numpy

问题 Trying to update each row from df1 to df2 if an unique value is matched. If not, append the row to df2 and assign new ID column. df1 ( NO ID COLUMN ): unique_value Status Price 0 xyz123 bad 6.67 1 eff987 bad 1.75 2 efg125 okay 5.77 df2: unique_value Status Price ID 0 xyz123 good 1.25 1000 1 xyz123 good 1.25 1000 2 xyz123 good 1.25 1000 3 xyz123 good 1.25 1000 4 xyz985 bad 1.31 1001 5 abc987 okay 4.56 1002 6 eff987 good 9.85 1003 7 asd541 excellent 8.85 1004 Desired output for updated df2:

问题 I recently encountered a thing which made me notice that numpy.var() and pandas.DataFrame.var() or pandas.Series.var() are giving different values. I want to know if there is any difference between them or not? Here is my dataset. Country GDP Area Continent 0 India 2.79 3.287 Asia 1 USA 20.54 9.840 North America 2 China 13.61 9.590 Asia Here is my code: from sklearn.preprocessing import StandardScaler ss = StandardScaler() catDf.iloc[:,1:-1] = ss.fit_transform(catDf.iloc[:,1:-1]) Now checking

问题 I am given 8000x3 data set similar to this one: import pandas as pd import numpy as np df = pd.DataFrame(np.random.rand(8000,3), columns=list('XYZ')) So for a visual reference, df.head(5) looks like this: X Y Z 0 0.462433 0.559442 0.016778 1 0.663771 0.092044 0.636519 2 0.111489 0.676621 0.839845 3 0.244361 0.599264 0.505175 4 0.115844 0.888622 0.766014 I'm trying to implement a method that when given an index from the dataset, it will return similar items from the dataset (in some reasonable

问题 I have a data set that has employees clocking in and out. It looks like this (note two entries per employee): Employee Date Time Emp1 1/1/16 06:00 Emp1 1/1/16 13:00 Emp2 1/1/16 09:00 Emp2 1/1/16 17:00 Emp3 1/1/16 11:00 Emp3 1/1/16 18:00 I want to get the data to look like this: Employee Date Start End Emp1 1/1/16 06:00 13:00 Emp2 1/1/16 09:00 17:00 Emp3 1/1/16 11:00 18:00 I would like to get it into a data frame format so that I can do some calculations. I currently have tried df['start'] =

问题 I have a data set that has employees clocking in and out. It looks like this (note two entries per employee): Employee Date Time Emp1 1/1/16 06:00 Emp1 1/1/16 13:00 Emp2 1/1/16 09:00 Emp2 1/1/16 17:00 Emp3 1/1/16 11:00 Emp3 1/1/16 18:00 I want to get the data to look like this: Employee Date Start End Emp1 1/1/16 06:00 13:00 Emp2 1/1/16 09:00 17:00 Emp3 1/1/16 11:00 18:00 I would like to get it into a data frame format so that I can do some calculations. I currently have tried df['start'] =

问题 I have a numpy array that has certain rows filled exclusively with "nan", i.e.: print(ar2[1560]) [ nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan

问题 When I show an array, the default __repr__() method for ndarray objects is too big for what I would like to do: a = np.eye(32) b = {'hello':42, 'array':a} b produces: {'array': array([[ 1., 0., 0., ..., 0., 0., 0.], [ 0., 1., 0., ..., 0., 0., 0.], [ 0., 0., 1., ..., 0., 0., 0.], ..., [ 0., 0., 0., ..., 1., 0., 0.], [ 0., 0., 0., ..., 0., 1., 0.], [ 0., 0., 0., ..., 0., 0., 1.]]), 'hello': 42} I tried an ugly solution, reassigning __repr__ : def wow(): return "wow!" a.__repr__ = wow which

问题 I would like to construct an array with the following structure: A[i,j,i,j,] = B[i,j] with all other entries 0: A[i,j,l,k]=0 # (i,j) =\= (l,k) I.e. if I have the B matrix constructed how can I create the matrix A , preferably in a vectorized manner. Explicitly, let B = [[1,2],[3,4]] Then: A[1,1,:,:]=[[1,0],[0,0]] A[1,2,:,:]=[[0,2],[0,0]] A[2,1,:,:]=[[0,0],[3,0]] A[2,2,:,:]=[[0,0],[0,4]] 回答1: We can use an open grid to assign to A broadcasting the indexing arrays across the axes: B = np.array(

问题 I would like to construct an array with the following structure: A[i,j,i,j,] = B[i,j] with all other entries 0: A[i,j,l,k]=0 # (i,j) =\= (l,k) I.e. if I have the B matrix constructed how can I create the matrix A , preferably in a vectorized manner. Explicitly, let B = [[1,2],[3,4]] Then: A[1,1,:,:]=[[1,0],[0,0]] A[1,2,:,:]=[[0,2],[0,0]] A[2,1,:,:]=[[0,0],[3,0]] A[2,2,:,:]=[[0,0],[0,4]] 回答1: We can use an open grid to assign to A broadcasting the indexing arrays across the axes: B = np.array(

问题 I am trying to extract features from .wav files by using MFCC's of the sound files. I am getting an error when I try to convert my list of MFCC's to a numpy array. I am quite sure that this error is occurring because the list contains MFCC values with different shapes (But am unsure of how to solve the issue). I have looked at 2 other stackoverflow posts, however these don't solve my problem because they are too specific to a certain task. ValueError: could not broadcast input array from