Python Pandas: Replacing float values in String typed columns?

Question

Pandas has isnull() and fillna() methods to replace NaN values in DataFrames. I have a dataset that has mostly string typed columns, but some columns have a few floating point values scattered in them. Are there some equivalent methods in Pandas for finding and replacing these?

So if I have a DataFrame like this:

In [60]: df1=pd.DataFrame([[1.0,'foo'],[2.0,1.0],[float('NaN'),'bar'],[4.0,0.0],[5.0,'baz']],columns=['fval','sval'])
In [61]: df1
Out[61]: 
   fval sval
0   1.0  foo
1   2.0    1
2   NaN  bar
3   4.0    0
4   5.0  baz

In [63]: df1.isnull()
Out[63]: 
    fval   sval
0  False  False
1  False  False
2   True  False
3  False  False
4  False  False

...I can replace the NaN values in the 'fval' column like this:

In [64]: df1.fillna(2.5)
Out[64]: 
   fval sval
0   1.0  foo
1   2.0    1
2   2.5  bar
3   4.0    0
4   5.0  baz

Is there convenient method in Pandas to replace the 0 and 1 values in the 'sval' column with, say, 'na'? How about an equivalent to is isnull() for out-of-place values?

same methods should work..? if the NaN are string you can replace them first with np.nan using df.replace — anky
– anky, Commented Mar 29, 2020 at 12:35

DataBach · Accepted Answer · 2020-03-29 16:13:42Z

1

If you want to manullay replace strings you can use the following replace statement:

df1.replace([0, 1], "na")

All values that are 0 or 1 will be replaced with the string "na".

However, as @anky_91 pointed out, you can also replace your specified values with np.nan. After your replacement, you can identify your NaN values just like the once in the float typed columns. This probably what you are actually looking for.

df1.replace([0, 1], np.nan)

More Information on how to use replace you can find here.

answered Mar 29, 2020 at 16:13

DataBach

1,6953 gold badges24 silver badges47 bronze badges

Sign up to request clarification or add additional context in comments.

1 Comment

zaphodb Over a year ago

+1 for the array arg on replace(). But that doesn't really generalize well in real situations, I have a more diverse set of values.

zaphodb · Accepted Answer · 2020-04-03 19:55:45Z

0

Guess there's no Pandas-native way of doing this. But using apply gets what I want:

df1['sval'].apply(lambda val: str(val) if type(val)!=str else val)

answered Apr 3, 2020 at 19:55

zaphodb

5051 gold badge7 silver badges12 bronze badges

Collectives™ on Stack Overflow

Python Pandas: Replacing float values in String typed columns?

2 Answers 2

1 Comment

Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

2 Answers 2

1 Comment

Comments

Your Answer

Sign up or log in

Post as a guest

Related