Develop Reference

Divide and Conquer Lists in Python (to read sav files using pyreadstat)

I am trying to read sav files using pyreadstat in python but for some rare scenarios I am getting error of UnicodeDecodeError since the string variable has special characters.
To handle this I think instead of loading the entire variable set I will load only variables which do not have this error.
Below is the pseudo-code that I have with me. This is not a very efficient code since I check for error in each item of list using try and except.
# Reads only the medata to get information about the variables
df, meta = pyreadstat.read_sav('Test.sav', metadataonly=True)
list = meta.column_names # All variables are stored in list
result = []
for var in list:
print(var)
try:
df, meta = pyreadstat.read_sav('Test.sav', usecols=[str(var)])
# If no error that means we can store this variable in result
result.append(var)
except:
pass
# This will finally load the sav for non error variables
df, meta = pyreadstat.read_sav('Test.sav', usecols=result)
For a sav file with 1000+ variables it takes a long amount of time to process this.
I was thinking if there is a way to use divide and conquer approach and do it faster. Below is my suggested approach but I am not very good in implementing recursion algorithm. Can someone please help me with pseudo code it would be very helpful.
Take the list and try to read sav file
In case of no error then output can be stored in result and then we read the sav file
In case of error then split the list into 2 parts and run these again ....
Step 3 needs to run again until we have a list where it does not give any error
Using the second approach 90% of my sav files will get loaded on the first pass itself hence I think recursion is a good method
You can try to reproduce the issue for sav file here

For this specific case I would suggest a different approach: you can give an argument "encoding" to pyreadstat.read_sav to manually set the encoding. If you don't know which one it is, what you can do is iterate over the list of encodings here: https://gist.github.com/hakre/4188459 to find out which one makes sense. For example:
# here codes is a list with all the encodings in the link mentioned before
for c in codes:
try:
df, meta = p.read_sav("Test.sav", encoding=c)
print(encoding)
print(df.head())
except:
pass
I did and there were a few that may potentially make sense, assuming that the string is in a non-latin alphabet. However the most promising one is not in the list: encoding="UTF8" (the list contains UTF-8, with dash and that fails). Using UTF8 (no dash) I get this:
నేను గతంలో వాడిన బ
which according to google translate means "I used to come b" in Telugu. Not sure if that fully makes sense, but it's a way.
The advantage of this approach is that if you find the right encoding, you will not be loosing data, and reading the data will be fast. The disadvantage is that you may not find the right encoding.
In case you would not find the right encoding, you anyway would be reading the problematic columns very fast, and you can discard them later in pandas by inspecting which character columns do not contain latin characters. This will be much faster than the algorithm you were suggesting.

Python Generic Data Engine

I have been working on Python for about 1.5yrs and looking for some direction. This is the first time I can't find what I need after doing a lot of searching and must be missing something- most likely searching the wrong terms.
Problem: I am working on an app that has many processes (Could be hundreds or even thousands). Each process may have a unique input and output data format - could be multiline strings, comma separated strings, excel or csv with or without varying headers and many others. I need something that will format the input correctly and handle the output based upon the process. New processes also need to be easily added/defined. I am open to whatever is the best approach, but my thoughts are to use a database that stores the template/data definition and use that to know the format given a process. However, I'm struggling to come up with exactly how, if this is really the best approach, but it needs to be a solution that is scalable. Any direction would be appreciated. Thank you.
A couple simple examples of data
Process 1 example data (multi line string with Header)
Input of
[ABC123, XYZ453, CDE987]
and the resulting data input below would be created:
Barcode
ABC123
XYZ453
CDE987
This code below works, but is not reusable for the example 2.
list = [ABC123, XYZ453, CDE987]
input = "Barcode /r/n"
for l in list:
input = input + l + '/r/n'
Process 2 example input template (comma separated with Header):
Barcode,Location,Param1,Param2
Item1,L1,11,A
Item1,L1,22,B
Item2,L1,33,C
Item2,L2,44,F
Item3,L2,55,B
Item3,L2,66,P
Process 2 example resulting input data (comma separated with Header):
Input of
{'Barcode':['ABC123', 'XYZ453', 'CDE987', 'FGH487', 'YTR123'], 'Location':['Shelf1', 'Shelf2']}
and using the template to create the input data below:
Barcode,Location,Param1,Param2
ABC123,Shelf1,11,A
ABC123,Shelf1,22,B
XYZ453,Shelf1,33,C
XYZ453,Shelf2,44,F
CDE987,Shelf2,55,B
CDE987,Shelf2,66,P
FGH487,Shelf1,11,A
FGH487,Shelf1,22,B
YTR123,Shelf1,33,C
YTR123,Shelf2,44,F
I know how to handle each process with hardcoded loop/dataframe merge, etc. Ive done some abstraction in other cases with dicts. However, how to define/store each format that vary so much and create reusable abstracted code is where I am stuck.

