How do you replace a value in python?
Replace values given in to_replace with value. Show Values of the DataFrame are replaced with other values dynamically. This differs from updating with How to find the values that will be replaced.
See the examples section for examples of each of these. valuescalar, dict, list, str, regex, default NoneValue to replace any values matching to_replace with. For a DataFrame a dict of values can be used to specify which value to use for each column (columns not in the dict will not be filled). Regular expressions, strings and lists or dicts of such objects are also allowed. inplacebool, default FalseWhether to modify the DataFrame rather than creating a new one. limitint, default NoneMaximum size gap to forward or backward fill. regexbool or same types as to_replace, default FalseWhether to interpret to_replace and/or value as regular expressions. If this is The method to use when for replacement, when to_replace is a scalar, list or tuple and value is Changed in version 0.23.0: Added to DataFrame. ReturnsDataFrameObject after replacement. RaisesAssertionError
Notes
Examples Scalar `to_replace` and `value` >>> s = pd.Series([1, 2, 3, 4, 5]) >>> s.replace(1, 5) 0 5 1 2 2 3 3 4 4 5 dtype: int64 >>> df = pd.DataFrame({'A': [0, 1, 2, 3, 4], ... 'B': [5, 6, 7, 8, 9], ... 'C': ['a', 'b', 'c', 'd', 'e']}) >>> df.replace(0, 5) A B C 0 5 5 a 1 1 6 b 2 2 7 c 3 3 8 d 4 4 9 e List-like `to_replace` >>> df.replace([0, 1, 2, 3], 4) A B C 0 4 5 a 1 4 6 b 2 4 7 c 3 4 8 d 4 4 9 e >>> df.replace([0, 1, 2, 3], [4, 3, 2, 1]) A B C 0 4 5 a 1 3 6 b 2 2 7 c 3 1 8 d 4 4 9 e >>> s.replace([1, 2], method='bfill') 0 3 1 3 2 3 3 4 4 5 dtype: int64 dict-like `to_replace` >>> df.replace({0: 10, 1: 100}) A B C 0 10 5 a 1 100 6 b 2 2 7 c 3 3 8 d 4 4 9 e >>> df.replace({'A': 0, 'B': 5}, 100) A B C 0 100 100 a 1 1 6 b 2 2 7 c 3 3 8 d 4 4 9 e >>> df.replace({'A': {0: 100, 4: 400}}) A B C 0 100 5 a 1 1 6 b 2 2 7 c 3 3 8 d 4 400 9 e Regular expression `to_replace` >>> df = pd.DataFrame({'A': ['bat', 'foo', 'bait'], ... 'B': ['abc', 'bar', 'xyz']}) >>> df.replace(to_replace=r'^ba.$', value='new', regex=True) A B 0 new abc 1 foo new 2 bait xyz >>> df.replace({'A': r'^ba.$'}, {'A': 'new'}, regex=True) A B 0 new abc 1 foo bar 2 bait xyz >>> df.replace(regex=r'^ba.$', value='new') A B 0 new abc 1 foo new 2 bait xyz >>> df.replace(regex={r'^ba.$': 'new', 'foo': 'xyz'}) A B 0 new abc 1 xyz new 2 bait xyz >>> df.replace(regex=[r'^ba.$', 'foo'], value='new') A B 0 new abc 1 new new 2 bait xyz Compare the behavior of >>> s = pd.Series([10, 'a', 'a', 'b', 'a']) When one uses a dict as the to_replace value, it is like the value(s) in the dict are equal to the
value parameter. >>> s.replace({'a': None}) 0 10 1 None 2 None 3 b 4 None dtype: object When >>> s.replace('a') 0 10 1 10 2 10 3 b 4 b dtype: object On the other hand, if >>> s.replace('a', None) 0 10 1 None 2 None 3 b 4 None dtype: object
How do you replace one value to another in Python?replace() function is used to replace a string, regex, list, dictionary, series, number, etc. from a Pandas Dataframe in Python. Every instance of the provided value is replaced after a thorough search of the full DataFrame.
What does replace () mean in Python?Definition and Usage
The replace() method replaces a specified phrase with another specified phrase. Note: All occurrences of the specified phrase will be replaced, if nothing else is specified.
How do you replace a value in a string?The Java string replace() method will replace a character or substring with another character or string. The syntax for the replace() method is string_name. replace(old_string, new_string) with old_string being the substring you'd like to replace and new_string being the substring that will take its place.
How do I replace a specific value in a column in pandas?replace() function is used to replace values in column (one value with another value on all columns). This method takes to_replace, value, inplace, limit, regex and method as parameters and returns a new DataFrame. When inplace=True is used, it replaces on existing DataFrame object and returns None value.
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