From 6d7df3da30945f2141475134d6665c793a8eaebf Mon Sep 17 00:00:00 2001 From: Zero Date: Sun, 10 Oct 2021 18:06:39 +0800 Subject: [PATCH] DOC: cancel replace's doc list item text bold effect --- pandas/core/shared_docs.py | 82 +++++++++++++++++++------------------- 1 file changed, 41 insertions(+), 41 deletions(-) diff --git a/pandas/core/shared_docs.py b/pandas/core/shared_docs.py index a3fa24c7ee1e0..7d89f2e8b7789 100644 --- a/pandas/core/shared_docs.py +++ b/pandas/core/shared_docs.py @@ -414,51 +414,51 @@ * numeric, str or regex: - numeric: numeric values equal to `to_replace` will be - replaced with `value` + replaced with `value` - str: string exactly matching `to_replace` will be replaced - with `value` + with `value` - regex: regexs matching `to_replace` will be replaced with - `value` + `value` * list of str, regex, or numeric: - First, if `to_replace` and `value` are both lists, they - **must** be the same length. + **must** be the same length. - Second, if ``regex=True`` then all of the strings in **both** - lists will be interpreted as regexs otherwise they will match - directly. This doesn't matter much for `value` since there - are only a few possible substitution regexes you can use. + lists will be interpreted as regexs otherwise they will match + directly. This doesn't matter much for `value` since there + are only a few possible substitution regexes you can use. - str, regex and numeric rules apply as above. * dict: - Dicts can be used to specify different replacement values - for different existing values. For example, - ``{{'a': 'b', 'y': 'z'}}`` replaces the value 'a' with 'b' and - 'y' with 'z'. To use a dict in this way the `value` - parameter should be `None`. + for different existing values. For example, + ``{{'a': 'b', 'y': 'z'}}`` replaces the value 'a' with 'b' and + 'y' with 'z'. To use a dict in this way the `value` + parameter should be `None`. - For a DataFrame a dict can specify that different values - should be replaced in different columns. For example, - ``{{'a': 1, 'b': 'z'}}`` looks for the value 1 in column 'a' - and the value 'z' in column 'b' and replaces these values - with whatever is specified in `value`. The `value` parameter - should not be ``None`` in this case. You can treat this as a - special case of passing two lists except that you are - specifying the column to search in. + should be replaced in different columns. For example, + ``{{'a': 1, 'b': 'z'}}`` looks for the value 1 in column 'a' + and the value 'z' in column 'b' and replaces these values + with whatever is specified in `value`. The `value` parameter + should not be ``None`` in this case. You can treat this as a + special case of passing two lists except that you are + specifying the column to search in. - For a DataFrame nested dictionaries, e.g., - ``{{'a': {{'b': np.nan}}}}``, are read as follows: look in column - 'a' for the value 'b' and replace it with NaN. The `value` - parameter should be ``None`` to use a nested dict in this - way. You can nest regular expressions as well. Note that - column names (the top-level dictionary keys in a nested - dictionary) **cannot** be regular expressions. + ``{{'a': {{'b': np.nan}}}}``, are read as follows: look in column + 'a' for the value 'b' and replace it with NaN. The `value` + parameter should be ``None`` to use a nested dict in this + way. You can nest regular expressions as well. Note that + column names (the top-level dictionary keys in a nested + dictionary) **cannot** be regular expressions. * None: - This means that the `regex` argument must be a string, - compiled regular expression, or list, dict, ndarray or - Series of such elements. If `value` is also ``None`` then - this **must** be a nested dictionary or Series. + compiled regular expression, or list, dict, ndarray or + Series of such elements. If `value` is also ``None`` then + this **must** be a nested dictionary or Series. See the examples section for examples of each of these. value : scalar, dict, list, str, regex, default None @@ -497,17 +497,17 @@ TypeError * If `to_replace` is not a scalar, array-like, ``dict``, or ``None`` * If `to_replace` is a ``dict`` and `value` is not a ``list``, - ``dict``, ``ndarray``, or ``Series`` + ``dict``, ``ndarray``, or ``Series`` * If `to_replace` is ``None`` and `regex` is not compilable - into a regular expression or is a list, dict, ndarray, or - Series. + into a regular expression or is a list, dict, ndarray, or + Series. * When replacing multiple ``bool`` or ``datetime64`` objects and - the arguments to `to_replace` does not match the type of the - value being replaced + the arguments to `to_replace` does not match the type of the + value being replaced ValueError * If a ``list`` or an ``ndarray`` is passed to `to_replace` and - `value` but they are not the same length. + `value` but they are not the same length. See Also -------- @@ -518,17 +518,17 @@ Notes ----- * Regex substitution is performed under the hood with ``re.sub``. The - rules for substitution for ``re.sub`` are the same. + rules for substitution for ``re.sub`` are the same. * Regular expressions will only substitute on strings, meaning you - cannot provide, for example, a regular expression matching floating - point numbers and expect the columns in your frame that have a - numeric dtype to be matched. However, if those floating point - numbers *are* strings, then you can do this. + cannot provide, for example, a regular expression matching floating + point numbers and expect the columns in your frame that have a + numeric dtype to be matched. However, if those floating point + numbers *are* strings, then you can do this. * This method has *a lot* of options. You are encouraged to experiment - and play with this method to gain intuition about how it works. + and play with this method to gain intuition about how it works. * When dict is used as the `to_replace` value, it is like - key(s) in the dict are the to_replace part and - value(s) in the dict are the value parameter. + key(s) in the dict are the to_replace part and + value(s) in the dict are the value parameter. Examples --------