Capture Hard Drive Folder Structure with Python

Use glob and pandas to create a snapshot of any computers current folder structure.

Suppose we want to grab the folder structure of a computer without backing up every single file. Maybe we want to index the folder structure, maybe find all of the .py files scattered across our computer, or we want to take a look at all of the files/folders that exist on another computer.  Here we’ll capture the contents (all files and folders) of our input folder, along with the contents of every sub-folder.

 

folderhierarchy

To begin, we’ll define a few functions:

def FileOrFolder(filepath):
    if "." in filepath:
        return('File')
    else:
        return('Folder')

def StillFolders(dfcolumn):
    FolderCount=0
    for item in dfcolumn:
        if item=='Folder':
            FolderCount+=1
        else:
            pass 
    if FolderCount>0:
        return('Still Folders')
    else:
        return('No Folders')

We’ll want to continue looping through each sub-folder (and their sub-folders) until there are no more folders to look in. “FileOrFolder” identifies whether a given filepath is a File or Folder. “StillFolders” looks in a single column of a DataFrame and identifies whether or not any Folders are remaining.

def find_contents(folderpath):
    #Find contents of intial input
    contents=pd.DataFrame(glob.glob(folderpath + '*'),columns=[('Path')])
    #http://stackoverflow.com/questions/12356501/pandas-create-two-new-columns-in-a-dataframe-with-values-calculated-from-a-pre?rq=1
    contents['FileOrFolder']=contents['Path'].map(FileOrFolder)
    return contents

The “find_contents” function uses glob to find all of the contents of a given folderpath. The contents is returned as a DataFrame.

In order to find all lower-level files and folders, we’ll write a short procedure to continue identifying the contents of sub-folders while the previous “order” folder still contains folders. So the full code will look something like this:

# -*- coding: utf-8 -*-
"""
Purpose: Returns all Folders and Files in a parent folder with hierarchical order.

Input: A folderpath.
Output: An excel file with four columns
            A. Index - Integer.
            B. Path - String.
            C. FileOrFolder - String.
            D. Order - Integer.  "0" is the input folderpath.
"""
import glob
import pandas as pd
from tkinter import Tk
from tkinter import filedialog

Tk().withdraw()

def FileOrFolder(filepath):
    if "." in filepath:
        return('File')
    else:
        return('Folder')

def StillFolders(dfcolumn):
    FolderCount=0
    for item in dfcolumn:
        if item=='Folder':
            FolderCount+=1
        else:
            pass 
    if FolderCount>0:
        return('Still Folders')
    else:
        return('No Folders')

def find_contents(folderpath):
    #Find contents of intial input
    contents=pd.DataFrame(glob.glob(folderpath + '*'),columns=[('Path')])
    #http://stackoverflow.com/questions/12356501/pandas-create-two-new-columns-in-a-dataframe-with-values-calculated-from-a-pre?rq=1
    contents['FileOrFolder']=contents['Path'].map(FileOrFolder)
    return contents

folder=filedialog.askdirectory(initialdir=r'C:\\',title='Please select folder')

all_levels={}
all_levels[0]=pd.DataFrame()
all_levels[0]=find_contents(folder)
all_levels[0]['Order']=0

level=1

while StillFolders(all_levels[level-1]['FileOrFolder'])=='Still Folders':
    all_levels[level]=pd.DataFrame()  
    #http://stackoverflow.com/questions/7837722/what-is-the-most-efficient-way-to-loop-through-dataframes-with-pandas        
    for index, row in all_levels[level-1][all_levels[level-1]['FileOrFolder']=='Folder'].iterrows():               
        all_levels[level]=all_levels[level].append(find_contents(row['Path'] + '\\'),ignore_index=True)
        all_levels[level]['Order']=level
    level+=1

#Concatenate all dataframes in all_levels
combined_all_levels=pd.concat([all_levels[level] for level in all_levels])
#Save to excel on one sheet
combined_all_levels.to_excel('FolderHierarchyResults.xlsx',index_label='Index')

Our output is temporarily stored as a dictionary of DataFrames, which we then concatenate into a single DataFrame, and then finally use to_excel() to write our results into a spreadsheet.

