I'm generating an csv file that is opened by excel and converted to xlsx manually.
The csv contains some path to .txt files.
Is it possible to build the file path in such way that when the csv is converted to xlsx , they became clickable hyperlinks ?
Thanks.
I would be interested to understand your workflow a bit better, but to try and help with your specific request:
The HYPERLINK solution proposed in the comments looks like a good one
If you are able to implement that upstream in the csv generation step then great
If not and/or you are interested in automating the conversion process, consider using the pandas library:
Create a DataFrame object from a csv using the pandas.read_csv method
Convert your paths to HYPERLINKs
Write back to xlsx using the pandas.DataFrame.to_excel method
E.g. if you have a file original.csv and the relevant column header is file_paths:
import pandas as pd
df = pd.read_csv('original.csv')
df['file_paths'] = '=HYPERLINK("' + df['file_paths'] + '")'
df.to_excel('new.xlsx', index=False)
Hope that helps!
Jon
Related
From Python i want to export to csv format a dataframe
The dataframe contains two columns like this
So when i write this :
df['NAME'] = df['NAME'].astype(str) # or .astype('string')
df.to_csv('output.csv',index=False,sep=';')
The excel output in csv format returns this :
and reads the value "MAY8218" as a date format "may-18" while i want it to be read as "MAY8218".
I've tried many ways but none of them is working. I don't want an alternative like putting quotation marks to the left and the right of the value.
Thanks.
If you want to export the dataframe to use it in excel just export it as xlsx. It works for me and maintains the value as string in the original format.
df.to_excel('output.xlsx',index=False)
The CSV format is a text format. The file contains no hint for the type of the field. The problem is that Excel has the worst possible support for CSV files: it assumes that CSV files always use its own conventions when you try to read one. In short, one Excel implementation can only read correctly what it has written...
That means that you cannot prevent Excel to interpret the csv data the way it wants, at least when you open a csv file. Fortunately you have other options:
import the csv file instead of opening it. This time you have options to configure the way the file should be processed.
use LibreOffice calc for processing CSV files. LibreOffice is a little behind Microsoft Office on most points except for csv file handling where it has an excellent support.
Simple problem that has me completely dumbfounded. I am trying to read an Excel document with pandas but I am stuck with this error:
ValueError: Worksheet index 0 is invalid, 0 worksheets found
My code snippet works well for all but one Excel document linked below. Is this an issue with my Excel document (which definitely has sheets when I open it in Excel) or am I missing something completely obvious?
Excel Document
EDIT - Forgot the code. It is quite simply:
import pandas as pd
df = pd.read_excel(FOLDER + 'omx30.xlsx')
FOLDER Is the absolute path to the folder in which the file is located.
Your file is saved as Strict Open XML Spreadsheet (*.xlsx). Because it shares the same extension as Excel Workbook, it isn't obvious that the format is different. Open the file in Excel and Save As. If the selected option is Strict Open XML Spreadsheet (*.xlsx), change it to Excel Workbook (*.xlsx), save it and try loading it again with pandas.
EDIT: with the info that you have the original .csv, re-do your cleaning and save it as a .csv from Excel; or, if you prefer, pd.read_csv the original, and do your cleaning from the CLI with pandas directly.
It maybe your excel delete the first sheet of index 0, and now the actual index is > 0, but the param sheet_name of function pd.read_excel is 0, so the error raised.
It seems there indeed is a problem with my excel file. We have not been able to figure out what though. For now the path of least resistance is simply saving as a .csv in excel and using pd.read_csv to read this instead.
My Python program converts Excel files (.xlsx) into a CSV file using Panda's read_excel and to_csv function, and at some point in the future, the CSV is converted back into an Excel file. Maintaining the data is fine, but of course all of the formatting and styling is gone. So I could use some help in being able to capture the that information to use when after converting the CSV back into an Excel file.
import pandas as pd
import xlsxwriter
EXCEL_PATH_FROM = r'C:\absolute\path\to\excel.xlsx'
EXCEL_PATH_TO = r'C:\absolute\path\to\other\excel.xlsx'
CSV_PATH = r'C:\absolute\path\to\csv.csv'
# read excel and convert to csv
def saveData():
read_excel = pd.read_excel(EXCEL_PATH_FROM)
print("writing csv...")
read_excel.to_csv(CSV_PATH, index=None, header=True)
# get csv data and import that data into an excel file
def createFromData():
csv = pd.read_csv(CSV_PATH)
excel = pd.ExcelWriter(EXCEL_PATH_TO, engine='xlsxwriter')
csv.to_excel(excel, index=None)
excel.save()
Some ideas I had were to save the Excel as a XML and insert format and style information as attributes or something, or to create both a CSV and XML from the Excel (one for data and one for styling). One problem I have is figuring out how to access that information.
