CODEWITHSUNDEEP
pythonpandas
Javier
Javier

Reputation: 730

Manipulating Date in Pandas

I'm trying to understand various functions in Python as I come from an R background.

The question I face is: How do I add and subtract days/years/months from pandas based on a condition? In R, I can use the dplyr package where mutate and ifelse will allow me to achieve it together with the lubridate package.

Here is my reproducible data in R:

df = data.frame(date1=c("2017-07-07", "2017-02-11", "2017-05-22", "2017-04-27")) 

library(lubridate)
df$date1 <- ymd(df$date1) + years(2)
df$day <- wday(df$date1, label=TRUE)

Input

       date1 day
1 2019-07-07 Sun
2 2019-02-11 Mon
3 2019-05-22 Wed
4 2019-04-27 Sat

Task: Add a year to the date if the day is "Sun" and subtract a year from the date if day is "Sat", else IGNORE

R Code

library(dplyr)

df %>% mutate(newdate = ifelse(df$day == "Sun", date1 %m+% years(1), 
                               ifelse(df$day == "Sat", date1 %m-% years(1), date1))) -> df

df$newdate <- as.Date(df$newdate, origin = "1970-01-01")
df$newday <- wday(df$newdate, label=T)
df

Output

       date1 day    newdate newday
1 2019-07-07 Sun 2020-07-07    Tue
2 2019-02-11 Mon 2019-02-11    Mon
3 2019-05-22 Wed 2019-05-22    Wed
4 2019-04-27 Sat 2018-04-27    Fri

Could someone share with me how to achieve this output using Pandas?

Upvotes: 1

Views: 368

Answers (2)

keshav
keshav

Reputation: 146

This should work fine for you:

df = pd.DataFrame(data = {'date1':["2017-07-07", "2017-02-11", "2017-05-22", "2017-04-27"], 'day':["Sun", "Mon", "Wed", "Sat"]})


df['date1']= pd.to_datetime(df['date1'])
df['date1'] = df['date1'] + pd.DateOffset(years=2)

def func_year(row):
if row['day'] == 'Sun':
    date = row['date1'] +  pd.DateOffset(years=1)
elif row['day'] == 'Sat':
    date = row['date1'] -  pd.DateOffset(years=1)
else:
    date = row['date1']
return date

df['new_date'] = df.apply(func_year, axis=1)

Upvotes: 1

jezrael
jezrael

Reputation: 862406

Use DateOffset for add years with Series.dt.strftime and %a fo names of days:

df = pd.DataFrame({'date1':pd.to_datetime(["2017-07-07", 
                                           "2017-02-11", 
                                           "2017-05-22", 
                                           "2017-04-27"])}) 

df['date1'] += pd.offsets.DateOffset(years=2)
df['day'] = df['date1'].dt.strftime('%a')

For set values by multiple boolean masks use numpy.select:

masks = [df['day'] == 'Sun', 
         df['day'] == 'Sat']
vals = [df['date1'] + pd.offsets.DateOffset(years=1),
        df['date1'] - pd.offsets.DateOffset(years=1)]

df['newdate'] = np.select(masks, vals, default=df['date1'])
df['newday'] = df['newdate'].dt.strftime('%a')

print (df)
       date1  day    newdate newday
0 2019-07-07  Sun 2020-07-07    Tue
1 2019-02-11  Mon 2019-02-11    Mon
2 2019-05-22  Wed 2019-05-22    Wed
3 2019-04-27  Sat 2018-04-27    Fri

Upvotes: 2

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