group-by

问题 I have a dataframe that gives upper and lower values of each indicator as follows df = pd.DataFrame( {'indicator': ['indicator 1', 'indicator 1', 'indicator 2', 'indicator 2'], 'year':[2014,2014,2015,2015], 'value type': ['upper', 'lower', 'upper', 'lower'], 'value':[12.3, 10.2, 15.4, 13.2] }, index=[1,2,3,4]) I want to remove the upper and lower values and replace that with the mean of two values. How can I do that? 回答1: You could groupby and transform by mean . df['value'] = df.groupby(

问题 What is pivot? How do I pivot? Is this a pivot? Long format to wide format? I've seen a lot of questions that ask about pivot tables. Even if they don't know that they are asking about pivot tables, they usually are. It is virtually impossible to write a canonical question and answer that encompasses all aspects of pivoting.... ... But I'm going to give it a go. The problem with existing questions and answers is that often the question is focused on a nuance that the OP has trouble

问题 What is pivot? How do I pivot? Is this a pivot? Long format to wide format? I've seen a lot of questions that ask about pivot tables. Even if they don't know that they are asking about pivot tables, they usually are. It is virtually impossible to write a canonical question and answer that encompasses all aspects of pivoting.... ... But I'm going to give it a go. The problem with existing questions and answers is that often the question is focused on a nuance that the OP has trouble

问题 Given a DataFrame with an ID column and corresponding values column, how can I aggregate (let's say sum) the values within blocks of repeating IDs? Example DF: import numpy as np import pandas as pd df = pd.DataFrame( {'id': ['a', 'a', 'a', 'a', 'a', 'b', 'b', 'b', 'a', 'a', 'b', 'a', 'b', 'b', 'b'], 'v': np.ones(15)} ) Note that there's only two unique IDs, so a simple groupby('id') won't work. Also, the IDs don't alternate/repeat in a regular manner. What I came up with was to recreate the

问题 Given a DataFrame with an ID column and corresponding values column, how can I aggregate (let's say sum) the values within blocks of repeating IDs? Example DF: import numpy as np import pandas as pd df = pd.DataFrame( {'id': ['a', 'a', 'a', 'a', 'a', 'b', 'b', 'b', 'a', 'a', 'b', 'a', 'b', 'b', 'b'], 'v': np.ones(15)} ) Note that there's only two unique IDs, so a simple groupby('id') won't work. Also, the IDs don't alternate/repeat in a regular manner. What I came up with was to recreate the

问题 I am not sure what title has to be here to correctly reflect my question, I can only describe what I want. There is a table with fields: id, name, city There are next rows: 1 John London 2 Mary Paris 3 John Paris 4 Samy London I want to get a such result: London Paris Total 2 2 John 1 1 Mary 0 1 Samy 1 0 So, I need to take all unique values of name and find an appropriate quantity for unique values of another field (city) Also I want to get a total quantity of each city Simple way to do it is

问题 I am not sure what title has to be here to correctly reflect my question, I can only describe what I want. There is a table with fields: id, name, city There are next rows: 1 John London 2 Mary Paris 3 John Paris 4 Samy London I want to get a such result: London Paris Total 2 2 John 1 1 Mary 0 1 Samy 1 0 So, I need to take all unique values of name and find an appropriate quantity for unique values of another field (city) Also I want to get a total quantity of each city Simple way to do it is

问题 I am not sure what title has to be here to correctly reflect my question, I can only describe what I want. There is a table with fields: id, name, city There are next rows: 1 John London 2 Mary Paris 3 John Paris 4 Samy London I want to get a such result: London Paris Total 2 2 John 1 1 Mary 0 1 Samy 1 0 So, I need to take all unique values of name and find an appropriate quantity for unique values of another field (city) Also I want to get a total quantity of each city Simple way to do it is

问题 When using pandas groupby functions and manipulating the output after the groupby, I've noticed that some functions behave differently in terms of what is returned as the index and how this can be manipulated. Say we have a dataframe with the following information: Name Type ID 0 Book1 ebook 1 1 Book2 paper 2 2 Book3 paper 3 3 Book1 ebook 1 4 Book2 paper 2 if we do df.groupby(["Name", "Type"]).sum() we get a DataFrame : ID Name Type Book1 ebook 2 Book2 paper 4 Book3 paper 3 which contains a

问题 I have a data set that looks like this Id Subject Date Vitals Value 10 John 2001-05-29 HeartRate 65 10 John 2001-05-29 HeartRate 68 10 John 2001-05-29 BP-Arterial 48 10 John 2001-05-29 PulseRate 64 34 Pete 2005-08-15 HeartRate 68 34 Pete 2005-08-15 BP-Arterial 56 10 John 2004-09-25 HeartRate 65 10 John 2004-09-25 BP-Arterial 64 10 John 2004-09-25 PulseRate 63 34 Pete 2007-07-21 BP-Arterial 68 34 Pete 2007-07-21 PulseRate 56 I want to do two things, 1) group by Vitals. 2) Count the number of