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Make select by substring
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‎books.xml

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<?xml version="1.0"?>
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<catalog>
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<book id="bk101">
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<author>Gambardella, Matthew</author>
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<title>XML Developer's Guide</title>
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<genre>Computer</genre>
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<price>44.95</price>
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<publish_date>2000-10-01</publish_date>
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<description>An in-depth look at creating applications
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with XML.</description>
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</book>
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<book id="bk102">
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<author>Ralls, Kim</author>
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<title>Midnight Rain</title>
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<genre>Fantasy</genre>
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<price>5.95</price>
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<publish_date>2000-12-16</publish_date>
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<description>A former architect battles corporate zombies,
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an evil sorceress, and her own childhood to become queen
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of the world.</description>
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</book>
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<book id="bk103">
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<author>Corets, Eva</author>
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<title>Maeve Ascendant</title>
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<genre>Fantasy</genre>
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<price>5.95</price>
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<publish_date>2000-11-17</publish_date>
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<description>After the collapse of a nanotechnology
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society in England, the young survivors lay the
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foundation for a new society.</description>
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</book>
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<book id="bk104">
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<author>Corets, Eva</author>
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<title>Oberon's Legacy</title>
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<genre>Fantasy</genre>
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<price>5.95</price>
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<publish_date>2001-03-10</publish_date>
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<description>In post-apocalypse England, the mysterious
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agent known only as Oberon helps to create a new life
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for the inhabitants of London. Sequel to Maeve
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Ascendant.</description>
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</book>
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<book id="bk105">
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<author>Corets, Eva</author>
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<title>The Sundered Grail</title>
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<genre>Fantasy</genre>
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<price>5.95</price>
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<publish_date>2001-09-10</publish_date>
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<description>The two daughters of Maeve, half-sisters,
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battle one another for control of England. Sequel to
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Oberon's Legacy.</description>
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</book>
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<book id="bk106">
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<author>Randall, Cynthia</author>
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<title>Lover Birds</title>
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<genre>Romance</genre>
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<price>4.95</price>
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<publish_date>2000-09-02</publish_date>
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<description>When Carla meets Paul at an ornithology
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conference, tempers fly as feathers get ruffled.</description>
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</book>
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<book id="bk107">
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<author>Thurman, Paula</author>
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<title>Splish Splash</title>
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<genre>Romance</genre>
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<price>4.95</price>
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<publish_date>2000-11-02</publish_date>
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<description>A deep sea diver finds true love twenty
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thousand leagues beneath the sea.</description>
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</book>
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<book id="bk108">
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<author>Knorr, Stefan</author>
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<title>Creepy Crawlies</title>
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<genre>Horror</genre>
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<price>4.95</price>
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<publish_date>2000-12-06</publish_date>
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<description>An anthology of horror stories about roaches,
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centipedes, scorpions and other insects.</description>
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</book>
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<book id="bk109">
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<author>Kress, Peter</author>
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<title>Paradox Lost</title>
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<genre>Science Fiction</genre>
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<price>6.95</price>
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<publish_date>2000-11-02</publish_date>
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<description>After an inadvertant trip through a Heisenberg
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Uncertainty Device, James Salway discovers the problems
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of being quantum.</description>
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</book>
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<book id="bk110">
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<author>O'Brien, Tim</author>
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<title>Microsoft .NET: The Programming Bible</title>
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<genre>Computer</genre>
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<price>36.95</price>
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<publish_date>2000-12-09</publish_date>
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<description>Microsoft's .NET initiative is explored in
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detail in this deep programmer's reference.</description>
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</book>
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<book id="bk111">
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<author>O'Brien, Tim</author>
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<title>MSXML3: A Comprehensive Guide</title>
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<genre>Computer</genre>
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<price>36.95</price>
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<publish_date>2000-12-01</publish_date>
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<description>The Microsoft MSXML3 parser is covered in
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detail, with attention to XML DOM interfaces, XSLT processing,
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SAX and more.</description>
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</book>
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<book id="bk112">
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<author>Galos, Mike</author>
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<title>Visual Studio 7: A Comprehensive Guide</title>
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<genre>Computer</genre>
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<price>49.95</price>
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<publish_date>2001-04-16</publish_date>
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<description>Microsoft Visual Studio 7 is explored in depth,
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looking at how Visual Basic, Visual C++, C#, and ASP+ are
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integrated into a comprehensive development
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environment.</description>
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</book>
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</catalog>

