I used to use requests lib to load single line via url:
import requests
def get_line(url):
resp = requests.get(url, stream=True)
for line in resp.iter_lines(decode_unicode=True):
yield line
line = get_line(url)
print(next(line))
A text files loading perfectly. But if I want to load .xlsx, result looks like unprintable symbols:
PK [symbols] [Content_Types].xml [symbols]
Is there a way to load single row of cells?
You can't just read raw HTTP response and seek for the particular Excel data. In order to get xlsx file contents in proper format you need to use an appropriate library.
One of the common libraries is xlrd, you can install it with pip:
sudo pip3 install xlrd
Example:
import requests
import xlrd
example_url = 'http://www.excel-easy.com/examples/excel-files/fibonacci-sequence.xlsx'
r = requests.get(example_url) # make an HTTP request
workbook = xlrd.open_workbook(file_contents=r.content) # open workbook
worksheet = workbook.sheet_by_index(0) # get first sheet
first_row = worksheet.row(0) # you can iterate over rows of a worksheet as well
print(first_row) # list of cells
xlrd documentation
If you want to be able to read your data line by line - you should switch to more simple data representation format, like .csv or simple text files.
Related
I am requesting a URL and getting a return in bytes. I want to store this in a data frame and then to CSV.
#Get Data from the CSV
url = "someURL"
req = requests.get(URL)
url_content = req.content
csv_file = open('test.txt', 'wb')
print(type(url_content))
print(url_content)
csv_file.write(url_content)
csv_file.close()
I tried many approaches, but couldn't find the solution. The above code is storing the output in CSV, but getting the below error. My end objective is to store this in CSV then send it to google cloud. And create a google big query table.
Output:
<class 'bytes'>
b'PK\x03\x04\x14\x00\x08\x08\x08\x009bwR\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x13\x00\x00\x00[Content_Types].xml\xb5S\xcbn\xc20\x10\xfc\x95\xc8\xd76\xf4PU\x15\x81C\x1f\xc7\x16\xa9\xf4\x03\{\x93X\xf8%\xaf\xa1\xf0\xf7]\x078\x94R\x89\nq\xf2cfgfW\xf6d\xb6q\xb6ZCB\x13|\xc3\xc6|\xc4\xf0h\xe3\xbb\x86},^\xea{Va\x96^K\x1b<4\xcc\x076\x9bN\x16\xdb\x08XQ\xa9\xc7\x86\xf59\xc7\x07!P\xf5\xe0$\xf2\x10\xc1\x13\xd2\x86\xe4d\xa6c\xeaD\x94j);\x10\xb7\xa3\xd1\x9dP\xc1g\xf0\xb9\xceE\x83M'O\xd0\xca\x95\xcd\xd5\xe3\xee\xbeH7L\xc6h\x8d\x92\x99R\x89\xb5\xd7G\xa2\xf5^\x90'\xb0\x03\x07{\x13\xf1\x86\x08\xacz\xde\x90\xca\xae\x1bB\x91\x893\x1c\x8e\x0b\xcb\x99\xea\xdeh.\xc9h\xf8W\xb4\xd0\xb6F\x81\x0ej\xe5\xa8\x84CQ\xd5\xa0\xeb\x98\x88\x98\xb2\x81}\xce\xb9L\xf9U:\x12\x14D\x9e\x13\x8a\x82\xa4\xf9%\xde\x87\xb1\xa8\x90\xe0,\xc3B\xbc\xc8\xf1\xa8[\x8c\t\xa4\xc6\x1e ;\xcb\xb1\x97\t\xf4{N\xf4\x98~\x87\xd8X\xf1\x83p\xc5\x1cykOL\xa1\x04\x18\x90kN\x80V\xee\xa4\xf1\xa7\xdc\xbfBZ~\x86\xb0\xbc\x9e\x7fq\x18\xf6\x7f\xd9\x0f \x8aa\x19\x1fr\x88\xe1{O\xbf\x01PK\x07\x08z\x94\xcaq;\x01\x00\x00\x1c\x04\x00\x00PK\x03\x04\x14\x00\x08\x08\x08\x009bwR\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x0b\x00\x00\x00_rels/.rels\xad\x92\xc1j\xc30\x0c\x86_\xc5\xe8\xde8\xed`\x8cQ\xb7\x972\xe8m\x8c\xee\x014[ILb\xcb\xd8\xda\x96\xbd\xfd\xcc.[K\n\x1b\xec($}\xff\x07\xd2v?\x87I\xbdQ.\x9e\xa3\x81u\xd3\x82\xa2h\xd9\xf9\xd8\x1bx>=\xac\xee#\x15\xc1\xe8p\xe2H\x06"\xc3~\xb7}\xa2\t\xa5n\x94\xc1\xa7\xa2"\x16\x03\x83H\xba\xd7\xba\xd8\x81\x02\x96\x86\x13\xc5\xda\xe98\x07\x94Z\xe6^'\xb4#\xf6\xa47m{\xab\xf3O\x06\x9c3\xd5\xd1\x19\xc8G\xb7\x06u\xc2\xdc\x93\x18\x98'\xfd\xcey|a\x1e\x9b\x8a\xad\x8d\x8fD\xbf\t\xe5\xae\xf3\x96\x0el_\x03EY\xc8\xbe\x98\x00\xbd\xec\xb2\xf9vql\x1f3\xd7ML\xe9\xbfeh\x16\x8a\x8e\xdc*\xd5\x04\xca\xe2\xa9\3\xbaY0\xb2\x9c\xe9oJ\xd7\x8f\xa2\x03\t:\x14\xfc\xa2^\x08\xe9\xb3\x1f\xd8}\x02PK\x07\x08\xa7\x8cz\xbd\xe3\x00\x00\x00I\x02\x00\x00PK\x03\x04\x14\x00\x08\x08\x08\x009bwR\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x10\x00\x00\x00docProps/app.xmlM\x8e\xc1\n\xc20\x10D\xef~E\xc8\xbd\xdd\xeaAD\xd2\x94\x82\x08\x9e\xecA? \xa4\xdb6\xd0lB\xb2J?
