Recently I have posted a problem regarding writing a spilted dataframe into different excel sheets this post, and I somehow find an answer which resulted in writing each of them in a separate excel file rather than separate excel sheets. I have also read the recommended post here but that also was not much help. I am wondering if I could find a solution to my question and I really feel that it is not that much complicated but since I am new in this field, I could not find it.
Combining solutions from both the links that you posted, here is the solution
# define an excel writer first
writer = pd.ExcelWriter("output.xlsx", engine = 'xlsxwriter')
df_split = np.array_split(promotion1, 4)
for index, df_sub in enumerate(df_split):
#print(df_sub.head())
# save each of your splitted dataframes using excel writer
df_sub.to_excel(writer, sheet_name = 'sheet' + str(index))
I am coming from java background and have minimal idea regarding python. I have to read an excel file and validate one of it's column values in the DB to verify that those rows exist in the DB or not.
I know the exact libraries and steps in java using which I can do this work.
But I am facing problems in choosing the ways to do this work in python.
till now I am able to identify some things which I can do.
Read excel file in python using python.
Use pyodbc to validate the values.
Can pandas help me to refine those steps. Rather doing things the hard way.
Yes pandas can help. But you phrase the question in a "please google this for me" way. Expect this question to be down-voted a lot.
I will give you the answer for the excel part. Surely you could have found this yourself with a little effort?
import pandas as pd
df = pd.read_excel('excel_file.xls')
Read the documentation.
Using xlrd module, one can retrieve information from a spreadsheet. For example, reading, writing or modifying the data can be done in Python. Also, a user might have to go through various sheets and retrieve data based on some criteria or modify some rows and columns and do a lot of work.
xlrd module is used to extract data from a spreadsheet.
# Reading an excel file using Python
import xlrd
# Give the location of the file
loc = ("path of file")
# To open Workbook
wb = xlrd.open_workbook(loc)
sheet = wb.sheet_by_index(0)
# For row 0 and column 0
sheet.cell_value(0, 0)
put open_workbook under try statement and use pyodbc.Error as exe in except to catch the error if there is any.
I have been given a CSV file with more than the MAX Excel can handle, and I really need to be able to see all the data. I understand and have tried the method of "splitting" it, but it doesnt work.
Some background: The CSV file is an Excel CSV file, and the person who gave the file has said there are about 2m rows of data.
When I import it into Excel, I get data up to row 1,048,576, then re-import it in a new tab starting at row 1,048,577 in the data, but it only gives me one row, and I know for a fact that there should be more (not only because of the fact that "the person" said there are more than 2 million, but because of the information in the last few sets of rows)
I thought that maybe the reason for this happening is because I have been provided the CSV file as an Excel CSV file, and so all the information past 1,048,576 is lost (?).
DO I need to ask for a file in an SQL database format?
You should try delimit it can open up to 2 billion rows and 2 million columns very quickly has a free 15 day trial too. Does the job for me!
I would suggest to load the .CSV file in MS-Access.
With MS-Excel you can then create a data connection to this source (without actual loading the records in a worksheet) and create a connected pivot table. You then can have virtually unlimited number of lines in your table (depending on processor and memory: I have now 15 mln lines with 3 Gb Memory).
Additional advantage is that you can now create an aggregate view in MS-Access. In this way you can create overviews from hundreds of millions of lines and then view them in MS-Excel (beware of the 2Gb limitation of NTFS files in 32 bits OS).
Excel 2007+ is limited to somewhat over 1 million rows ( 2^20 to be precise), so it will never load your 2M line file. I think that the technique you refer to as splitting is the built-in thing Excel has, but afaik that only works for width problems, not for length problems.
The really easiest way I see right away is to use some file splitting tool - there's tons of 'em and use that to load the resulting partial csv files into multiple worksheets.
ps: "excel csv files" don't exist, there are only files produced by Excel that use one of the formats commonly referred to as csv files...
You can use PowerPivot to work with files of up to 2GB, which will be enough for your needs.
First you want to change the file format from csv to txt. That is simple to do, just edit the file name and change csv to txt. (Windows will give you warning about possibly corrupting the data, but it is fine, just click ok). Then make a copy of the txt file so that now you have two files both with 2 millions rows of data. Then open up the first txt file and delete the second million rows and save the file. Then open the second txt file and delete the first million rows and save the file. Now change the two files back to csv the same way you changed them to txt originally.
I'm surprised no one mentioned Microsoft Query. You can simply request data from the large CSV file as you need it by querying only that which you need. (Querying is setup like how you filter a table in Excel)
Better yet, if one is open to installing the Power Query add-in, it's super simple and quick. Note: Power Query is an add-in for 2010 and 2013 but comes with 2016.
If you have Matlab, you can open large CSV (or TXT) files via its import facility. The tool gives you various import format options including tables, column vectors, numeric matrix, etc. However, with Matlab being an interpreter package, it does take its own time to import such a large file and I was able to import one with more than 2 million rows in about 10 minutes.
