I created some data and stored it several times like this:
with open('filename', 'a') as f:
pickle.dump(data, f)
Every time the size of file increased, but when I open file
with open('filename', 'rb') as f:
x = pickle.load(f)
I can see only data from the last time.
How can I correctly read file?
Pickle serializes a single object at a time, and reads back a single object -
the pickled data is recorded in sequence on the file.
If you simply do pickle.load you should be reading the first object serialized into the file (not the last one as you've written).
After unserializing the first object, the file-pointer is at the beggining
of the next object - if you simply call pickle.load again, it will read that next object - do that until the end of the file.
objects = []
with (open("myfile", "rb")) as openfile:
while True:
try:
objects.append(pickle.load(openfile))
except EOFError:
break
There is a read_pickle function as part of pandas 0.22+
import pandas as pd
obj = pd.read_pickle(r'filepath')
The following is an example of how you might write and read a pickle file. Note that if you keep appending pickle data to the file, you will need to continue reading from the file until you find what you want or an exception is generated by reaching the end of the file. That is what the last function does.
import os
import pickle
PICKLE_FILE = 'pickle.dat'
def main():
# append data to the pickle file
add_to_pickle(PICKLE_FILE, 123)
add_to_pickle(PICKLE_FILE, 'Hello')
add_to_pickle(PICKLE_FILE, None)
add_to_pickle(PICKLE_FILE, b'World')
add_to_pickle(PICKLE_FILE, 456.789)
# load & show all stored objects
for item in read_from_pickle(PICKLE_FILE):
print(repr(item))
os.remove(PICKLE_FILE)
def add_to_pickle(path, item):
with open(path, 'ab') as file:
pickle.dump(item, file, pickle.HIGHEST_PROTOCOL)
def read_from_pickle(path):
with open(path, 'rb') as file:
try:
while True:
yield pickle.load(file)
except EOFError:
pass
if __name__ == '__main__':
main()
I developed a software tool that opens (most) Pickle files directly in your browser (nothing is transferred so it's 100% private):
https://pickleviewer.com/ (formerly)
Now it's hosted here: https://fire-6dcaa-273213.web.app/
Edit: Available here if you want to host it somewhere: https://github.com/ch-hristov/Pickle-viewer
Feel free to host this somewhere.
I have a .blf file, I have to convert that to a .asc file so that my ASCREADER is able to read the data.
from can.io.blf import BLFReader
blf_file = "/home/ranjeet/Downloads/CAN/BLF_Files/input.blf"
with BLFReader(blf_file) as can_log:
for msg in can_log:
print(msg)
I've tried this so far.
Able to read BLF File, need to write data as per .asc file
Very similar to my other answer you should read your blf file in binary mode then write the messages in the asc one:
import can
with open(blf_file, 'rb') as f_in:
log_in = can.io.BLFReader(f_in)
with open("file_out.asc", 'w') as f_out:
log_out = can.io.ASCWriter(f_out)
for msg in log_in:
log_out.on_message_received(msg)
log_out.stop()
I have managed to get my first python script to work which downloads a list of .ZIP files from a URL and then proceeds to extract the ZIP files and writes them to disk.
I am now at a loss to achieve the next step.
My primary goal is to download and extract the zip file and pass the contents (CSV data) via a TCP stream. I would prefer not to actually write any of the zip or extracted files to disk if I could get away with it.
Here is my current script which works but unfortunately has to write the files to disk.
