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python_ui
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## Beyond Compare
Collected notes from: [Learn How to Quickly Create UIs in Python](https://towardsdatascience.com/learn-how-to-quickly-create-uis-in-python-a97ae1394d5).
<div style="text-align: right">Photo by Eftakher Alam on Unsplash</div>
### Motivation
Sometimes, however, your target audience is not technical enough.\\
They’d love to use your python scripts but only as long as they didn’t have to look at a single line of code.\\
They need a Uer Interface (UI) in such a case.\\
\* years ago, I used to code UIs using Java/Python/Matlab. They are good, but time-consuming in some cases. Some environments also provide graphical operations for UI design, which is essentially UI for UI designers :)
### The task
Design a UI to check if two files are identical, without mannually referecing to the Python scripts.\\
\* for file compare algorithms, pls check a previous post: [Beyond Compare](yongchaohuang/github.io/beyond_compare)\\
We essentially need a way to load up two files, and then choose the encryption we would like to use to do the file comparison.
## Python Libraries Available for UI usage
There are essentially 3 big Python UI libraries; Tkinter, wxPython and PyQT.
## Code the UI
To build that UI, we can use the following code:
```
import PySimpleGUI as sg
layout = [
[sg.Text('File 1'), sg.InputText(), sg.FileBrowse(),
sg.Checkbox('MD5'), sg.Checkbox('SHA1')
],
[sg.Text('File 2'), sg.InputText(), sg.FileBrowse(),
sg.Checkbox('SHA256')
],
[sg.Output(size=(88, 20))],
[sg.Submit(), sg.Cancel()]
]
window = sg.Window('File Compare', layout)
while True: # The Event Loop
event, values = window.read()
# print(event, values) #debug
if event in (None, 'Exit', 'Cancel'):
break
```
which results in:
### The Pythonic way
The original article introduces a Python-based approach to quickly compare two files, this is the focus of this note.
\\
1. Check the integrity of data
* - MD5 Checksum: returns a hexadecimal number for the contents of a file.
```
import hashlib, sys
files = [sys.argv[1], sys.argv[2]] #these are the arguments we take
def md5(fname):
md5hash = hashlib.md5()
with open(fname) as handle: #opening the file one line at a time for memory considerations
for line in handle:
md5hash.update(line.encode('utf-8'))
return(md5hash.hexdigest())
print('Comparing Files:',files[0],'and',files[1])
if md5(files[0]) == md5(files[1]):
print('Matched')
else:
print('Not Matched')
```
Running above gives: \\
* - the SHA1 algorithm: another hexadecimal algorithm that converts file contents into a string.
```
import hashlib, sys
files = [sys.argv[1], sys.argv[2]] #these are the arguments we take
def sha1(fname):
sha1hash = hashlib.sha1()
with open(fname) as handle: #opening the file one line at a time for memory considerations
for line in handle:
sha1hash.update(line.encode('utf-8'))
return(sha1hash.hexdigest())
print('Comparing Files:',files[0],'and',files[1])
if sha1(files[0]) == sha1(files[1]):
print('Matched')
else:
print('Not Matched')
```
Running above gives: \\
2. Check data contents with SQL\\
Using a couple of Python libraries, we can import our files into an SQL database, and use the Except Operator to highlight any differences.\\
\* The only thing to note is that Except expects the data to be ordered; otherwise, it will highlight everything as a difference.
```
import sys, sqlite3, pandas as pd
files = [sys.argv[1], sys.argv[2]] #these are the arguments we take
conn = sqlite3.connect(':memory:') #we are spinning an SQL db in memory
cur = conn.cursor()
chunksize = 10000
i=0
for file in files:
i = i+1
for chunk in pd.read_csv(file, chunksize=chunksize): #load the file in chunks in case its too big
chunk.columns = chunk.columns.str.replace(' ', '_') #replacing spaces with underscores for column names
chunk.to_sql(name='file' + str(i), con=conn, if_exists='append')
print('Comparing', files[0], 'to', files[1]) #Compare if all data from File[0] are present in File[1]
cur.execute( '''SELECT * FROM File1
EXCEPT
SELECT * FROM File2''')
i=0
for row in cur:
print(row)
i=i+1
if i==0: print('No Differences')
print('Comparing', files[1], 'to', files[0]) #Compare if all data from File[1] are present in File[0]
cur.execute( '''SELECT * FROM File2
EXCEPT
SELECT * FROM File1''')
i=0
for row in cur:
print(row)
i=i+1
if i==0: print('No Differences')
cur.close()
```
Running above gives: \\
3. Check data contents with Pandas\\
\* prepare data in a dataframe
- Using the .equals() method
```
import sys, sqlite3, pandas as pd
files = [sys.argv[1], sys.argv[2]] #these are the arguments we take
df1 = pd.read_csv(files[0])
df2 = pd.read_csv(files[1])
df3 = df1.equals(df2)
print('Matches:', df3)
```
Running above gives: \\
- Using the .any()
```
import sys, sqlite3, pandas as pd
files = [sys.argv[1], sys.argv[2]] #these are the arguments we take
df1 = pd.read_csv(files[0])
df2 = pd.read_csv(files[1])
df3 = (df1 != df2).any(axis=None)
print('Differences in file:', df3)
df3 = (df1 != df2).any(1)
ne_stacked = (df1 != df2).stack()
changed = ne_stacked[ne_stacked]
changed.index.names = ['id', 'col']
print('Differences In:')
print(changed)
```
Running above gives: \\
- Using the .Eq()
```
import sys, sqlite3, pandas as pd, numpy as np
files = [sys.argv[1], sys.argv[2]] #these are the arguments we take
df1 = pd.read_csv(files[0])
df2 = pd.read_csv(files[1])
df3 = df1.eq(df2)
print(df3.all())
#print(df3.all(axis=1))
df4 = df3.all(axis=1)
df4 = pd.DataFrame(df4, columns=['Columns'])
print(df4[df4['Columns']==False])
```
Running above gives: \\
