I have an Excel file uploaded to my ML workspace.
I can access the file as an azure FileDataset object. However, I don't know how to get it into a pandas DataFrame since 'FileDataset' object has no attribute 'to_dataframe'.
Azure ML notebooks seem to make a point of avoiding pandas for some reason.
Does anyone know how to get blob files into pandas dataframes from within Azure ML notebooks?
To explore and manipulate a dataset, it must first be downloaded from the blob source to a local file, which can then be loaded in a pandas DataFrame.
Here are the steps to follow for this procedure:
Download the data from Azure blob with the following Python code sample using Blob service. Replace the variable in the following code with your specific values:
from azure.storage.blob import BlobServiceClient
import pandas as pd
STORAGEACCOUNTURL= <storage_account_url>
STORAGEACCOUNTKEY= <storage_account_key>
LOCALFILENAME= <local_file_name>
CONTAINERNAME= <container_name>
BLOBNAME= <blob_name>
#download from blob
t1=time.time()
blob_service_client_instance =
BlobServiceClient(account_url=STORAGEACCOUNTURL,
credential=STORAGEACCOUNTKEY)
blob_client_instance =
blob_service_client_instance.get_blob_client(CONTAINERNAME, BLOBNAME,
snapshot=None)
with open(LOCALFILENAME, "wb") as my_blob:
blob_data = blob_client_instance.download_blob()
blob_data.readinto(my_blob)
t2=time.time()
print(("It takes %s seconds to download "+BLOBNAME) % (t2 - t1))
Read the data into a pandas DataFrame from the downloaded file.
#LOCALFILE is the file path
dataframe_blobdata = pd.read_csv(LOCALFILENAME)
For more details you can follow this link
Related
How can I change the dictionary to dataframe in colab?
I added two pictures. One from colab and the other from notebook.
https://i.stack.imgur.com/o9yMf.png
https://i.stack.imgur.com/DcY8T.png
Thanks!
Using your notebook you have read your data using Pandas library
data = pd.read_csv('data.csv')
And that's why you it was uploaded as dataframe. While the files.upload() funstion it uplodes your files as dictionary and you need to read it as dataframe. However, you just need to read your data again after it has been uploeded using
data = pd.read_csv('DailyDelhiClimateTest.csv.csv')
Best of luck :)
I've tried looking for this and haven't had any meaningful results.
I have a keras model that has multi input and my data was getting too large for my pandas approach so I preprocessed it and saved it parquet file. I'm not sure how to open it with keras.
I looked up tf.datasets but I still cannot figure out how to read a parquet file that I can pass to my model.
Does anyone know how to use open parquet files? I can't seem to figure out how to do this in tensorflow and can't find anything related to it in keras.
You can probably keep your pandas approach, but you would have to breakdown your data into chunks.
If you have already broken it down to create your parquet file, you should be able to use the same method to have only a subset of your data opened in pandas at a time.
If you need to extract the data from your parquet file here's a link on how to create chunks of data for a pandas dataframe:
How to read a CSV file subset by subset with Pandas?
Once you have a chunk of data you can call model.fit on that chunk of data and then go on to the next chunk and call model.fit
You can look into TensorFlow I/O which is a collection of file systems and file formats that are not available in TensorFlow's built-in support. Here you can find functionalities such tfio.IODataset.from_parquet, and also tfio.IOTensor.from_parquet to work with the parquet file formats.
!pip install tensorflow_io -U -q
import tensorflow_io as tfio
df = pd.DataFrame({"data": tf.random.normal([20], 0, 1, tf.float32),
"label": np.random.randint(2, size=(20))})
df.to_parquet("df.parquet")
pd.read_parquet('/content/df.parquet')[:2]
data label
0 0.721347 1
1 -1.215225 1
ds = tfio.IODataset.from_parquet('/content/df.parquet')
ds
FYI, I think you should also consider using the feather format rather than the parquet file format, AFAIK, the parquet file can be really heavy to load and can slow down your training pipelines, whereas feather is comparatively fast (very fast).
I am trying to read a bunch of CSV files from Google Cloud Storage into pandas dataframes as explained in Read csv from Google Cloud storage to pandas dataframe
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
blobs = bucket.list_blobs(prefix=prefix)
list_temp_raw = []
for file in blobs:
filename = file.name
temp = pd.read_csv('gs://'+bucket_name+'/'+filename+'.csv', encoding='utf-8')
list_temp_raw.append(temp)
df = pd.concat(list_temp_raw)
It shows the following error message while importing gcfs. The packages 'dask' and 'gcsfs' have already been installed on my machine; however, cannot get rid of the following error.
