pandas

1. Pandas Library in Python
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2. Introduction to Pandas
• Pandas is a Python library for data manipulation & analysis.
• • Built on NumPy
• • Fast, powerful & flexible
• • Essential for data science

3. Pandas Data Structures
• • Series → 1D labeled array
• • DataFrame → 2D table
• Used for cleaning, transforming & analyzing data.

4. Series Example (Code)
• import pandas as pd
• s = pd.Series([10,20,30])
• print(s)

5. DataFrame Example (Code)
• import pandas as pd
• info = { 'Name':['A','B'], 'Marks':[90,80] }
• df = pd.DataFrame(info)
• print(df)

6. Reading CSV Files
• df = pd.read_csv('data.csv')
• df.head()

7. Filtering Data
• filtered = df[df['Marks'] > 80]
• print(filtered)

8. Grouping Data
• df.groupby('Category').sum()

9. Merging DataFrames
• pd.merge(df1, df2, on='id')

10. Why Pandas is Important
• • Cleaning messy data
• • Handling duplicates & missing values
• • Fast operations on large datasets
• • Works with CSV, Excel, SQL, JSON

11. DataFrame Example (Visual)

12. DataFrame Example (Visual)