Convert a column in pandas dataframe from String to Float

Question

I've already read about various solutions, and tried the solution stated here: Pandas: Converting to numeric, creating NaNs when necessary

But it didn't really solve my problem: I have a dataframe contains multiple columns, in where a column ['PricePerSeat_Outdoor'] contains some float values, some empty values, and some '-'

    print type(df_raw['PricePerSeat_Outdoor'][99])
    print df_raw['PricePerSeat_Outdoor'][95:101]
    df_raw['PricePerSeat_Outdoor'] = df_raw['PricePerSeat_Outdoor'].apply(pd.to_numeric, errors='coerce')
    print type(df_raw['PricePerSeat_Outdoor'][99])

Then I got:

<type 'str'>
95     17.21
96     17.24
97         -
98         -
99      17.2
100    17.24
Name: PricePerSeat_Outdoor, dtype: object
<type 'str'>

Values at row #98 and 99 didn't get converted. Again, I've already tried multiple methods including following but it just didn't work. Much appreciated if someone can give me some hints.

df_raw['PricePerSeat_Outdoor'] = df_raw['PricePerSeat_Outdoor'].apply(pd.to_numeric, errors='coerce')

Also, how can I convert multiple columns to numeric at once? Thanks.

MaxU - stand with Ukraine · Accepted Answer · 2016-04-26 21:30:06Z

19

try this:

df_raw['PricePerSeat_Outdoor'] = pd.to_numeric(df_raw['PricePerSeat_Outdoor'], errors='coerce')

Here is an example:

In [97]: a = pd.Series(['17.21','17.34','15.23','-','-','','12.34']

In [98]: b = pd.Series(['0.21','0.34','0.23','-','','-','0.34'])

In [99]: df = pd.DataFrame({'a':a, 'b':b})

In [100]: df['c'] = np.random.choice(['a','b','b'], len(df))

In [101]: df
Out[101]:
       a     b  c
0  17.21  0.21  a
1  17.34  0.34  b
2  15.23  0.23  b
3      -     -  b
4      -        b
5            -  b
6  12.34  0.34  b

In [102]: cols_to_convert = ['a','b']

In [103]: cols_to_convert
Out[103]: ['a', 'b']

In [104]: for col in cols_to_convert:
   .....:         df[col] = pd.to_numeric(df[col], errors='coerce')
   .....:

In [105]: df
Out[105]:
       a     b  c
0  17.21  0.21  a
1  17.34  0.34  b
2  15.23  0.23  b
3    NaN   NaN  b
4    NaN   NaN  b
5    NaN   NaN  b
6  12.34  0.34  b

check:

In [106]: df.dtypes
Out[106]:
a    float64
b    float64
c     object
dtype: object

edited Apr 26, 2016 at 21:30

answered Apr 26, 2016 at 19:56

MaxU - stand with Ukraine

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2 Comments

Kevin Over a year ago

However, do you happen to know how can I apply the same with multiple columns?

MaxU - stand with Ukraine Over a year ago

@Kevin, i've added an example - please check

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