I have the following problem. I want to create a date from another. To do this, I extract the year from the database date and then create the chosen date (day = 30 and month = 9) being the year extracted from the database.
The code is the following
bbdd20Q3['year']=(pd.DatetimeIndex(bbdd20Q3['datedaymonthyear']).year)
y=(bbdd20Q3['year'])
m=int(9)
d=int(30)
bbdd20Q3['mydate']=dt.datetime(y,m,d)
But error message is this
"cannot convert the series to <class 'int'>"
I think dt mean datetime, so the line 'dt.datetime(y,m,d)' create datetime object type.
bbdd20Q3['mydate'] should get int?
If so, try to think of another way to store the date (8 numbers maybe).
hope I helped :)
I assume that you did import datetime as dt then by doing:
bbdd20Q3['year']=(pd.DatetimeIndex(bbdd20Q3['datedaymonthyear']).year)
y=(bbdd20Q3['year'])
m=int(9)
d=int(30)
bbdd20Q3['mydate']=dt.datetime(y,m,d)
You are delivering series as first argument to datetime.datetime, when it excepts int or something which can be converted to int. You should create one datetime.datetime for each element of series not single datetime.datetime, consider following example
import datetime
import pandas as pd
df = pd.DataFrame({"year":[2001,2002,2003]})
df["day"] = df["year"].apply(lambda x:datetime.datetime(x,9,30))
print(df)
Output:
year day
0 2001 2001-09-30
1 2002 2002-09-30
2 2003 2003-09-30
Here's a sample code with the required logic -
import pandas as pd
df = pd.DataFrame.from_dict({'date': ['2019-12-14', '2020-12-15']})
print(df.dtypes)
# convert the date in string format to datetime object,
# if the date column(Series) is already a datetime object then this is not required
df['date'] = pd.to_datetime(df['date'])
print(f'after conversion \n {df.dtypes}')
# logic to create a new data column
df['new_date'] = pd.to_datetime({'year':df['date'].dt.year,'month':9,'day':30})
#eollon I see that you are also new to Stack Overflow. It would be better if you can add a simple sample code, which others can tryout independently
(keeping the comment here since I don't have permission to comment :) )
Related
I'm using pandas to analyze some data about the House Price Index of all states from quandl:
HPI_Data = quandl.get("FMAC/HPI_AK")
The data looks something like this:
HPI Alaska
Date
1975-01-31 35.105461
1975-02-28 35.465209
1975-03-31 35.843110
and so on.
I've got a second dataframe with some special dates in it:
Date
Name
David 1979-08
Allen 1980-08
Hugo 1989-09
The values for "Date" here are of "string" type and not "date".
I'd like to go 6 months back from each date in the special dataframe and see the values in the HPI dataframe.
I'd like to use .loc but I have not been able to convert the first dataframe's index from "END OF MONTH" to "MONTH". even after resampling to "1D" then back to "M".
I'd would appreciate any help, if it solves the problem a different way or the janky data deleting way I want :).
Not sure if I understand correctly. So please clarify your question if this is not correct.
You can convert a string to a pandas date time object using pd.to_datetime and use the format parameter to specify how to parse the string
import pandas as pd
# Creating a dummy Series
sr = pd.Series(['2012-10-21 09:30', '2019-7-18 12:30', '2008-02-2 10:30',
'2010-4-22 09:25', '2019-11-8 02:22'])
# Convert the underlying data to datetime
sr = pd.to_datetime(sr)
# Subtract 6 months of the datetime series
sr-pd.DateOffset(month=6)
In regards to changing the datetime to just month i.e. 2012-10-21 09:30 --> 2012-10 I would do this:
sr.dt.to_period('M')
I have a date column in a dataset where the dates are like 'Apr-12','Jan-12' format. I would like to change the format to 04-2012,01-2012. I am looking for a function which can do this.
I think I know one guy with the same name. Jokes apart here is the solution to your problem.
We do have an inbuilt function named as strptime(), so it takes up the string and then convert into the format you want.
You need to import datetime first since it is the part of the datetime package of python. Don't no need to install anything, just import it.
Then this works like this: datetime.strptime(your_string, format_you_want)
# You can also do this, from datetime import * (this imports all the functions of datetime)
from datetime import datetime
str = 'Apr-12'
date_object = datetime.strptime(str, '%m-%Y')
print(date_object)
I hope this will work for you. Happy coding :)
You can do following:
import pandas as pd
df = pd.DataFrame({
'date': ['Apr-12', 'Jan-12', 'May-12', 'March-13', 'June-14']
})
pd.to_datetime(df['date'], format='%b-%y')
This will output:
0 2012-04-01
1 2012-01-01
2 2012-05-01
Name: date, dtype: datetime64[ns]
Which means you can update your date column right away:
df['date'] = pd.to_datetime(df['date'], format='%b-%y')
You can chain a couple of pandas methods together to get this the desired output:
df = pd.DataFrame({'date_fmt':['Apr-12','Jan-12']})
df
Input dataframe:
date_fmt
0 Apr-12
1 Jan-12
Use pd.to_datetime chained with .dt date accessor and strftime
pd.to_datetime(df['date_fmt'], format='%b-%y').dt.strftime('%m-%Y')
Output:
0 04-2012
1 01-2012
Name: date_fmt, dtype: object
I've got a dataset which goes back to 15-Jan-0001 (yes that is 1 CE!), it was originally 0 CE but since that year doesn't exist I cut those 12 months out of the data.
I am trying to get pandas to convert to date-time string in my datasdf.datetime=pd.to_datetime(df.datetime) to an internal datetime object.
