loc

Does loc in pandas use vectorised logic or a for loop?

由 南楼画角 提交于 2019-12-13 03:44:55
问题 I access rows in pandas with the loc function as below: pdf.loc[pdf.a>2] Is this vectorised? Is it better than using numpy pdf[pdf.a>2] 回答1: This timing suggests there is no slow down with loc testa = pd.DataFrame(np.arange(10000000),columns =['q']) %timeit testb = testa.loc[testa.q>6] %timeit testc = testa[testa.q>7] 1 loop, best of 3: 207 ms per loop 1 loop, best of 3: 208 ms per loop 来源： https://stackoverflow.com/questions/55874138/does-loc-in-pandas-use-vectorised-logic-or-a-for-loop

Can't call handler in an AsyncTask?

由 我怕爱的太早我们不能终老 提交于 2019-12-11 19:19:47
问题 I want to create a background thread (execute even if my activity dies) that update my data location et send it to my server with an http resquest. But it occurs an exception: 02-01 07:50:18.172: ERROR/Exception1(277): Can't create handler inside thread that has not called Looper.prepare() My code: public class Messagerie extends Activity { private LocationManager locationManager; private String locationProvider = LocationManager.NETWORK_PROVIDER; public void onCreate(Bundle

Using groupby and loc to set up a new dataframe

由 ╄→гoц情女王★ 提交于 2019-12-11 12:05:14
问题 Hi I have a data frame as follow: df = pd.DataFrame() df['Team1'] = ['A','B','C','D','E','F','A','B','C','D','E','F'] df['Score1'] = [1,2,3,1,2,4,1,2,3,1,2,4] df['Team2'] = ['U','V','W','X','Y','Z','U','V','W','X','Y','Z'] df['Score2'] = [2,1,2,2,3,3,2,1,2,2,3,3] df['Match'] = df['Team1'] + ' Vs '+ df['Team2'] df['Match_no']= [1,2,3,4,5,6,1,2,3,4,5,6] df['model'] = ['ELO','ELO','ELO','ELO','ELO','ELO','xG','xG','xG','xG','xG','xG'] winner = df.Score1>df.Score2 df['winner'] = np.where(winner

Resources containing cross-language benchmarks?

由 喜夏-厌秋 提交于 2019-12-05 16:37:56
What resources are available that use benchmarks for comparing programming languages? I am interested in both How quickly a program in a given language can execute a given benchmark? How many lines of code are required in a given language to implement a given benchmark? There is a long-standing web site called the Computer Language Benchmarks Game , originally created by Doug Bagley as the "Great Computer Language Shootout". (You can view a little history at Portland Patterns Repository.) Is anyone aware of other resources that enable programmers to compare performance and size of programs

interacting over a dateframe with functions

由 送分小仙女□ 提交于 2019-12-04 12:32:22
if I have a date frame like this: N EG_00_04 NEG_04_08 NEG_08_12 NEG_12_16 NEG_16_20 NEG_20_24 \ datum_von 2017-10-12 21.69 15.36 0.87 1.42 0.76 0.65 2017-10-13 11.85 8.08 1.39 2.86 1.02 0.55 2017-10-14 7.83 5.88 1.87 2.04 2.29 2.18 2017-10-15 14.64 11.28 2.62 3.35 2.13 1.25 2017-10-16 5.11 5.82 -0.30 -0.38 -0.24 -0.10 2017-10-17 12.09 9.61 0.20 1.09 0.39 0.57 And I wanna check the values that are above 0 and change them to zero when they are lower. Not sure how should I use the function iterrows() and the loc() function to do so. clip_lower and mask solutions are good. Here is another one

Pandas loc multiple conditions [duplicate]

由 余生长醉 提交于 2019-12-01 23:03:45
This question already has an answer here: Selecting with complex criteria from pandas.DataFrame 4 answers I have a dataframe and I want to delete all rows where column A is equal to blue and also col B is equal to green. I though the below should work, but its not the case. Can anyone see the problem df=df.loc[~(df['A']=='blue' & df['B']=='green')] You should separate the two propositions: df1=df.loc[~(df['A']=='blue') & ~(df['B']=='green')] use eq instead of == : df.loc[~(df['A'].eq('blue') & df['B'].eq('green'))] query Note the != and or as consequence of De Morgan's Law df.query('A != "blue

Python: Pandas Series - Why use loc?

由 随声附和 提交于 2019-11-26 18:27:55
Why do we use 'loc' for pandas dataframes? it seems the following code with or without using loc both compile anr run at a simulular speed %timeit df_user1 = df.loc[df.user_id=='5561'] 100 loops, best of 3: 11.9 ms per loop or %timeit df_user1_noloc = df[df.user_id=='5561'] 100 loops, best of 3: 12 ms per loop So why use loc? Edit: This has been flagged as a duplicate question. But although pandas iloc vs ix vs loc explanation? does mention that * you can do column retrieval just by using the data frame's getitem : * df['time'] # equivalent to df.loc[:, 'time'] it does not say why we use loc,

Python: Pandas Series - Why use loc?

由 梦想的初衷 提交于 2019-11-26 05:19:02
问题 Why do we use \'loc\' for pandas dataframes? it seems the following code with or without using loc both compile anr run at a simulular speed %timeit df_user1 = df.loc[df.user_id==\'5561\'] 100 loops, best of 3: 11.9 ms per loop or %timeit df_user1_noloc = df[df.user_id==\'5561\'] 100 loops, best of 3: 12 ms per loop So why use loc? Edit: This has been flagged as a duplicate question. But although pandas iloc vs ix vs loc explanation? does mention that * you can do column retrieval just by
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