How to take only integer value in pandas dataframe [duplicate]

Question

I am having below ct_data dataframe

imjp_number,imct_id
182467224,'ed3baabac3ce4d86801d8490ea474963|pXJjGxufodMVq5FBSzHc2A'
307291224,'__gde66a472fe104ab381456ee059751d9d|Qujk8BKa0XkkpJMCstCYBw'
214278175,'mbKKbkKpiTsIAyCE8y07rw|e8133ceeca654d169532b4ad4de661d5'
tes123456,'tMyM0un_ptsHHC-lET6tkQ|87538a4436af47a7a9b8b9bc2b3ec5ba'
Not Found,'pXJjGxufodMVq5FBSzHc2A'

I am applying below logic but it's not working.

ct_data['imjp_number']  = ct_data.loc[ct_data['imjp_number'].apply(lambda x: isinstance(x,int)), 'imjp_number']

Please suggest me best way to select ct_data df having integer value only and remove 'tes12345' and 'Not found' value from imjp_number columns

ct_data[ct_data.imjp_number.str.isdigit()] . https://pandas.pydata.org/docs/user_guide/text.html#text-string-methods — wwii
– wwii, Commented Feb 26, 2021 at 3:10
Does Remove non-numeric rows in one column with pandas answer your question? Or Filtering string/float/integer values in pandas dataframe columns? — wwii
– wwii, Commented Feb 26, 2021 at 3:15
@wwii your logic works but I have to remove entire row of tes12345 and not found along with imjp_number and imct_id so that dataframe column indexes should be equal — Dharmendra Yadav
– Dharmendra Yadav, Commented Feb 26, 2021 at 3:23
ct_data.imjp_number.str.isdigit() creates a boolean Series. ct_data[ct_data.imjp_number.str.isdigit()] Will return a DataFrame without those two rows. https://pandas.pydata.org/docs/user_guide/indexing.html#boolean-indexing — wwii
– wwii, Commented Feb 26, 2021 at 3:26
@wwii It still contains tes123456 Not Found as rows and imct_id column is not coming after print(ct_data) — Dharmendra Yadav
– Dharmendra Yadav, Commented Feb 26, 2021 at 3:29

wwii · Accepted Answer · 2021-02-26 03:36:22Z

>>> print(df.to_string()) 
  imjp_number                                                     imct_id
0   182467224     ed3baabac3ce4d86801d8490ea474963|pXJjGxufodMVq5FBSzHc2A
1   307291224  __gde66a472fe104ab381456ee059751d9d|Qujk8BKa0XkkpJMCstCYBw
2   214278175     mbKKbkKpiTsIAyCE8y07rw|e8133ceeca654d169532b4ad4de661d5
3   tes123456     tMyM0un_ptsHHC-lET6tkQ|87538a4436af47a7a9b8b9bc2b3ec5ba
4   Not Found                                      pXJjGxufodMVq5FBSzHc2A

>>> print(df.imjp_number.str.isdigit().to_string())
0     True
1     True
2     True
3    False
4    False

>>> print(df[df.imjp_number.str.isdigit()].to_string())
  imjp_number                                                     imct_id
0   182467224     ed3baabac3ce4d86801d8490ea474963|pXJjGxufodMVq5FBSzHc2A
1   307291224  __gde66a472fe104ab381456ee059751d9d|Qujk8BKa0XkkpJMCstCYBw
2   214278175     mbKKbkKpiTsIAyCE8y07rw|e8133ceeca654d169532b4ad4de661d5
>>>

From the second question I linked to in the comment.

>>> print(df.to_string())
  imjp_number                                                     imct_id
0   182467224     ed3baabac3ce4d86801d8490ea474963|pXJjGxufodMVq5FBSzHc2A
1   307291224  __gde66a472fe104ab381456ee059751d9d|Qujk8BKa0XkkpJMCstCYBw
2   214278175     mbKKbkKpiTsIAyCE8y07rw|e8133ceeca654d169532b4ad4de661d5
3   tes123456     tMyM0un_ptsHHC-lET6tkQ|87538a4436af47a7a9b8b9bc2b3ec5ba
4   Not Found                                      pXJjGxufodMVq5FBSzHc2A
>>>
>>> print(pd.to_numeric(df.imjp_number, errors='coerce').to_string())
0    182467224.0
1    307291224.0
2    214278175.0
3            NaN
4            NaN
>>>
>>> print(pd.to_numeric(df.imjp_number, errors='coerce').notnull().to_string())
0     True
1     True
2     True
3    False
4    False
>>>
>>> print(df[pd.to_numeric(df.imjp_number, errors='coerce').notnull()].to_string())
  imjp_number                                                     imct_id
0   182467224     ed3baabac3ce4d86801d8490ea474963|pXJjGxufodMVq5FBSzHc2A
1   307291224  __gde66a472fe104ab381456ee059751d9d|Qujk8BKa0XkkpJMCstCYBw
2   214278175     mbKKbkKpiTsIAyCE8y07rw|e8133ceeca654d169532b4ad4de661d5
>>>
>>> df = df[pd.to_numeric(df.imjp_number, errors='coerce').notnull()]              
>>> print(df.to_string())                                                           
  imjp_number                                                     imct_id
0   182467224     ed3baabac3ce4d86801d8490ea474963|pXJjGxufodMVq5FBSzHc2A
1   307291224  __gde66a472fe104ab381456ee059751d9d|Qujk8BKa0XkkpJMCstCYBw
2   214278175     mbKKbkKpiTsIAyCE8y07rw|e8133ceeca654d169532b4ad4de661d5
>>>

where can i use inplace=True so this logic persist in my entire dataframe ?
@DharmendraYadav - none of those methods have an inplace parameter.

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