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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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How to Write Complex Python Script: Explained Each Step

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 Creating a complex Python script is challenging, but I can provide you with a simplified example of a script that simulates a basic bank account system. In a real-world application, this would be much more elaborate, but here's a concise version. Python Complex Script Here is an example of a Python script that explains each step: class BankAccount:     def __init__(self, account_holder, initial_balance=0):         self.account_holder = account_holder         self.balance = initial_balance     def deposit(self, amount):         if amount > 0:             self.balance += amount             print(f"Deposited ${amount}. New balance: ${self.balance}")         else:             print("Invalid deposit amount.")     def withdraw(self, amount):         if 0 < amount ...
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