I will explain in detail:
I have an Excel file and my client is using one tool which reads .csv format files only.
Now I am opening the Excel file in Excel and saving into .CSV format by using Save As option in excel. let me take this is a File_1.
I wrote Python code by using pandas module and i converted that Excel file into csv. let me take this is as a File_2.
My client tool is able to read File_1 but not File_2. Why? What would be the problem?
My observations:
When I am reading File_1 in pandas (which is converted into .CSV manually) I had to mention --> encoding = "ISO-8859-1", otherwise it is giving Unicode error.
Ex: pd.read_csv("File_1.csv", encoding = 'ISO-8859-1")
But when I am reading File_2 in pandas, it simply reading and not giving any error.
Ex: pd.read_csv("File_2.csv")
So what would be the reason to not read File_2 by client tool? Is it Unicode problem or any other?
Related
I have an excel that is generated daily and can have up to 50k+ rows. Is there a way to read only the last row (which is the sum of the columns)?
right now I am just reading the entire sheet and keeping only the last row but it is taking up a huge amount of runtime.
my code:
df=pd.read_excel(filepath,header=1,usecols="O:AC")
df=df.tail(1)
Pandas is quite slow, especially with large in memory data. You can think about a lazy loading method, for example check dask.
Else you can read the file using "open" and read the last line :
with open(filepath, "r") as file:
last_line = file.readlines()[-1]
I dont think there is a way to decrease runtime when you read excel file.
When you read a excel or one sheet of excel,you would load excel all data into dask,even you use pd.read_excel skiprows,Its just keep the row the skiprows choose after you load all data into dask.So it cant decrease runtime.
If you really want decrease runtime of read file,you should save the file into another format,.csv or .txt and so on.
AND you generally you can't read Microsoft Excel files as a text files using methods like readlines or read. You should convert files to another format before (good solution is .csv which can be readed by csv module) or use a special python modules like pyexcel and openpyxl to read .xlsx files directly.
Problem Statement :
I have a directory with gzip files , and each gzip file contains a text file.
I have written a code in such a way that it unzips all the gzip files and then used to read each unzipped text file and then combined that output to one text file, then applied a condition , if that condition meets then it writes to excel.
The above process is bit tedious and lengthy.
Can anyone please help me out in writing the code where the data is read directly from the txt file which is gzipped and write it contents to excel.
IIUC you can use pandas using first read_csv:
df = read_csv('yourfile.gzip', compression='gzip')
then apply your conditions on df and write back the dataframe to excel using to_excel:
df.to_excel(file.xls)
I am trying to convert a CSV to an xlxs file format because I have a code that is meant to read a an excel file, but ended up getting a CSV. Is there a way to convert a CSV file to an TEMP excel file and have it not destroyed until the reading process is done. I have tried using openpyxl but it ends up not working and throwing an error saying it's not a good zip file. I even tried converting the CSV to text and then storing it in a dictionary but it writing to excel using xlrd pakage did not work aswell. I was wondering if there is a way do it in a cc
Seems like you open the file in text mode. Try this to open file
open('sample.csv', "rt", encoding="utf8")
or
open('sample.csv', "rt", encoding="ascii")
depending on the encoding of the file
I have 5 Excel files that have to be compiled into one csv file that can be uploaded to our website for our affiliated stores database. Until now we've had someone manually cut and paste the rows of each file into one master csv file in Excel then they upload that file to the website.
I've been trying to use Python to consolidate the files so the user would just have to run the Python script that would do this for her. The problem is that the Excel files are encoded in Shift-JIS and when I use CSV writer in Python they get converted to UTF-8. The website we upload them to will only accept files in Shift-JIS, so I have to keep all of this data in Shift-JIS.
Since DOS automatically defaults to ascii encoding, I first have to run this:
import codecs, sys, xlrd, csv
reload(sys)
sys.setdefaultencoding('shift_jis')
Here is a sample of the code for one of the Excel files, which has data on 2 separate worksheets:
with xlrd.open_workbook('Circle.xls') as wb:
for sheet in wb.sheets():
fn = 'store-'
print "Converting files.."
with open(fn + sheet.name + ".csv","wb") as f:
c = csv.writer(f,dialect="excel")
for r in range(sheet.nrows):
c.writerow(sheet.row_values(r))
The conversion runs until it finds a UTF-8 character that doesn't exist in shift-JIS, then it errors out.
Is there a way to convert from Excel to a csv purely in shift-JIS?
(If my question has a flaw, please ask me to edit it before marking it down! I will edit it!)
I have many Python scripts that output CSV files. It is occasionally convenient to open these files in Excel. After installing OS X Mavericks, Excel no longer opens these files properly: Excel doesn't parse the files and it duplicates the rows of the file until it runs out of memory. Specifically, when Excel attempts to open the file, a prompt appears that reads: "File not loaded completely."
Example of code I'm using to generate the CSV files:
import csv
with open('csv_test.csv', 'wb') as f:
writer = csv.writer(f)
writer.writerow([1,2,3])
writer.writerow([4,5,6])
Even the simple file generated by the above code fails to load in Excel. However, if I open the CSV file in a text editor and copy/paste the text into Excel, parse it with text to columns, and then save as CSV from Excel, then I can reopen the CSV file in Excel without issue. Do I need to pass an additional parameter in my scripts to make Excel parse the CSV files the same way it used to? Or is there some setting I can change in OS X Mavericks or Excel? Thanks.
Maybe I had the similar problem, the error message "SYLK: File format is not valid" when open python autogenerated csv file. The solution is really funny. The first two characters must not be I and D in uppercase (ID). Also see "SYLK: File format is not valid" error message when you open file.
Possible solution1: use *.txt instead of *.csv. In this case Excel (at least, 2010) will show you an import data wizard where you can specify delimiters, character encoding, field types, etc.
UPD: Solution2:
The python "csv" module has a "dialect" feature. For example, the following modification of your code generates valid csv file for my environment (Python 2.7, Excel 2010, Windows7, locale with ";" list delimiters):
import csv
with open('csv_test2.csv', 'wb') as f:
csv.excel.delimiter=';'
writer = csv.writer(f, dialect=csv.excel)
writer.writerow([1,2,3])
writer.writerow([4,5,6])