How YData Profiling helped me with data analysis | Sophia Konobe posted on the topic | LinkedIn (2024)

Understanding Key Python and Pandas Concepts for Data AnalysisAs a data analyst or data scientist, mastering Python and its powerful libraries like Pandas and NumPy is crucial. Here’s a quick rundown of some essential concepts and differences you should know, along with simple examples.Differences Between Lists and Tuples in PythonMutability: Lists are mutable (elements can be changed), while tuples are immutable.Syntax: Lists use square brackets [], while tuples use parentheses ().Performance: Tuples are faster due to their immutability.Usage: Lists are used for collections that may change and tuples for fixed collections.Differences Between NumPy and PandasPurpose: NumPy is for numerical computations and arrays. Pandas is for data manipulation and analysis.Functionality: Pandas allows for advanced data manipulation, including merging datasets and handling missing data.Data Structures: NumPy arrays are hom*ogeneous. Pandas DataFrames can hold heterogeneous data.What is a DataFrame in Pandas?A DataFrame is a 2D, size-mutable, and heterogeneous tabular data structure with labeled axes (rows and columns), similar to a table in a database.Handling Missing Data in PandasCheck for Missing Data: df.isna()Drop Missing Data: df.dropna() ''' Used when missing data is negligible '''Fill Missing Data: df.fillna(value)Renaming a Column in a Pandas DataFramedf.rename(columns={'old_column_name': 'new_column_name'}, inplace=True) Using groupby in Pandasgroupby is used to split data into groups based on some criteria and perform operations on these groups independently.Merging Two DataFrames in PandasUse the merge() function to combine DataFrames.Purpose of the apply Function in Pandasapply is used to apply a function along an axis of the DataFrame.Lambda Functions in PythonSmall anonymous functions defined with lambda.Difference Between loc and iloc in Pandasloc: Label-based indexing.iloc: Integer-location-based indexing.Gainingmasteryoftheseideasandresourceswillimproveyourabilityto manipulateandanalyzedata,whichwillimprovetheeffectivenessand efficiencyofyourwork. Cheerstoyournextcodingadventure! Feel free to connect and share your thoughts or questions on these topics!#Python #BigData #DataAnalysis #Programming #NumPy #MachineLearning #AI #Tech #Coding #DataScienceTips

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I'mexcitedtosharemyexperiencelearningPython!🔍 Learning Python has been thrilling and difficult at the same time. Understanding the syntax was one of the first things I had to learn, despite the fact that Python is renowned for being readable, it took some getting used to understand the basic ideas. Nevertheless, it's getting much more natural with time and practice.Exploring different functions and their use, felt both scary and exciting. There were things like methods for working with and manipulating data, it fells like there are endless ways to do things! Despite the challenges, I've been able to build a calculator using Python. I had to keep trying different codes, dealing with and debugging different error messages to find the one would work best for the task I had to complete.This calculator can perform all the basic arithmetic operations you'd expect: addition ➕, subtraction ➖, multiplication ✖️, and division ➗. Plus, it's super easy to use! With just a few lines of code.#Calculator #Python #CodingCommunity #DataScienceCheck out a snippet of the first few lines of codes:

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Excited to put my data science skills to work! My fourth week at GoMyCode focused on implementing a comprehensive relational model using SQL - I went from creating basic tables to creating more complex databases, handling both the DDL and DML parts. Diving into some intriguing queries to extract valuable insights from my database, here are a few hurdles I tackled:* Mastering the syntax and structure of SQL commands – it's like learning a whole new language! * Wrapping my head around fundamental database concepts like tables, keys, and relationships. * Crafting effective queries to retrieve specific data – it's like solving puzzles! * Getting the hang of data manipulation tasks like inserting, updating, and deleting records. * Figuring out how to join tables to access data from multiple sources. * Ensuring data integrity through constraints.* Navigating through errors and debugging SQL queries – trial and error at its finest! 😅 Every challenge is an opportunity to learn and grow, and I'm excited to overcome these challenges on my SQL journey! As my Instructor says "Everything is Figureoutable" 🧠 #SQL #DataScience #ChallengeAccepted

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In today's digital age, numerous everyday human activities generate data, often without us even realizing it.Internet Browsing, Social Media Usage, Streaming Services, Banking Transactions are all activities that generate data.Data is being generated at a rate that has never been seen before.Nonetheless, this vast volume of data may yield insights that are even more valuable when correctly analysed.It’s been three weeks at the GoMyCode hacker space. My journey to becoming a Data scientist has gone from understanding the terms used in the data world to writing basic codes to create tables, manipulate tables and retrieve data from databases using the SQL programming language.Learning the basis and creating a solid foundation in data manipulation is the first step to unlocking the secrets of large datasets.#DigitalCulture #DataDriven #DataUsage #BigData #SQL

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Navigating the various applications of data science and finding a way to combine my passions into one has led me to discover Augmented Reality. I am excited to explore the possibilities of blending data science with AR.

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I'm excited to announce that I'm joining GOMYCODE as a data scientist student.

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