I have read a csv file and made a dataframe where timestamp column is in format "11/12/2020 3:01".
How do I convert this into "yyyy-mm-dd hh:mm:ss.ssssss" format for the data of that particular timestamp column?
import org.apache.spark.sql.functions._
df.withColumn("timestamp_col",
date_format(
unix_timestamp($"timestamp_col", "dd/MM/yyyy h:mm").cast("timestamp"),
"yyyy-MM-dd hh:mm:ss.SSSSSS"
)
)
watch for .strftime in the documentation
https://docs.python.org/3/library/datetime.html
timestamp.strftime("%d.%m.%Y")
Related
In a spark dataframe, I will like to convert date column, "Date" which is in string format (eg. 20220124) to 2022-01-24 and then to date format using python.
df_new= df.withColumn('Date',to_date(df.Date, 'yyyy-MM-dd'))
You can do it with to_date function which gets the input col and format of your date.
from pyspark.sql import functions as F
df.withColumn('date', F.to_date('date', 'yyyyMMdd'))
In pyspark sql, I have unix timestamp column that is a long - I tried using the following but the output was not correct.
from_unixtime(col("firstAvailableDateTimeUnix"), "yyyy-MM-dd HH:mm:ss")
from_unixtime output
import org.apache.spark.sql.functions._
val df = Seq(1651484635297L).toDF("firstAvailableDateTimeUnix").withColumn("goodDate",
from_unixtime(col("firstAvailableDateTimeUnix")/1000, "yyyy-MM-dd HH:mm:ss")
)
df.show(false)
// +--------------------------+-------------------+
// |firstAvailableDateTimeUnix|goodDate |
// +--------------------------+-------------------+
// |1651484635297 |2022-05-02 12:43:55|
// +--------------------------+-------------------+
All you need to do is casting and then use date_format to format your timestamp:
date_format(col("firstAvailableDateTimeUnix").cast('timestamp'), "yyyy-MM-dd HH:mm:ss"))
I am currently learning pyspark and I need to convert a COLUMN of strings in format 13/09/2021 20:45 into a timestamp of just the hour 20:45.
Now I figured that I can do this with q1.withColumn("timestamp",to_timestamp("ts")) \ .show() (where q1 is my dataframe, and ts is a column we are speaking about) to convert my input into a DD/MM/YYYY HH:MM format, however values returned are only null. I therefore realised that I need an input in PySpark timestamp format (MM-dd-yyyy HH:mm:ss.SSSS) to convert it to a proper timestamp. Hence now my question:
How can I convert the column of strings dd/mm/yyyy hh:mm into a format understandable for pyspark so that I can convert it to timestamp format?
There are different ways you can do that
from pyspark.sql import functions as F
# use substring
df.withColumn('hour', F.substring('A', 12, 15)).show()
# use regex
df.withColumn('hour', F.regexp_extract('A', '\d{2}:\d{2}', 0)).show()
# use datetime
df.withColumn('hour', F.from_unixtime(F.unix_timestamp('A', 'dd/MM/yyyy HH:mm'), 'HH:mm')).show()
# Output
# +----------------+-----+
# | A| hour|
# +----------------+-----+
# |13/09/2021 20:45|20:45|
# +----------------+-----+
unix_timestamp may be a help for your problem.
Just try this:
Convert pyspark string to date format
I have a string in format 05/26/2021 11:31:56 AM for mat and I want to convert it to a date format like 05-26-2021 in pyspark.
I have tried below things but its converting the column type to date but making the values null.
df = df.withColumn("columnname", F.to_date(df["columnname"], 'yyyy-MM-dd'))
another one which I have tried is
df = df.withColumn("columnname", df["columnname"].cast(DateType()))
I have also tried the below method
df = df.withColumn(column.lower(), F.to_date(F.col(column.lower())).alias(column).cast("date"))
but in every method I was able to convert the column type to date but it makes the values null.
Any suggestion is appreciated
# Create data frame like below
df = spark.createDataFrame(
[("Test", "05/26/2021 11:31:56 AM")],
("user_name", "login_date"))
# Import functions
from pyspark.sql import functions as f
# Create data framew with new column new_date with data in desired format
df1 = df.withColumn("new_date", f.from_unixtime(f.unix_timestamp("login_date",'MM/dd/yyyy hh:mm:ss a'),'yyyy-MM-dd').cast('date'))
The above answer posted by #User12345 works and the below method is also works
df = df.withColumn(column, F.unix_timestamp(column, "MM/dd/YYYY hh:mm:ss aa").cast("double").cast("timestamp"))
df = df.withColumn(column, F.from_utc_timestamp(column, 'Z').cast(DateType()))
Use this
df=data.withColumn("Date",to_date(to_timestamp("Date","M/d/yyyy")))
I am trying to save a dataframe to a csv file, that contains a timestamp.
The problem that this column changes of format one written in the csv file. Here is the code I used:
val spark = SparkSession.builder.master("local").appName("my-spark-app").getOrCreate()
val df = spark.read.option("header",true).option("inferSchema", "true").csv("C:/Users/mhattabi/Desktop/dataTest2.csv")
//val df = spark.read.option("header",true).option("inferSchema", "true").csv("C:\\dataSet.csv\\datasetTest.csv")
//convert all column to numeric value in order to apply aggregation function
df.columns.map { c =>df.withColumn(c, col(c).cast("int")) }
//add a new column inluding the new timestamp column
val result2=df.withColumn("new_time",((unix_timestamp(col("time"))/300).cast("long") * 300).cast("timestamp")).drop("time")
val finalresult=result2.groupBy("new_time").agg(result2.drop("new_time").columns.map((_ -> "mean")).toMap).sort("new_time") //agg(avg(all columns..)
finalresult.coalesce(1).write.option("header",true).option("inferSchema","true").csv("C:/mydata.csv")
when display via df.show it shoes the correct format
But in the csv file it shoes this format:
Use option to format timestamp into desired one which you need:
finalresult.coalesce(1).write.option("header",true).option("inferSchema","true").option("dateFormat", "yyyy-MM-dd HH:mm:ss").csv("C:/mydata.csv")
or
finalresult.coalesce(1).write.format("csv").option("delimiter", "\t").option("header",true).option("inferSchema","true").option("dateFormat", "yyyy-MM-dd HH:mm:ss").option("escape", "\\").save("C:/mydata.csv")
Here is the code snippet that worked for me to modify the CSV output format for timestamps.
I needed a 'T' character in there, and no seconds or microseconds. The timestampFormat option did work for this.
DF.write
.mode(SaveMode.Overwrite)
.option("timestampFormat", "yyyy-MM-dd'T'HH:mm")
Such as 2017-02-20T06:53
If you substitute a space for 'T' then you get this:
DF.write
.mode(SaveMode.Overwrite)
.option("timestampFormat", "yyyy-MM-dd HH:mm")
Such as 2017-02-20 06:53