Are there currently any packages that support Python 3 (currently using 3.8) that could help simplify this process? I dug through openpyxl's documentation and they have some stylesheet classes that aren't meant to be used directly I don't think and I couldn't figure out how to use them directly.
I have a spark dataframe (hereafter spark_df) and I'd like to convert that to .csv format. I tried two following methods:
spark_df_cut.write.csv('/my_location/my_file.csv')
spark_df_cut.repartition(1).write.csv("/my_location/my_file.csv", sep=',')
where I get no error message for any of them and both get completed [it seems], but I cannot find any output .csv file in the target location! Any suggestion?
I'm on a cloud-based Jupyternotebook using spark '2.3.1'.
spark_df_cut.write.csv('/my_location/my_file.csv')
//will create directory named my_file.csv in your specified path and writes data in CSV format into part-* files.
We are not able to control the names of files while writing the dataframe, look for directory named my_file.csv in your location (/my_location/my_file.csv).
In case if you want filename ending with *.csv then you need to rename using fs.rename method.
spark_df_cut.write.csv save the files as part files. there is no direct solution available in spark to save as .csv file that can be opened directly with xls or some other. but there are multiple workarounds available one such work around is to convert spark Dataframe to panda Dataframe and use to_csv method like below
df = spark.read.csv(path='game.csv', sep=',')
pdf = df.toPandas()
pdf.to_csv(path_or_buf='<path>/real.csv')
this will save the data as .csv file
and another approach is using open the file using hdfs command and cat that to a file.
please post if you need more help
I have following N number of invoice data in Excel and I want to create CSV of that file so that it can be imported whenever needed...so how can I archive this?
Here is a screenshot:
Assuming you have a Folder "excel" full of Excel Files within your Project-Directory and you also have another folder "csv" where you intend to put your generated CSV Files, you could pretty much easily batch-convert all the Excel Files in the "excel" Directory into "csv" using Pandas.
It will be assumed that you already have Pandas installed on your System. Otherwise, you could do that via: pip install pandas. The fairly commented Snippet below illustrates the Process:
# IMPORT DATAFRAME FROM PANDAS AS WELL AS PANDAS ITSELF
from pandas import DataFrame
import pandas as pd
import os
# OUR GOAL IS:::
# LOOP THROUGH THE FOLDER: excelDir.....
# AT EACH ITERATION IN THE LOOP, CHECK IF THE CURRENT FILE IS AN EXCEL FILE,
# IF IT IS, SIMPLY CONVERT IT TO CSV AND SAVE IT:
for fileName in os.listdir(excelDir):
#DO WE HAVE AN EXCEL FILE?
if fileName.endswith(".xls") or fileName.endswith(".xlsx"):
#IF WE DO; THEN WE DO THE CONVERSION USING PANDAS...
targetXLFile = os.path.join(excelDir, fileName)
targetCSVFile = os.path.join(csvDir, fileName) + ".csv"
# NOW, WE READ "IN" THE EXCEL FILE
dFrame = pd.read_excel(targetXLFile)
# ONCE WE DONE READING, WE CAN SIMPLY SAVE THE DATA TO CSV
pd.DataFrame.to_csv(dFrame, path_or_buf=targetCSVFile)
Hope this does the Trick for you.....
Cheers and Good-Luck.
Instead of putting total output into one csv, you could go with following steps.
Convert your excel content to csv files or csv-objects.
Each object will be tagged with invoice id and save into dictionary.
your dictionary data structure could be like {'invoice-id':
csv-object, 'invoice-id2': csv-object2, ...}
write custom function which can reads your csv-object, and gives you
name,product-id, qty, etc...
Hope this helps.