guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2460 4.2.1 Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2461 Out[50]: 0 12 1 -$10 2 $10,000 dtype: string # We need to escape the special character (for >1 len patterns) In [51]: dollars.str.replace(r'-\$', '-') Out[51]: 0 12 1 -10 2 $10,000 dtype: string New in numeric (floating-point), character, and boolean. NA types are implemented by reserving special bit patterns for each type to be used as the missing value. While doing this with the full NumPy type hierarchy

guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2460 4.2.1 Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2461 Out[50]: 0 12 1 -$10 2 $10,000 dtype: string # We need to escape the special character (for >1 len patterns) In [51]: dollars.str.replace(r'-\$', '-') Out[51]: 0 12 1 -10 2 $10,000 dtype: string New in numeric (floating-point), character, and boolean. NA types are implemented by reserving special bit patterns for each type to be used as the missing value. While doing this with the full NumPy type hierarchy

guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2655 4.5.1 Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2655 be taken when dealing with regular expressions! The current behavior is to treat single character patterns as literal strings, even when regex is set to True. This behavior is deprecated and will be removed numeric (floating-point), character, and boolean. NA types are implemented by reserving special bit patterns for each type to be used as the missing value. While doing this with the full NumPy type hierarchy

guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2734 4.5.1 Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2735 be taken when dealing with regular expressions! The current behavior is to treat single character patterns as literal strings, even when regex is set to True. This behavior is deprecated and will be removed numeric (floating-point), character, and boolean. NA types are implemented by reserving special bit patterns for each type to be used as the missing value. While doing this with the full NumPy type hierarchy

guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2734 4.5.1 Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2735 be taken when dealing with regular expressions! The current behavior is to treat single character patterns as literal strings, even when regex is set to True. This behavior is deprecated and will be removed numeric (floating-point), character, and boolean. NA types are implemented by reserving special bit patterns for each type to be used as the missing value. While doing this with the full NumPy type hierarchy

Out[42]: 0 12 1 -$10 2 $10,000 dtype: string # We need to escape the special character (for >1 len patterns) In [43]: dollars.str.replace(r'-\$', '-') Out[43]: 0 12 1 -10 2 $10,000 dtype: string New in numeric (floating-point), character, and boolean. NA types are implemented by reserving special bit patterns for each type to be used as the missing value. While doing this with the full NumPy type hierarchy default), the given pat is compiled as a regex. When repl is a string, it replaces matching regex patterns as with re.sub(). NaN value(s) in the Series are left as is: >>> pd.Series(['foo', 'fuz', np.nan])

guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2367 4.2.1 Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2367 Out[42]: 0 12 1 -$10 2 $10,000 dtype: string # We need to escape the special character (for >1 len patterns) In [43]: dollars.str.replace(r'-\$', '-') Out[43]: 0 12 1 -10 2 $10,000 dtype: string New in numeric (floating-point), character, and boolean. NA types are implemented by reserving special bit patterns for each type to be used as the missing value. While doing this with the full NumPy type hierarchy

guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2363 4.2.1 Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2363 Out[42]: 0 12 1 -$10 2 $10,000 dtype: string # We need to escape the special character (for >1 len patterns) In [43]: dollars.str.replace(r'-\$', '-') Out[43]: 0 12 1 -10 2 $10,000 dtype: string New in numeric (floating-point), character, and boolean. NA types are implemented by reserving special bit patterns for each type to be used as the missing value. While doing this with the full NumPy type hierarchy

guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2353 5.2.1 Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2353 Out[42]: 0 12 1 -$10 2 $10,000 dtype: string # We need to escape the special character (for >1 len patterns) In [43]: dollars.str.replace(r'-\$', '-') Out[43]: 0 12 1 -10 2 $10,000 dtype: string New in numeric (floating-point), character, and boolean. NA types are implemented by reserving special bit patterns for each type to be used as the missing value. While doing this with the full NumPy type hierarchy

guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2511 4.2.1 Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2512 be taken when dealing with regular expressions! The current behavior is to treat single character patterns as literal strings, even when regex is set to True. This behavior is deprecated and will be removed numeric (floating-point), character, and boolean. NA types are implemented by reserving special bit patterns for each type to be used as the missing value. While doing this with the full NumPy type hierarchy