CODEWITHSUNDEEP
pythonpandasdata-science
Michael
Michael

Reputation: 31

Pandas add a column whose value can be multiple different values

I'm trying to add a new column to a pandas data set that take the numeric month from one column and stores the corresponding season into the new column.

So in essence, 

if month == 12, season = 'Winter'
elif month == 2, season = 'Spring'
elif month == 5, season = 'Summer'
else month == 8, season = 'Fall'

I haven't seen a really clear solution for this. I've seen how to do it with 2 conditional values, but I'm still fairly new to pandas and python.

Edit: I was able to get it working with one of the solutions listed below (Thanks!), but I should have mentioned that I also need to include months 1,3,4,6,7,9,10,11

Upvotes: 1

Views: 90

Answers (5)

RavinderSingh13
RavinderSingh13

Reputation: 133600

Could you please try following. Fair warning, since no samples of Input and expected output are given so didn't test it.

import pandas as pd
import numpy as np
conditions=[df['month']==12,
            df['month']==2,
            df['month']==5,
            df['month']==8]
choices=['Winter','spring','Summer','fall']

df['season']=np.select(conditions,choices,default=np.nan)

Upvotes: 1

AMC
AMC

Reputation: 2702

There are some wild solutions in here.

This obviously isn’t a complete example, but it should be sufficient to understand what you need to do:

# map of month number -> season name
months_dict = {12: 'Winter', 2: 'Spring', ...}

df['season'] = df['month_number'].map(months_dict)

Yes, that’s it.

Let me know if you have any questions :)

Upvotes: 1

darthbhyrava
darthbhyrava

Reputation: 515

season_map = {12: "Winter",
              2: "Spring",
              5: "Summer",
              8: "Fall"} # Add the rest to this dictionary

df['season'] = df['month_num'].apply(lambda x: season_map[x]) 
# 'season' is the new column, replace 'month_num' accordingly

Upvotes: -1

mochsner
mochsner

Reputation: 325

Since you're already using numpy through pandas, the best way to do this would be through the np.where command.

df['season'] = np.where(df['month']==11|df['month']==12|df['month']==1, 'Winter')
df['season'] = np.where(df['month']==2|df['month']==3|df['month']==4, 'Spring')
df['season'] = np.where(df['month']==5|df['month']==6|df['month']==7, 'Summer')
df['season'] = np.where(df['month']==8|df['month']==9|df['month']==10, 'Fall')

Hope this helps!

Upvotes: -1

c0rias
c0rias

Reputation: 143

You can create a dictionary with corresponding numbers and season names, that you can then map onto your new series:

season_dict = {12: "Winter",
              2: "Spring",
              5: "Summer",
              8: "Fall"}

df["season"] = df.month.replace(season_dict)

Upvotes: 1

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