I am very new to Python and would like to use it for my mass spectrometry data analysis. I have a txt file that is separated by tabulator. I can import it into Excel with the import assistant.
I have also managed to import it into spyder with the import assistant, but I would like to automate the process.
Is there a way to "record" the import settings I use while manually loading the data? That way I would generate a code that I could use in the future for the other txt files.
I've tried using NumPy and pandas to import my data but my txt file contains strings and numbers (floats) and I have not managed to tell Python to distinguish between the two.
When in import the file manually I get the exat DataFrame I want with the first row as a header, and the strings, and numbers correctly formatted.
here is a sample of my txt file:
Protein.IDs Majority.protein.IDs Peptide.counts..all.
0 LmxM.01.0330.1-p1 LmxM.01.0330.1-p1 5
1 LmxM.01.0410.1-p1 LmxM.01.0410.1-p1 15
2 LmxM.01.0480.1-p1 LmxM.01.0480.1-p1 14
3 LmxM.01.0490.1-p1 LmxM.01.0490.1-p1 27
4 LmxM.01.0520.1-p1 LmxM.01.0520.1-p1 27
Using numpy or pandas is the best way to automate the process, so good job using the right tools.
I suggest that you look at all the options that the pandas read_csv function has to offer. There is most likely a single line of code that can import the data properly by using the right options.
In particular, look at the decimal option if the floats are not parsed correctly.
Other solutions, which you may still want to use even if you use pandas properly are:
Formatting the input data to make your life easier : either when it is generated, or using some notepad with good macros (Notepadd++ can replace expression or accomplish tedious repeating keystrokes for you).
Formatting the output of the pandas import. If you still have strings that should be interpreted as numeric values, maybe you can run a loop to check that all values are converted in the format that they should be in.
Finally, you may want to provide some examples when you ask technical questions: show an example of data, the code that you're using, and the output of your code would make answering your question easier :)
Edit:
From the data example that you posted, it seems to me that pandas should separate the data just fine and detect strings and numerical values without trouble.
Look at the options sep of read_csv. The default is ',', you probably want to switch it to a tabulation: '\t'
Try this:
pandas.read_csv(my_filename, sep='\t')
You may run into some header issue, which you can solve using the header and names options.
Related
I have a csv file containing numerical values such as 1524.449677. There are always exactly 6 decimal places.
When I import the csv file (and other columns) via pandas read_csv, the column automatically gets the datatype object. My issue is that the values are shown as 2470.6911370000003 which actually should be 2470.691137. Or the value 2484.30691 is shown as 2484.3069100000002.
This seems to be a datatype issue in some way. I tried to explicitly provide the data type when importing via read_csv by giving the dtype argument as {'columnname': np.float64}. Still the issue did not go away.
How can I get the values imported and shown exactly as they are in the source csv file?
Pandas uses a dedicated dec 2 bin converter that compromises accuracy in preference to speed.
Passing float_precision='round_trip' to read_csv fixes this.
Check out this page for more detail on this.
After processing your data, if you want to save it back in a csv file, you can passfloat_format = "%.nf" to the corresponding method.
A full example:
import pandas as pd
df_in = pd.read_csv(source_file, float_precision='round_trip')
df_out = ... # some processing of df_in
df_out.to_csv(target_file, float_format="%.3f") # for 3 decimal places
I realise this is an old question, but maybe this will help someone else:
I had a similar problem, but couldn't quite use the same solution. Unfortunately the float_precision option only exists when using the C engine and not with the python engine. So if you have to use the python engine for some other reason (for example because the C engine can't deal with regex literals as deliminators), this little "trick" worked for me:
In the pd.read_csv arguments, define dtype='str' and then convert your dataframe to whatever dtype you want, e.g. df = df.astype('float64') .
Bit of a hack, but it seems to work. If anyone has any suggestions on how to solve this in a better way, let me know.
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 csv file containing numerical values such as 1524.449677. There are always exactly 6 decimal places.
When I import the csv file (and other columns) via pandas read_csv, the column automatically gets the datatype object. My issue is that the values are shown as 2470.6911370000003 which actually should be 2470.691137. Or the value 2484.30691 is shown as 2484.3069100000002.
