CODEWITHSUNDEEP
pythonpandasdatetimeparsing
shuntera
shuntera

Reputation: 41

Pandas - adding new date column based on parsing other date column

I am trying to take a Pandas dataframe, parse a column that represents dates and add a new column to the dataframe with a simple mm/dd/yyyy format.

Here is the data and libraries:

import pandas as pd
import datetime
from dateutil.parser import parse

df = pd.DataFrame([['row1', 'Tue Jun 16 19:05:44 UTC 2020', 'record1'], ['row2', 'Tue Jun 16 17:10:02 UTC 2020', 'record2'], ['row3', 'Fri Jun 12 17:52:37 UTC 2020', 'record3']], columns=["row", "checkin", "record"])

From picking bits and pieces from around here I crafted this line to parse and add the new column of data:

df['NewDate'] = df.apply(lambda row: datetime.date.strftime(parse(df['checkin']), "%m/%d/%Y"), axis = 1)

But I end up with this error when run, can anyone suggest a fix or easier way to do this, seems like it should be simpler and more pythonic than I am finding

TypeError: ('Parser must be a string or character stream, not Series', 'occurred at index 0')

Thanks for any help you can offer.

Upvotes: 2

Views: 2236

Answers (2)

safiqul islam
safiqul islam

Reputation: 650

Just change df['checkin'] to row['checkin'] as below

df['NewDate'] = df.apply(lambda row: datetime.date.strftime(parse(row['checkin']), "%m/%d/%Y"), axis = 1)

Upvotes: 0

ScootCork
ScootCork

Reputation: 3676

You could do so without apply

df['newDate'] = pd.to_datetime(df.checkin).dt.strftime("%m/%d/%Y")

    row                       checkin   record     newDate
0  row1  Tue Jun 16 19:05:44 UTC 2020  record1  06/16/2020
1  row2  Tue Jun 16 17:10:02 UTC 2020  record2  06/16/2020
2  row3  Fri Jun 12 17:52:37 UTC 2020  record3  06/12/2020

Upvotes: 2

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