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• 7 Reasons to Migrate from Google Analytics to Matomo Now

  15 mai 2022, par Erin

  The release of Google Analytics 4 (GA4), and the subsequent depreciation of Universal Analytics, has caused a stir amongst webmasters, SEO experts, marketers and the likes.

  Google’s Universal Analytics is the most widely used web analytics platform in the world, but from 1 July 2023, it will no longer process any new data. Google is now pushing users to set up GA4 tracking imminently.

  If you’re like many and wondering if you should upgrade to Google Analytics 4, there are two key reasons why this might be a risk :

  1. GDPR violations : recent rulings have deemed Google Analytics illegal in France and Austria, and it’s likely that this trend will continue across the EU.
  2. Data loss : users switching to Google Analytics 4 can’t migrate their data from Universal Analytics.

  To mitigate these risks, many organisations are looking to switch to a Google Analytics alternative like Matomo. This is an ideal option for organisations that want to take ownership of their data, get compliant with privacy regulations and save themselves the stress of Google deprecating the software they rely on.

  Whilst there are two major reasons to steer clear of Google Analytics 4, there are 7 reasons why migrating to Matomo instead could save your business time, money and peace of mind.

  If you want to avoid the pitfalls of GA4 and are thinking about migrating from Universal Analytics to Matomo, here’s why you should make the switch now.

  1. Keep your historical Universal Analytics data

  Users switching to Google Analytics 4 will be disappointed to find out that GA4 does not accept data imports from Universal Analytics. On top of that, Google also announced that after Universal Analytics stops processing new data (1 July 2023), users will only be able to access this data for “at least six months”. 

  Years of valuable insights will be completely wiped and organisations will not be able to report on year over year results.

  Fortunately, any organisation using Universal Analytics can import this data into Matomo using our Google Analytics Importer plugin. So you can reduce business disruptions and retain years of valuable web analytics data when you switch to Matomo.

  Our comprehensive migration documentation features a handy video, written guides and FAQs to ensure a smooth migration process.

  2. Ease of use

  Web analytics is complicated enough without having to navigate confusing platform user interfaces (UIs). One of GA4’s biggest drawbacks is the “awful and unusable” interface which has received an overwhelming amount of negative backlash online. 

  Matomo’s intuitive UI contains many of the familiar features that made Universal Analytics so well-liked. You’ll find the same popular features like Visitors, Behaviour, and Acquisition to name a few.

  

  User Flow in Matomo

  When you switch to Matomo you can get up to speed quickly and spend more time focusing on high-value tasks, rather than learning about everything new in GA4.

  3. 100% accurate unsampled data

  GA4 implements data sampling and machine learning to fill gaps. Often what you are basing critical business decisions on is actually an estimate of activity. 

  Matomo does not use data sampling, so this guarantees you will always see the full picture.

  “My primary reason to use Matomo is to get the unsampled data, [...] if your website gets lots of traffic and you can’t afford an enterprise level tool like GA premium [GA360] then Matomo is your best choice.”

  With Matomo you can be confident your data-driven decisions are being made with real data.

  4. Privacy by design

  Built-in privacy has always been at the core of Matomo. One key method we use to achieve this, is by giving you 100% data ownership of your web analytics data. You don’t ever have to worry about the data landing in the wrong hands or being used in unethical ways – like unsolicited advertising. 

  On the contrary, Google Analytics is regularly under fire for controversial uses of data. While Google has made changes to make GA4 more privacy-focused, it’s all just smoke and mirrors. The data collected from Google Analytics accounts is used by Google to create digital profiles on internet users, which is then used for advertising. 

  Consumers are becoming increasingly concerned about how businesses are using their data. Businesses that develop privacy strategies, utilise privacy-focused tools will gain a competitive advantage and a loyal customer-base. 

  Prioritise the protection of your user data by switching to a privacy-by-design analytics solution.

  5. Compliance with global privacy laws

  To date, Google Analytics has been deemed illegal to use in France and Austria due to data transfers to the US. Upgrading to GA4 doesn’t make this problem go away either since data is still transferred to the US. 

  Matomo is easily configured to follow even the strictest of privacy laws like GDPR, HIPAA, CCPA, LGPD and PECR. Here’s how :

  • Matomo’s opt-out mechanism lets users opt-out of web analytics tracking
  • You can configure analytics for data retention of raw data and aggregated reports
  • Users can anonymise IP addresses as well as implement other data anonymisation techniques
  • Matomo can respect DoNotTrack setting
  • Users can set up Matomo so it doesn’t process any personal data or PII (personally identifiable information)
  • It’s easy to set shorter expiration dates for tracking cookies
  • Matomo allows you to disable Visits Log and Visitor Profile
  • Users aren’t tracked across websites unless specifically enabled

  Matomo can also be used without cookie consent banners (unlike with Google Analytics, which will always need user consent to track). Matomo has been approved by the French Data Protection Authority (CNIL) as one of the select few web analytics tools that can be used to collect data without tracking consent.

