The -Filter parameter can do more than just match on everything, which is effectively what -Filter * does. The -Filter string is very much like Powershell syntax (not quite, but most of the way there). You can use most of the same logical operators that Powershell supports, and they work much in the same way that Powershell operators do.

2010年2月9日 · Yes, definitely more powerful -- however fnmatch does support [0123456789] sequences , and it also has the fnmatch.filter() function which makes this loop slightly more efficient. – Ben Hoyt Commented Jan 10, 2013 at 22:42

2017年8月28日 · You should use filter method, which accepts a callback function. The filter() method creates a new array with all elements that pass the test implemented by the provided function. Also, use typeof operator in order to find out the type of item from array. The typeof operator returns a string indicating the type of the unevaluated operand.

2013年3月11日 · Using Python's built-in ability to write lambda expressions, we could filter by an arbitrary regex operation as follows: import re # with foo being our pd dataframe foo[foo['b'].apply(lambda x: True if re.search('^f', x) else False)] By using re.search you can filter by complex regex style queries, which is more powerful in my opinion.

2012年1月13日 · Oracle SQL filter results between two dates, but only display records within a time range. 0.

Setting the value of the filter query-string parameter to a string using those delimiters creates a list of name/value pairs which can be parsed easily on the server-side and utilized to enhance database queries as needed.

2015年8月5日 · And filter object: var filter = {address: 'England', name: 'Mark'}; For example i need to filter all users ...

2011年1月31日 · But we are restricted because the filter is formed statically and at runtime we may get null values for certain filter input. In such a scenario, there is not any easy alternative. It would be difficult to remove the predicate completely since that would involve some of amount of parsing the url and removing the null predicate

2014年2月28日 · Args: df (pd.DataFrame): dataframe filter_values (None or dict): Dictionary of the form: `{<field>: <target_values_list>}` used to filter columns data. """ import numpy as np if filter_values is None or not filter_values: return df return df[ np.logical_and.reduce([ df[column].isin(target_values) for column, target_values in filter_values.items

If you want to filter using both (or multiple) columns, there's any() and all() to reduce columns (axis=1) depending on the need. Select rows where at least one of A or B is in list_of_values: df[df[['A','B']].isin(list_of_values).any(1)] df.query("A in @list_of_values or B in @list_of_values") Select rows where both of A and B are in list_of ...