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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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Python Function Argument: How to Pass it to Decorator

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A decorator is a wrapper and provides additional functionality to a function. Also, it may modify the behavior, such as changing the return type/adding new abilities.


Python Decorators



Python Decorators


Precisely, it is another form of function pointers. Also, it accepts function argument, then either wraps the function or returns a new one. Moreover, it modifies the inputs/outputs supplied to it. It helps you add behavior to functions (objects) dynamically (without changing the function behavior).

Function Argument

Below, you will find an example of passing a function argument to a decorator. The below function modifies inputs and returns output.

def to_upper(func):
    text=func()
    if isinstance(text,str):
        return text.upper()

def say():
    return "welcome"

def hello():
    return "hello"
    
a = to_upper(say)
print(a) 

b = to_upper(hello)
print(b)  


Output


WELCOME
HELLO


** Process exited - Return Code: 0 **
Press Enter to exit terminal

References

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