I tried:
import pandas as pd
df = pd.load_csv(file)
df.datetime = pd.to_dtaetime(df.datetime)
and got:
OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 1-01-15 00:00:00
the first two lines of the csv file are:
datenum,year,month,day,datetime,data_mean_global,data_mean_nh,data_mean_sh
381,1,1,15,15-Jan-0001 00:00:00,277.876675965034,278.555895908363,277.197456021705
One way is convert this problematic values to NaTs:
df.datetime = pd.to_dtaetime(df.datetime, errors='coerce')
I have a SQL table that contains data of the mySQL time type as follows:
time_of_day
-----------
12:34:56
I then use pandas to read the table in:
df = pd.read_sql('select * from time_of_day', engine)
Looking at df.dtypes yields:
time_of_day timedelta64[ns]
My main issue is that, when writing my df to a csv file, the data comes out all messed up, instead of essentially looking like my SQL table:
time_of_day
0 days 12:34:56.000000000
I'd like to instead (obviously) store this record as a time, but I can't find anything in the pandas docs that talk about a time dtype.
Does pandas lack this functionality intentionally? Is there a way to solve my problem without requiring janky data casting?
Seems like this should be elementary, but I'm confounded.
Pandas does not support a time dtype series
Pandas (and NumPy) do not have a time dtype. Since you wish to avoid Pandas timedelta, you have 3 options: Pandas datetime, Python datetime.time, or Python str. Below they are presented in order of preference. Let's assume you start with the following dataframe:
df = pd.DataFrame({'time': pd.to_timedelta(['12:34:56', '05:12:45', '15:15:06'])})
print(df['time'].dtype) # timedelta64[ns]
Pandas datetime series
You can use Pandas datetime series and include an arbitrary date component, e.g. today's date. Underlying such a series are integers, which makes this solution the most efficient and adaptable.
The default date, if unspecified, is 1-Jan-1970:
df['time'] = pd.to_datetime(df['time'])
print(df)
# time
# 0 1970-01-01 12:34:56
# 1 1970-01-01 05:12:45
# 2 1970-01-01 15:15:06
You can also specify a date, such as today:
df['time'] = pd.Timestamp('today').normalize() + df['time']
print(df)
# time
# 0 2019-01-02 12:34:56
# 1 2019-01-02 05:12:45
# 2 2019-01-02 15:15:06
Pandas object series of Python datetime.time values
The Python datetime module from the standard library supports datetime.time objects. You can convert your series to an object dtype series containing pointers to a sequence of datetime.time objects. Operations will no longer be vectorised, but each underlying value will be represented internally by a number.
df['time'] = pd.to_datetime(df['time']).dt.time
print(df)
# time
# 0 12:34:56
# 1 05:12:45
# 2 15:15:06
print(df['time'].dtype)
# object
print(type(df['time'].at[0]))
# <class 'datetime.time'>
Pandas object series of Python str values
Converting to strings is only recommended for presentation purposes that are not supported by other types, e.g. Pandas datetime or Python datetime.time. For example:
df['time'] = pd.to_datetime(df['time']).dt.strftime('%H:%M:%S')
print(df)
# time
# 0 12:34:56
# 1 05:12:45
# 2 15:15:06
print(df['time'].dtype)
# object
print(type(df['time'].at[0]))
# <class 'str'>
it's a hack, but you can pull out the components to create a string and convert that string to a datetime.time(h,m,s) object
def convert(td):
time = [str(td.components.hours), str(td.components.minutes),
str(td.components.seconds)]
return datetime.strptime(':'.join(time), '%H:%M:%S').time()
df['time'] = df['time'].apply(lambda x: convert(x))
found a solution, but i feel like it's gotta be more elegant than this:
def convert(x):
return pd.to_datetime(x).strftime('%H:%M:%S')
df['time_of_day'] = df['time_of_day'].apply(convert)
df['time_of_day'] = pd.to_datetime(df['time_of_day']).apply(lambda x: x.time())
Adapted this code
I have a dataframe full of dates and I would like to select all dates where the month==12 and the day==25 and add replace the zero in the xmas column with a 1.
Anyway to do this? the second line of my code errors out.
df = DataFrame({'date':[datetime(2013,1,1).date() + timedelta(days=i) for i in range(0,365*2)], 'xmas':np.zeros(365*2)})
df[df['date'].month==12 and df['date'].day==25] = 1
Pandas Series with datetime now behaves differently. See .dt accessor.
This is how it should be done now:
df.loc[(df['date'].dt.day==25) & (cust_df['date'].dt.month==12), 'xmas'] = 1
Basically what you tried won't work as you need to use the & to compare arrays, additionally you need to use parentheses due to operator precedence. On top of this you should use loc to perform the indexing:
df.loc[(df['date'].month==12) & (df['date'].day==25), 'xmas'] = 1
An update was needed in reply to this question. As of today, there's a slight difference in how you extract months from datetime objects in a pd.Series.
So from the very start, incase you have a raw date column, first convert it to datetime objects by using a simple function:
import datetime as dt
def read_as_datetime(str_date):
# replace %Y-%m-%d with your own date format
return dt.datetime.strptime(str_date,'%Y-%m-%d')
then apply this function to your dates column and save results in a new column namely datetime:
df['datetime'] = df.dates.apply(read_as_datetime)
finally in order to extract dates by day and month, use the same piece of code that #Shayan RC explained, with this slight change; notice the dt.datetime after calling the datetime column:
df.loc[(df['datetime'].dt.datetime.month==12) &(df['datetime'].dt.datetime.day==25),'xmas'] =1