I am trying to save a huge pandas dataframe into dropbox without success.
For the moment my code looks as follows:
import dropbox
import csv
dbx = dropbox.Dropbox("<access_token>")
import dask.dataframe as dd
dask_merge_bodytextknown5 = dd.from_pandas(merge_bodytextknown5, npartitions=10)
#I THINK THIS IS THE PROBLEMATIC LINE:
dbx.files_upload(dask_merge_bodytextknown5.to_csv(index=False, single_file=True).encode(), "/df_compl_emakg.csv")
Could you please help me with this?
Furthermore, I would like to reduce the size of the pandas df and was thinking about downcasting the strings. In details, as you can see I have a lot of strings that are encoded as "objects" by pandas:
...
oecd_field object
oecd_subfield object
wosfield object
author float32
entity_id float32
affiliation2 float32
class object
foaf_name object
foundation_date float32
type_entities object
acronym object
pos#lat float32
pos#long float32
city_name object
city_lat float32
city_lon float32
state_name object
postcode object
country_name object
country_alpha2 object
country_alpha3 object
country_official_name object
...
I was wondering if something like:
df["col"] = df["col"].astype("|S")
for each of the object column might reduce the memory usage of the database.
Thank you
Related
I have gone days trying to figure this out, hopefully someone can help.
I am uploading a .mat file into python using scipy.io, placing the struct into a dataframe, which will then be used in Tensorflow.
from scipy.io import loadmat
import pandas as pd
import numpy as p
import matplotlib.pyplot as plt
#import TF
path = '/home/anthony/PycharmProjects/Deep_Learning_MATLAB/circuit-data/for tinghao/template1-lib5-eqns-CR-RESULTS-SET1-FINAL.mat'
raw_data = loadmat(path, squeeze_me=True)
data = raw_data['Graphs']
df = pd.DataFrame(data, dtype=int)
df.pop('transferFunc')
print(df.dtypes)
The out put is:
A object
Ln object
types object
nz int64
np int64
dtype: object
Process finished with exit code 0
The struct is (43249x6). Each cell in the 'A' column is a different sized matrix, i.e. 18x18, or 16x16 etc. Each cell in "Ln" is a row of letters each in their own separate cell. Each cell in 'Types' contains 12 columns of numbers, and 'nz' and 'np' i have no issues with.
I want to put all columns into a dataframe, and use column A or LN or Types as the 'Labels' and nz and np as 'features', again i do not have issues with the latter. Can anyone help with this or have some kind of work around.
The end goal is to have tensorflow train on nz and np and give me either a matrix, Ln, or Type.
What type of data is your .mat file of ? Is your application very time critical?
If you can collect all your data in a struct you could give jsonencode a try, make the struct a json file and load it back into python via json (see json documentation on loading data).
Then you can create a pandas dataframe via
pd.df.from_dict()
Of course this would only be a workaround. Still you would have to ensure your data in the MATLAB struct is correctly orderer to be then imported and transferred to a df.
raw_data = loadmat(path, squeeze_me=True)
data = raw_data['Graphs']
graph_labels = pd.DataFrame()
graph_labels['perf'] = raw_data['Objective'][0:1000]
graph_labels['np'] = data['np'][0:1000]
The code above helped out. Its very simple and drawn out, but it got the job done. But, it does not work in tensorflow because tensorflow does not accept this format, and that was my main issue. I have to convert adjacency matrices to networkx graphs, then upload them into stellargraph.
I have a big data dataframe and I want to write it to disk for quick retrieval. I believe to_hdf(...) infers the data type of the columns and sometimes gets it wrong. I wonder what the correct way is to cope with this.
import pandas as pd
import numpy as np
length = 10
df = pd.DataFrame({"a": np.random.randint(1e7, 1e8, length),})
# df.loc[1, "a"] = "abc"
# df["a"] = df["a"].astype(str)
print(df.dtypes)
df.to_hdf("df.hdf5", key="data", format="table")
Uncommenting various lines leads me to the following.
Just filling the column with numbers will lead to a data type int32 and stores without problem
Setting one element to abc changes the data to object, but it seems that to_hdf internally infers another data type and throws an error: TypeError: object of type 'int' has no len()
Explicitely converting the column to str leads to success, and to_hdf stores the data.
Now I am wondering what is happening in the second case, and is there a way to prevent this? The only way I found was to go through all columns, check if they are dtype('O') and explicitely convert them to str.
