I have an exelfile that I want to convert but the default type for numbers is float. How can I change it so xlwings explicitly uses strings and not numbers?
This is how I read the value of a field:
xw.Range(sheet, fieldname ).value
The problem is that numbers like 40 get converted to 40.0 if I create a string from that. I strip it with: str(xw.Range(sheetFronius, fieldname ).value).rstrip('0').rstrip('.') but that is not very helpful and leads to errors because sometimes the same field can contain both a number and a string. (Not at the same time, the value is chosen from a list)
With xlwings if no options are set during reading/writing operations single cells are read in as 'floats'. Also, by default cells with numbers are read as 'floats'. I scoured the docs, but don't think you can convert a cell that has numbers to a 'string' via xlwings outright. Fortunately all is not lost...
You could read in the cells as 'int' with xlwings and then convert the 'int' to 'string' in Python. The way to do that is as follows:
xw.Range(sheet, fieldname).options(numbers=int).value
And finally, you can read in your data this way (by packing the string conversion into the options upfront):
xw.Range(sheet, fieldname).options(numbers=lambda x: str(int(x))).value
Then you would just convert that to string in Python in the usual way.
Good luck!
In my case conclusion was, just adding one row to the last row of raw data.
Write any text in the column you want to change to str, save, load, and then delete the last line.
Related
I read from the following file my data, and create a table.
tracks=pd.read_csv('C:\\Users\\demet\\Desktop\\Internship\\scripts\\tracks-rainy.csv')
Yet when I print for instance an element instead of obtaining a float a get a string.
print(tracks.iloc[track_id][3][0])
What should I add to my project.
You can try:
tracks=pd.read_csv('C:\\Users\\demet\\Desktop\\Internship\\scripts\\tracks-rainy.csv', dtype={'track_id':'Float64'})
Which tell pandas to interpret the column as Float. (As Karl Knechtel said)
If you do not want to initiate the conversion when reading the csv file, you can always do list comprehension with a float conversion.
tracks['track_id'] = [float(i) for i in tracks['track_id']]
The conversion of xml to csv file, this is done by some code and the specifications that I have added.
As as result I get a csv file, once I open it I see some weird numbers that look something like this
1,25151E+21
Is there any way to eliminate this and show the whole numbers. The code itself that parses xml to csv is working fine so I’m assuming it is an excel thing.
I don’t want to go and do something manually every time I am generating a new csv file
Additional
The entire code can be found HERE and I have only long numbers in Quality
for qu in sn.findall('.//Qualify'):
repeated_values['qualify'] = qu.text
CSV doesn't pass any cell formatting rules to Excel. Hence if you open a CSV that has very large numbers in it, the default cell formatting will likely be Scientific. You can try changing the cell formatting to Number and if that changes the view to the entire number like you want, consider using the Xlsxwriter to apply cell formatting to the document while writing to Xlsx instead of CSV.
I often end up running a lambda on dataframes with this issue when I bring in csv, fwf, etc, for ETL and back out to XLSX. In my case they are all account numbers, so it's pretty bad when Excel helpfully overrides it to scientific notation.
If you don't mind the long number being a string, you can do this:
# First I force it to be an int column as I import everything as objects for unrelated reasons
df.thatlongnumber = df.thatlongnumber.astype(np.int64)
# Then I convert that to a string
df.thatlongnumber.apply(lambda x: '{:d}'.format(x))
Let me know if this is useful at all.
Scientific notation is a pain, what I've used before to handle situations like this is to cast it into a float and then use a format specifier, something like this should work:
a = "1,25151E+21"
print(f"{float(a.replace(',', '.')):.0f}")
>>> 1251510000000000065536
I have a table with columns: Location, Basic quals, Preferred quals, and Responsibilities.
The last three columns have string entries that I tokenized, I want to group the columns by Location. When I do this my strings Truncate eg. "we want an individual who knows python and java." turns into "we want an individual..."
How do I avoid this from happening?
grouped_location=pd.DataFrame(df1['Pref'].groupby(df1['Location']))
grouped_location.columns = ['Loaction','Pref']
grouped_location=grouped_location.set_index('Loaction')
grouped_location.iat[0,0]
I expect to get
17 [Experience, in, design, verification,, includ (full entry)]
but what I get is:
17 [Experience, in, design, verification,, includ...
Try saving out the dataframe to csv, it's probably only the display configuration that's truncating it.
I'm importing data coming from excel files that come from another office.
