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.
Related
I was wondering why I get funny behaviour using a csv file that has been "changed" in excel.
I have a csv file of around 211,029 rows and pass this csv file into pandas using a Jupyter-notebook
The simplest example I can give of a change is simply clicking on the filter icon in excel saving the file, unclicking the filter icon and saving again (making no physical changes in the data).
When I pass my csv file through pandas, after a few filter operations, some rows go missing.
This is in comparison to that of doing absolutely nothing with the csv file. Leaving the csv file completely alone gives me the correct number of rows I need after filtering compared to "making changes" to the csv file.
Why is this? Is it because of the number of rows in a csv file? Are we supposed to leave csv files untouched if we are planning to filter through pandas anyways?
(As a side note I'm using Excel on a MacBook.)
Excel does not leave any file "untouched". It applies formatting to every file it opens (e.g. float values like "5.06" will be interpreted as date and changed to "05 Jun"). Depending on the expected datatype these rows might be displayed wrongly or missing in your notebook.
Better use sed or awk to manipulate csv files (or a text editor for smaller files).
So I have a csv file with a column called reference_id. The values in reference id are 15 characters long, so something like '162473985649957'. When I open the CSV file, excel has changed the datatype to General and the numbers are something like '1.62474E+14'. To fix this in excel, I change the column type to Number and remove the decimals and it displays the correct value. I should add, it only does this in CSV file, if I output to xlsx, it works fine. PRoblem is, the file has to be csv.
Is there a way to fix this using python? I'm trying to automate a process. I have tried using the following to convert it to a string. It works in the sense that is converts the column to a string, but it still shows up incorrectly in the csv file.
df['reference_id'] = df['reference_id'].astype(str)
df.to_csv(r'Prev Day Branch Transaction Mems.csv')
Thanks
When I open the CSV file, excel has changed the data
This is an Excel problem. You can't fix how Excel decides to interpret your CSV. (You can work around some issues by using the text import format, but that's cumbersome.)
Either use XLS/XLSX files when working with Excel, or use eg. Gnumeric our something other that doesn't wantonly mangle your data.
I am attempting to read MapInfo .dat files into .csv files using Python. So far, I have found the easiest way to do this is though xlwings and pandas.
When I do this (below code) I get a mostly correct .csv file. The only issue is that some columns are appearing as symbols/gibberish instead of their real values. I know this because I also have the correct data on hand, exported from MapInfo.
import xlwings as xw
import pandas as pd
app = xw.App(visible=False)
tracker = app.books.open('./cable.dat')
last_row = xw.Range('A1').current_region.last_cell.row
data = xw.Range("A1:AE" + str(last_row))
test_dataframe = data.options(pd.DataFrame, header=True).value
test_dataframe.columns = list(schema)
test_dataframe.to_csv('./output.csv')
When I compare to the real data, I can see that the symbols do actually map the correct number (meaning that (1 = Â?, 2=#, 3=#, etc.)
Below is the first part of the 'dictionary' as to how they map:
My question is this:
Is there an encoding that I can use to turn these series of symbols into their correct representation? The floats aren't the only column affected by this, but they are the most important to my data.
Any help is appreciated.
import pandas as pd
from simpledbf import Dbf5
dbf = Dbf5('path/filename.dat')
df = dbf.to_dataframe()
.dat files are dbase files underneath https://www.loc.gov/preservation/digital/formats/fdd/fdd000324.shtml. so just use that method.
then just output the data
df.to_csv('outpath/filename.csv')
EDIT
If I understand well you are using XLWings to load the .dat file into excel. And then read it into pandas dataframe to export it into a csv file.
Somewhere along this it seems indeed that some binary data is not/incorrectly interpreted and then written as text to you csv file.
directly read dBase file
My first suggestion would be to try to read the input file directly into Python without the use of an excel instance.
According to Wikipedia, mapinfo .dat files are actually are dBase III files. These you can parse in python using a library like dbfread.
inspect data before writing to csv
Secondly, I would inspect the 'corrupted' columns in python instead of immediately writing them to disk.
Either something is going wrong in the excel import and the data of these columns gets imported as text instead of some binary number format,
Or this data is correctly into memory as a byte array (instead of a float), and when you write it to csv, it just gets byte-wise dumped to disk instead of interpreting it as a number format and making a text representation of it
note
Small remark about your initial question regarding mapping text to numbers:
Probably it will not be possible create a straightforward map of characters to numbers:
These numbers could have any encoding and might not be stored as decimal text values like you now seem to assume
These text representations are just a decoding using some character encoding (UTF-8, UTF-16). E.g. for UTF-8 several bytes might map to one character. And the question marks or squares you see, might indicate that one or more characters could not be decoded.
In any case you will be losing information if start from the text, you must start from the binary data to decode.
My data in Excel is not separated by ",". Twitter data separated by columns. When I throw it in Python, it automatically installs DataFrame and Tweets are not showed full text. How can I overcome this?
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If you have a copy open in Excel, the easiest solution would be to save a copy as a csv.
File -> Save As -> dropdown and select CSV.
But pandas also allows you to read excel files. This would be recommended if you have a lot of files and don't want to convert all of them.
df = pd.read_excel(<file>)
Now, if you're saying it isn't .xlsx and also not .csv, but you know the delimiter, then:
df = pd.read_csv(<file>, delimiter='\t') # for tab delimited, but you can change '\t' to any delimiter
I am asking a follow up question from here (File downloaded is different from what is on server).
I have datetime in csv file which is getting reformatted.
My CSV has data like this 1-Jan-15,1-Feb-15,1-Mar-15.
But, the reformated csv is like Jan-15, Feb-15, Mar-15.......
Is there any way to stop automatic reformatting of data?
Instead of opening the .csv file directly in Excel, open a new blank workbook in Excel and use Get Data from Text (under the Data tab of the Ribbon) to import the .csv file.
This will open the Text Import Wizard, which has 3 total screens.
On Step 1, choose Delimited.
On Step 2, choose Comma.
And on Step 3, highlight all columns with dates in them and choose Text.
Click Finish.
The General format (which is also what happens by default if you open a .csv file in Excel directly) will recognize the dates as being dates and reformat them according to your locale settings. By instructing Excel to interpret those columns as text, they will not be recognized as dates and therefore left as they are.