But readable date is returned null.
What am I missing here?
While formating or converting to date or timestamp you need to provide the date_format as is following your pattern , example in your case you need to modify your format as below and further which can be formatted depending on the final format you wish your date col to take using date_format
References to various patterns and parsing can be found here
To Timestamp
sql.sql("""
SELECT
TO_TIMESTAMP('01080600','ddMMhhmm') as date,
DATE_FORMAT(TO_TIMESTAMP('01080600','ddMMhhmm'),'MM/dd hh:mm') as formated_date
""").show()
+-------------------+-------------+
| date|formated_date|
+-------------------+-------------+
|1970-08-01 06:00:00| 08/01 06:00|
+-------------------+-------------+
I have a column where timestamp is 5/23/2022 8:45:34 PM. I want to create a new column with same data as old column but in UTC format 'yyyy-MM-dd HH:mm:ss' and this new datetime format is 7 hours behind UTC (UTC-7)
I tried doing in azure data factory derived column using toTimestamp before it converts to UTC but it always fail.
toTimestamp(column_name,'yyyy-MM-dd HH:mm:SS')
but it did not work and the result always NULL.
Can anyone help this data conversion to UTC ?
The reason you are getting null values for the newly added columns is because the format you specified in the toTimestamp() function is incorrect. The following is the sample data that I used to reproduce this issue. The date column here is of type String.
While using Derived column in the dataflow to create a new date column of timestamp type, write toTimestamp(date, 'MM/dd/yyyy hh:mm:ss a', 'UTC') expression as the value for this new column. Here, date is the column you want to convert to new date column and MM/dd/yyyy hh:mm:ss a is the format of the values in date column (a represents the AM/PM). You can also pass time zone value like UTC, GMT etc. which is optional.
The following is what the result looks like, with a new column of timestamp type. You can use this resulting data to perform further conversions in dataflow.
I have 2 dates one is stored inside my date and for other date I am using calculated column in order to store the end date into that, how an I calculate the difference in time period between those dates, I need the date period between all those dates is that possible with DAX?
How can I use calculated column inside my DAX and also I dont have a calender table inside my database.
2019-05-31 and end date is 2019-06-03 then the difference will give me 3 dates that is 2019-05-31,2019-06-01 2019-06-02 and 2019-06-03
Totally possible and easy. If you just need the difference between dates in two columns you can create a calculated column using the following:
DateDiff =
DATEDIFF ( 'Table'[Date1], 'Table'[Date2], DAY )
This will take the difference between Date1 and Date2 in days.
DECLARE #start_date [date] = CAST(‘2012-08-01’ as [date])
DECLARE #end_date [date] = CAST(‘2012-09-01’ as [date])
SELECT
DATEADD(day, [v].[number], #start_date)
FROM
[master].[dbo].[spt_values] [v]
WHERE
[v].[type] = ‘P’ AND
DATEADD(day, [v].[number], #start_date) <= #end_date
I have a spark dataframe having a timestamp Column.
I want to get previous day date of the column.Then add time (3,59,59) to the date.
Ex- value in current column(x1) : 2018-07-11 21:40:00
previous day date : 2018-07-10
after adding time(3,59,59) to the previous day date ,it should be like :
2018-07-10 03:59:59 (x2)
I want to add a column in the dataframe with "x2" values corresponding to "x1" values in all records.
I want one more column with values equal to difference of (x1-x2).totalDays in exact double values
Substracting day and adding time and converting to timestamp type
from pyspark.sql.types import *
from pyspark.sql import *
>>>df.withColumn('x2',concat(date_sub(col("x1"),1),lit(" 03:59:59")).cast("timestamp"))
Caluculating Time and Date difference:
Date Difference:-
Using datediff function we can caluculate date difference
>>>df1.withColumn("x3",datediff(col("x1"),col("x2")))
Time Difference
Calculate time difference for this convert to unix time then subtract x2 column from x1
>>>df1.withColumn("x3",unix_timestamp(col("x1"))-unix_timestamp(col("x2")))