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Top Questions People Ask About Pandas, NumPy, Matplotlib & Scikit-learn — Answered!

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 Whether you're a beginner or brushing up on your skills, these are the real-world questions Python learners ask most about key libraries in data science. Let’s dive in! 🐍 🐼 Pandas: Data Manipulation Made Easy 1. How do I handle missing data in a DataFrame? df.fillna( 0 ) # Replace NaNs with 0 df.dropna() # Remove rows with NaNs df.isna(). sum () # Count missing values per column 2. How can I merge or join two DataFrames? pd.merge(df1, df2, on= 'id' , how= 'inner' ) # inner, left, right, outer 3. What is the difference between loc[] and iloc[] ? loc[] uses labels (e.g., column names) iloc[] uses integer positions df.loc[ 0 , 'name' ] # label-based df.iloc[ 0 , 1 ] # index-based 4. How do I group data and perform aggregation? df.groupby( 'category' )[ 'sales' ]. sum () 5. How can I convert a column to datetime format? df[ 'date' ] = pd.to_datetime(df[ 'date' ]) ...
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2 Top Skills You Need For E-commerce

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The following skills are must for every data analytics engineer to be successful in e-commerce companies. Many software engineers are struggling to get this information. I am giving here both technical and general skills.

skills e-commerce

1# Technical Skills for Analytics Career

What skills they need to be successful in their analytics career. The skills required to get an entry into the Analytics job are here for your reference.

I have told in my previous posts that there are many branches in analytics. You need to apply domesticated techniques to extract actionable knowledge.

2# General Skills

I have selected the following mindset and skills that you need to get a job in data analytics. These are proven skills. Set a clear goal to acquire these skills.
  1. Strong interpersonal, oral and written communication and presentation skills; 
  2. Ability to communicate complex findings and ideas in plain language 
  3. Being able to work in teams towards a shared goal; 
  4. Ability to change direction quickly based on data analysis; 
  5. Enjoying discovering and solving problems; 
  6. Proactively seeking clarification of requirements and direction; take responsibility when needed; 
  7. Being able to work in a stressful situation when insights in (new) data sets are required quickly.

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