Syntax: DataFrame.replace (to_replace = None, value = None, inplace = False, limit = None, regex = False, method = `pad`, axis = None)
to_replace: [str, regex, list, dict, Series, numeric, or None] pattern that we are trying to replace in dataframe.
value: Value to use to fill holes (eg 0), alternately a dict of values specifying 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.
inplace: If True, in place. Note: this will modify any other views on this object (eg a column from a DataFrame). Returns the caller if this is True.
limit: Maximum size gap to forward or backward fill
regex: Whether to interpret to_replace and / or value as regular expressions. If this is True then to_replace must be a string. Otherwise, to_replace must be None because this parameter will be interpreted as a regular expression or a list, dict, or array of regular expressions.
method: Method to use when for replacement, when to_replace is a list.
Returns: filled: NDFrame
To link to the CSV file used in the code, press here
Example # 1: replace the Boston Celtics command to the Omega Warrior in nba.csv
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We`re going to replace the Boston Celtics command with Omega Warrior in the df data frame
Example # 2: Replacing more than one value at a time ... Using Python List as Argument
We`re going to replace Boston Celtics and Texas with Omega Warrior in the df data frame.
Notice the College column in the first row: Texas has been replaced by Omega Warriors.
Example # 3: strong> replace the Nan value in the data frame with -99999.
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Note that all
Nan values in the data frame have been replaced with -99999. Although for practical purposes, we have to be careful what value we replace with