I am trying to calculate basic statistics using pandas. I have precip values for a whole year from 1956. I created a "Date" column that has date for the entire year using pd.date_range. Then I calculated the max value for the year and the date of maximum value. The date of maximum value show "Timestamp('1956-06-19 00:00:00" as the output. How do I extract just the date. I do not need the timestamp or the 00:00:00 time
#Create Date Column
year = 1956
start_date = datetime.date(year,1,1)
end_date = datetime.date(year,12,31)
precip_file["Date"] = pd.date_range(start=start_date,end=end_date,freq="D")
#Yearly maximum value and date of maximum value
yearly_max = precip_file["Precip (mm)"].max(skipna=True)
max_index = precip_file["Precip (mm)"].idxmax()
yearly_max_date = precip_file.iat[max_index,2
Image of output dictionary I am trying to create
May be a duplicate of this question, although I can't tell whether you are trying to convert one DateTime or a column of DateTimes.
Related
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 large data frame with transaction data. What I am trying to do is use python to aggregate the data starting with zip codes, then a year and month, finally the total number of transactions for that month.
My Df:
Date VAR1 VAR2 ZipCode Transactions
YYYY-MM-DD. X. Y. 12345. 1.
So the first thing I did was convert the to date time
df['Date'] = pd.to_datetime(df['Date'])
df.info()
# Date datetime64[ns]
Then I split the data into year-month and number of transactions:
# grouping the data by year and month
per = df.Date.dt.to_period("M")
g = df.groupby(per)
g.sum() # so now that this works, we need to break it up into zip codes
Which gives an output of:
Date. Transactions
YYYY-MM. X
YYYY-MM. Y
My questions is, what am I missing to get the zipcodes in front:
ZipCode. Date. Transactions
123345. YYYY-MM. sum()
Any and all help is greatly apprecaited
I believe you need add column ZipCode to groupby if need grouping per zip and per months:
per = df.Date.dt.to_period("M")
df1 = df.groupby(['ZipCode',per])['Transactions'].sum().reset_index()
In the image I have a dataframe.In that I have a column called timestamp ,from that I want to seperate month and have to make it as a new column.How to do that?
If your Timestamp is not already datetime than convert like so:
df["Timestamp_converted"] = pd.to_datetime(df["Timestamp"], format="%Y-%m-%d %H:%M:%S")
You get the month as a separate column with this:
df["month"] = df.Timestamp_converted.dt.month
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")))
I am parsing a dataframe from a sas7bdat file and I want to convert the index into datetime to resample the data.
I have one column with the Date which is type String and another column of the time which is of type datetime.time. Does anybody know how to convert this to one column of datetime?
I already tried the pd.datetime like this but it requires individual columns for year, month and day:
df['TimeIn']=str(df['TimeIn'])
df['datetime']=pd.to_datetime(df[['Date', 'TimeIn']], dayfirst=True)
This gives me a value error:
ValueError: to assemble mappings requires at least that [year, month, day] be specified: [day,month,year] is missing
DataFrame column headers
If you convert both the date and time column to str then you can concatenate them and then call to_datetime:
In[155]:
df = pd.DataFrame({'Date':['08/05/2018'], 'TimeIn':['10:32:12']})
df
Out[155]:
Date TimeIn
0 08/05/2018 10:32:12
In[156]:
df['new_date'] = pd.to_datetime(df['Date']+' '+df['TimeIn'])
df
Out[156]:
Date TimeIn new_date
0 08/05/2018 10:32:12 2018-08-05 10:32:12