import urllib, urllister
import zipfile
import urllib2
import os
import time
import pickle
# check for extraction directories existence
if not os.path.isdir('downloaded'):
os.makedirs('downloaded')
if not os.path.isdir('extracted'):
os.makedirs('extracted')
# open logfile for downloaded data and save to local variable
if os.path.isfile('downloaded.pickle'):
downloadedLog = pickle.load(open('downloaded.pickle'))
else:
downloadedLog = {'key':'value'}
# remove entries older than 5 days (to maintain speed)
# path of zip files
zipFileURL = "http://www.thewebserver.com/that/contains/a/directory/of/zip/files"
# retrieve list of URLs from the webservers
usock = urllib.urlopen(zipFileURL)
parser = urllister.URLLister()
parser.feed(usock.read())
usock.close()
parser.close()
# only parse urls
for url in parser.urls:
if "PUBLIC_P5MIN" in url:
# download the file
downloadURL = zipFileURL + url
outputFilename = "downloaded/" + url
# check if file already exists on disk
if url in downloadedLog or os.path.isfile(outputFilename):
print "Skipping " + downloadURL
continue
print "Downloading ",downloadURL
response = urllib2.urlopen(downloadURL)
zippedData = response.read()
# save data to disk
print "Saving to ",outputFilename
output = open(outputFilename,'wb')
output.write(zippedData)
output.close()
# extract the data
zfobj = zipfile.ZipFile(outputFilename)
for name in zfobj.namelist():
uncompressed = zfobj.read(name)
# save uncompressed data to disk
outputFilename = "extracted/" + name
print "Saving extracted file to ",outputFilename
output = open(outputFilename,'wb')
output.write(uncompressed)
output.close()
# send data via tcp stream
# file successfully downloaded and extracted store into local log and filesystem log
downloadedLog[url] = time.time();
pickle.dump(downloadedLog, open('downloaded.pickle', "wb" ))
Below is a code snippet I used to fetch zipped csv file, please have a look:
Python 2:
from StringIO import StringIO
from zipfile import ZipFile
from urllib import urlopen
resp = urlopen("http://www.test.com/file.zip")
myzip = ZipFile(StringIO(resp.read()))
for line in myzip.open(file).readlines():
print line
Python 3:
from io import BytesIO
from zipfile import ZipFile
from urllib.request import urlopen
# or: requests.get(url).content
resp = urlopen("http://www.test.com/file.zip")
myzip = ZipFile(BytesIO(resp.read()))
for line in myzip.open(file).readlines():
print(line.decode('utf-8'))
Here file is a string. To get the actual string that you want to pass, you can use zipfile.namelist(). For instance,
resp = urlopen('http://mlg.ucd.ie/files/datasets/bbc.zip')
myzip = ZipFile(BytesIO(resp.read()))
myzip.namelist()
# ['bbc.classes', 'bbc.docs', 'bbc.mtx', 'bbc.terms']
My suggestion would be to use a StringIO object. They emulate files, but reside in memory. So you could do something like this:
# get_zip_data() gets a zip archive containing 'foo.txt', reading 'hey, foo'
import zipfile
from StringIO import StringIO
zipdata = StringIO()
zipdata.write(get_zip_data())
myzipfile = zipfile.ZipFile(zipdata)
foofile = myzipfile.open('foo.txt')
print foofile.read()
# output: "hey, foo"
Or more simply (apologies to Vishal):
myzipfile = zipfile.ZipFile(StringIO(get_zip_data()))
for name in myzipfile.namelist():
[ ... ]
In Python 3 use BytesIO instead of StringIO:
import zipfile
from io import BytesIO
filebytes = BytesIO(get_zip_data())
myzipfile = zipfile.ZipFile(filebytes)
for name in myzipfile.namelist():
[ ... ]
I'd like to offer an updated Python 3 version of Vishal's excellent answer, which was using Python 2, along with some explanation of the adaptations / changes, which may have been already mentioned.
from io import BytesIO
from zipfile import ZipFile
import urllib.request
url = urllib.request.urlopen("http://www.unece.org/fileadmin/DAM/cefact/locode/loc162txt.zip")
with ZipFile(BytesIO(url.read())) as my_zip_file:
for contained_file in my_zip_file.namelist():
# with open(("unzipped_and_read_" + contained_file + ".file"), "wb") as output:
for line in my_zip_file.open(contained_file).readlines():
print(line)
# output.write(line)
Necessary changes:
There's no StringIO module in Python 3 (it's been moved to io.StringIO). Instead, I use io.BytesIO]2, because we will be handling a bytestream -- Docs, also this thread.
urlopen:
"The legacy urllib.urlopen function from Python 2.6 and earlier has been discontinued; urllib.request.urlopen() corresponds to the old urllib2.urlopen.", Docs and this thread.
Note:
In Python 3, the printed output lines will look like so: b'some text'. This is expected, as they aren't strings - remember, we're reading a bytestream. Have a look at Dan04's excellent answer.
A few minor changes I made:
I use with ... as instead of zipfile = ... according to the Docs.
The script now uses .namelist() to cycle through all the files in the zip and print their contents.
I moved the creation of the ZipFile object into the with statement, although I'm not sure if that's better.
I added (and commented out) an option to write the bytestream to file (per file in the zip), in response to NumenorForLife's comment; it adds "unzipped_and_read_" to the beginning of the filename and a ".file" extension (I prefer not to use ".txt" for files with bytestrings). The indenting of the code will, of course, need to be adjusted if you want to use it.
Need to be careful here -- because we have a byte string, we use binary mode, so "wb"; I have a feeling that writing binary opens a can of worms anyway...