This seems to be a datatype issue in some way. I tried to explicitly provide the data type when importing via read_csv by giving the dtype argument as {'columnname': np.float64}. Still the issue did not go away.
How can I get the values imported and shown exactly as they are in the source csv file?
Pandas uses a dedicated dec 2 bin converter that compromises accuracy in preference to speed.
Passing float_precision='round_trip' to read_csv fixes this.
Check out this page for more detail on this.
After processing your data, if you want to save it back in a csv file, you can passfloat_format = "%.nf" to the corresponding method.
A full example:
import pandas as pd
df_in = pd.read_csv(source_file, float_precision='round_trip')
df_out = ... # some processing of df_in
df_out.to_csv(target_file, float_format="%.3f") # for 3 decimal places
I realise this is an old question, but maybe this will help someone else:
I had a similar problem, but couldn't quite use the same solution. Unfortunately the float_precision option only exists when using the C engine and not with the python engine. So if you have to use the python engine for some other reason (for example because the C engine can't deal with regex literals as deliminators), this little "trick" worked for me:
In the pd.read_csv arguments, define dtype='str' and then convert your dataframe to whatever dtype you want, e.g. df = df.astype('float64') .
Bit of a hack, but it seems to work. If anyone has any suggestions on how to solve this in a better way, let me know.
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 have to dump data from SAS datasets. I found a Python module called sas7bdat.py that says it can read SAS .sas7bdat datasets, and I think it would be simpler and more straightforward to do the project in Python rather than SAS due to the other functionality required. However, the help(sas7bdat) in interactive Python is not very useful and the only example I was able to find to dump a dataset is as follows:
import sas7bdat
from sas7bdat import *
# following line is sas dataset to convert
foo = SAS7BDAT('/support/sas/locked_data.sas7bdat')
#following line is txt file to create
foo.convertFile('/support/textfiles/locked_data.txt','\t')
This doesn't do what I want because a) it uses the SAS variable names as column headers and I need it to use the variable labels, and b) it uses "nan" to denote missing numeric values where I'd rather just leave the value blank.
Can anyone point me to some useful documentation on the methods included in sas7bdat.py? I've Googled every permutation of key words that I could think of, with no luck. If not, can someone give me an example or two of using readColumnAttributes(), readColumnLabels(), and/or readColumnNames()?
Thanks, all.
As time passes, solutions become easier. I think this one is easiest if you want to work with pandas:
import pandas as pd
df = pd.read_sas('/support/sas/locked_data.sas7bdat')
Note that it is easy to get a numpy array by using df.values
This is only a partial answer as I've found no [easy to read] concrete documentation.
You can view the source code here
This shows some basic info regarding what arguments the methods require, such as:
readColumnAttributes(self, colattr)
readColumnLabels(self, collabs, coltext, colcount)
readColumnNames(self, colname, coltext)
I think most of what you are after is stored in the "header" class returned when creating an object with SAS7BDAT. If you just print that class you'll get a lot of info, but you can also access class attributes as well. I think most of what you may be looking for would be under foo.header.cols. I suspect you use various header attributes as parameters for the methods you mention.
Maybe something like this will get you closer?
from sas7bdat import SAS7BDAT
foo = SAS7BDAT(inFile) #your file here...
for i in foo.header.cols:
print '"Atrributes"', i.attr
print '"Labels"', i.label
print '"Name"', i.name
edit: Unrelated to this specific question, but the type() and dir() commands come in handy when trying to figure out what is going on in an unfamiliar class/library
I know I'm late for the answer, but in case someone searches for similar question. The best option is:
import sas7bdat
from sas7bdat import *
foo = SAS7BDAT('/support/sas/locked_data.sas7bdat')
# This converts to dataframe:
ds = foo.to_data_frame()
Personally I think the better approach would be to export the data using SAS then process the external file as needed using Python.
In SAS, you can do this...
libname datalib "/support/sas";
filename sasdump "/support/textfiles/locked_data.txt";
proc export
data = datalib.locked_data
outfile = sasdump
dbms = tab
label
replace;
run;
The downside to this is that while the column labels are used rather than the variable names, the labels are enclosed in double quotes. When processing in Python, you may need to programmatically remove them if they cause a problem. I hope that helps even though it doesn't use Python like you wanted.