reshape

问题 Assume this simplified example: L <- list() L$Foo <- list() L$Foo$Bar <- list() L$Foo$Bar$VAR <- TRUE L$Lorem <- list() L$Lorem$Ipsum <- list() L$Lorem$Ipsum$Dolor <- list() L$Lorem$Ipsum$Dolor$VAR <- TRUE I will then melt this list with reshape2::melt(L) . That will output the following: value L3 L2 L4 L1 1 TRUE VAR Bar <NA> Foo 2 TRUE Dolor Ipsum VAR Lorem After some operations on certain cells in the value column, I'm then looking to recast this melted list into the exact same nested list

问题 I have data in long format as seen below: Data: id code 1 EP 2 EP 3 EP 4 UM 5 UM 1 UM 2 UM 10 UM 6 BZ 7 BZ 14 BZ 2 BZ 8 TVOL 9 TVOL 16 TVOL 10 NW 11 NW 7 NW 12 SM 13 SM 3 SM 14 GS 15 GS 1 GS 2 GS 9 GS I would like to create a wide dataframe with each "code" as its own column marked TRUE/FALSE depending on whether there's an associated "id" as seen in the minimal example below: id code.EP code.UM code.BZ code.TVOL code.NW code.SM code.GS 1 TRUE TRUE FALSE FALSE FALSE FALSE TRUE 2 TRUE FALSE

问题 Let's say I have this array x: x = array([1, 2, 3, 4, 5, 6, 7, 8]) x.shape = (8,1) I want to reshape it to become array([[1, 3, 5, 7], [2, 4, 6, 8]]) this is a reshape(2, 4) on x but in the straight forward way: y = x.reshape(2,4) y becomes array([[1, 2, 3, 4], [5, 6, 7, 8]]) and that's not what I want. Is there a way to transform the array in that specific way? 回答1: In[4]: x.reshape(4, 2).T Out[4]: array([[1, 3, 5, 7], [2, 4, 6, 8]]) 回答2: The easiest way to do this is to specify the order

问题 I am reshaping a 1D array into 3D using the following. It works fine but it throws an error when x is 7267. I understand that it is not possible to slice an odd number as an int without losing some values. Would appreciate any solution to this. code x = 7248 y= 24 A = np.arange(x) A.reshape(int(x/y),y,1).transpose() output array([[[ 0, 24, 48, ..., 7176, 7200, 7224], [ 1, 25, 49, ..., 7177, 7201, 7225], [ 2, 26, 50, ..., 7178, 7202, 7226], ..., [ 21, 45, 69, ..., 7197, 7221, 7245], [ 22, 46,

问题 For a research project, I need to process every individual's information from the website into an excel file. I have copied and pasted everything I need from the website onto a single column in an excel file, and I loaded that file using PANDAS. However, I need to present each individual's information horizontally instead of vertically like it is now. For example, this is what I have right now. I only have one column of unorganized data. df= pd.read_csv("ior work.csv", encoding = "ISO-8859-1"

问题 For a research project, I need to process every individual's information from the website into an excel file. I have copied and pasted everything I need from the website onto a single column in an excel file, and I loaded that file using PANDAS. However, I need to present each individual's information horizontally instead of vertically like it is now. For example, this is what I have right now. I only have one column of unorganized data. df= pd.read_csv("ior work.csv", encoding = "ISO-8859-1"

问题 I have this badly organized data table given to me, in which there are hundreds of columns (subset is given below) Names of columns are dot delimited where the first field holds information about a type of object (e.g. Item123, object_AB etc.) without any naming convention. There is no specific order for these columns as well. Other columns share the type of object field and also have the name of some property for that object (e.g. color, manufacturer etc.). Item123.type.value Item123.mass

问题 For a 2D array like this: table = np.array([[11,12,13],[21,22,23],[31,32,33],[41,42,43]]) Is it possible to use np.reshape on table to get an array single_column where each column of table is stacked vertically? This can be accomplished by splitting table and combining with vstack . single_column = np.vstack(np.hsplit(table , table .shape[1])) Reshape can combine all the rows into a single row, I'm wondering if it can combine the columns as well to make the code cleaner and possibly faster.

问题 For a 2D array like this: table = np.array([[11,12,13],[21,22,23],[31,32,33],[41,42,43]]) Is it possible to use np.reshape on table to get an array single_column where each column of table is stacked vertically? This can be accomplished by splitting table and combining with vstack . single_column = np.vstack(np.hsplit(table , table .shape[1])) Reshape can combine all the rows into a single row, I'm wondering if it can combine the columns as well to make the code cleaner and possibly faster.

问题 I have an issue in reshaping a pandas DatFrame. It looks like this (the numbers of lines and columns can vary) : columns col1 col2 col3 col4 Species sp1 218.000000 521.000000 533.000000 793.000000 sp1 0.105569 0.252300 0.258111 0.384019 sp1 2 2 2 3 sp2 225.000000 521.000000 540.000000 800.000000 sp2 0.107862 0.249760 0.258869 0.383509 sp2 2 2 2 3 sp3 217.000000 477.000000 512.000000 725.000000 sp3 0.112377 0.247022 0.265148 0.375453 sp3 1 1 3 3 The column Species is my index. I want to