5 Python Pandas tips to solve your tasks efficiently: melt, select_dtypes, get_dummies, agg. import pandas as pd # Creating a basic example dataset data = { 'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Emily'], 'Age': [25, 30, 22, 35, 28], 'Salary': [50000, 60000, 45000, 70000, 55000], 'Department': ['HR', 'Finance', 'IT', 'Marketing', 'HR'] } df = pd.DataFrame(data) print(df) # Select columns based on data types numeric_columns = df.select_dtypes(include=['int', 'float']).columns print(df[nume
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5 Python Pandas tips to solve your tasks efficiently: melt, select_dtypes, get_dummies, agg. import pandas as pd # Creating a basic example dataset data = { 'Name': ['Alice',
Karina.DataScientist(@karinadatascientist). original sound - Karina.DataScientist. 5 Python Pandas tips to solve your tasks efficiently: melt, select_dtypes, get_dummies, agg. import pandas as pd # Creating a basic example dataset data = { 'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Emily'], 'Age': [25, 30, 22, 35, 28], 'Salary': [50000 ...
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