I Want a Separate column which returns "Yes" if the column "ID" contains all numeric values and 'No' if it contains alphabets or alphanumeric values.
ID Result
3965 Yes
wyq8 No
RO_123 No
CMD_ No
2976 Yes
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3 Answers
You can use pd.Series.str.isnumeric here.
df['Result'] = np.where(df['ID'].str.isnumeric(), 'YES', 'NO')
ID Result
0 3965 YES
1 wyq8 NO
2 RO_123 NO
3 CMD_ NO
4 2976 YES
There's a caveat with using isnumeric it doesn't identify float numbers.
test = pd.Series(["9.0", "9"])
test.str.isnumeric()
0 False
1 True
dtype: bool
If you strictly mark YES for int then use isnumeric else you can use pd.Series.str.fullmatch(available from version 1.1.0) here.
df['Result'] = np.where(df['ID'].str.fullmatch(r"\d+|\d+\.\d+", 'YES', 'NO')
For version <1.1.0 you use re.fullmatch
numeric_pat = re.compile(r"\d+|\d+\.\d+")
def numeric(val):
match = numeric_pat.fullmatch(val)
if match: return 'YES'
else: return 'NO'
df['Result'] = df['ID'].apply(numeric)
Or we can use pd.to_numeric with boolean masking using pd.Series.isna
m = pd.to_numeric(df['ID'], errors='coerce').isna()
df['Result'] = np.where(m, 'NO', 'YES')
With errors parameter set to 'coerce' values which cannot be turned into numeic value will set to Nan.
test = pd.Series(['3965', 'wyq8', 'RO_123', 'CMD_', '2976'])
pd.to_numeric(test)
0 3965.0
1 NaN
2 NaN
3 NaN
4 2976.0
Name: ID, dtype: float64
Or you can build a custom function
def numeric(val):
try:
float(val) # Using just `float` would suffice as int can be
return 'YES' # converted to `float` so both `int`
# and `float` wouldnot raise any error
except ValueError:
return 'NO'
df['Result'] = df['ID'].apply(numeric)
Note: float handles scientic notation too, float("1e6") -> 1000000.0.
test = pd.Series(['1e6', '1', 'a 10', '1E6'])
test.apply(numeric)
0 YES
1 YES
2 NO
3 YES
dtype: object
Comments
Check if ID contains non-digitsand reverse the Boolean selection using ~. Using np.where, allocate option
df['Result']=np.where(~df.ID.str.contains('(\D+)'),'Yes','N0')
ID Result
0 3965 Yes
1 wyq8 N0
2 RO_123 N0
3 CMD_ N0
4 2976 Yes
As noted by @Cameron Riddell. You could also skip inverting the boolean and do the following;
df['Result']=np.where(df.ID.str.contains('(\D+)'),'No','Yes')
3 Comments
Cameron Riddell
Instead of inverting the boolean array, why not just make
"No" the True value and "Yes" the False value? np.where(df.ID.str.contains('(\D+)'),'No','Yes')
wwnde
@Cameron Riddell we actually could. My philosophy is to make answer as retraceable as possible for the OP if they choose to run break it down. Good point though.
Ch3steR
This would fail when there's a string
'123 SO', using re.fullmatch would counter that example.You can use .isnumeric() method:
df3["Result"] = df3["ID"].str.isnumeric().apply(lambda x: "No" if x == False else "Yes")
[UPDATE]: This method works only with integers numbers, please view the @Ch3steR answer for other cases.