Instead of using hdf5, I have found a generic pickling library which seems to be perfect for the job: jiblib
Storing and loading data is straight forward:
import joblib
joblib.dump(df, "file.jl")
df2 = joblib.load("file.jl")
Well, it seems like a couple of similar questions were asked here in stack overflow, but none of them seem like answered correctly or properly, nor they described the exact examples.
I have a problem with saving array or list into hdf5 ...
I have a several files contains list of (n, 35) dimensions, where n may be different in each file. Each of them can be saved in hdf5 with code below.
hdf = hf.create_dataset(fname, data=d)
However, if I want to merge them to make in 3d the error occurs as below.
Object dtype dtype('O') has no native HDF5 equivalent
I have no idea why it turns to dtype object, since what I have done is only this
all_data = list()
for fname in file_list:
d = np.load(fname)
all_data.append(d)
hdf = hf.create_dataset('all_data', data=all_data)
How can I save such data?
I tried a couple of tests, and it seems like all_data turns to dtype with 'object' when I change them with
all_data = np.array(all_data)
Which looks it has the similar problem with saving hdf5.
Again, how can I save such data in hdf5?
I was running into a similar issue with h5py, and changing the type of the NumPy array using array.astype worked for me (I believe this changes the type from dtype('O') to the data type you specify). Please see the code snippet below:
import numpy as np
print(X.dtype)
--> dtype('O')
print(X.astype(np.float64).dtype)
--> dtype('float64')
When I ran h5.create_dataset with this data type conversion, I was able to successfully create a h5 dataset. Hope this helps!
ONE ADDITIONAL UPDATE: I believe the NumPy object type 'O' is created when the NumPy array itself has mixed element types (e.g. np.int8 and np.float32).
dtype('O') stands for object. In my case I had a list of lists where the lengths were different and got the same error. If you convert it to a numpy array numpy warns Creating an ndarray from ragged nested sequences. h5 files can't handle this type of data for more info see this post
This error comes when I use:
with h5py.File(peakfilename, 'w') as pfile: # saves the data
pfile['peakY'] = np.array(X)
pfile['peakX'] = np.array(Y)
However when I used dtype when saving the arrays... the problem went away... I guess h5py is not able to create datasets from undefined data types.
with h5py.File(peakfilename, 'w') as pfile: # saves the data
pfile['peakY'] = np.array(X, dtype=np.float32)
pfile['peakX'] = np.array(Y, dtype=np.float32)
I would like to store a image represented as a numpy array in a Pyspark data frame.
When I try the I get an error data type not supported.
looking at the data types supported in Pyspark I don't see numpy, wondering if there's a way to store array.
I also tried numpy as string but the string for some reason is truncated contains ...
Any suggestions or solutions?
I am trying to create a 78TB HDF5 dataset by filling it in a 2d block-partition manner. This is very slow when the block I'm writing spans rows that haven't ever been written to, because HDF5 is going in and allocating the diskspace and filling in the missing entries with zero.
Instead, I would like h5py to allocate the disk space for my dataset as soon as its created, and never fill it. This is possible with the C api according to Table 16 in the HDF5 Dataset documentation, but how can I do this with h5py, preferably with the high level interface?
I believe that you want to set the fill time to "never", with the H5Pset_fill_time() routine, but I don't know the h5py way to do that.
As Quincey suggested. You can use the low-level H5py API to create the dataset with the FILL_TIME_NEVER property then convert it back to a high-level Dataset object:
# create the rows dataset using the low-level api so I can force it to not do zero-filling, then convert to a high level object
spaceid = h5py.h5s.create_simple((numRows, numCols))
plist = h5py.h5p.create(h5py.h5p.DATASET_CREATE)
plist.set_fill_time(h5py.h5d.FILL_TIME_NEVER)
plist.set_chunk((rowchunk, colchunk))
datasetid = h5py.h5d.create(fout.id, "rows", h5py.h5t.NATIVE_DOUBLE, spaceid, plist)
rows = h5py.Dataset(datasetid)
Try specifying a chunk shape that matches your write pattern. For example if you are writing in blocks of 1024x1024, it would look like this:
import h5py
import numpy as np
f = h5py.File('mybigdset.h5', 'w')
dset = f.create_dataset('dset', (78*1024*1024, 1024*1024), dtype='f4', chunks=(1024,1024))
arr = np.random.rand(1024,1024)
dset[0:1024, 0:1024] = arr
f.close()
Thankfully, this didn't use 78TB of disk, the file size was just 4MB.