I have dates in this format: 2015-02-02 14:19:00.
I use this code:
dateparse = lambda dates: pd.datetime.strptime(dates, '%Y/%m/%d %H:%M:%S')
df = pd.read_csv('3df_uniti.csv', parse_dates=True, index_col='date', date_parser=dateparse)
df.head()
but it doesn't work because it gives me the follow error:
time data does not match format
Can you help me to set the right format?
Your format uses / instead of -. Try changing it to %Y-%m-%d %H:%M:%S.
Related
I'm curious about how to use pandas to deal with this sort of info in a .csv file:
2022-08-11 11:50:01 America/Los_Angeles
My goal is to extract the date, hour and minute, and the timezone info for further analysis.
I have tried to lift out the date and time using:
df['Date'] = pd.to_datetime(df['datetime']).dt.date
but got an error because of the string at the end. Other than extracting the date and time using specific indices, is there any better and quicker way? Thank you so much.
pandas cannot handle a datetime column with different timezones. You can start by splitting the datetime and timezone in separate columns:
df[['datetime', 'timezone']] = df['datetime'].str.rsplit(' ', n=1, expand=True)
df['datetime'] = pd.to_datetime(df['datetime']) # this column now has the datetime64[ns] type
Now you are able to do the following:
df['date_only'] = df['datetime'].dt.date
If you want to express all local date/times in America/Los_Angeles time:
df['LA_datetime'] = df.apply(lambda x: x['datetime'].tz_localize(tz=x['timezone']).tz_convert('America/Los_Angeles'), axis = 1)
You can change America/Los_Angeles to the timezone of your liking.
I am trying to get a date time field in Pandas in the below format
df['date'] = pd.to_datetime(df['date'])
The above code returns date time column in the below format
2021-11-27 03:30:00
I would like to get an output of 27/11/2021 (format is dd/mm/yyyy) and the data type of the column needs to be datetime and not object.
If your column is a string, you will need to first use pd.to_datetime,
df['Date'] = pd.to_datetime(df['Date'])
Then, use .dt datetime accessor with strftime:
df = pd.DataFrame({'Date':pd.date_range('2017-01-01', periods = 60, freq='D')})
df.Date.dt.strftime('%Y%m%d').astype(int)
Or use lambda function:
df.Date.apply(lambda x: x.strftime('%Y%m%d')).astype(int)
I am looking to convert datetime to date for a pandas datetime series.
I have listed the code below:
df = pd.DataFrame()
df = pandas.io.parsers.read_csv("TestData.csv", low_memory=False)
df['PUDATE'] = pd.Series([pd.to_datetime(date) for date in df['DATE_TIME']])
df['PUDATE2'] = datetime.datetime.date(df['PUDATE']) #Does not work
Can anyone guide me in right direction?
You can access the datetime methods of a Pandas series by using the .dt methods (in a aimilar way to how you would access string methods using .str. For your case, you can extract the date of your datetime column as:
df['PUDATE'].dt.date
This is a simple way to get day of month, from a pandas
#create a dataframe with dates as a string
test_df = pd.DataFrame({'dob':['2001-01-01', '2002-02-02', '2003-03-03', '2004-04-04']})
#convert column to type datetime
test_df['dob']= pd.to_datetime(test_df['dob'])
# Extract day, month , year using dt accessor
test_df['DayOfMonth']=test_df['dob'].dt.day
test_df['Month']=test_df['dob'].dt.month
test_df['Year']=test_df['dob'].dt.year
I think you need to specify the format for example
df['PUDATE2']=datetime.datetime.date(df['PUDATE'], format='%Y%m%d%H%M%S')
So you just need to know what format you are using
I am getting date data in string format in pandas like 10-Oct,11-Oct but i want to make it date data type like this format 2019-10-10,2019-10-11
is there any easy way available in pandas?
Use to_datetime with added year and parameter format:
df = pd.DataFrame({'date':['10-Oct', '11-Oct']})
df['date'] = pd.to_datetime(df['date'] + '-2019', format='%d-%b-%Y')
print (df)
date
0 2019-10-10
1 2019-10-11
I'm trying to get today's date in a few different formats and I keep getting errors:
pd.to_datetime('Today',format='%m/%d/%Y') + MonthEnd(-1)
ValueError: time data 'Today' does not match format '%m/%d/%Y' (match)
What is the correct syntax to get todays date in yyyy-mm-dd and yyyymm formats?
For YYYY-MM-DD format, you can do this:
import datetime as dt
print(dt.datetime.today().date())
2017-05-23
For YYYY-MM format, you can do this:
print(dt.datetime.today().date().strftime('%Y-%m'))
2017-05
If you need to do this on just a few columns you can use:
import pandas as pd
dataframe_name['Date_Column_name'].apply(pd.tslib.normalize_date)
This method doesn't use any other module except pandas. If you need a "custom" date format you can always do:
from datetime import datetime as dt
dataframe_name['Date_Column_name'].dt.strftime('%d/%m/%Y')
Here is a list of strftime options.