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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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Data science these IT skills you need to learn to get job

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The most lucrative analytics skills include MapReduce, Apache Pig, Machine Learning, Apache Hive and Apache Hadoop.

Machine learning, big data, and data science skills are the most challenging to recruit for and potentially can create the greatest disruption to ongoing product development and go-to-market strategies if not filled.
In demand IT skills
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Great in demand it skills...
  1. Big Data (Information Technology): 3,977%
  2. Node.js (Design): 2,493%
  3. Tableau (Research and Analysis): 1,581%
  4. NoSQL (Information Technology): 1,002%
  5. Apache Hadoop (Information Technology): 704%
  6. HTML5 (Information Technology): 612%
  7. Python (Research and Analysis): 456%
  8. Oracle (Sales): 382%
  9. JSON (Information Technology): 318%
  10. Salesforce CRM (Sales): 292%

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