  Every year more countries are drafting legislation that mirrors the European Union’s GDPR (like the Brazilian LGPD). Matomo is designed to stay data-privacy law compliant, and always will be.

  Stay on top of global privacy laws and reduce the time you spend on compliance by switching to a privacy-compliant solution. 

  6. All-in-one web analytics

  Matomo gives you easy access to Heatmaps, Session Recordings, A/B testing, Funnels analytics, and more right out of the box. This means that digital marketing, UX and procurement teams won’t need to set up and manage multiple tools for behavioural analytics – it’s all in one place.

  Learn more about your audience, save money and reduce complexity by switching to an all-in-one analytics solution.

  Check out Matomo’s extensive product features.

  

  Page Scroll Depth in Matomo

  7. Tag Manager built-in

  Unlike GA4, the Matomo Tag Manager comes built-in for an efficient and consistent user experience. Matomo Tag Manager offers a pain-free solution for embedding tracking codes on your website without needing help from a web developer or someone with technical knowledge.

  Help your Marketing team track more website actions and give time back to your web developer by switching to Matomo Tag Manager.

  Final Thoughts

  Google Analytics is free to use, but the surrounding legal issues with the platform and implications of switching to GA4 will make migrating a tough choice for many businesses. 

  Now is the chance for organisations to step away from the advertising tech giant, take ownership of web analytics data and get compliant. Switch to the leading Google Analytics alternative and see why over 1 million websites choose Matomo for their web analytics.

  Ready to get started with your own Google Analytics to Matomo migration ? Try Matomo free for 21 days now – no credit card required. 

  21 day free trial. No credit card required.

  Your email address
  

  Your website address
  

  Your Analytics subdomain will be .matomo.cloud (change)

  Choose your analytics subdomain
  

  .matomo.cloud

  I hereby accept the terms and conditions and the data processing agreeement (DPA).

  
  Your information will be used to create an account on our cloud service. For more information please consult our privacy policy.
  
  

  » Start improving your websites now

  

• How to visualize matplotlib animation in Jupyter notebook

  23 avril 2020, par anonymous13

  I am trying to create a racing bar chart similar to the one in the link (https://towardsdatascience.com/bar-chart-race-in-python-with-matplotlib-8e687a5c8a41). 
However I am unable to see the animation in my Jupyter notebook

  



  code

  



  import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import matplotlib.animation as animation
from IPython.display import HTML

df = pd.read_csv('https://gist.githubusercontent.com/johnburnmurdoch/4199dbe55095c3e13de8d5b2e5e5307a/raw/fa018b25c24b7b5f47fd0568937ff6c04e384786/city_populations', 
                 usecols=['name', 'group', 'year', 'value'])

current_year = 2018
dff = (df[df['year'].eq(current_year)]
       .sort_values(by='value', ascending=True)
       .head(10))

colors = dict(zip(
    ['India', 'Europe', 'Asia', 'Latin America',
     'Middle East', 'North America', 'Africa'],
    ['#adb0ff', '#ffb3ff', '#90d595', '#e48381',
     '#aafbff', '#f7bb5f', '#eafb50']
))
group_lk = df.set_index('name')['group'].to_dict()


fig, ax = plt.subplots(figsize=(15, 8))
def draw_barchart(year):
    dff = df[df['year'].eq(year)].sort_values(by='value', ascending=True).tail(10)
    ax.clear()
    ax.barh(dff['name'], dff['value'], color=[colors[group_lk[x]] for x in dff['name']])
    dx = dff['value'].max() / 200
    for i, (value, name) in enumerate(zip(dff['value'], dff['name'])):
        ax.text(value-dx, i,     name,           size=14, weight=600, ha='right', va='bottom')
        ax.text(value-dx, i-.25, group_lk[name], size=10, color='#444444', ha='right', va='baseline')
        ax.text(value+dx, i,     f'{value:,.0f}',  size=14, ha='left',  va='center')
    # ... polished styles
    ax.text(1, 0.4, year, transform=ax.transAxes, color='#777777', size=46, ha='right', weight=800)
    ax.text(0, 1.06, 'Population (thousands)', transform=ax.transAxes, size=12, color='#777777')
    ax.xaxis.set_major_formatter(ticker.StrMethodFormatter('{x:,.0f}'))
    ax.xaxis.set_ticks_position('top')
    ax.tick_params(axis='x', colors='#777777', labelsize=12)
    ax.set_yticks([])
    ax.margins(0, 0.01)
    ax.grid(which='major', axis='x', linestyle='-')
    ax.set_axisbelow(True)
    ax.text(0, 1.12, 'The most populous cities in the world from 1500 to 2018',
            transform=ax.transAxes, size=24, weight=600, ha='left')
    ax.text(1, 0, 'by @pratapvardhan; credit @jburnmurdoch', transform=ax.transAxes, ha='right',
            color='#777777', bbox=dict(facecolor='white', alpha=0.8, edgecolor='white'))
    plt.box(False)

