Python Libraries for Data Science

1. What is NumPy used for?
👉 Works with large arrays & matrices, speeds up math operations.

2. What is SciPy used for?
👉 Advanced math, linear algebra, optimization, and statistics.

3. What is Pandas used for?
👉 Helps store, manipulate, and analyze table-like data (like Excel sheets).

4. What is Scikit-Learn used for?
👉 Provides machine learning tools for classification, regression, and clustering.

5. What is Matplotlib used for?
👉 Creates basic graphs like line plots, bar charts, and histograms.

6. What is Seaborn used for?
👉 Makes fancy statistical graphs, built on top of Matplotlib.

7. How do you load a dataset in Pandas?
📌 df = pd.read_csv("data.csv")

8. How do you view the first 5 rows of a dataset?
📌 df.head()

9. How do you check data types in Pandas?
📌 df.dtypes

10. How do you filter data in Pandas?
📌 df[df["salary"] > 120000] (Select rows where salary > 120K)

11. How do you group data in Pandas?
📌 df.groupby("rank").mean() (Find average salary per job rank)

12. What is missing data in Pandas?
👉 Data that is blank or NaN (Not a Number).

13. How do you remove missing data?
📌 df.dropna() (Drops rows with missing values).

14. How do you replace missing data?
📌 df.fillna(0) (Fills missing values with 0).

15. How do you sort data in Pandas?
📌 df.sort_values(by="salary") (Sorts data by salary).

16. How do you visualize data in Seaborn?
📌 sns.histplot(df["age"]) (Creates a histogram of ages).