CODEWITHSUNDEEP
pythonpandasdataframe
Scorgio345
Scorgio345

Reputation: 55

Nested if conditions to create a new column in pandas dataframe

I have a dataframe that looks like below:

|userid|rank2017|rank2018|
|212   |'H'     |'H'     |    
|322   |'L'     |'H      |
|311   |'H'     |'L'     |

I want to create a new column called progress in the the dataframe above that will output 1 if rank2017 is equal to rank2018, 2 if rank2017 is 'H' and rank2018 is 'L' else 3. can anybody help me execute this in python

Upvotes: 3

Views: 1832

Answers (2)

sacuL
sacuL

Reputation: 51395

Here is a way using np.select:

# Set your conditions:
conds = [(df['rank2017'] == df['rank2018']), 
         (df['rank2017'] == 'H') & (df['rank2018'] == 'L')]

# Set the values for each conditions
choices = [1, 2]

# Use np.select with a default of 3 (your "else" value)    
df['progress'] = np.select(conds, choices, default = 3)

Returns:

>>> df
   userid rank2017 rank2018  progress
0     212        H        H         1
1     322        L        H         3
2     311        H        L         2

Upvotes: 3

jpp
jpp

Reputation: 164773

Here is one way. You do not need to use nested if statements.

df = pd.DataFrame({'user': [212, 322, 311],
                   'rank2017': ['H', 'L', 'H'],
                   'rank2018': ['H', 'H', 'L']})

df['progress'] = 3
df.loc[(df['rank2017'] == 'L') & (df['rank2018'] == 'H'), 'progress'] = 2
df.loc[df['rank2017'] == df['rank2018'], 'progress'] = 1

#   rank2017 rank2018  user  progress
# 0        H        H   212         1
# 1        L        H   322         2
# 2        H        L   311         3

Upvotes: 3

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