The tool is accessible via Matlab's Home tab by clicking on the "Import Data" button. An example image of a large file upload is shown below:
Once imported, the data appears on the right-hand-side Workspace, which can then be double-clicked in an Excel-like format and even be plotted in different formats.
I was able to edit a large 17GB csv file in Sublime Text without issue (line numbering makes it a lot easier to keep track of manual splitting), and then dump it into Excel in chunks smaller than 1,048,576 lines. Simple and quite quick - less faffy than researching into, installing and learning bespoke solutions. Quick and dirty, but it works.
Try PowerPivot from Microsoft. Here you can find a step by step tutorial. It worked for my 4M+ rows!
"DO I need to ask for a file in an SQL database format?" YES!!!
Use a database, is the best option for this problem.
Excel 2010 specifications .
Use MS Access. I have a file of 2,673,404 records. It will not open in notepad++ and excel will not load more than 1,048,576 records. It is tab delimited since I exported the data from a mysql database and I need it in csv format. So I imported it into Access. Change the file extension to .txt so MS Access will take you through the import wizard.
MS Access will link to your file so for the database to stay intact keep the csv file
The best way to handle this (with ease and no additional software) is with Excel - but using Powerpivot (which has MSFT Power Query embedded). Simply create a new Power Pivot data model that attaches to your large csv or text file. You will then be able to import multi-million rows into memory using the embedded X-Velocity (in-memory compression) engine. The Excel sheet limit is not applicable - as the X-Velocity engine puts everything up in RAM in compressed form. I have loaded 15 million rows and filtered at will using this technique. Hope this helps someone... - Jaycee
I found this subject researching.
There is a way to copy all this data to an Excel Datasheet.
(I have this problem before with a 50 million line CSV file)
If there is any format, additional code could be included.
Try this.
Sub ReadCSVFiles()
Dim i, j As Double
Dim UserFileName As String
Dim strTextLine As String
Dim iFile As Integer: iFile = FreeFile
UserFileName = Application.GetOpenFilename
Open UserFileName For Input As #iFile
i = 1
j = 1
Check = False
Do Until EOF(1)
Line Input #1, strTextLine
If i >= 1048576 Then
i = 1
j = j + 1
Else
Sheets(1).Cells(i, j) = strTextLine
i = i + 1
End If
Loop
Close #iFile
End Sub
You can try to download and install TheGun Text Editor. Which can help you to open large csv file easily.
You can check detailed article here https://developingdaily.com/article/how-to/what-is-csv-file-and-how-to-open-a-large-csv-file/82
Split the CSV into two files in Notepad. It's a pain, but you can just edit each of them individually in Excel after that.
I have an xls. file that contains the following information
I want to delete from R columns D and E and of course that the data of F moves to the left. How I can do that in R?
Thanks
Read the .xls file into R with one of the several packages (eg. xlsReadWrite:read.xls()), delete the columns :
data$Column <- NULL
or
data <- data[ ,-c(4,5)]
and then write the new data to a .xls file with one of the mentioned packages.
You can convert it into a csv file and use read.csv function to load it in R. Then, you delete the columns you want to and save it with write.csv.
How can I save data from an Excel sheet to .RData file in R? I want to use one of the packages in R and to load my dataset as data(dataset) i think i have to save the data as .RData file and then load that into the package. My data currently is in an Excel spreadsheet.
my excel sheets has column names like x, y , time.lag.
I have saved it as .csv
then i use:
x=read.csv('filepath', header=T,)
then i say
data(x)
and it shows dataset 'x' not found
There are also several packages that allow directly reading from XLS and XLSX files. We've even had a question on that topic here and here for example. However you decide to read in the data, saving into an RData can be handled with save, save.image, saveRDS and probably some others I'm not thinking about.
save your Excel data as a .csv file and import it using read.csv() or read.table().
Help on each will explain the options.
For example, you have a file called myFile.xls, save it as myFile.csv.
library(BBMM)
# load an example dataset from BBMM
data(locations)
# from the BBMM help file
BBMM <- brownian.bridge(x=locations$x, y=locations$y, time.lag=locations$time.lag[-1], location.error=20, cell.size=50)
bbmm.summary(BBMM)
# output of summary(BBMM)
Brownian motion variance : 3003.392
Size of grid : 138552 cells
Grid cell size : 50
# subsitute locations for myData for your dataset that you have read form a myFile.csv file
myData <- read.csv(file='myFile.csv', header=TRUE)
head(myData) # will show the first 5 entries in you imported data
# use whatever you need from the BBMM package now ....
Check RODBC package. You can find an example in R Data Import/Export. You can query data from excel sheet as if from a database table.
The benefit of reading Excel sheet with RODBC is that you get dates (if you work with any) in a proper format. With intermediate CSV, you'd need to specify a column type, unless you want it to be a factor or string. Also you can query only a portion of your data if you need so thus making subset() unnecessary.