Defi_Auditor
@Defi_Auditor
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Onchain data validator breaking meaningful insights.
Cryptoverse
Joined June 2024
Data analysis is like cooking: Pandas = your knife (cuts & preps raw data) Matplotlib = your pan (basic cooking/charts) Seaborn = your seasoning (makes it beautiful) Together they turn messy spreadsheets into insights that actually matter. #DataScience #MediaPilot
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Python isn’t just a coding skill — it’s a gateway to staying relevant in today’s innovation race. From AI and Machine Learning to data analytics, it scales effortlessly to handle huge, messy datasets and turns raw data into value. Learning Python is learning the future.
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Explore NumPy's power for data handling: iterate effortlessly, join & split arrays seamlessly, sort data to organize, and filter for condition-based insights. Perfect for finance or any data domain, these functions streamline complex array manipulations with speed and ease.
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After NumPy: visualize insights with Matplotlib. Turning raw data into clear charts makes meaning visible—just like Power BI or Tableau. Essential for analysts to present trends, patterns, and decisions in impactful ways. #DataViz #Python #Matplotlib
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Python automates business, analyzes data, builds websites, and powers web3 (blockchain, smart contracts, analytics). Careers: data analyst, automation specialist, web developer, blockchain dev, quant, DevOps, AI/ML engineer, and more.
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NumPy beats Excel in efficiency for both basics and complex tasks. Example: sum large data arrays instantly with np.sum(arr) vs. slow manual Excel formulas. Fast, scalable, automatable — ideal for analysts driving smarter insights! #DataScience #Python #NumPy
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NumPy is the go-to Python library for data and business analysts. It powers fast, efficient numerical computing with arrays, matrices, and math functions — forming the core of data analysis, machine learning, and insights generation. Stay tuned for more! #Python #DataAnalytics
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Below are few popular Python libraries explained in brief : - NumPy: fast math & arrays. - Pandas: data analysis. - Matplotlib/Seaborn: charts. - SciPy: scientific computing. - Scikit-learn: machine learning. - TensorFlow/Keras/PyTorch: deep learning. - Flask/Django: web apps.
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7/7 cont... Playlist: Spotify Fav - Bohemian Rhapsody - Wake me up when September ends - Hotel California - The Scientist If you have reached here so far, congrats now you’ve coded your own playlist! 🎉 #Python #Coding #SpotifyPlaylist #Learnandgrow #MediaPilot
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6/7 cont... myplaylist.remove_song("ABCFG") https://t.co/F5p1vJn4MU_songs() Output: Added song: Bohemian Rhapsody Added song: Wake me up when September ends Added song: ABCFG Added song: Hotel California Added song: The Scientist Removed song: ABCFG cont...
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5/7 Run it & see the magic: myplaylist = playlist("Spotify Fav") myplaylist.add_song("Bohemian Rhapsody") myplaylist.add_song("Wake me up when September ends") myplaylist.add_song("ABCFG") myplaylist.add_song("Hotel California") myplaylist.add_song("The Scientist") cont...
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4/7 Show your final favorite playlist: def show_songs(self): print(f"Playlist: { https://t.co/OVAeSel9hr}") for song in self.songs: print(f" - {song}") See your favorite songs in order! 📋
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3/7 Remove songs with remove_song(): def remove_song(self, song): if song in self.songs: self.songs.remove(song) print(f"Removed song: {song}") No more unwanted tracks! 🚫
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2/7 Add songs using the add_song() method: def add_song(self, song): self.songs.append(song) print(f"Added song: {song}") Each song gets added to your playlist! 🎧
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1/7 First, create a Playlist class: class playlist: def __init__(self, name): https://t.co/OVAeSel9hr = name self.songs = [] This sets up your playlist with a name and an empty song list. 🎵
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Want to build your own Spotify playlist using Python? Let’s do it in 7 easy steps! 🎶🧵
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Something exciting is coming your way tomorrow! Stay tuned for a special walkthrough that'll strike a chord with every music lover. 🎶👀 #Python #MusicTech
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Why @Python_Dv wins for beginners: Learn once, code everywhere! While Java needs verbose syntax, JavaScript changes constantly, R is niche, and SQL only handles databases - Python does web dev, data science, AI, automation & more with clean, readable code. Your gateway to tech!
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Virtual environments in Python = your coding lifesaver! They isolate project dependencies, preventing version conflicts & keeping your system clean. Think of it as separate toolboxes for each project. Fun bonus: Install cowsay & get ASCII cows greeting you in terminal! 🐄
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🐍 New to Python? Master these math functions in minutes! abs(-5) → 5 math.sqrt(16) → 4.0 math.ceil(3.2) → 4 math.floor(3.8) → 3 math.pi → 3.14159... Pro tip: Try Googling "recursion" for a fun surprise! 😉 #PythonBasics #MediaPilot
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