pandas

问题 I've got a dataframe of pandas with a series of dates (all Sundays) like this: Date Year Week 2011-01-02 2011 52 2011-01-23 2011 3 2011-01-23 2011 3 2011-01-30 2011 4 2011-01-30 2011 4 The week is given by df['Date'].dt.week , and what I want is set Sundays as the first day of the week, so I can get: Date Year Week 2011-01-02 2011 1 2011-01-23 2011 4 2011-01-23 2011 4 2011-01-30 2011 5 2011-01-30 2011 5 How can I do that in the simplest way? P.S. I have failed to mention that there're

问题 I've got a dataframe of pandas with a series of dates (all Sundays) like this: Date Year Week 2011-01-02 2011 52 2011-01-23 2011 3 2011-01-23 2011 3 2011-01-30 2011 4 2011-01-30 2011 4 The week is given by df['Date'].dt.week , and what I want is set Sundays as the first day of the week, so I can get: Date Year Week 2011-01-02 2011 1 2011-01-23 2011 4 2011-01-23 2011 4 2011-01-30 2011 5 2011-01-30 2011 5 How can I do that in the simplest way? P.S. I have failed to mention that there're

问题 I've got a dataframe of pandas with a series of dates (all Sundays) like this: Date Year Week 2011-01-02 2011 52 2011-01-23 2011 3 2011-01-23 2011 3 2011-01-30 2011 4 2011-01-30 2011 4 The week is given by df['Date'].dt.week , and what I want is set Sundays as the first day of the week, so I can get: Date Year Week 2011-01-02 2011 1 2011-01-23 2011 4 2011-01-23 2011 4 2011-01-30 2011 5 2011-01-30 2011 5 How can I do that in the simplest way? P.S. I have failed to mention that there're

问题 When using Pandas to update the value of a column for specif subset of rows, what is the best way to do it? Easy example: import pandas as pd df = pd.DataFrame({'name' : pd.Series(['Alex', 'John', 'Christopher', 'Dwayne']), 'value' : pd.Series([1., 2., 3., 4.])}) Objective: update the value column based on names length and the initial value of the value column itself. The following line achieves the objective: df.value[df.name.str.len() == 4 ] = df.value[df.name.str.len() == 4] * 1000 However

问题 I have a nested list of strings which I would like to extract them the date. The date format is: Two numbers (from 01 to 12 ) hyphen tree letters (a valid month) hyphen two numbers, for example: 08-Jan—07 or 03-Oct—01 I tried to use the following regex: r'\d{2}(—|-)(?:Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)-\d{2,4}' Then I tested it as follows: import pandas as pd df = pd.DataFrame({'blobs':['6-Feb- 1 4 Facebook’s virtual-reality division created a 3-EBÚ7 11 network of 500 free demo

问题 I have a nested list of strings which I would like to extract them the date. The date format is: Two numbers (from 01 to 12 ) hyphen tree letters (a valid month) hyphen two numbers, for example: 08-Jan—07 or 03-Oct—01 I tried to use the following regex: r'\d{2}(—|-)(?:Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)-\d{2,4}' Then I tested it as follows: import pandas as pd df = pd.DataFrame({'blobs':['6-Feb- 1 4 Facebook’s virtual-reality division created a 3-EBÚ7 11 network of 500 free demo

问题 I am trying to use Pandas to represent motion-capture data, which has T measurements of the (x, y, z) locations of each of N markers. For example, with T=3 and N=4, the raw CSV data looks like: T,Ax,Ay,Az,Bx,By,Bz,Cx,Cy,Cz,Dx,Dy,Dz 0,1,2,1,3,2,1,4,2,1,5,2,1 1,8,2,3,3,2,9,9,1,3,4,9,1 2,4,5,7,7,7,1,8,3,6,9,2,3 This is really simple to load into a DataFrame, and I've learned a few tricks that are easy (converting marker data to z-scores, or computing velocities, for example). One thing I'd like

问题 I am trying to use Pandas to represent motion-capture data, which has T measurements of the (x, y, z) locations of each of N markers. For example, with T=3 and N=4, the raw CSV data looks like: T,Ax,Ay,Az,Bx,By,Bz,Cx,Cy,Cz,Dx,Dy,Dz 0,1,2,1,3,2,1,4,2,1,5,2,1 1,8,2,3,3,2,9,9,1,3,4,9,1 2,4,5,7,7,7,1,8,3,6,9,2,3 This is really simple to load into a DataFrame, and I've learned a few tricks that are easy (converting marker data to z-scores, or computing velocities, for example). One thing I'd like

问题 I have two disjoint time series objects, for example -ts1 Date Price 2010-01-01 1800.0 2010-01-04 1500.0 2010-01-08 1600.0 2010-01-09 1400.0 Name: Price, dtype: float64 -ts2 Date Price 2010-01-02 2000.0 2010-01-03 2200.0 2010-01-05 2010.0 2010-01-07 2100.0 2010-01-10 2110.0 How I could merge the two into a single time series that should be sorted on date? like -ts3 Date Price 2010-01-01 1800.0 2010-01-02 2000.0 2010-01-03 2200.0 2010-01-04 1500.0 2010-01-05 2010.0 2010-01-07 2100.0 2010-01-08

问题 I have a pandas data frame like x = pd.DataFrame(['05/06/2015 00:00', '22/06/2015 00:00', None], columns=['myDate']) I want to find out the number of days between the dates in the myDate column and the current date. How can I do this? I tried the below without much success pd.to_datetime(x['myDate']) - pd.datetime.now().date() 回答1: the following works for me: In [9]: df = pd.DataFrame(['05/06/2015 00:00', '22/06/2015 00:00', None], columns=['myDate']) df['myDate']= pd.to_datetime(df['myDate']