Python | Pandas dataframe.replace()
Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
dataframe.replace() function is used to replace a string, regex, list, dictionary, series, number etc. from a dataframe. This is a very rich function as it has many variations.
The most powerful thing about this function is that it can work with Python regex (regular expressions).
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 (e.g. 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 (e.g. 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
For link to CSV file Used in Code, click here
Example #1: Replace team “Boston Celtics” with “Omega Warrior” in the nba.csv file
We are going to replace team “Boston Celtics” with “Omega Warrior” in the ‘df’ data frame
Example #2: Replacing more than one value at a time. Using python list as an argument
We are going to replace team “Boston Celtics” and “Texas” with “Omega Warrior” in the ‘df’ dataframe.
Notice the College column in the first row, “Texas” has been replaced with “Omega Warriors”
Example #3: Replace the Nan value in the data frame with -99999 value.
Notice all the
Nan value in the data frame has been replaced by -99999. Though for practical purposes we should be careful with what value we are replacing
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