CODEWITHSUNDEEP
pythonpython-3.xseries
twhale
twhale

Reputation: 755

Why does Python function return 1.0 (float) when `return 1` is specified?

I have a lot of strings, some of which consist of 1 sentence and some consisting of multiple sentences. My goal is to determine which one-sentence strings end with an exclamation mark '!'.

My code gives a strange result. Instead of returning '1' if found, it returns 1.0. I have tried: return int(1) but that does not help. I am fairly new to coding and do not understand, why is this and how can I get 1 as an integer?

'Sentences'                                                                        
0  [This is a string., And a great one!]      
1  [It's a wonderful sentence!]
2  [This is yet another string!]
3  [Strange strings have been written.]                
4  etc. etc.                                  

e = df['Sentences']

def Single(s):
    if len(s) == 1: # Select the items with only one sentence
        count = 0
        for k in s: # loop over every sentence
            if (k[-1]=='!'): # check if sentence ends with '!'
                count = count+1
        if count == 1: 
        return 1
    else:
        return '' 

df['Single'] = e.apply(Single)

This returns the the correct result, except that there should be '1' instead of '1.0'.

'Single'                                                                        
0  NaN
1  1.0
2  1.0
3                                  
4  etc. etc.

Why does this happen?

Upvotes: 1

Views: 1079

Answers (2)

jpp
jpp

Reputation: 164783

The reason is np.nan is considered float. This makes the series of type float. You cannot avoid this unless you want your column to be of type Object [i.e. anything]. This is inefficient and inadvisable, and I refuse to show you how to do this.

If there is an alternative value you can use instead of np.nan, e.g. 0, then there is a workaround. You can replace NaN values with 0 and then convert to int:

s = pd.Series([1, np.nan, 2, 3])

print(s)
# 0    1.0
# 1    NaN
# 2    2.0
# 3    3.0
# dtype: float64

s = s.fillna(0).astype(int)

print(s)
# 0    1
# 1    0
# 2    2
# 3    3
# dtype: int32

Upvotes: 2

Rakesh
Rakesh

Reputation: 82785

Use astype(int)

Ex:

df['Single'] = e.apply(Single).astype(int)

Upvotes: 0

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