draw_barchart(2018)

import matplotlib.animation as animation
from IPython.display import HTML
fig, ax = plt.subplots(figsize=(15, 8))
animator = animation.FuncAnimation(fig, draw_barchart, frames=range(1968, 2019))
HTML(animator.to_jshtml()) 



  



  Below is what I tried using and the errors

  



  HTML(animator.to_jshtml())  <-- Static output with buttons unable to visualize animation
plt.rcParams["animation.html"] = "jshtml"  <- no error and output
HTML(animator.to_html5_video())  <---Requested MovieWriter (ffmpeg) not available 



  



  Note I have FFmpeg installed in my system.
Can you help me with the issue

  


• Translating code snippet function from c++ to python, using ffmpeg to crop imageFile [closed]

  5 octobre 2022, par Minai

  The code above is written in C++. It takes an input image file and crops its imageHeight and Width using ffmpeg libray, and saves the output image fractures to a txt file. I want the same code written in python. The fractures are saved using the new name of the original file plus the crop size to differentiate each image grid.

  


  void shatterImages(string imagefiles, int imgWidth, int imgHeight, 
    int cropSizeW, int cropSizeH)
{
    cout << "Calculating subimage fractures for " << imagefiles << " fullframe images..." << endl;
    if(!imagefiles.isnull()){
        string outputCommand="";
        int i,j;
        ofstream myfile2 ("outputfile.txt");
        if (myfile2.is_open()) {
                //cout << "Fracturing: " << imagefiles << endl;
                outputCommand = "ffmpeg";
                for (i = 0; i < ceil(imgWidth / (0.0+cropSizeW)); i += 1)  //loop vert over image
                {
                    for (j = 0; j < ceil(imgHeight / (0.0+cropSizeH)); j += 1)  //loop horizontally over image
                    {
                        //replace shattered images from fullframes to subframes
                         outputCommand +=
                                " -i " + imagefiles + " -qscale:v 1 -vf \"crop=" + to_string(cropSizeW) + ":" +
                                to_string(cropSizeH) + ":" + to_string(i * cropSizeW) + ":" + to_string(j * cropSizeH) +
                                "\" " + imagefiles.substr(0, imagefiles.length() - 4) + "-" +
                                to_string(i * cropSizeW) + "-" + to_string(j * cropSizeH) + ".jpg";
                    }
                }
                //print(outputCommand)
                myfile2 << outputCommand << endl;
            myfile2.close();
            cout << "Finished writing to: fractureAllImages.sh" << endl;
        }
        else
            cout << "Unable to open output file for: fractureAllImages.sh" << endl;
    }
}


  


  I Transalted into python like this but I am getting an error :

  


  import csv
from csv import reader, writer
from math import ceil
import string

#Using context managers to open file
with open ("coral.txt", 'w') as infile:
    outputFile = writer(infile)

    # writes to the file
    #infile.write('Test me, 123, abc, xyz')

    
#creating the main fucntion
def coral (imageFiles, imageWidth, imageHeight, cropWidth, cropHeight):
    print("calculating the subimage fractures for", imageFiles, "full frame images")
    
    if imageFiles != None:
        def outputCommand (i, j):
            outputFile.write("coral.txt")
            if (infile is open):
                print ("fracturing:", imageFiles)
                outputCommand = "ffmpeg"
            
                #looping vertically over the image
                for (i = 0, i < ceil(imageWidth/(0.0+cropWidth), i++)){

                    #Looping horizontally over the image
                    for (int j = 0; j(0.0+cropWidth); j++)){

                        #replace shattered images from fullframes to subframes and saving
                        outputCommand += " -i " + imageFiles + " -qscale:v 1 -vf \"crop=" + to_string(cropWidth) + ":" + to_string(cropHeight) + ":" + to_string(i * cropWidth) + ":" + to_string(j * cropHeight) + "\" " + imageFiles.substr(0, imageFiles.length() - 4) + "-" + to_string(i * cropWidth) + "-" + to_string(j * cropHeight) + ".jpg";
                
                    }

                }


    
                #printing to the file coral.txt
                infile(outputCommand)
            infile.close() 
            print("Finnished writing to text file")
    

    else:
        print("Unable to open output file for fracturing")


#remember to close opened files
outputFile.close()