Filter All Rows That Do Not Contain Letters (Alpha) in 'Pandas'

Filter all rows that do not contain letters (alpha) in ´pandas´

I think you'd need str.contains to filter values which contain letters by the means of boolean indexing:

df =  df[df['Col A.'].str.contains('[A-Za-z]')]
print (df)
       Col A.
2         dog
3     cat 583
4  rabbit 444

If there are some NaNs values you can pass a parameter:

df = df[df['Col A.'].str.contains('[A-Za-z]', na=False)]    
print (df)
       Col A.
3         dog
4     cat 583
5  rabbit 444

Filtering out rows with non-alphanumeric characters

You're looking for str.isalpha:

df[df.my_column.str.isalpha()]

   some_col my_column
0         1      some
1         2      word

A similar method is str.isalnum, if you want to retain letters and digits.

If you want to handle letters and whitespace characters, use

df[~df.my_column.str.contains(r'[^\w\s]')]

   some_col my_column
0         1      some
1         2      word

Lastly, if you are looking to remove punctuation as a whole, I've written a Q&A here which might be a useful read: Fast punctuation removal with pandas

How to remove non-alpha-numeric characters from strings within a dataframe column in Python?

Use str.replace.

df
  strings
0  a#bc1!
1   a(b$c

df.strings.str.replace('[^a-zA-Z]', '')
0    abc
1    abc
Name: strings, dtype: object

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To retain alphanumeric characters (not just alphabets as your expected output suggests), you'll need:

df.strings.str.replace('\W', '')
0    abc1
1     abc
Name: strings, dtype: object 

Choose empty rows only from a dataframe

See below for a table summarizing various methods to identify null/False/empty elements in pandas

Depending on what other values you can have in "Alpha" you could use:

Keep only spaces

df2.loc[df2['Alpha'].eq('')]

Keep all "Falsy" values

df2.loc[~df2['Alpha'].astype(bool)]

output:

       Date FN  AuM Alpha
0  01012021  A   10      
1  01012021  B   20      

table of empty/falsy/zero-length values

df = pd.DataFrame({'data': [1, 'abc', True, 0, '', None,
                            float('nan'), False, [],
                            {}, set(), tuple([])]
                    })

df['type']   = df['data'].apply(lambda x: type(x).__name__)
df['isna']   = df['data'].isna().map({True: 'X', False: ''})
df['isnull'] = df['data'].isnull().map({True: 'X', False: ''})
df['~bool']  = (~df['data'].astype(bool)).map({True: 'X', False: ''})
df['len']    = df['data'].str.len().convert_dtypes()
df['len==0'] = df['data'].str.len().eq(0).map({True: 'X', False: ''})
print(df)

output:

     data      type isna isnull ~bool   len len==0
0       1       int                    <NA>       
1     abc       str                       3       
2    True      bool                    <NA>       
3       0       int                 X  <NA>       
4               str                 X     0      X
5    None  NoneType    X      X     X  <NA>       
6     NaN     float    X      X        <NA>       
7   False      bool                 X  <NA>       
8      []      list                 X     0      X
9      {}      dict                 X     0      X
10     {}       set                 X     0      X
11     ()     tuple                 X     0      X

Remove all alphanumeric words from a string using pandas

You can use

data_df = pd.DataFrame({'Vendor': ['2fvRE-Ku89lkRVJ44QQFN ABACUS LABS, INC', 'abc123 CAT LABS, INC']})
data_df['Vendor'].str.replace(r'^(?:[A-Za-z-]+\d|[\d-]+[A-Za-z])[\w-]*\s*', '', regex=True)
# => 0    ABACUS LABS, INC
#    1       CAT LABS, INC
#    Name: Vendor, dtype: object

See the regex demo.

Regex details

• ^ - start of string
• (?:[A-Za-z-]+\d|[\d-]+[A-Za-z]) - either one or more letters/dashes and then a digit or a one or more digits/dashes and then a letter
• [\w-]* - zero or more word or - chars
• \s* - zero or more whitespace chars.

filter pandas dataframe by two columns where one column is a list

You can use apply():

print(x[x.apply(lambda row: row.Letter in row.Alpha, axis=1)])
#       Alpha Letter
#0     [a, z]      a
#2          c      c
#4  [e, q, m]      e

pandas iterate throw rows to see if value is alpha numeric

You can use a list comprehension:

df['col1'] = [''.join([i for i in x if i.isalpha()]) for x in df['col1']]

print(df)

  col1
0   Hi
1   Hi
2   hi
3   Hi

If you have NaN or float values, remove them first by converting them to empty string:

df.loc[pd.to_numeric(df['col1'], errors='coerce').notnull(), 'col1'] = ''

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