Segregate a column data based on regex using pandas

I have a dataframe like as shown below

df = pd.DataFrame({'val': ['>1234','<>','<1000','<test','31sadj',123,43.21]})

I would like to create 3 new columns

val_num – will store ONLY NUMBER values that comes along with symbols ex: 1234 (from >1234) and 1000 (from <1000) but WILL NOT STORE 31 (from 31sadj) because it doesn’t have any symbol

val_str – will store only values a mix of NUMBER,symbols,ALPHABETS or just plain alphabets ex: 31sadj. It can have any symbols except >,<,=

val_symbol – will store ONLY 3 symbols like >, <, =

I tried the below but it isn’t accurate

df['val_SYMBOL'] = df['val'].str.extract(r'([<>=]+)').fillna('=')
df['val_num'] = df['val'].str.extract(r'([0-9]+)')
df['val_str'] = df['val'].str.extract(r'([a-zA-Z0-9s-]+)') 

I expect my output to be like as shown below

enter image description here


You can use

df['val_SYMBOL'] = df['val'].astype(str).str.extract(r'([<>=]+)').fillna('=')
df['val_num'] = df['val'].astype(str).str.extract(r'b(d+(?:.d+)?)b')
df['val_str'] = df['val'].astype(str).str.extract(r'([^<>=]*[a-zA-Z][^<>=]*)')

You want to work on a mixed data type column, so the first operation is to convert the data to string with astype(str).

The val_num column is populated with b(d+(?:.d+)?)b matches, integer or float numbers matched as whole words (b stands for a word boundary).

The val_str column is populated with ([^<>=]*[a-zA-Z][^<>=]*) matches, that searches for zero or more chars other than <, > and =, then a letter and then again zero or more chars other than <, > and =.

The output I get:

>>> df
      val val_SYMBOL val_num val_str
0   >1234          >    1234     NaN
1      <>         <>     NaN     NaN
2   <1000          <    1000     NaN
3   <test          <     NaN    test
4  31sadj          =     NaN  31sadj
5     123          =     123     NaN
6   43.21          =   43.21     NaN