Generating Math Tests with Python

Auto-Generate Unique Tests

This is a script for generating a bunch of unique math tests from a “Test Template” and a spreadsheet containing test inputs and problem solutions.

In our Test Template we set the layout of our test and define our test problems. Our test problems will have variable placeholders (TestID, Question ID, VarA, etc.) that we will replace with data from our “Test Data” spreadsheet.

In our excel file, we random generate values for the A, B, and C variables (using the =RANDBETWEEN() function) and clearly identify which Question, Equation, and Test ID they correspond to. In the Excel file, we’ll calculate solutions using the input data and equation listed for each entry.

Next, we can run our script. This is dependent on the docx (Note: pip install python-docx), docx2txt, re, pandas, and tkinter libraries.  Forms will pop-up prompting you for the Test Template and Test Data files.

"""
Creates unique test documents with data
taken from a DataFrame (which is populated from an excel file).

Input: Test Template (Word Document).  Test Data (Excel File)
Output: 20 Unique Tests (Test Data)
"""
#Import modules
import docx
import docx2txt
import pandas as pd
import re
from tkinter import Tk
from tkinter import filedialog

Tk().withdraw()

#Define "Test" template
template_file=filedialog.askopenfilename(title="Please select Word template")
testdata_file=filedialog.askopenfilename(title="Please select Test Data spreadsheet")

#Read file data
template_text=docx2txt.process(template_file)
testdata=pd.read_excel(testdata_file)

#Produce 20 unique tests
for i in range(20):
    new_text=template_text
    #Add data for 10 unique questions
    for j in range(10):
        #Define replacement dictionary
        #http://stackoverflow.com/questions/6116978/python-replace-multiple-strings
        rep={'QuestionID':str(testdata['Question'][i+j*20]),
             'VarA':str(testdata['VarA'][i+j*20]),
             'VarB':str(testdata['VarB'][i+j*20]),
             'VarC':str(testdata['VarC'][i+j*20])}
        rep=dict((re.escape(k),v) for k, v in rep.items())
        pattern=re.compile("|".join(rep.keys()))
    
        if j==0:
            new_text=pattern.sub(lambda m: rep[re.escape(m.group(0))],template_text,count=4)
            new_text=new_text.replace('TestID','Test #' + str(i+1))
        else:
            new_text=pattern.sub(lambda m: rep[re.escape(m.group(0))],new_text,count=4)
            
    #Create and save new test document
    test_doc=docx.Document()
    test_doc.add_paragraph(new_text)
    test_doc.save(r'C:\Users\Craig\Documents\Python Scripts\Test #'+str(i+1)+'.docx')

After the files have been selected, the script reads the Test Template text and loads the Test Data into a DataFrame. We then loop through the Test Data and produce 20 unique test documents by substituting the placeholder variables with values from the Test Data spreadsheet. Each test document is clearly labeled and we can use our original Test Data as our answer key.

Thanks to Andrew Clark for his code for replacing multiple text strings.

Reading & Writing Excel Data with Python

Using pandas to read/write data in Excel.

In this post we’re going to explore how easy it is to read and write data in Excel using Python.  There’s a few different ways to do this.  We’re going to use pandas.  The pandas DataFrame  is the main data structure that we’re going to be working with.

Reading

The sample Excel data we’ll be using is available on Tableau’s Community page.

To load a single sheet of the Excel file into Python, we’ll use the read_excel function:

import pandas as pd
sales_data=pd.read_excel(r'C:\Users\Craig\Downloads\Sample - Superstore Sales (Excel).xls')

This loads one tab of the spreadsheet (.xls, .xlsx, or .xlsm) into a DataFrame.

