33. Pandas Replace | Handling Missing Values Using Pandas | Part 6
Handling Missing Values in Pandas Using Replace,at, iat, iloc and loc methods
Replace values given in 'to_replace' with 'value'.
---------------------------------------------
data.replace(
to_replace=None,
value=None,
inplace=False,
limit=None,
regex=False,
method='pad',
)
---------------------------------------------
to_replace
str, regex, list, dict, Series, int, float, or None
How to find the values that will be replaced.
---------------------------------------------
value
scalar, dict, list, str, regex, default None
Value to replace any values matching `to_replace` with.
---------------------------------------------
inplace : bool, default False
---------------------------------------------
limit : int, default None
Maximum size gap to forward or backward fill.
---------------------------------------------
regex :
bool or same types as `to_replace`, default False
Whether to interpret `to_replace` and/or `value` as regular expressions.
---------------------------------------------
method : {'pad', 'ffill', 'bfill', `None`}
The method to use when for replacement.
---------------------------------------------
iat:
Access a single value for a row/column pair by integer position.
Similar to 'iloc', in that both provide integer-based lookups. Use
'iat' if you only need to get or set a single value in a DataFrame
or Series.
---------------------------------------------
at:
Access a single value for a row/column label pair.
---------------------------------------------
DataFrame.loc : Access a group of rows and columns by label(s).
--------------------------------------------
DataFrame.iloc : Access a group of rows and columns by integer position(s).
Github Link: https://github.com/PRIYANG-BHATT/Data...
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