Github Python
Lorraine Karmazyn
The problems I'm having are: (a) how can I make scalable the way to extract all the .csv files? and (b) is there any way to extract ONLY the filenames that end with .csv from the github folder? In order to do (2) of above.
I am still trying to find a better solution but below is a workaround that I use with my code to pull from a github directory. Unfortunately, I still have not found a way to just get a list of CSVs in the github directoy like you can if it was on a local drive.
The data we will be using is taken from the gapminderdataset. To obtain it, download and unzip the file python-novice-gapminder-data.zip.In order to follow the presented material, you should launch theJupyterLab server in the root directory (see StartingJupyterLab).
Python.NET (pythonnet) is a package that gives Python programmersnearly seamless integration with .NET Framework, .NET Core and Monoruntime on Windows, Linux and macOS. Python.NETprovides a powerful application scripting tool for .NET developers.Using this package you can script .NET applications or build entireapplications in Python, using .NET services and components written inany language that targets the CLR (C#, VB.NET, F#, C++/CLI).
To create a netCDF file from python, you simply call the Datasetconstructor. This is also the method used to open an existing netCDFfile.If the file is open for write access (mode='w', 'r+' or 'a'), you maywrite any type of data including new dimensions, groups, variables andattributes.netCDF files come in five flavors (NETCDF3_CLASSIC,NETCDF3_64BIT_OFFSET, NETCDF3_64BIT_DATA, NETCDF4_CLASSIC, and NETCDF4).NETCDF3_CLASSIC was the original netcdf binary format, and was limitedto file sizes less than 2 Gb. NETCDF3_64BIT_OFFSET was introducedin version 3.6.0 of the library, and extended the original binary formatto allow for file sizes greater than 2 Gb.NETCDF3_64BIT_DATA is a new format that requires version 4.4.0 ofthe C library - it extends the NETCDF3_64BIT_OFFSET binary format toallow for unsigned/64 bit integer data types and 64-bit dimension sizes.NETCDF3_64BIT is an alias for NETCDF3_64BIT_OFFSET.NETCDF4_CLASSIC files use the version 4 disk format (HDF5), but omits featuresnot found in the version 3 API. They can be read by netCDF 3 clientsonly if they have been relinked against the netCDF 4 library. They canalso be read by HDF5 clients. NETCDF4 files use the version 4 diskformat (HDF5) and use the new features of the version 4 API.ThenetCDF4 module can read and write files in any of these formats. Whencreating a new file, the format may be specified using the formatkeyword in the Dataset constructor.The default format isNETCDF4. To see how a given file is formatted, you can examine thedata_model attribute.Closing the netCDF file isaccomplished via the Dataset.close() method of the Datasetinstance.
netCDF version 4 added support for organizing data in hierarchicalgroups, which are analogous to directories in a filesystem. Groups serveas containers for variables, dimensions and attributes, as well as othergroups.A Dataset creates a special group, calledthe 'root group', which is similar to the root directory in a unixfilesystem.To create Group instances, use theDataset.createGroup() method of a Dataset or Groupinstance. Dataset.createGroup() takes a single argument, apython string containing the name of the new group. The new Groupinstances contained within the root group can be accessed by name usingthe groups dictionary attribute of the Dataset instance.OnlyNETCDF4 formatted files support Groups, if you try to create a Groupin a netCDF 3 file you will get an error message.
Using the python len function with a Dimension instance returnscurrent size of that dimension.Dimension.isunlimited() method of a Dimension instancebe used to determine if the dimensions is unlimited, or appendable.
netCDF variables behave much like python multidimensional array objectssupplied by the numpy module. However,unlike numpy arrays, netCDF4 variables can be appended to along one ormore 'unlimited' dimensions. To create a netCDF variable, use theDataset.createVariable() method of a Dataset orGroup instance. The Dataset.createVariable()j methodhas two mandatory arguments, the variable name (a Python string), andthe variable datatype. The variable's dimensions are given by a tuplecontaining the dimension names (defined previously withDataset.createDimension()). To create a scalarvariable, simply leave out the dimensions keyword. The variableprimitive datatypes correspond to the dtype attribute of a numpy array.You can specify the datatype as a numpy dtype object, or anything thatcan be converted to a numpy dtype object.Valid datatype specifiersinclude: 'f4' (32-bit floating point), 'f8' (64-bit floatingpoint), 'i4' (32-bit signed integer), 'i2' (16-bit signedinteger), 'i8' (64-bit signed integer), 'i1' (8-bit signedinteger), 'u1' (8-bit unsigned integer), 'u2' (16-bit unsignedinteger), 'u4' (32-bit unsigned integer), 'u8' (64-bit unsignedinteger), or 'S1' (single-character string).The old Numericsingle-character typecodes ('f','d','h','s','b','B','c','i','l'), corresponding to('f4','f8','i2','i2','i1','i1','S1','i4','i4'),will also work. The unsigned integer types and the 64-bit integer typecan only be used if the file format is NETCDF4.
