I would like to upload csv as parquet file to S3 bucket. Below is the code snippet.
df = pd.read_csv('right_csv.csv')
csv_buffer = BytesIO()
df.to_parquet(csv_buffer, compression='gzip', engine='fastparquet')
csv_buffer.seek(0)
Above is giving me an error: TypeError: expected str, bytes or os.PathLike object, not _io.BytesIO
How to make it work?
As per the documentation, when fastparquet is used as the engine, io.BytesIO cannot be used. auto or pyarrow engine have to be used. Quoting from the documentation.
The engine fastparquet does not accept file-like objects.
Below code works without any issues.
import io
f = io.BytesIO()
df.to_parquet(f, compression='gzip', engine='pyarrow')
f.seek(0)
As mentioned in the other answer, this is not supported. One work around would be to save as parquet to a NamedTemporaryFile. Then copy the content to a BytesIO buffer:
import tempfile
with tempfile.NamedTemporaryFile() as tmp:
df.to_parquet(tmp.name, compression='gzip', engine='fastparquet')
with open(tmp.name, 'rb') as fh:
buf = io.BytesIO(fh.read())
Related
I am trying to save a Pandas DataFrame to HDFS in CSV format using pyarrow upload method, but the CSV file saved is empty. The code example can be found below.
import io
import pandas as pd
import pyarrow as pa
df = pd.DataFrame({"x": [1, 2, 3]})
buf = io.StringIO()
df.to_csv(buf)
hdfs = pa.hdfs.connect()
hdfs.upload("path/to/hdfs/test.csv", buf)
When I check the contents of test.csv on HDFS it is empty. What did I do wrong? Thanks.
You need to call buf.seek(0) before uploading.
Basically you need to rewind to the begining of the buffer otherwise hdfs thinks there's nothing to upload:
>>> buf.read()
''
>>> buf.seek(0)
0
>>> buf.read()
',x\n0,1\n1,2\n2,3\n'
>>> buf.read()
''
I want to load a pytorch model (model.pt) from a S3 bucket. I wrote the following code:
from smart_open import open as smart_open
import io
load_path = "s3://serial-no-images/yolo-models/model4/model.pt"
with smart_open(load_path) as f:
buffer = io.BytesIO(f.read())
model.load_state_dict(torch.load(buffer))
This results in the following error:
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x80 in position 64: invalid start byte
One solution would be to download the model locally, but I want to avoid this and load the model directly from S3. Unfortunately, I couldn't find a good solution for that online. Can someone help me out here?
According to the documentation, the following works:
from smart_open import open as smart_open
import io
load_path = "s3://serial-no-images/yolo-models/model4/model.pt"
with smart_open(load_path, 'rb') as f:
buffer = io.BytesIO(f.read())
model.load_state_dict(torch.load(buffer))
I have tried this before, but didn't see that I have to set 'rb' as argument.
I am trying the following:
import pandas as pd
loc = r'T:\Analysis\calibraer19.zip\col1\profiles\myfile.csv'
pd.read_csv(loc)
But I keep getting file not exists error. I am not sure how to read this file as the zip folder size is very large with 100s of files in it so unzipping is not a good option.
You can use the zipfile library to extract only the file you want to read:
import zipfile
with zipfile.ZipFile(r'T:\Analysis\calibraer19.zip') as z:
with open('myfile.csv', 'wb') as f:
f.write(z.read(r'col1\profiles\myfile.csv'))
df = pd.read_csv('myfile.csv')
You can try the following approach with zipfile module:
import zipfile
with zipfile.ZipFile("Desktop.zip") as z:
data = z.read("pandas_test_data.csv").decode("utf-8-sig")
lines = (elem for elem in data.split("\r\n"))
# lines = (elem for elem in data.split("\n")) if you're csv contains \n instead of \r\n
rows_of_data = (elem.split(",") for elem in lines)
df = pd.DataFrame(rows_of_data)
You read the data once and then simply create generators for subsequent steps. The generators can be consumed by the pandas DataFrame class's constructor.
Note: I added the decode("utf-8-sig") since i have encountered UTF-BOM characters when reading Zip Files.
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.