Maybe you can do the output of the functions as a tuple with the keys "datatype" and "output" for the actual output

check csv every 5 rows with condition using python3.x

csv data:
>c1,v1,c2,v2,Time
>13.9,412.1,29.7,177.2,14:42:01
>13.9,412.1,29.7,177.2,14:42:02
>13.9,412.1,29.7,177.2,14:42:03
>13.9,412.1,29.7,177.2,14:42:04
>13.9,412.1,29.7,177.2,14:42:05
>0.1,415.1,1.3,-0.9,14:42:06
>0.1,408.5,1.2,-0.9,14:42:07
>13.9,412.1,29.7,177.2,14:42:08
>0.1,413.4,1.3,-0.9,14:42:09
>0.1,413.8,1.3,-0.9,14:42:10
My current code that I have:
import pandas as pd
import csv
import datetime as dt
#Read .csv file, get timestamp and split it into date and time separately
Data = pd.read_csv('filedata.csv', parse_dates=['Time_Stamp'], infer_datetime_format=True)
Data['Date'] = Data.Time_Stamp.dt.date
Data['Time'] = Data.Time_Stamp.dt.time
#print (Data)
print (Data['Time_Stamp'])
Data['Time_Stamp'] = pd.to_datetime(Data['Time_Stamp'])
#Read timestamp within a certain range
mask = (Data['Time_Stamp'] > '2017-06-12 10:48:00') & (Data['Time_Stamp']<= '2017-06-12 11:48:00')
june13 = Data.loc[mask]
#print (june13)
What I'm trying to do is to read every 5 secs of data, and if 1 out of 5 secs of data of c1 is 10.0 and above, replace that value of c1 with 0.
I'm still new to python and I could not find examples for this. May I have some assistance as this problem is way beyond my python programming skills for now. Thank you!

I don't know the modules around csv files so my answer might look primitive, and I'm not quite sure what you are trying to accomplish here, but have you though of dealing with the file textually ?
From what I get, you want to read every c1, check the value and modify it.
To read and modify the file, you could do:
with open('filedata.csv', 'r+') as csv_file:
lines = csv_file.readlines()
# for each line, isolate data part and check - and modify, the first one if needed.
# I'm seriously not sure, you might have wanted to read only one out of five lines.
# For that, just do a while loop with an index, which increments through lines by 5.
for line in lines:
line = line.split(',') # split comma-separated-values
# Check condition and apply needed change.
if float(line[0]) >= 10:
line[0] = "0" # Directly as a string.
# Transform the list back into a single string.
",".join(line)
# Rewrite the file.
csv_file.seek(0)
csv_file.writelines(lines)
# Here you are ready to use the file just like you were already doing.
# Of course, the above code could be put in a function for known advantages.
(I don't have python here, so I couldn't test it and typos might be there.)
If you only need the dataframe without the file being modified:
Pretty much the same to be honest.
Instead of the file-writing at the end, you could do :
from io import StringIO # pandas needs stringIO instead of strings.
# Above code here, but without the last 6 lines.
Data = pd.read_csv(
StringIo("\n".join(lines)),
parse_dates=['Time_Stamp'],
infer_datetime_format=True
)
This should give you the Data you have, with changed values where needed.
Hope this wasn't completely off. Also, some people might find this approach horrible ; we have already coded working modules to do that kind of things, so why botter and dealing with the rough raw data ourselves ? Personally, I think that it's often much easier than learning all of the external modules I'll be using in my life if I don't try to understand how the text representation of files can be used. Your opinion might differ.
Also, this code might result in performances being lower, as we need to iterate through the text twice (pandas does it when reading). However, I don't think you'd get faster result by reading the csv like you already do, then iterate through data anyway to check condition. (You might win a cast per c1 checked value, but the difference is small and iterating through pandas dataframe might as well be slower than a list, depending on the state of their current optimisation.)
Of course, if you don't really need the pandas dataframe format, you could completely do it manually, it would take only a few more lines (or not, tbh) and shouldn't be slower, as the amount of iterations would be minimized : you could check conditions on data at the same time as you read it. It's getting late and I'm sure you can figure that out by yourself so I won't code it in my great editor (known as stackoverflow), ask if there's anything !

Reading multidimensional array data into Python

I have data in the format of 10000x500 matrix contained in a .txt file. In each row, data points are separated from each other by one whitespace and at the end of each row there a new line starts.
Normally I was able to read this kind of multidimensional array data into Python by using the following snippet of code:
with open("position.txt") as f:
data = [line.split() for line in f]
# Get the data and convert to floats
ytemp = np.array(data)
y = ytemp.astype(np.float)
This code worked until now. When I try to use the exact some code with another set of data formatted in the same way, I get the following error:
setting an array element with a sequence.
When I try to get the 'shape' of ytemp, it gives me the following:
(10001,)
So it converts the rows to array, but not the columns.
I thought of any other information to include, but nothing came to my mind. Basically I'm trying to convert my data from a .txt file to a multidimensional array in Python. The code worked before, but now for some reason that is unclear to me it doesn't work. I tried to look compare the data, of course it's huge, but everything seems quite similar between the data that is working and the data that is not working.
I would be more than happy to provide any other information you may need. Thanks in advance.

Use numpy's builtin function:
data = numpy.loadtxt('position.txt')
Check out the documentation to explore other available options.

Shortening code for formatted output

I must write a two dimensional array (let's say array[100][20]) into a file. This is just an example of my code:
for i in range(0,99):
print >> file, "%15.9e%24.15e%3d..." % (array[i][0], array[i][1], array[i][2], ... )
Is it possible to shorten it after "%" symbol with a slicing or something similar notation?

You could do:
print >> file, format_string % tuple(array[i])
That said, I think that I would probably use numpy here to save the data in a format that isn't ascii for efficiency (both in terms of time spent doing IO and disk space).

At the risk of being down-voted, are you sure you're asking the right question? I've always found that if something is ugly or hard, it's probably not the best approach, especially in Python.
You might check out the csv module---what format do you want to write, exactly?
There's also the texttable module, that makes pretty tables for you, and will even dump them to a text file.
Finally, if you should be so inclined, there are the Python Excel Utilities, which will dump things into Excel spreadsheets. I have written some wrappers around the excel read and excel write interfaces in that module that I'll post here if you want---they're nothing special, but they may save you some time.