In fact, if we didn’t want to download the Excel file locally, we can load it into Python directly from the URL:

sales_data_fromURL=pd.read_excel('https://community.tableau.com/servlet/JiveServlet/downloadBody/1236-102-1-1149/Sample%20-%20Superstore%20Sales%20(Excel).xls')

Note that we can load specific sheets (sheetname), grab specific columns (parse_cols), and handle N/A values (na_values) by using the optional keyword arguments.

To load all of the sheets/tabs within an Excel file into Python, we can set sheetname=None:

sales_data_all=pd.read_excel(r'C:\Users\Craig\Downloads\Sample - Superstore Sales (Excel).xls', sheetname=None)

This will return a dictionary of DataFrames – one for each sheet.

Writing

Writing existing Python data to an Excel file is just as straightforward.  If our data is already a DataFrame, we can call the pd.DataFrame.to_excel(‘filename.xlsx’) function.  If not, we can just convert the data into a DataFrame and then call to_excel.

import pandas as pd
import numpy as np
df=pd.DataFrame(np.random.randn(50,50))
df.to_excel('MyDataFrame.xlsx')

This will work for the .xls, .xlsx, and .xlsm.  Pandas also writer functions such as to_csv, to_sql, to_html, and a few others.

To write data on multiple sheets, we can use the pd.ExcelWriter function as shown in the pandas documentation:

with pd.ExcelWriter('filename.xlsx') as writer:
    df1.to_excel(writer, sheet_name='Sheet1')
    df2.to_excel(writer, sheet_name='Sheet2')

Quick Data Grabs

Try experimenting with the

pd.read_clipboard() #and
pd.to_clipboard()

functions to quickly transfer data from Excel to Python and vice-versa.

Thank you, pandas, for creating and maintaining excellent documentation.

Creating Images with PyQRCode

Mass generation of QR codes with Python.

This is a script for taking a list of URLs from a spreadsheet and generating a captioned QR code for each entry.

Specifically, the script reads the ‘LongURLs‘ input file, shortens the URLs, creates QR Codes, adds captions, and saves each QR code as a .PNG image file.

We shorten the URLs to reduce the complexity of the QR code, which makes it less likely to become unreadable from printing imperfections and dirt smudges.

We’ll use:

1. Numpy
2. Pandas
3. PyQRCode
4. pyshorteners
5. PIL
6. PyPNG

We load our URLs and IDs (captions) using the LongURLs template.

longurl_09172016
LongURLs Template

Next, we run the script and our QR codes will be output as PNG files in the same directory as our script.

Email links (such as “mailto:test@mailinator.com”) can be used as input URLs, but you’ll need to disable the ValueError:’Please enter a valid url’ that pyshorteners will raise.

import numpy as np
import pyqrcode
import pandas as pd
from pyshorteners import Shortener
from PIL import ImageFont
from PIL import Image
from PIL import ImageDraw

shortener=Shortener('Tinyurl',timeout=10)
DF = pd.DataFrame(pd.read_excel(r'C:\Users\Craig\Documents\Python Scripts\LongURLs.xlsx',
                                sheetname='LongURLs',parse_cols='A:B'))
LongURL=DF.iloc[:,0]
ID=DF.iloc[:,1]

ShortURL=np.array(LongURL, dtype='str')

for i in range(0,len(LongURL)):
    ShortURL[i]=shortener.short(LongURL[i])
    code=pyqrcode.create(ShortURL[i])
    code.png(ID[i] + '.png', scale=6, module_color=[0,0,0,128],quiet_zone=7) 

    #Adds caption
    img=Image.open(ID[i] + '.png')
    draw=ImageDraw.Draw(img)
    font = ImageFont.truetype("ariblk.ttf", 20)
    xcor=100
    draw.text((xcor,245),str(ID[i]),font=font)
    img.save(str(ID[i]) + '.png')

book-1book-2book-3book-4book-5

With pyshorteners, we have the option of using a bunch of different URL shorteners – in this case we used TinyURL.  See the pyshorteners github for a full list.