‎pandas/core/frame.py

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TYPE_CHECKING,
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Any,
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Literal,
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List,
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cast,
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overload,
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Union
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)
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import warnings
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Nauru 337000 182 NR
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"""
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return selectn.SelectNFrame(self, n=n, keep=keep, columns=columns).nsmallest()
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def select_by_substr(
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self,
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substr: Union[str, List[str]],
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ignore_case: bool = True,
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) -> DataFrame | None:
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"""
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Return columns whose names contain the specified substring(s).
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Select and return all columns from the DataFrame whose names contain
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the given substring or any of a list of substrings. By default, the
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search is case-insensitive.
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Parameters
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----------
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substr : str or list of str
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Substring or list of substrings to search for in column names.
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ignore_case : bool, default True
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Whether to ignore case when searching for substrings.
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Returns
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-------
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DataFrame or None
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DataFrame containing only the columns whose names match the
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specified substring(s). Returns None if no columns match.
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See Also
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--------
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DataFrame.filter : Subset the columns or rows of a DataFrame according to labels or a boolean array.
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DataFrame.loc : Access a group of rows and columns by label(s) or a boolean array.
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Notes
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-----
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All columns containing at least one of the provided substrings will be
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returned. If no columns match, None is returned.
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Examples
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--------
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>>> df = pd.DataFrame({
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... "first_name": ["Alice", "Bob"],
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... "last_name": ["Smith", "Jones"],
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... "age": [25, 30],
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... "city": ["NY", "LA"]
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... })
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>>> df.select_by_substr("name")
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first_name last_name
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0 Alice Smith
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1 Bob Jones
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>>> df.select_by_substr(["name", "city"])
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first_name last_name city
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0 Alice Smith NY
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1 Bob Jones LA
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>>> df.select_by_substr("AGE", ignore_case=False) # No match due to case
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None
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>>> df.select_by_substr("AGE", ignore_case=True)
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age
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0 25
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1 30
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"""
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substr = [substr] if isinstance(substr, str) else substr
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selected_cols = self.columns
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if ignore_case:
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selected_cols = [
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col for col in self.columns
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if any(sub.casefold() in col.casefold()
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for sub in substr)
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]
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else:
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selected_cols = [
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col for col in self.columns
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if any(sub in col
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for sub in substr)
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]
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selected_cols = list(set(selected_cols))
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return self[selected_cols]
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def swaplevel(self, i: Axis = -2, j: Axis = -1, axis: Axis = 0) -> DataFrame:
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"""

‎pandas/tests/frame/test_

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‎placeholder.txt

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Return the first `n` rows ordered by `columns` in ascending order.
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Return the first `n` rows with the smallest values in `columns`, in
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ascending order. The columns that are not specified are returned as
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well, but not used for ordering.
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This method is equivalent to
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``df.sort_values(columns, ascending=True).head(n)``, but more
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performant.
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Parameters
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----------
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n : int
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Number of items to retrieve.
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columns : list or str
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Column name or names to order by.
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keep : {'first', 'last', 'all'}, default 'first'
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Where there are duplicate values:
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- ``first`` : take the first occurrence.
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- ``last`` : take the last occurrence.
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- ``all`` : keep all the ties of the largest item even if it means
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selecting more than ``n`` items.
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Returns
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-------
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DataFrame
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DataFrame with the first `n` rows ordered by `columns` in ascending order.
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See Also
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--------
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DataFrame.nlargest : Return the first `n` rows ordered by `columns` in
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descending order.
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DataFrame.sort_values : Sort DataFrame by the values.
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DataFrame.head : Return the first `n` rows without re-ordering.
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Examples
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--------
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>>> df = pd.DataFrame(
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... {
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... "population": [
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... 59000000,
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... 65000000,
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... 434000,
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... 434000,
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... 434000,
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... 337000,
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... 337000,
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... 11300,
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... 11300,
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... ],
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... "GDP": [1937894, 2583560, 12011, 4520, 12128, 17036, 182, 38, 311],
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... "alpha-2": ["IT", "FR", "MT", "MV", "BN", "IS", "NR", "TV", "AI"],
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... },
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... index=[
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... "Italy",
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... "France",
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... "Malta",
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... "Maldives",
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... "Brunei",
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... "Iceland",
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... "Nauru",
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... "Tuvalu",
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... "Anguilla",
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... ],
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... )
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>>> df
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population GDP alpha-2
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Italy 59000000 1937894 IT
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France 65000000 2583560 FR
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Malta 434000 12011 MT
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Maldives 434000 4520 MV
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Brunei 434000 12128 BN
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Iceland 337000 17036 IS
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Nauru 337000 182 NR
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Tuvalu 11300 38 TV
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Anguilla 11300 311 AI
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In the following example, we will use ``nsmallest`` to select the
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three rows having the smallest values in column "population".
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>>> df.nsmallest(3, "population")
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population GDP alpha-2
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Tuvalu 11300 38 TV
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Anguilla 11300 311 AI
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Iceland 337000 17036 IS
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When using ``keep='last'``, ties are resolved in reverse order:
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>>> df.nsmallest(3, "population", keep="last")
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population GDP alpha-2
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Anguilla 11300 311 AI
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Tuvalu 11300 38 TV
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Nauru 337000 182 NR
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When using ``keep='all'``, the number of element kept can go beyond ``n``
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if there are duplicate values for the largest element, all the
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ties are kept.
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>>> df.nsmallest(3, "population", keep="all")
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population GDP alpha-2
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Tuvalu 11300 38 TV
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Anguilla 11300 311 AI
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Iceland 337000 17036 IS
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Nauru 337000 182 NR
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However, ``nsmallest`` does not keep ``n`` distinct
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smallest elements:
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>>> df.nsmallest(4, "population", keep="all")
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population GDP alpha-2
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Tuvalu 11300 38 TV
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Anguilla 11300 311 AI
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Iceland 337000 17036 IS
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To order by the smallest values in column "population" and then "GDP", we can
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specify multiple columns like in the next example.
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>>> df.nsmallest(3, ["population", "GDP"])
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population GDP alpha-2
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Tuvalu 11300 38 TV
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Anguilla 11300 311 AI
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Nauru 337000 182 NR
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"""

‎test.py

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# contributing guide: https://pandas.pydata.org/docs/dev/development/contributing.html#pushing-your-changes
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import pandas as pd
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df = pd.DataFrame({
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"yes": [5000, 2, 3],
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"Byesyes": [4, 5, 6],
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"no": [7, 8, 9],
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"Byesno": [10, 11, 12],
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"YES": [13, 14, 15],
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"NO": [16, 17, 18],
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"YESYES": [19, 20, 21],
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})
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# Test the DataFrame creation
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print(df.select_by_substr("skibidi"))

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