multi-index

问题 The data: index = [('A', 'aa', 'aaa'), ('A', 'aa', 'aab'), ('B', 'bb', 'bbb'), ('B', 'bb', 'bbc'), ('C', 'cc', 'ccc') ] values = [0.07, 0.04, 0.04, 0.06, 0.07] s = pd.Series(data=values, index=pd.MultiIndex.from_tuples(index)) s A aa aaa 0.07 aab 0.04 B bb bbb 0.04 bbc 0.06 C cc ccc 0.07 To get a mean of first two levels is easy: s.mean(level=[0,1]) Result: A aa 0.055 B bb 0.050 C cc 0.070 But to get a count on first two levels does not work the same: #s.count(level=[0,1]) # does not work I

问题 I'm trying to rename a single row of a pandas dataframe by it's tuple. For example: import pandas as pd df = pd.DataFrame(data={'i1':[0,0,0,0,1,1,1,1], 'i2':[0,1,2,3,0,1,2,3], 'x':[1.,2.,3.,4.,5.,6.,7.,8.], 'y':[9,10,11,12,13,14,15,16]}) df.set_index(['i1','i2'], inplace=True) Creates df: x y i1 i2 0 0 1.0 9 1 2.0 10 2 3.0 11 3 4.0 12 1 0 5.0 13 1 6.0 14 2 7.0 15 3 8.0 16 I'd like to be able to use something like: df.rename(index={(0,1):(0,9)},inplace=True) to get: x y i1 i2 0 0 1.0 9 9 2.0

问题 I have a multi-indexed dataframe with names attached to the column levels. The data table looks something like this: (df1) TIME TMC 111N1 111P2 111N3 111P4 DATE EPOCH 0 143 113 103 NaN 1 183 NaN NaN NaN 2 NaN NaN NaN NaN 3 143 NaN NaN NaN I'd like to shuffle the columns around so that they match the order specified by the rows index of a reference dataframe (df2): A1 A2 A3 A4 A5 Name 111N3 PA PL er 0.75543 35 111P4 PA PL er 0.09413 35 111N1 PA PL er 4.21557 35 111P2 PA PL er 1.31989 35 i.e.

问题 I'm new to pandas. I'm trying to create the following table: | C | Perl | | line | func | line | func | version | covered | total | covered | total | There is the following structure of JSON that I have created: { "version1":{ "perl":{ "line_covered":207, "line_total":312, "func_covered":15, "func_total":18 }, "C":{ "line_covered":321, "line_total":512, "func_covered":10, "func_total":10 } } } I want to iterate over this JSON and create the table. My first problem is that I can't seem to

问题 boost::multi_index_container enables the construction of containers maintaining one or more indices with different sorting and access semantics like relational database. And I use boost::multi_index_container with composite key to handle something like this: struct Person { Person(int id, string name): m_id(id), m_name(name) { } int m_id; string m_name; }; typedef multi_index_container< Person, indexed_by< ordered_unique< member<Person, int, &Person::m_id> >, ordered_unique< composite_key<

问题 I've a dataframe which looks like this some feature another feature label sample 0 ... ... ... and I'd like to get a dataframe with multiindexed columns like this features label sample some another 0 ... ... ... From the API it's not clear to me how to use from_arrays() , from_product() , from_tuples() or from_frame() correctly. The solution shall not depend on string parsing of the feature columns ( some feature , another feature ). The last column for the label is the last column and it's

问题 My .csv file looks like: Area When Year Month Tickets City Day 2015 1 14 City Night 2015 1 5 Rural Day 2015 1 18 Rural Night 2015 1 21 Suburbs Day 2015 1 15 Suburbs Night 2015 1 21 City Day 2015 2 13 containing 75 rows. I want both a row multiindex and column multiindex that looks like: Area City Rural Suburbs When Day Night Day Night Day Night Year Month 2015 1 5.0 3.0 22.0 11.0 13.0 2.0 2 22.0 8.0 4.0 16.0 6.0 18.0 3 26.0 25.0 22.0 23.0 22.0 2.0 2016 1 20.0 25.0 39.0 14.0 3.0 10.0 2 4.0 14

问题 My .csv file looks like: Area When Year Month Tickets City Day 2015 1 14 City Night 2015 1 5 Rural Day 2015 1 18 Rural Night 2015 1 21 Suburbs Day 2015 1 15 Suburbs Night 2015 1 21 City Day 2015 2 13 containing 75 rows. I want both a row multiindex and column multiindex that looks like: Area City Rural Suburbs When Day Night Day Night Day Night Year Month 2015 1 5.0 3.0 22.0 11.0 13.0 2.0 2 22.0 8.0 4.0 16.0 6.0 18.0 3 26.0 25.0 22.0 23.0 22.0 2.0 2016 1 20.0 25.0 39.0 14.0 3.0 10.0 2 4.0 14

问题 I have a pandas DataFrame looking like this : x1 x2 x3 x4 Date Time 2017-01-03 09:00:00 0.000097 0.000259 0.000629 0.000142 09:20:00 0.000046 0.000044 0.000247 0.000134 09:40:00 0.000021 0.000032 0.000171 0.000105 10:00:00 0.000033 0.000040 0.000136 0.000178 10:20:00 0.000079 0.000157 0.000094 0.000083 ..... 17:00:00 0.000032 0.000137 0.000024 0.000028 However, i want to reindex the second index, by one 20min bin and I would like it to look like this: x1 x2 x3 x4 Date Time 2017-01-03 09:20:00

问题 I have a hierarchical table with time series data in a MultiIndex DataFrame - see sample data below - note that Level 1 index is unevenly populated. How do I resample the DataFrame to result in a weekly time series that resamples weekly backwards from the current date. I have tried this but failed: df.index.levels[0].name = 'date' df.reset_index().groupby(pandas.Grouper(key='date', freq='W')) Sample data: Price Sector 2016-08-08 Equity(24 [AAPL]) 107.47 311 Equity(4151 [JNJ]) 124.19 206