What is the output of the code given above?
Anonymous Quiz
7%
11
10%
13
35%
33
6%
23
4%
21
37%
Error
Image To Text
🔸Python-tesseract is an optical character recognition (OCR) tool for python. With this library you can easily detect and extract the text embedded in images.
⚙️Installation
Windows:
1. pip install pytesseract
2. Download Tesseract OCR here and install it.
3. Add tesseract installation path to PATH environment variable (here's a video in case you get stuck).
Linux:
sudo apt-get update
sudo apt-get install tesseract-ocr
sudo apt-get install libtesseract-dev
Python-tesseract can read all image types supported by the Pillow and Leptonica imaging libraries, including jpeg, png, gif, bmp, tiff, and others. Additionally, if used as a script, Python-tesseract will print the recognized text instead of writing it to a file. You can also work with multiple languages not only with English!
🔗GitHub
#pytesseract #OCR
🔸Python-tesseract is an optical character recognition (OCR) tool for python. With this library you can easily detect and extract the text embedded in images.
⚙️Installation
Windows:
1. pip install pytesseract
2. Download Tesseract OCR here and install it.
3. Add tesseract installation path to PATH environment variable (here's a video in case you get stuck).
Linux:
sudo apt-get update
sudo apt-get install tesseract-ocr
sudo apt-get install libtesseract-dev
Python-tesseract can read all image types supported by the Pillow and Leptonica imaging libraries, including jpeg, png, gif, bmp, tiff, and others. Additionally, if used as a script, Python-tesseract will print the recognized text instead of writing it to a file. You can also work with multiple languages not only with English!
🔗GitHub
#pytesseract #OCR
Pandas Tutorials 📺
Pandas is a data analysis library that allows you to easily work with different types of data. If you're getting into the data science field, then this library is going to be essential to learn.
1. Getting Started with Data Analysis - Installation and Loading Data [YouTube]
2. DataFrame and Series Basics - Selecting Rows and Columns [YouTube]
3. Indexes - How to Set, Reset, and Use Indexes [YouTube]
4. Filtering - Using Conditionals to Filter Rows and Columns [YouTube]
5. Updating Rows and Columns - Modifying Data Within DataFrames [YouTube]
...
11. Reading/Writing Data to Different Sources - Excel, JSON, SQL, Etc [YouTube]
🔗Full YouTube playlist
#materials #pandas #dataanalysis
Pandas is a data analysis library that allows you to easily work with different types of data. If you're getting into the data science field, then this library is going to be essential to learn.
1. Getting Started with Data Analysis - Installation and Loading Data [YouTube]
2. DataFrame and Series Basics - Selecting Rows and Columns [YouTube]
3. Indexes - How to Set, Reset, and Use Indexes [YouTube]
4. Filtering - Using Conditionals to Filter Rows and Columns [YouTube]
5. Updating Rows and Columns - Modifying Data Within DataFrames [YouTube]
...
11. Reading/Writing Data to Different Sources - Excel, JSON, SQL, Etc [YouTube]
🔗Full YouTube playlist
#materials #pandas #dataanalysis
What is the output of the code given above?
Anonymous Quiz
13%
! !
28%
execute
9%
!
11%
2
38%
execute execute
The easiest way to prepare data in Python
🔸DataPrep is an open-source python library that allows you to prepare your data with just a few lines of code.
⚙️Installation
1. DataPrep.Connector – API wrapper that speeds up development by standardizing calls to multiple APIs as a simple workflow. Basically, it allows you to collect data from common data sources.
2. DataPrep.EDA allows you to perform Exploratory Data Analysis with a few lines of code in seconds.
3. DataPrep.Clean aims to provide a large number of functions with a unified interface for cleaning and standardizing data of various semantic types in a Pandas or Dask DataFrame.
🔗GitHub
🔗Docs
#dataprep #datascience
🔸DataPrep is an open-source python library that allows you to prepare your data with just a few lines of code.
⚙️Installation
pip install dataprep
The library consists of 3 main modules:1. DataPrep.Connector – API wrapper that speeds up development by standardizing calls to multiple APIs as a simple workflow. Basically, it allows you to collect data from common data sources.
2. DataPrep.EDA allows you to perform Exploratory Data Analysis with a few lines of code in seconds.
3. DataPrep.Clean aims to provide a large number of functions with a unified interface for cleaning and standardizing data of various semantic types in a Pandas or Dask DataFrame.
🔗GitHub
🔗Docs
#dataprep #datascience
Text-to-Speech
🔸gTTS (Google Text-to-Speech) is a Python library and CLI tool to interface with Google Translate's text-to-speech API.
⚙️Installation
🔗GitHub
#gtts #texttospeech
🔸gTTS (Google Text-to-Speech) is a Python library and CLI tool to interface with Google Translate's text-to-speech API.
⚙️Installation
pip install gTTS
The gTTS API supports several languages including English, Hindi, Tamil, French, German and many more. The speech can be delivered in any one of the two available audio speeds, fast or slow.🔗GitHub
#gtts #texttospeech
What is the output of the code given above?
Anonymous Quiz
12%
10 15 20
9%
5 25 125
60%
25 125 625
12%
None None None
7%
Error
is vs ==
❓You might have heard somewhere that the Python identity operator (is) is faster than the equality operator (==), or you may feel that it looks more Pythonic. But actually there's a subtle difference between this operators.❗️The
== operator compares the value or equality of two objects, whereas the is operator checks whether two variables point to the same object in memory. In the vast majority of cases, this means you should use the equality operators == and !=, except when you’re comparing to None.#tips
What is the output of the code given above?
Anonymous Quiz
34%
0 1 2 3
22%
1 2 3 4
25%
Error, because __next__ method is not implemented.
19%
Error, because __iter__ must yield the result instead of returning it.
Comprehensive Python Cheatsheet 📚
Without a doubt, this the biggest Python cheat sheet I've ever seen.
This guide essentially breaks down Collections Types, Syntax, System commands, Data Types, Advanced commands and a lot of the basics that are pretty much a requirement to know. Whether you plan on going into software engineering, data science or anything else, learning these concepts will be the fundamental building blocks for your programs.
🔗Comprehensive Python Cheatsheet
🔗GitHub repo
P.S. Thank @b0nd07 for sharing this 🤗
#materials #cheatsheet
Without a doubt, this the biggest Python cheat sheet I've ever seen.
This guide essentially breaks down Collections Types, Syntax, System commands, Data Types, Advanced commands and a lot of the basics that are pretty much a requirement to know. Whether you plan on going into software engineering, data science or anything else, learning these concepts will be the fundamental building blocks for your programs.
🔗Comprehensive Python Cheatsheet
🔗GitHub repo
P.S. Thank @b0nd07 for sharing this 🤗
#materials #cheatsheet