The font of your caption can be adjusted by taking the desired font’s .tff file (found in Control Panel > Appearance and Personalization > Fonts), copying it into the same folder as your script, and updating line 25.

You might need to adjust the “xcor” value (based on the length of your IDs) to get your caption centered under the QR image.  If your ID lengths are all different, consider adding a few lines of code to detect the ID length and update “xcor” dynamically.

Finding Words with PyPDF2

Find all instances of words in a PDF with Python’s PyPDF2 library.

This is a script for finding all instances of a given search word (or multiple search words) in a PDF.

For our example, we’ll be using a PDF of Romeo and Juliet.  In this case, our search terms are “Romeo” and “Juliet” (search is not case-sensitive).

import PyPDF2
import re

pdfFileObj=open(r'C:\Users\Craig\RomeoAndJuliet.pdf',mode='rb')
pdfReader=PyPDF2.PdfFileReader(pdfFileObj)
number_of_pages=pdfReader.numPages

pages_text=[]
words_start_pos={}
words={}

searchwords=['romeo','juliet']

with open('FoundWordsList.csv', 'w') as f:
    f.write('{0},{1}\n'.format("Sheet Number", "Search Word"))
    for word in searchwords:
        for page in range(number_of_pages):
            print(page)
            pages_text.append(pdfReader.getPage(page).extractText())
            words_start_pos[page]=[dwg.start() for dwg in re.finditer(word, pages_text[page].lower())]
            words[page]=[pages_text[page][value:value+len(word)] for value in words_start_pos[page]]
        for page in words:
            for i in range(0,len(words[page])):
               if str(words[page][i]) != 'nan':
                    f.write('{0},{1}\n'.format(page+1, words[page][i]))
                    print(page, words[page][i])

We run the script and get an output that shows each instance of each search word and the associated PDF page number:
foundsearchwords

This script can be used for a variety of other applications by updating the file path (line 4) and the search terms (line 12).

A few ideas for modification include:

  • Frequency counts of words in books/lyrics (ATS has an awesome frequency count graph generator)
  • Finding reference drawing numbers in a document
  • Identify search terms by prefixes rather than whole words
  • Identifying sheets that need to be updated
  • Using glob to iterate through multiple files

How else would you modify this script?  Let me know!

Thanks for reading!

Special thanks to these sources:

Automate the Boring Stuff with Python
ritesh_shrv on Stack Overflow

Automated Email with Python

Automated email notifications and task tracking system.

This article explains how to use a Python script in conjunction with a simple Action Tracking spreadsheet to create an automated email notifications and task-tracking system for your team.

To begin, let’s setup our “ActionTracker” spreadsheet as shown below:

ActionTracker_Overview

We can use the expression “=IF(ISBLANK(H2)=TRUE,”Active”,”Closed”)” in our Status column to acknowledge when a date has been entered in the “Completion Date” column.  This will help our script later on.

The “Days Open” column can be calculated using “=IF(ISBLANK(H2)=FALSE,H2-F2,TODAY()-F2)”.  As your list grows, be sure to drag down your formulas.

It can be helpful to apply conditional formatting here in order to see which items are “Open” and late, so that we know which items we expect to send notifications about.  This can be accomplished by the expression shown below, but it is not a necessary step.  Again, remember to update your applicable range as your list grows.

ConditionalFormatting

On our “Email” tab, we’ll list our unique assignees by name and add their email addresses (separated by a comma and a space) in column B.

EmailTab

In order to minimize errors, we can apply a Data Validation rule to our “Assignee” column on the “ActionList” tab.  We’ll select all of the unique names on our “Email” tab as the Source validation criteria.  New emails can easily be added to this list, however, we must update our Source range.

DataValidation

Here’s a download link for the ActionTracker template.

Next, we’ll use the following Python script to send automated email notifications to our team for any Open actions that are open for more than three days.  The three day threshold can be easily adjusted in line #50 of the script below.