File "C:\Program Files\Anaconda3\lib\site-packages\gcsfs\dask_link.py", line
121, in register
dask.bytes.core._filesystems['gcs'] = DaskGCSFileSystem
AttributeError: module 'dask.bytes.core' has no attribute '_filesystems'
It seems there is some error or conflict between the gcsfs and dask packages. In fact, the dask library is not needed for your code to work. The minimal configuration for your code to run is to install the libraries ( I am posting its latest versions):
google-cloud-storage==1.14.0
gcsfs==0.2.1
pandas==0.24.1
Also, the filename already contains the .csv extension. So change the 9th line to this:
temp = pd.read_csv('gs://' + bucket_name + '/' + filename, encoding='utf-8')
With this changes I ran your code and it works. I suggest you to create a virtual env and install the libraries and run the code there:
This has been tested and seen to work from elsewhere - whether reading directly from GCS or via Dask. You may wish to try import of gcsfs and dask, see if you can see the _filesystems and see its contents
In [1]: import dask.bytes.core
In [2]: dask.bytes.core._filesystems
Out[2]: {'file': dask.bytes.local.LocalFileSystem}
In [3]: import gcsfs
In [4]: dask.bytes.core._filesystems
Out[4]:
{'file': dask.bytes.local.LocalFileSystem,
'gcs': gcsfs.dask_link.DaskGCSFileSystem,
'gs': gcsfs.dask_link.DaskGCSFileSystem}
As of https://github.com/dask/gcsfs/pull/129 , gcsfs behaves better if it is unable to register itself with Dask, so updating may solve your problem.
Few things to point out in the text above:
bucket_name and prefixes needed to be defined.
and the iteration over the filenames should append the each dataframe each time. Otherwise it is the last one that gets concatenated.
from google.cloud import storage
import pandas as pd
storage_client = storage.Client()
buckets_list = list(storage_client.list_buckets())
bucket_name='my_bucket'
bucket = storage_client.bucket(bucket_name)
blobs = bucket.list_blobs()
list_temp_raw = []
for file in blobs:
filename = file.name
temp = pd.read_csv('gs://'+bucket_name+'/'+filename, encoding='utf-8')
print(filename, temp.head())
list_temp_raw.append(temp)
df = pd.concat(list_temp_raw)
I am new to python and I have a scenario where there are multiple parquet files with file names in order. ex: par_file1,par_file2,par_file3 and so on upto 100 files in a folder.
I need to read these parquet files starting from file1 in order and write it to a singe csv file. After writing contents of file1, file2 contents should be appended to same csv without header. Note that all files have same column names and only data is split into multiple files.
I learnt to convert single parquet to csv file using pyarrow with the following code:
import pandas as pd
df = pd.read_parquet('par_file.parquet')
df.to_csv('csv_file.csv')
But I could'nt extend this to loop for multiple parquet files and append to single csv.
Is there a method in pandas to do this? or any other way to do this would be of great help. Thank you.
I ran into this question looking to see if pandas can natively read partitioned parquet datasets. I have to say that the current answer is unnecessarily verbose (making it difficult to parse). I also imagine that it's not particularly efficient to be constantly opening/closing file handles then scanning to the end of them depending on the size.