In one of the columns, for each cell, I have lists of numbers used as tags. These were manually inserted, by different people and (my guess) using computers with different thousands settings, so the result is very heterogeneous.
As an example I have:
tags= ['205', '306.3', '3,206,302','7.205.206']
If this was a CSV file (I tried converting one single file to check), using
pd.read_csv(my_file,sep=';')
would give me exactly the above mentioned list.
Unfortunately as said, we're talking about excel files (plural) and I have to deal with it, and using
pd.read_excel(my_file,sheetname=my_sheet,encoding='utf-16',converters{'my_column':str})
what I get instead is:
tags= ['205', '306.3', '3,206,302','7205206']
As you see, whenever the number can be expressed logically in thousands (so, not the second number in my list) the dot is recognised as a thousands separator and I get a single number, instead of three.
I tried reading documentation, and searching on stackoverflow and google, but the keywords to describe this problem are too vague and I didn't find a viable solution, yet.
How can I get the right list using excel files?
Thanks.
This problem is likely happening because pandas is running their number parser before their date parser.
One possible fix is to add a thousands separator. For example, if you are actually using ',' as your thousands separator, you could add thousands=',' in your excel reader:
pd.read_excel(my_file,sheetname=my_sheet,encoding='utf-16',thousands=',',converters{'my_column':str})
You could also pick an arbitrary thousand separator that doesn't exist in your data to make the output stay the same if thousands=None (which should be the default according to documentation), doesn't already deal with your problem. You should also make sure that you are converting the fields to str (in which case using thousands is kind of redundant, as it's not applied to trings either way).
EDIT:
I tried using the following dummy data ('test.xlsx'):
a b c d
205 306.3 3,206,302 7.205.206
and with
dataf = pandas.read_excel('test.xlsx', header=0, converters={'a':str, 'b':str,'c':str,'d':str})
print(dataf.to_string)
I got the following output:
Columns: [205, 306.3, 3,206,302, 7.205.206]
Which is exactly what you were looking for. Are you sure you have the latest version of pandas and that you are in fact not using converters = {'col':int} or float in your converters keyword?
As it stands, it sounds like you are either converting your fields to numeric (int or float), or there is a problem elsewhere in your code. The pandas read_excel seems to work as described, and I can get the results you specified with the code specified above. In other wods: Your code should work, if it doesn't it might be due to outdated pandas version, other parts in your code or even problems with the source data. As it stands, it's not possible to answer your question further with the information you have provided.
I want the following records (currently displaying as 3.200000e+18 but actually (hopefully) each a different long integer), created using pd.read_excel(), to be interpreted differently:
ipdb> self.after['class_parent_ref']
class_id
3200000000000515954 3.200000e+18
3200000000000515951 NaN
3200000000000515952 NaN
3200000000000515953 NaN
3200000000000515955 3.200000e+18
3200000000000515956 3.200000e+18
Name: class_parent_ref, dtype: float64
Currently, they seem to 'come out' as scientifically notated strings:
ipdb> self.after['class_parent_ref'].iloc[0]
3.2000000000005161e+18
Worse, though, it's not clear to me that the number has been read correctly from my .xlsx file:
ipdb> self.after['class_parent_ref'].iloc[0] -3.2e+18
516096.0
The number in Excel (the data source) is 3200000000000515952.
This is not about the display, which I know I can change here. It's about keeping the underlying data in the same form it was in when read (so that if/when I write it back to Excel, it'll look the same and so that if I use the data, it'll look like it did in Excel and not Xe+Y). I would definitely accept a string if I could count on it being a string representation of the correct number.
You may notice that the number I want to see is in fact (incidentally) one of the labels. Pandas correctly read those in as strings (perhaps because Excel treated them as strings?) unlike this number which I entered. (Actually though, even when I enter ="3200000000000515952" into the cell in question before redoing the read, I get the same result described above.)
How can I get 3200000000000515952 out of the dataframe? I'm wondering if pandas has a limitation with long integers, but the only thing I've found on it is 1) a little dated, and 2) doesn't look like the same thing I'm facing.
Thank you!
Convert your column values with NaN into 0 then typcast that column as integer to do so.
df[['class_parent_ref']] = df[['class_parent_ref']].fillna(value = 0)
df['class_parent_ref'] = df['class_parent_ref'].astype(int)
Or in reading your file, specify keep_default_na = False for pd.read_excel() and na_filter = False for pd.read_csv()