Note: In order to allow the script to access your gmail account, make sure that your less secure app access settings are currently turned on.

import smtplib
import pandas as pd
import sys
from tkinter import Tk
from tkinter import filedialog
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart

#Define email login information.
from_email="_____@gmail.com" #Replace with your email address.  Must be gmail.
pw="_____" #Replace with gmail password.

#Select file to use.
Tk().withdraw()
filepath=filedialog.askopenfilename(defaultextension='*.xlsx',
                                    filetypes=[('.xlsx files','*.xlsx'),
                                               ('All files','*.*')],
                                    initialdir=r'C:\Users')
if filepath=="." or filepath=="":
    sys.exit(0)

#Import ActionTracker
ActionTracker = pd.DataFrame(pd.read_excel(filepath,sheetname='ActionList',
                                           parse_cols='A:E'))
ActionTracker_maxrow=len(ActionTracker)
status=ActionTracker.iloc[:,0]
LineItem=ActionTracker.iloc[:,1]
action=ActionTracker.iloc[:,2]
person=ActionTracker.iloc[:,3]
DaysOpen=ActionTracker.iloc[:,4]

#Import Email Addresses by name
EmailList=pd.DataFrame(pd.read_excel(filepath,sheetname='Email',index_col=0,
                                     parse_cols='A:B'))

#Establish connection to gmail server, login.
server = smtplib.SMTP('smtp.gmail.com',587)
server.starttls()
server.login(from_email, pw)

msg=MIMEMultipart('alternative')
msg.set_charset('utf8')
msg['FROM']=from_email

#Initialize a list of submittals with invalid email addresses
invalid_addresses=[]

#Send emails to late Action Tracker assignees
for i in range(0,ActionTracker_maxrow):
    if status[i]=='Active' and DaysOpen[i]>3:
        print('Active Line Item #'+str(LineItem[i])+': '+person[i])
        msg=MIMEText("Action Tracker Line Item #" + str(LineItem[i]) + " has been open for " +
                     str(DaysOpen[i]) + " days.\n\n" + str(action[i]) +
                     "\n\nPlease take action.",_charset="UTF-8")
        msg['Subject']="Open Action #" + str(LineItem[i])
        msg['TO']=str(EmailList.iloc[EmailList.index.get_loc(person[i]),0])
        try:
            server.sendmail(from_email, msg['TO'].split(","),
                            msg.as_string())
        except smtplib.SMTPRecipientsRefused:
            invalid_addresses.append(LineItem[i])
            print('Line Item #' + str(LineItem[i]) + 'has an invalid email address.')

if len(invalid_addresses) != 0:
    for i in range(0,len(invalid_addresses)):
        invalid_addresses[i]=invalid_addresses[i].strip('\xa0')
    try:
        if len(invalid_addresses)==1:
            msg=MIMEText(str(invalid_addresses) +
            " has an invalid email address associated with the responsible party.",
            _charset="UTF-8")
        else:
            msg=MIMEText(str(invalid_addresses) +
                             " have invalid email addresses associated with the responsible parties.",
                             _charset="UTF-8")
        msg['Subject']='Invalid Email Addresses'
        msg['TO']=str(from_email)
        server.sendmail(from_email, msg['TO'].split(","),
                        msg.as_string())
    except smtplib.SMTPRecipientsRefused:
        print('Invalid Email Address notification email failed.')

server.quit()

 

And that’s it.  Full automation can be achieved by hard-coding in the file location and using Windows Task Scheduler to execute the Python script.

Finding Correlations

Script for normalizing and finding correlations across variables in a numeric dataset.  Data can be analyzed as a whole or split into ‘n’ many subsets.  When split, normalizations are calculated and correlations are found for each subset.

Input is read from a .csv file with any number of columns (as shown below).  Each column must have the same number of samples.  Script assumes there are headers in the first row.