A better alternative would be to read all the parquet files into a single DataFrame, and write it once:
from pathlib import Path
import pandas as pd
data_dir = Path('dir/to/parquet/files')
full_df = pd.concat(
pd.read_parquet(parquet_file)
for parquet_file in data_dir.glob('*.parquet')
)
full_df.to_csv('csv_file.csv')
Alternatively, if you really want to just append to the file:
data_dir = Path('dir/to/parquet/files')
for i, parquet_path in enumerate(data_dir.glob('*.parquet')):
df = pd.read_parquet(parquet_path)
write_header = i == 0 # write header only on the 0th file
write_mode = 'w' if i == 0 else 'a' # 'write' mode for 0th file, 'append' otherwise
df.to_csv('csv_file.csv', mode=write_mode, header=write_header)
A final alternative for appending each file that opens the target CSV file in "a+" mode at the onset, keeping the file handle scanned to the end of the file for each write/append (I believe this works, but haven't actually tested it):
data_dir = Path('dir/to/parquet/files')
with open('csv_file.csv', "a+") as csv_handle:
for i, parquet_path in enumerate(data_dir.glob('*.parquet')):
df = pd.read_parquet(parquet_path)
write_header = i == 0 # write header only on the 0th file
df.to_csv(csv_handle, header=write_header)
I'm having a similar need and I read current Pandas version supports a directory path as argument for the read_csv function. So you can read multiple parquet files like this:
import pandas as pd
df = pd.read_parquet('path/to/the/parquet/files/directory')
It concats everything into a single dataframe so you can convert it to a csv right after:
df.to_csv('csv_file.csv')
Make sure you have the following dependencies according to the doc:
pyarrow
fastparquet
This helped me to load all parquet files into one data frame
import glob
files = glob.glob("*.snappy.parquet")
data = [pd.read_parquet(f,engine='fastparquet') for f in files]
merged_data = pd.concat(data,ignore_index=True)
If you are going to copy the files over to your local machine and run your code you could do something like this. The code below assumes that you are running your code in the same directory as the parquet files. It also assumes the naming of files as your provided above: "order. ex: par_file1,par_file2,par_file3 and so on upto 100 files in a folder." If you need to search for your files then you will need to get the file names using glob and explicitly provide the path where you want to save the csv: open(r'this\is\your\path\to\csv_file.csv', 'a') Hope this helps.
import pandas as pd
# Create an empty csv file and write the first parquet file with headers
with open('csv_file.csv','w') as csv_file:
print('Reading par_file1.parquet')
df = pd.read_parquet('par_file1.parquet')
df.to_csv(csv_file, index=False)
print('par_file1.parquet appended to csv_file.csv\n')
csv_file.close()
# create your file names and append to an empty list to look for in the current directory
files = []
for i in range(2,101):
files.append(f'par_file{i}.parquet')
# open files and append to csv_file.csv
for f in files:
print(f'Reading {f}')
df = pd.read_parquet(f)
with open('csv_file.csv','a') as file:
df.to_csv(file, header=False, index=False)
print(f'{f} appended to csv_file.csv\n')
You can remove the print statements if you want.
Tested in python 3.6 using pandas 0.23.3
a small change for those trying to read remote files, which helps to read it faster (direct read_parquet for remote files was doing this much slower for me):
import io
merged = []
# remote_reader = ... <- init some remote reader, for example AzureDLFileSystem()
for f in files:
with remote_reader.open(f, 'rb') as f_reader:
merged.append(remote_reader.read())
merged = pd.concat((pd.read_parquet(io.BytesIO(file_bytes)) for file_bytes in merged))
Adds a little temporary memory overhead though.
You can use Dask to read in the multiple Parquet files and write them to a single CSV.
Dask accepts an asterisk (*) as wildcard / glob character to match related filenames.
Make sure to set single_file to True and index to False when writing the CSV file.
import pandas as pd
import numpy as np
# create some dummy dataframes using np.random and write to separate parquet files
rng = np.random.default_rng()
for i in range(3):
df = pd.DataFrame(rng.integers(0, 100, size=(10, 4)), columns=list('ABCD'))
df.to_parquet(f"dummy_df_{i}.parquet")
# load multiple parquet files with Dask
import dask.dataframe as dd
ddf = dd.read_parquet('dummy_df_*.parquet', index=False)
# write to single csv
ddf.to_csv("dummy_df_all.csv",
single_file=True,
index=False
)
# test to verify
df_test = pd.read_csv("dummy_df_all.csv")
Using Dask for this means you won't have to worry about the resulting file size (Dask is a distributed computing framework that can handle anything you throw at it, while pandas might throw a MemoryError if the resulting DataFrame is too large) and you can easily read and write from cloud data storage like Amazon S3.
I have been trying to write a function that loads multiple files from a Google Cloud Storage bucket into a single Pandas Dataframe, however I cannot seem to make it work.
import pandas as pd
from google.datalab import storage
from io import BytesIO
def gcs_loader(bucket_name, prefix):
bucket = storage.Bucket(bucket_name)
df = pd.DataFrame()
for shard in bucket.objects(prefix=prefix):
fp = shard.uri
%gcs read -o $fp -v tmp
df.append(read_csv(BytesIO(tmp))
return df
When I try to run it says:
undefined variable referenced in command line: $fp
Sure, here's an example:
https://colab.research.google.com/notebook#fileId=0B7I8C_4vGdF6Ynl1X25iTHE4MGc
This notebook shows the following:
Create two random CSVs
Upload both CSV files to a GCS bucket
Uses the GCS Python API to iterate over files in the bucket. And,
Merge each file into a single Pandas DataFrame.