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Showing posts with the label Relational Operators

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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' ]) ...

Relational Operators in Python: A Quick Guide On How to Use Them

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Relational operators in Python are helpful, If you are working with numeric values to compare them. Here we explore eight different relational operators and provide examples of how each one works. So to compare numeric values it is a useful guide to refresh. Python Relational Operators Here's a frequently used list of relational operators, and these you can use to compare numeric values. The list shows how to use each operator helpful for data analysis . < <= > >= == != Is is not Python program: How to use relational operators Assign 23 to a and 11 to b. Then, apply all the comparison operators. The output is self-explanatory. Bookmark this article to refresh when you are in doubt. Example a = 23 b = 11 print("Is a greater than b?", a > b) #greater than print("Is a less than b?", a < b) #less than print("Is a greater or equal to b?", a >= b) #greater or equal print("Is a less or equal to b?", a <= b) #less or equal pr...