hdf5

问题 My issue is that when I try to look for a string that is NOT contained in the DataFrame (which is stored in an hdf5 file), it takes a very long time to complete the query. For example: I have a df that contains 2*10^9 rows. It is stored in an HDF5 file. I have a string column named "code", that was marked as "data_column" (therefore it is indexed). When I search for a code that exists in the dataset ( store.select('df', 'code=valid_code') ) it takes around 10 seconds to get 70K rows. However,

问题 Perhaps this question has been asked before, but I'm having trouble finding relevant info for my situation. I'm using PyTorch to create a CNN for regression with image data. I don't have a formal, academic programming background, so many of my approaches are ad-hoc and just terribly inefficient. May times I can go back through my code and clean things up later because the inefficiency is not so drastic that performance is significantly affected. However, in this case, my method for using the

问题 I am hoping for some help in opening a corrupted HDF5 file. I am accessing PyTables via Pandas , but a pd.read_hdf() call produces the following error. I don't know anything about the inner workings of PyTables . I believe the error was created because the process saving to the file (appending every 10 seconds or so) got duplicated, so there were then 2 identical processes appending. I am not sure why this would corrupt the file rather than duplicate data, but the two errors occurred together

问题 I want to use HDF5 file to input my data and labels in my CNN. I created the hdf5 file with matlab. Here is my code: h5create(['uNetDataSet.h5'],'/home/alexandra/Documents/my-u-net/warwick_dataset/Warwick_Dataset/train/image',[522 775 3 numFrames]); h5create(['uNetDataSet.h5'],'/home/alexandra/Documents/my-u-net/warwick_dataset/Warwick_Dataset/train/anno',[522 775 3 numFrames]); h5create(['uNetDataSet.h5'],'/home/alexandra/Documents/my-u-net/warwick_dataset/Warwick_Dataset/label',[1 numFrames

问题 I just came across this issue when adding a multi-index to my pandas dataframe. I am using the pandas HDFStore with the option format='table' , which I prefer because the saved data frame is easier to understand and load when not using pandas. (For details see this SO answer: Save pandas DataFrame using h5py for interoperabilty with other hdf5 readers .) But I ran into a problem because I was setting the multi-index using drop=False when calling set_index, which keeps the index columns as

问题 Situation: I want to create a program to read something from a .hdf5 file. What i did: Nothing, but adding the hdf5.lib to the project. Problem: I get two Errors when i try to run the program. cannot find -lhdf5d error: ld returned 1 exit status My Code: HDF5_Test.pro : TEMPLATE = app CONFIG += console c++11 CONFIG -= app_bundle CONFIG -= qt SOURCES += \ main.cpp win32:CONFIG(release, debug|release): LIBS += -L'C:/Program Files/HDF_Group/HDF5/1.10.2/lib/' -lhdf5 else:win32:CONFIG(debug, debug

问题 I'm just getting started with HDF5 and would appreciate some advice on the following. I have a 2-d array: data[][] passed into a method. The method looks like: void WriteData( int data[48][100], int sizes[48]) The size of the data is not actually 48 x 100 but rather 48 x sizes[i]. I.e. each row could be a different length! In one simple case I'm dealing with, all rows are the same size (but not 100), so you can say that the array is 48 X sizes[0]. How best to write this to HDF5? I have some

问题 I have an h5 file containing multiple groups and datasets. Each dataset has associated attributes. I want to find/filter the datasets in this h5 file based upon the respective attribute associated with it. Example: dataset1 =cloudy(attribute) dataset2 =rainy(attribute) dataset3 =cloudy(attribute) I want to find the datasets having weather attribute/metadata as cloudy What will be the simplest approach to get this done in pythonic way. 回答1: There are 2 ways to access HDF5 data with Python:

问题 I have a a number of large (13GB+ in size) h5 files, each h5 file has two datasets that were created with pandas: df.to_hdf('name_of_file_to_save', 'key_1',table=True) df.to_hdf('name_of_file_to_save', 'key_2', table=True) # saved to the same h5 file as above I've seen a post here: Concatenate two big pandas.HDFStore HDF5 files on using odo to concatenate h5 files. What I want to do is for each h5 file that was created, each having key_1 and key_2 , combine them so that all of the key_1 data

问题 How to open a HDF5 file with pandas.read_hdf when the keys are not known? from pandas.io.pytables import read_hdf read_hdf(path_or_buf, key) pandas.__version__ == '0.14.1' Here the key parameter is not known. Thanks 回答1: Having never worked with hdf files before I was able to use the online docs to cook an example: In [59]: # create a temp df and store it df_tl = pd.DataFrame(dict(A=list(range(5)), B=list(range(5)))) df_tl.to_hdf('store_tl.h5','table',append=True) In [60]: # we can simply