The original URL (now edited out of the question) suggests that the downloaded file is in .xlsx format. The .xlsx format is essentially one or more xml files in a zip archive (iBug's answer is correct in this respect).
Therefore if you want to get the file's data in a dataframe, tell Pandas to read it as an excel file.
import pandas as pd
url = "someURL"
req = requests.get(URL)
url_content = req.content
# Load into a dataframe
df = pd.read_excel(url_content)
# Write to csv
df.to_csv('data.csv')
The initial bytes PK\x03\x04 suggest that it's PK Zip format. Try unzipping it first, either with unzip x <filename> or with Python builtin zipfile module.
I am re-framing an existing question for simplicity. I have the following code to download Excel files from a company Share Point site.
import requests
import pandas as pd
def download_file(url):
filename = url.split('/')[-1]
r = requests.get(url)
with open(filename, 'wb') as output_file:
output_file.write(r.content)
df = pd.read_excel(r'O:\Procurement Planning\QA\VSAF_test_macro.xlsm')
df['Name'] = 'share_point_file_path_documentName' #i'm appending the sp file path to the document name
file = df['Name'] #I only need the file path column, I don't need the rest of the dataframe
# for loop for download
for url in file:
download_file(url)
The downloads happen and I don't get any errors in Python, however when I try to open them I get an error from Excel saying Excel cannot open the file because the file format or extension is not valid. If I print the link in Jupyter Notebooks it does open correctly, the issue appears to be with the download.
Check r.status_code. This must be 200 or you have the wrong url or no permission.
Open the downloaded file in a text editor. It might be a HTML file (Office Online)
If the URL contains a web=1 query parameter, remove it or replace it by web=0.
I have a web link which downloads an excel file directly. It opens a page writing "your file is downloading" and starts downloading the file.
Is there any way i can automate it using requests module ?
I am able to do it with selenium but i want it to run in background so i was wondering if i can use request module.
I have used request.get but it simply gives the text i.e "your file is downloading" but somehow i am not able to get the file.
This Python3 code downloads any file from web to a memory:
import requests
from io import BytesIO
url = 'your.link/path'
def get_file_data(url):
response = requests.get(url)
f = BytesIO()
for chunk in response.iter_content(chunk_size=1024):
f.write(chunk)
f.seek(0)
return f
data = get_file_data(url)
You can use next code to read the Excel file:
import pandas as pd
xlsx = pd.read_excel(data, skiprows=0)
print(xlsx)
It sounds like you don't actually have a direct URL to the file, and instead need to engage with some javascript. Perhaps there is an underlying network call that you can find by inspecting the page traffic in your browser that shows a direct URL for downloading the file. With that you can actually just read the excel file URL directly with pandas:
import pandas as pd
url = "https://example.com/some_file.xlsx"
df = pd.read_excel(url)
print(df)
This is nice and tidy, but if you really want to use requests (or avoid pandas) you can download the raw file content as shown in this answer and then use the pyexcel_xlsx package's get_xlsx function to read it without any pandas involvement.
I have this website that requires log in to access data.
import pandas as pd
import requests
r = requests.get(my_url, cookies=my_cookies) # my_cookies are imported from a selenium session.
df = pd.io.excel.read_excel(r.content, sheetname=0)
Reponse:
IOError: [Errno 2] No such file or directory: 'Ticker\tAction\tName\tShares\tPrice\...
Apparently, the str is processed as a filename. Is there a way to process it as a file? Alternatively can we pass cookies to pd.get_html?
