python

问题 I am using Python 2.7.3. Can anybody explain the difference between the literals: '\u0391' and: u'\u0391' and the different way they are echoed in the REPL below (especially the extra slash added to a1): >>> a1='\u0391' >>> a1 '\\u0391' >>> type(a1) <type 'str'> >>> >>> a2=u'\u0391' >>> a2 u'\u0391' >>> type(a2) <type 'unicode'> >>> 回答1: You can only use unicode escapes ( \uabcd ) in a unicode string literal. They have no meaning in a byte string. A Python 2 Unicode literal ( u'some text' )

问题 I am using Python 2.7.3. Can anybody explain the difference between the literals: '\u0391' and: u'\u0391' and the different way they are echoed in the REPL below (especially the extra slash added to a1): >>> a1='\u0391' >>> a1 '\\u0391' >>> type(a1) <type 'str'> >>> >>> a2=u'\u0391' >>> a2 u'\u0391' >>> type(a2) <type 'unicode'> >>> 回答1: You can only use unicode escapes ( \uabcd ) in a unicode string literal. They have no meaning in a byte string. A Python 2 Unicode literal ( u'some text' )

问题 I have a situation with some parallel lists that need to be filtered based on the values in one of the lists. Sometimes I write something like this to filter them: lista = [1, 2, 3] listb = [7, 8, 9] filtered_a, filtered_b = zip(*[(a, b) for (a, b) in zip(lista, listb) if a < 3]) This gives filtered_a == (1, 2) and filtered_b == (7, 8) However, changing the final condition from a < 3 to a < 0 causes an exception to be raised: Traceback (most recent call last): ... ValueError: need more than 0

问题 I am trying to use APScheduler to run periodic jobs with an IntervalTrigger, I've intentionally set the maximum number of running instances to one because I don't want jobs to overlap. Problem is that after some time the scheduler starts reporting that the maximum number of running instance for a job have been reached even after it previously informed that the job finished successfully, I found this on the logs: 2015-10-28 22:17:42,137 INFO Running job "ping (trigger: interval[0:01:00], next

问题 I have a situation with some parallel lists that need to be filtered based on the values in one of the lists. Sometimes I write something like this to filter them: lista = [1, 2, 3] listb = [7, 8, 9] filtered_a, filtered_b = zip(*[(a, b) for (a, b) in zip(lista, listb) if a < 3]) This gives filtered_a == (1, 2) and filtered_b == (7, 8) However, changing the final condition from a < 3 to a < 0 causes an exception to be raised: Traceback (most recent call last): ... ValueError: need more than 0

问题 I have a situation with some parallel lists that need to be filtered based on the values in one of the lists. Sometimes I write something like this to filter them: lista = [1, 2, 3] listb = [7, 8, 9] filtered_a, filtered_b = zip(*[(a, b) for (a, b) in zip(lista, listb) if a < 3]) This gives filtered_a == (1, 2) and filtered_b == (7, 8) However, changing the final condition from a < 3 to a < 0 causes an exception to be raised: Traceback (most recent call last): ... ValueError: need more than 0

问题 Given a pandas.DataFrame with a column holding mixed datatypes, like e.g. df = pd.DataFrame({'mixed': [pd.Timestamp('2020-10-04'), 999, 'a string']}) I was wondering how to obtain the datatypes of the individual objects in the column (Series)? Suppose I want to modify all entries in the Series that are of a certain type, like multiply all integers by some factor. I could iteratively derive a mask and use it in loc , like m = np.array([isinstance(v, int) for v in df['mixed']]) df.loc[m, 'mixed

问题 Given a pandas.DataFrame with a column holding mixed datatypes, like e.g. df = pd.DataFrame({'mixed': [pd.Timestamp('2020-10-04'), 999, 'a string']}) I was wondering how to obtain the datatypes of the individual objects in the column (Series)? Suppose I want to modify all entries in the Series that are of a certain type, like multiply all integers by some factor. I could iteratively derive a mask and use it in loc , like m = np.array([isinstance(v, int) for v in df['mixed']]) df.loc[m, 'mixed

问题 Given a pandas.DataFrame with a column holding mixed datatypes, like e.g. df = pd.DataFrame({'mixed': [pd.Timestamp('2020-10-04'), 999, 'a string']}) I was wondering how to obtain the datatypes of the individual objects in the column (Series)? Suppose I want to modify all entries in the Series that are of a certain type, like multiply all integers by some factor. I could iteratively derive a mask and use it in loc , like m = np.array([isinstance(v, int) for v in df['mixed']]) df.loc[m, 'mixed

问题 I'm trying to use the rolling() function on a pandas data frame with monthly data. However, I dropped some NaN values, so now there are some gaps in my time series. Therefore, the basic window parameter gives a misleading answer since it just looks at the previous observation: import pandas as pd import numpy as np import random dft = pd.DataFrame(np.random.randint(0,10,size=len(dt)),index=dt) dft.columns = ['value'] dft['value'] = np.where(dft['value'] < 3,np.nan,dft['value']) dft = dft