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Showing posts with the label tibco-spot-fire

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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' ]) ...

What is Tibco Spotfire - Visualization tool

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[Tibco online learning] When you start Spotfire for the first time, your first task is to load some data. This data can come from a file, a database, or even the clipboard. Data is at the heart of all analysis, and it's important that you know, not only how to load data into Spotfire, but also how data works. If you handle a lot of data in spreadsheet form, you will no doubt understand its content and meaning very well. You might even have developed advanced and insightful representations of your data. However, there is so much more you can do with Spotfire to improve the handling of this subject matter. Importing data into Spotfire is just the beginning. To progress into its rich analytic world, you will have to become familiar with the relational database model. You will have to learn some formal data concepts. We will therefore spend some time taking a look at some basic database principles to set you on your way to advance quickly beyond the limited world of the sprea...