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Showing posts with the label python dictionary

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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' ]) ...

Python Dictionary Vs List With Examples

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Dictionary and List we use interchangeably in Python to store values. For beginners, both look the same. In reality, they both differ. Here are the differences. Dictionary Vs Lists Values in lists are accessed by means of integers called indices, which indicate where in the list a given value is found. Dictionaries access values by means of integers, strings, or other Python objects called keys , which indicate where in the dictionary a given value is found.  In other words, both lists and dictionaries provide indexed access to arbitrary values, but the set of items that can be used as dictionary indices is much larger than, and contains, the set of items that can be used as list indices.   Also, the mechanism that dictionaries use to provide indexed access is quite different from that used by lists. Both lists and dictionaries can store objects of any type. Values stored in a list are implicitly ordered by their positions in the list because the indices that access these val...