By default, netcdf4-python returns numpy masked arrays with values equal to themissing_value or _FillValue variable attributes masked for primitive andenum data types.The Dataset.set_auto_mask() Dataset and Variable methodscan be used to disable this feature so thatnumpy arrays are always returned, with the missing values included. Prior toversion 1.4.0 the default behavior was to only return masked arrays when therequested slice contained missing values.This behavior can be recoveredusing the Dataset.set_always_mask() method. If a masked array iswritten to a netCDF variable, the masked elements are filled with thevalue specified by the missing_value attribute.If the variable hasno missing_value, the _FillValue is used instead.
If you want to read data from a variable that spans multiple netCDF files,you can use the MFDataset class to read the data as if it werecontained in a single file. Instead of using a single filename to createa Dataset instance, create a MFDataset instance with either a listof filenames, or a string with a wildcard (which is then converted toa sorted list of files using the python glob module).Variables in the list of files that share the same unlimiteddimension are aggregated together, and can be sliced across multiplefiles.To illustrate this, let's first create a bunch of netCDF files withthe same variable (with the same unlimited dimension).The filesmust in be in NETCDF3_64BIT_OFFSET, NETCDF3_64BIT_DATA, NETCDF3_CLASSIC orNETCDF4_CLASSIC format (NETCDF4 formatted multi-filedatasets are not supported).
If your data only has a certain number of digits of precision (say forexample, it is temperature data that was measured with a precision of0.1 degrees), you can dramatically improve compression byquantizing (or truncating) the data. There are two methods supplied fordoing this.You can use the least_significant_digitkeyword argument to Dataset.createVariable() to specifythe power of ten of the smallest decimal place inthe data that is a reliable value. For example if the data has aprecision of 0.1, then setting least_significant_digit=1 will causedata the data to be quantized using numpy.around(scale*data)/scale, wherescale = 2**bits, and bits is determined so that a precision of 0.1 isretained (in this case bits=4).This is done at the python level and isnot a part of the underlying C library.Starting with netcdf-c version 4.9.0,a quantization capability is provided in the library.This can beused via the significant_digits Dataset.createVariable() kwarg (new inversion 1.6.0).The interpretation of significant_digits is different than least_signficant_digitin that it specifies the absolute number of significant digits independentof the magnitude of the variable (the floating point exponent).Either of these approaches makes the compression'lossy' instead of 'lossless', that is some precision in the data issacrificed for the sake of disk space.
Since there is no native vlen datatype in numpy, vlen arrays are representedin python as object arrays (arrays of dtype object). These are arrays whoseelements are Python object pointers, and can contain any type of python object.For this application, they must contain 1-D numpy arrays all of the same typebut of varying length.In this case, they contain 1-D numpy int32 arrays of random length between1 and 10.
Numpy object arrays containing python strings can also be written as vlenvariables,For vlen strings, you don't need to create a vlen data type.Instead, simply use the python str builtin (or a numpy string datatypewith fixed length greater than 1) when calling theDataset.createVariable() method.
Here's an example of using an Enum type to hold cloud type data.The base integer data type and a python dictionary describing the allowedvalues and their names are used to define an Enum data type usingDataset.createEnumType().
If MPI parallel enabled versions of netcdf and hdf5 or pnetcdf are detected,and mpi4py is installed, netcdf4-python willbe built with parallel IO capabilities enabled. Parallel IO of NETCDF4 orNETCDF4_CLASSIC formatted files is only available if the MPI parallel HDF5library is available. Parallel IO of classic netcdf-3 file formats is onlyavailable if the PnetCDF library isavailable. To use parallel IO, your program must be running in an MPIenvironment using mpi4py.
To run an MPI-based parallel program like this, you must use mpiexec to launch severalparallel instances of Python (for example, using mpiexec -np 4 python mpi_example.py).The parallel features of netcdf4-python are mostly transparent -when a new dataset is created or an existing dataset is opened,use the parallel keyword to enable parallel access.
The most flexible way to store arrays of strings is with theVariable-length (vlen) string data type. However, this requiresthe use of the NETCDF4 data model, and the vlen type does not map very wellnumpy arrays (you have to use numpy arrays of dtype=object, which are arrays ofarbitrary python objects). numpy does have a fixed-width string arraydata type, but unfortunately the netCDF data model does not.Instead fixed-width byte strings are typically stored as arrays of 8-bitcharacters.To perform the conversion to and from character arrays to fixed-width numpy string arrays, thefollowing convention is followed by the python interface.If the _Encoding special attribute is set for a character array(dtype S1) variable, the chartostring() utility function is used to convert the array ofcharacters to an array of strings with one less dimension (the last dimension isinterpreted as the length of each string) when reading the data. The characterset (usually ascii) is specified by the _Encoding attribute. If _Encodingis 'none' or 'bytes', then the character array is converted to a numpyfixed-width byte string array (dtype S#), otherwise a numpy unicode (dtypeU#) array is created.When writing the data,stringtochar() is used to convert the numpy string array to an array ofcharacters with one more dimension. For example,
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