Input

import numpy as np

#Divides a list (or np.array) into N equal parts.
#http://stackoverflow.com/questions/4119070/how-to-divide-a-list-into-n-equal-parts-python
def slice_list(input, size):
    input_size = len(input)
    slice_size = input_size // size
    remain = input_size % size
    result = []
    iterator = iter(input)
    for i in range(size):
        result.append([])
        for j in range(slice_size):
            result[i].append(iterator.__next__())
        if remain:
            result[i].append(iterator.__next__())
            remain -= 1
    return result

#Functions below are from Data Science From Scratch by Joel Grus
def mean(x):
    return sum(x)/len(x)

def de_mean(x):
    x_bar=mean(x)
    return [x_i-x_bar for x_i in x]

def dot(v,w):
    return sum(v_i*w_i for v_i, w_i in zip(v,w))

def sum_of_squares(v):
    return dot(v,v)

def variance(x):
    n=len(x)
    deviations=de_mean(x)
    return sum_of_squares(deviations)/(n-1)

def standard_deviation(x):
    return np.sqrt(variance(x))  

def covariance(x,y):
    n=len(x)
    return dot(de_mean(x),de_mean(y))/(n-1)

def correlation(x,y):
    stdev_x=standard_deviation(x)
    stdev_y=standard_deviation(y)
    if stdev_x >0 and stdev_y>0:
        return covariance(x,y)/stdev_x/stdev_y
    else:
        return 0

#Read data from CSV
input_data=np.array(np.genfromtxt(r'C:\Users\Craig\Documents\GitHub\normalized\VariableTimeIntervalInput.csv',delimiter=",",skip_header=1))
var_headers=np.genfromtxt(r'C:\Users\Craig\Documents\GitHub\normalized\VariableTimeIntervalInput.csv',delimiter=",",dtype=str,max_rows=1)

#Determine number of samples & variables
number_of_samples=len(input_data[0:,0])
number_of_allvars=len(input_data[0,0:])

#Define number of samples (and start/end points) in full time interval
full_sample=number_of_samples
full_sample_start=0
full_sample_end=number_of_samples

#Define number of intervals to split data into
n=2
dvar_sublists={}
max_sublists=np.zeros((number_of_allvars,n))
min_sublists=np.zeros((number_of_allvars,n))
subnorm_test=np.zeros((full_sample_end, number_of_allvars+1))

#Slice variable lists
for dvar in range(0,number_of_allvars):
    dvar_sublists[dvar]=slice_list(input_data[:,dvar],n)
    for sublist in range(0,n):
        max_sublists[dvar,sublist]=np.max(dvar_sublists[dvar][sublist])
        min_sublists[dvar,sublist]=np.min(dvar_sublists[dvar][sublist])

var_interval_sublists=max_sublists-min_sublists

#Normalize each sublist.
for var in range(0, number_of_allvars):
    x_count=0
    for n_i in range(0,n):
        sublength=len(dvar_sublists[var][n_i])
        for x in range(0,sublength):
            subnorm_test[x_count,var]=(dvar_sublists[var][n_i][x]-min_sublists[var,n_i])/var_interval_sublists[var,n_i]
            subnorm_test[x_count,6]=n_i
            x_count+=1

var_sub_correlation=np.zeros((n,number_of_allvars,number_of_allvars),float)

#Check for correlation between each variable
for n_i in range(0,n):
    for i in range(0,number_of_allvars):
        icount=0
        for j in range(0,number_of_allvars):
            jcount=0
            starti=icount*len(dvar_sublists[i][n_i])
            endi=starti+len(dvar_sublists[i][n_i])
            startj=icount*len(dvar_sublists[j][n_i])
            endj=startj+len(dvar_sublists[j][n_i])
            var_sub_correlation[n_i,i,j]=correlation(subnorm_test[starti:endi,i],subnorm_test[startj:endj,j])

#Writes to CSV
np.savetxt(r'C:\Users\Craig\Documents\GitHub\normalized\sublists_normalized.csv',subnorm_test, delimiter=",") 

print(var_sub_correlation, 'variable correlation matrix')