EDIT: After further processing we can now see that this is actually a csv file. The content of the downloaded file is:
In [201]: r.content
Out [201]: 'Ticker\tAction\tName\tShares\tPrice\tCommission\tAmount\tTarget Weight\nBRSS\tSELL\tGlobal Brass and Copper Holdings Inc\t400.0\t17.85\t-1.00\t7,140\t0.00\nCOHU\tSELL\tCohu Inc\t700.0\t12.79\t-1.00\t8,953\t0.00\nUNTD\tBUY\tUnited Online Inc\t560.0\t15.15\t-1.00\t-8,484\t0.00\nFLXS\tBUY\tFlexsteel Industries Inc\t210.0\t40.31\t-1.00\t-8,465\t0.00\nUPRO\tCOVER\tProShares UltraPro S&P500\t17.0\t71.02\t-0.00\t-1,207\t0.00\n'
Notice that it is tab delimited. Still, trying:
# csv version 1
df = pd.read_csv(r.content)
# Returns error, file does not exist. Apparently read_csv() is also trying to read it as a file.
# csv version 2
fh = io.BytesIO(r.content)
df = pd.read_csv(fh) # ValueError: No columns to parse from file.
# csv version 3
s = StringIO(r.content)
df = pd.read_csv(s)
# No error, but the resulting df is not parsed properly; \t's show up in the text of the dataframe.
Simply wrap the file contents in a BytesIO:
with io.BytesIO(r.content) as fh:
df = pd.io.excel.read_excel(fh, sheetname=0)
This functionality was included in an update from 2014. According to the documentation it is as simple as providing the url:
The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. For instance, a local file could be file://localhost/path/to/workbook.xlsx
Based on the code you've provided, it looks like you are using pandas 0.13.x? If you can upgrade to a newer version (code below is tested with 0.16.x) you can get this to work without the additional utilization of the requests library. This was added in 0.14.1
data2 = pd.read_excel(data_url)
As an example of a full script (with the example XLS document taken from the original bug report stating the read_excel didn't accept a URL):
import pandas as pd
data_url = "http://www.eia.gov/dnav/pet/xls/PET_PRI_ALLMG_A_EPM0_PTC_DPGAL_M.xls"
data = pd.read_excel(data_url, "Data 1", skiprows=2)
I would like to automate the download of CSV files from the World Bank's dataset.
My problem is that the URL corresponding to a specific dataset does not lead directly to the desired CSV file but is instead a query to the World Bank's API. As an example, this is the URL to get the GDP per capita data: http://api.worldbank.org/v2/en/indicator/ny.gdp.pcap.cd?downloadformat=csv.
If you paste this URL in your browser, it will automatically start the download of the corresponding file. As a consequence, the code I usually use to collect and save CSV files in Python is not working in the present situation:
baseUrl = "http://api.worldbank.org/v2/en/indicator/ny.gdp.pcap.cd?downloadformat=csv"
remoteCSV = urllib2.urlopen("%s" %(baseUrl))
myData = csv.reader(remoteCSV)
How should I modify my code in order to download the file coming from the query to the API?
This will get the zip downloaded, open it and get you a csv object with whatever file you want.
import urllib2
import StringIO
from zipfile import ZipFile
import csv
baseUrl = "http://api.worldbank.org/v2/en/indicator/ny.gdp.pcap.cd?downloadformat=csv"
remoteCSV = urllib2.urlopen(baseUrl)
sio = StringIO.StringIO()
sio.write(remoteCSV.read())
# We create a StringIO object so that we can work on the results of the request (a string) as though it is a file.
z = ZipFile(sio, 'r')
# We now create a ZipFile object pointed to by 'z' and we can do a few things here:
print z.namelist()
# A list with the names of all the files in the zip you just downloaded
# We can use z.namelist()[1] to refer to 'ny.gdp.pcap.cd_Indicator_en_csv_v2.csv'
with z.open(z.namelist()[1]) as f:
# Opens the 2nd file in the zip
csvr = csv.reader(f)
for row in csvr:
print row
For more information see ZipFile Docs and StringIO Docs
import os
import urllib
import zipfile
from StringIO import StringIO
package = StringIO(urllib.urlopen("http://api.worldbank.org/v2/en/indicator/ny.gdp.pcap.cd?downloadformat=csv").read())
zip = zipfile.ZipFile(package, 'r')
pwd = os.path.abspath(os.curdir)
for filename in zip.namelist():
csv = os.path.join(pwd, filename)
with open(csv, 'w') as fp:
fp.write(zip.read(filename))
print filename, 'downloaded successfully'
From here you can use your approach to handle CSV files.
We have a script to automate access and data extraction for World Bank World Development Indicators like: https://data.worldbank.org/indicator/GC.DOD.TOTL.GD.ZS
The script does the following:
Downloading the metadata data
Extracting metadata and data
Converting to a Data Package
The script is python based and uses python 3.0. It has no dependencies outside of the standard library. Try it:
python scripts/get.py
python scripts/get.py https://data.worldbank.org/indicator/GC.DOD.TOTL.GD.ZS
You also can read our analysis about data from World Bank:
https://datahub.io/awesome/world-bank
Just a suggestion than a solution. You can use pd.read_csv to read any csv file directly from a URL.
import pandas as pd
data = pd.read_csv('http://url_to_the_csv_file')