I am using an example file, the UN/LOCODE text archive:
What I didn't do:
NumenorForLife asked about saving the zip to disk. I'm not sure what he meant by it -- downloading the zip file? That's a different task; see Oleh Prypin's excellent answer.
Here's a way:
import urllib.request
import shutil
with urllib.request.urlopen("http://www.unece.org/fileadmin/DAM/cefact/locode/2015-2_UNLOCODE_SecretariatNotes.pdf") as response, open("downloaded_file.pdf", 'w') as out_file:
shutil.copyfileobj(response, out_file)
I'd like to add my Python3 answer for completeness:
from io import BytesIO
from zipfile import ZipFile
import requests
def get_zip(file_url):
url = requests.get(file_url)
zipfile = ZipFile(BytesIO(url.content))
files = [zipfile.open(file_name) for file_name in zipfile.namelist()]
return files.pop() if len(files) == 1 else files
write to a temporary file which resides in RAM
it turns out the tempfile module ( http://docs.python.org/library/tempfile.html ) has just the thing:
tempfile.SpooledTemporaryFile([max_size=0[,
mode='w+b'[, bufsize=-1[, suffix=''[,
prefix='tmp'[, dir=None]]]]]])
This
function operates exactly as
TemporaryFile() does, except that data
is spooled in memory until the file
size exceeds max_size, or until the
file’s fileno() method is called, at
which point the contents are written
to disk and operation proceeds as with
TemporaryFile().
The resulting file has one additional
method, rollover(), which causes the
file to roll over to an on-disk file
regardless of its size.
The returned object is a file-like
object whose _file attribute is either
a StringIO object or a true file
object, depending on whether
rollover() has been called. This
file-like object can be used in a with
statement, just like a normal file.
New in version 2.6.
or if you're lazy and you have a tmpfs-mounted /tmp on Linux, you can just make a file there, but you have to delete it yourself and deal with naming
Adding on to the other answers using requests:
# download from web
import requests
url = 'http://mlg.ucd.ie/files/datasets/bbc.zip'
content = requests.get(url)
# unzip the content
from io import BytesIO
from zipfile import ZipFile
f = ZipFile(BytesIO(content.content))
print(f.namelist())
# outputs ['bbc.classes', 'bbc.docs', 'bbc.mtx', 'bbc.terms']
Use help(f) to get more functions details for e.g. extractall() which extracts the contents in zip file which later can be used with with open.
All of these answers appear too bulky and long. Use requests to shorten the code, e.g.:
import requests, zipfile, io
r = requests.get(zip_file_url)
z = zipfile.ZipFile(io.BytesIO(r.content))
z.extractall("/path/to/directory")
Vishal's example, however great, confuses when it comes to the file name, and I do not see the merit of redefing 'zipfile'.
Here is my example that downloads a zip that contains some files, one of which is a csv file that I subsequently read into a pandas DataFrame:
from StringIO import StringIO
from zipfile import ZipFile
from urllib import urlopen
import pandas
url = urlopen("https://www.federalreserve.gov/apps/mdrm/pdf/MDRM.zip")
zf = ZipFile(StringIO(url.read()))
for item in zf.namelist():
print("File in zip: "+ item)
# find the first matching csv file in the zip:
match = [s for s in zf.namelist() if ".csv" in s][0]
# the first line of the file contains a string - that line shall de ignored, hence skiprows
df = pandas.read_csv(zf.open(match), low_memory=False, skiprows=[0])
(Note, I use Python 2.7.13)
This is the exact solution that worked for me. I just tweaked it a little bit for Python 3 version by removing StringIO and adding IO library
Python 3 Version
from io import BytesIO
from zipfile import ZipFile
import pandas
import requests
url = "https://www.nseindia.com/content/indices/mcwb_jun19.zip"
content = requests.get(url)
zf = ZipFile(BytesIO(content.content))
for item in zf.namelist():
print("File in zip: "+ item)
# find the first matching csv file in the zip:
match = [s for s in zf.namelist() if ".csv" in s][0]
# the first line of the file contains a string - that line shall de ignored, hence skiprows
df = pandas.read_csv(zf.open(match), low_memory=False, skiprows=[0])
It wasn't obvious in Vishal's answer what the file name was supposed to be in cases where there is no file on disk. I've modified his answer to work without modification for most needs.
from StringIO import StringIO
from zipfile import ZipFile
from urllib import urlopen
def unzip_string(zipped_string):
unzipped_string = ''
zipfile = ZipFile(StringIO(zipped_string))
for name in zipfile.namelist():
unzipped_string += zipfile.open(name).read()
return unzipped_string
Use the zipfile module. To extract a file from a URL, you'll need to wrap the result of a urlopen call in a BytesIO object. This is because the result of a web request returned by urlopen doesn't support seeking:
from urllib.request import urlopen
from io import BytesIO
from zipfile import ZipFile
zip_url = 'http://example.com/my_file.zip'
with urlopen(zip_url) as f:
with BytesIO(f.read()) as b, ZipFile(b) as myzipfile:
foofile = myzipfile.open('foo.txt')
print(foofile.read())
If you already have the file downloaded locally, you don't need BytesIO, just open it in binary mode and pass to ZipFile directly:
from zipfile import ZipFile
zip_filename = 'my_file.zip'
with open(zip_filename, 'rb') as f:
with ZipFile(f) as myzipfile:
foofile = myzipfile.open('foo.txt')
print(foofile.read().decode('utf-8'))
Again, note that you have to open the file in binary ('rb') mode, not as text or you'll get a zipfile.BadZipFile: File is not a zip file error.
It's good practice to use all these things as context managers with the with statement, so that they'll be closed properly.
I have a python code that takes multiple text files as input and generates output in separate CSV file so if my text files are ABC.txt and XYX.txt then my code is generating output in 2 CSV files ABC.csv and XYX.csv. My ultimate goal is get one single CSV file with all the outputs. Since I am more comfortable with sql I was thinking about uploading all the files to a database and then combine them using sql but I was wondering if I can modify my python code below to generate one single CSV file containing all output. Here is my code:
import json
from watson_developer_cloud import ToneAnalyzerV3Beta
import urllib.request
import codecs
import csv
import os
import re
import sys
import collections
import glob
import xlwt
from bs4 import BeautifulSoup
ipath = 'C:/TEMP/' # input folder
opath = 'C:/TEMP/' # output folder
reader = codecs.getreader("utf-8")
tone_analyzer = ToneAnalyzerV3Beta(
url='https://gateway.watsonplatform.net/tone-analyzer/api',
username='1f2fd51b-d0fb-45d8-aba2-08e22777b77d',
password='DykYfXjV4UXP',
version='2016-02-11')
path = 'C:/TEMP/*.html'
file = glob.glob(path)
# iterate over the list getting each file
writer = csv.writer(open('C:/TEMP/test', mode='w'))
# now enter our input loop
for fle in file:
# open the file and then call .read() to get the text
with open(fle) as f:
...
# output tone name and score to file
for i in tonename:
writer.writerows((tone['tone_name'],tone['score']) for tone in cat['tones'])
Modifying your existing code as little as possible ... you simply need to open the csv file before entering your loop that reads the text files:
...
path = 'C:/TEMP/*.html'
file = glob.glob(path)
# !! open our output csv
writer = csv.writer(open('our-merged-data', mode='w'))
# iterate over the list getting each file
for fle in file:
# open the file and then call .read() to get the text
with open(fle) as f:
...
# output tone name and score to file
for i in tonename:
writer.writerows((tone['tone_name'],tone['score'],Date,Title) for tone in cat['tones'])
Here is a test I created to recreate a problem I was having when I used
tempfile.NamedTemporaryFile(). The problem is that when I use tempfile the
data in my CSV is truncated off the end of the file.
When you run this test script, temp2.csv will get truncated and temp1.csv
will be the same size as the original CSV.
I'm using Python 2.7.1.
You can download the sample CSV from http://explore.data.gov/Energy-and-Utilities/Residential-Energy-Consumption-Survey-RECS-Files-A/eypy-jxs2
#!/usr/bin/env python
import tempfile
import shutil
def main():
f = open('RECS05alldata.csv')
data = f.read()
f.close()
f = open('temp1.csv', 'w+b')
f.write(data)
f.close()
temp = tempfile.NamedTemporaryFile()
temp.write(data)
shutil.copy(temp.name, 'temp2.csv')
temp.close()
if __name__ == '__main__':
main()
Add temp.flush() after temp.write(data).
You copy the file before you close it. Files are buffered, which means that some of it will remain in the buffer while it is waiting to be written to the file. The close will write out all remaining data from the buffer to the file as part of the closing of the file.
This has nothing to do with NamedTemporaryFile.
I think your problem is that Python has not flushed the entire file to disk when you call shutil.copy.
Change
temp = tempfile.NamedTemporaryFile()
temp.write(data)
shutil.copy(temp.name, 'temp2.csv')
temp.close()
to
temp = tempfile.NamedTemporaryFile()
temp.write(data)
temp.close()
shutil.copy(temp.name, 'temp2.csv')