series

问题 I have a dataframe df : df = pandas.DataFrame(pd.read_csv(loggerfile, header = 2)) values = df.as_matrix() df2 = pd.DataFrame.from_records(values, index = datetimeIdx, columns = Columns) EDIT: Now reading the data this way as suggested: df2 = pd.read_csv(loggerfile, header = None, skiprows = [0,1,2]) Sample: 0 1 2 3 4 5 6 7 8 \ 0 2014-03-19T12:44:32.695Z 1395233072695 703425 0 2 1 13 5 21 1 2014-03-19T12:44:32.727Z 1395233072727 703425 0 2 1 13 5 21 9 10 11 12 13 14 15 16 0 25 0 25 209 0 145

问题 Which programming language or a library is able to process infinite series (like geometric or harmonic)? It perhaps must have a database of some well-known series and automatically give proper values in case of convergence, and maybe generate an exception in case of divergence. For example, in Python it could look like: sum = 0 sign = -1.0 for i in range(1,Infinity,2): sign = -sign sum += sign / i then, sum must be math.pi/4 without doing any computations in the loop (because it's a well

问题 I have some data I'm trying to organize into a DataFrame in Pandas . I was trying to make each row a Series and append it to the DataFrame . I found a way to do it by appending the Series to an empty list and then converting the list of Series to a DataFrame e.g. DF = DataFrame([series1,series2],columns=series1.index) This list to DataFrame step seems to be excessive. I've checked out a few examples on here but none of the Series preserved the Index labels from the Series to use them as

问题 I have some data I'm trying to organize into a DataFrame in Pandas . I was trying to make each row a Series and append it to the DataFrame . I found a way to do it by appending the Series to an empty list and then converting the list of Series to a DataFrame e.g. DF = DataFrame([series1,series2],columns=series1.index) This list to DataFrame step seems to be excessive. I've checked out a few examples on here but none of the Series preserved the Index labels from the Series to use them as

问题 I have a pandas series say import pandas as pd a = pd.Series([ [1, 2, 3, 4, 5], [6, 7, 8, 3, 334], [333, 4, 5, 3, 4] ]) I want to find the largest element in all lists, which is 334, what is the easy way to do it? 回答1: Option 1 Only works if elements are actually list . This is because sum concatenates lists. This is also likely very slow. max(a.sum()) 334 Option 2 minimal two tiered application of max max(map(max, a)) 334 Option 3 Only works if all lists are same length np.max(a.tolist())

问题 I have a pandas series say import pandas as pd a = pd.Series([ [1, 2, 3, 4, 5], [6, 7, 8, 3, 334], [333, 4, 5, 3, 4] ]) I want to find the largest element in all lists, which is 334, what is the easy way to do it? 回答1: Option 1 Only works if elements are actually list . This is because sum concatenates lists. This is also likely very slow. max(a.sum()) 334 Option 2 minimal two tiered application of max max(map(max, a)) 334 Option 3 Only works if all lists are same length np.max(a.tolist())

问题 I've got a dataframe containing country names & their percentage of energy output. I need to add a new column that assigns a 1 or 0, based on whether the country's energy output is above or below the median of energy output. Some dummy code is: import pandas as pd def answer(): df = pd.DataFrame({'name':['china', 'america', 'canada'], 'output': [33.2, 15.0, 5.0]}) df['newcol'] = df.where(df['output'] > df['output'].median(), 1, 0) return df['newcol'] answer() the code returns ValueError:

问题 Suppose I have some number a , and I want to get vector [ 1 , a , a^2 , ... , a^N ] . I use [ 1 , cumprod( a * ones( 1 , N - 1 ) ) ] code. What is the best (and propably efficient) way to do it? 回答1: What about a.^[0:N] ? 回答2: ThibThib's answer is absolutely correct, but it doesn't generalize very easily if a happens to a vector. So as a starting point: > a= 2 a = 2 > n= 3 n = 3 > a.^[0: n] ans = 1 2 4 8 Now you could also utilize the built-in function vander (although the order is different,

问题 In my script I have df['Time'] as shown below. 497 2017-08-06 11:00:00 548 2017-08-08 15:00:00 580 2017-08-10 04:00:00 646 2017-08-12 23:00:00 Name: Time, dtype: datetime64[ns] But when i do t1=pd.Timestamp(df['Time'][0]) I get an error like this : KeyError: 0 Do I need any type conversion here, if yes then how it can be fixed? 回答1: You're looking for df.iloc . df['Time'].iloc[0] df['Time'][0] would've worked if your series had an index beginning from 0 And if need scalar only use Series.iat:

问题 In my script I have df['Time'] as shown below. 497 2017-08-06 11:00:00 548 2017-08-08 15:00:00 580 2017-08-10 04:00:00 646 2017-08-12 23:00:00 Name: Time, dtype: datetime64[ns] But when i do t1=pd.Timestamp(df['Time'][0]) I get an error like this : KeyError: 0 Do I need any type conversion here, if yes then how it can be fixed? 回答1: You're looking for df.iloc . df['Time'].iloc[0] df['Time'][0] would've worked if your series had an index beginning from 0 And if need scalar only use Series.iat: