I have a data frame in pandas which includes number of days since an event occurred. I want to create a new column that calculates the date of the event by subtracting the number of days from the current date. Every time I attempt to apply pd.offsets.Day or pd.Timedelta I get an error stating that Series are an unsupported type. This also occurs when I use apply. When I use map I receive a runtime error saying "maximum recursion depth exceeded while calling a Python
object".
For example, assume my data frame looked like this:
index days_since_event
0 5
1 7
2 3
3 6
4 0
I want to create a new column with the date of the event, so my expected outcome [using today's date of 12/29/2015]
index days_since_event event_date
0 5 2015-12-24
1 7 2015-12-22
2 3 2015-12-26
3 6 2015-12-23
4 0 2015-12-29
I have attempted multiple ways to do this, but have received errors for each.
One method I tried was:
now = pd.datetime.date[pd.datetime.now[]]
df['event_date'] = now - df.days_since_event.apply[pd.offsets.Day]
With this I received an error saying that Series are an unsupported type.
I tried the above with .map instead of .apply, and received the error that
"maximum recursion depth exceeded while calling a Python object".
I also attempted to convert the days into timedelta, such as:
df.days_since_event = [dt.timedelta[days = df.days_since_event]].apply
This also received an error referencing the series being an unsupported type.
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Let’s discuss how to convert an Integer to Datetime in it. Now to convert Integers to Datetime in Pandas DataFrame.
Syntax of pd.to_datetime
df['DataFrame Column'] = pd.to_datetime[df['DataFrame Column'], format=specify your format]
Create the DataFrame to Convert Integer to Datetime in Pandas
Check data type for the ‘Dates’ column is Integer.
Python
import pandas as pd
values = {'Dates': [20190902, 20190913, 20190921],
'Attendance': ['Attended', 'Not Attended', 'Attended']
}
df =
pd.DataFrame[values, columns=['Dates', 'Attendance']]
print[df]
print[df.dtypes]
Output:
Example 1:
Now to convert it into Datetime we use the previously mentioned syntax. Since in this example the date format is yyyymmdd, the date format can be represented as follows:
format= '%Y%m%d'
Python3
import pandas as pd
values = {'Dates': [20190902, 20190913, 20190921],
'Attendance': ['Attended', 'Not Attended', 'Attended']
}
df =
pd.DataFrame[values, columns=['Dates', 'Attendance']]
df['Dates'] = pd.to_datetime[df['Dates'], format='%Y%m%d']
print[df]
print[df.dtypes]
Output:
Example 2:
Now, suppose the Pandas DataFrame has a date in the format yymmdd. In this case, the date format would now contain ‘y’ in lowercase:
format='%y%m%d'
Python3
import pandas as pd
values = {'Dates': [190902, 190913, 190921],
'Attendance': ['Attended', 'Not Attended', 'Attended']
}
df = pd.DataFrame[values, columns=['Dates', 'Attendance']]
df['Dates'] = pd.to_datetime[df['Dates'], format='%y%m%d']
print[df]
print[df.dtypes]
Output:
Example 3:
Now, let’s suppose that your integers contain both date and time. In that case, the format that you should specify is:
format='%Y%m%d%H%M%S'
Python3
import pandas as pd
values = {'Dates': [20190902093000, 20190913093000, 20190921200000],
'Attendance': ['Attended', 'Not Attended', 'Attended']
}
df = pd.DataFrame[values, columns=['Dates', 'Attendance']]
df['Dates'] =
pd.to_datetime[df['Dates'], format='%Y%m%d%H%M%S']
print[df]
print[df.dtypes]
Output:
Example 4:
Consider this DataFrame with microseconds in our DateTime values. In this case, the format should be specified as:
format='%Y%m%d%H%M%S%F'
Python3
import pandas as pd
values = {'Dates': [20190902093000912, 20190913093000444,
20190921200000009],
'Attendance': ['Attended', 'Not Attended', 'Attended']
}
df = pd.DataFrame[values, columns=['Dates', 'Attendance']]
df['Dates'] = pd.to_datetime[df['Dates'], format='%Y%m%d%H%M%S%F']
print[df]
print[df.dtypes]
Output: