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
Related
How do I convert Excel date format to number in Python? I'm importing a number of Excel files into Pandas dataframe in a loop and some values are formatted incorrectly in Excel. For example, the number column is imported as date and I'm trying to convert this date value into numeric.
Original New
1912-04-26 00:00:00 4500
How do I convert the date value in original to the numeric value in new? I know this code can convert numeric to date, but is there any similar function that does the opposite?
df.loc[0]['Date']= xlrd.xldate_as_datetime(df.loc[0]['Date'], 0)
I tried to specify the data type when I read in the files and also tried to simply change the data type of the column to 'float' but both didn't work.
Thank you.
I found that the number means the number of days from 1900-01-00.
Following code is to calculate how many days passed from 1900-01-00 until the given date.
import pandas as pd
from datetime import datetime, timedelta
df = pd.DataFrame(
{
'date': ['1912-04-26 00:00:00'],
}
)
print(df)
# date
#0 1912-04-26 00:00:00
def date_to_int(given_date):
given_date = datetime.strptime(given_date, '%Y-%m-%d %H:%M:%S')
base_date = datetime(1900, 1, 1) - timedelta(days=2)
delta = given_date - base_date
return delta.days
df['date'] = df['date'].apply(date_to_int)
print(df)
# date
#0 4500
I'm using spark 2.4.5. I want to add two new columns, date & calendar week, in my pyspark data frame df.
So I tried the following code:
from pyspark.sql.functions import lit
df.withColumn('timestamp', F.lit('2020-05-01'))
df.show()
But I'm getting error message: AssertionError: col should be Column
Can you explain how to add date column & calendar week?
Looks like you missed the lit function in your code.
Here's what you were looking for:
df = df.withColumn("date", lit('2020-05-01'))
This is your answer if you want to hardcode the date and week. If you want to programmatically derive the current timestamp, I'd recommend using a UDF.
I see two questions here: First, how to cast a string to a date. Second, how to get the week of the year from a date.
Cast string to date
You can either simply use cast("date") or the more specific F.to_date.
df = df.withColumn("date", F.to_date("timestamp", "yyyy-MM-dd"))
Extract week of year
Using format date allows you to format a date column to any desired format. w is the week of the year. W would be the week of the month.
df = df.withColumn("week_of_year", F.date_format("date", "w"))
Related Question: pyspark getting weeknumber of month
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.
I am trying to plot a graph with dates format. The thing is that I have problem with the format of the dates column.
I have tried to use the solution like this:
df['Date'] = pd.to_datetime(df['Date'])
It works. But the problem is that when I append the value of the Date in the dataframe into a list, the format of my Date column turns back to String. How do I solve this case?
hope this will work
solution1
df['Date']=df['Date'].astype('datetime64[ns]')
Solution2
date is the list of date in string formate,if u want to convert into datetime then try this code
dates_list = [dt.datetime.strptime(date, '"%Y-%m-%d"').date() for date in dates]
I am trying to change a column type from object to a datetime64 but want it to display only the time as hours:minute.
The column is a string formatted 13:45:00. When I change the data type to datetime64 it now prints it with a made up date (1900-01-01 13:45:00).
I want the column data type to be a datetime64 type (so I can do comparisons and operations later) but only I want it display the time in hour:minute format without the seconds and without the date.
Example - 13:45
Everything I can find in google is about getting only the date to show and maintain the datetime64 datatype, which I was able to do.
I have tried messing with the pd.to_datetime().dt.strftime('%H:%M'). It correctly formats the column but its datatype is object not datetime64.
cycle_trips_df['Checkout Date'] = pd.to_datetime(
cycle_trips_df['Checkout Date'], infer_datetime_format=True
).dt.normalize() #strftime('%m/%d/%Y') # format='%m/%d/%Y').dt.date
cycle_trips_df['Checkout Time'] = pd.to_datetime(
cycle_trips_df['Checkout Time'], format='%H:%M:%S'
).dt.strftime('%H:%M')
print(cycle_trips_df.dtypes)
[Output]
Checkout Date datetime64[ns]
Checkout Time object
Use a timedelta rather than a datetime:
In [11]: s = pd.Series(['13:45:00'])
In [12]: pd.to_timedelta(s)
Out[12]:
0 13:45:00
dtype: timedelta64[ns]
Distinguish between the data and your views of that data. A datetime64 is a datetime64 and will be printed by default as a full date string. You can use strftime to get the time part.
str = "13:45:00" # Your string.
dt64 = pd.to_datetime(str) # the datetime64 object
timestr = dt64.strftime("%H:%M:%S") # extracting the time string from the datetime64.
May use some like:
df['time'] = df['time'].apply(lambda x: datetime.strptime(x, "%H:%M:%S").time())
It will be object