python - pandas: Dataframe.replace() with regex -

i have table looks this:

df_raw = pd.dataframe(dict(a = pd.series(['1.00','-1']), b = pd.series(['1.0','-45.00','-'])))            b 0   1.00    1.0 1   -1      -45.00 2   nan     - 

i replace '-' '0.00' using dataframe.replace() struggles because of negative values, '-1', '-45.00'.

how can ignore negative values , replace '-' '0.00' ?

my code:

df_raw = df_raw.replace(['-','\*'], ['0.00','0.00'], regex=true).astype(np.float64) 

error code:

valueerror: invalid literal float(): 0.0045.00 

your regex matching on - characters:

in [48]: df_raw.replace(['-','\*'], ['0.00','0.00'], regex=true)  out[48]:                 b 0   1.00        1.0 1  0.001  0.0045.00 2    nan       0.00 

if put additional boundaries matches single character termination works expected:

in [47]: df_raw.replace(['^-$'], ['0.00'], regex=true)  out[47]:             b 0  1.00     1.0 1    -1  -45.00 2   nan    0.00 

here ^ means start of string , $ means end of string match on single character.

or can use replace match on exact matches:

in [29]:  df_raw.replace('-',0) out[29]:             b 0  1.00     1.0 1    -1  -45.00 2   nan       0