Pandas

🔹 1. Introduction to Pandas

• What is Pandas?

• History and evolution of the Pandas library

• Importance of Pandas in data analysis, data science, and machine learning

• Installing Pandas (pip install pandas)

• Importing the library (import pandas as pd)

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🔹 2. Core Data Structures in Pandas

• Series

o Creating a Series from a list, NumPy array, or dictionary

o Accessing and modifying Series elements

o Indexing, slicing, and filtering

• DataFrame

o Creating DataFrames from lists, dictionaries, NumPy arrays, or CSV files

o DataFrame structure: rows, columns, index

o Head, tail, shape, info, and describe methods

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🔹 3. Reading and Writing Data

• Reading data from files:

o read_csv(), read_excel(), read_json(), read_sql(), etc.

• Writing data to files:

o to_csv(), to_excel(), to_json()

• Reading from and writing to databases (using SQLAlchemy)

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🔹 4. Data Selection and Filtering

• Accessing rows and columns:

o df['column'], df.column, df.loc[], df.iloc[]

• Conditional filtering

• Slicing and subsetting data

• Boolean indexing and multiple conditions

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🔹 5. Data Cleaning and Preparation

• Handling missing values:

o isnull(), notnull(), fillna(), dropna()

• Replacing values: replace()

• Removing duplicates: drop_duplicates()

• Renaming columns: rename()

• Changing data types: astype()

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🔹 6. Data Transformation

• Applying functions:

o apply(), map(), applymap()

• Lambda functions in Pandas

• Using replace() and where()

• String operations: str.lower(), str.contains(), etc.

• Date and time handling: pd.to_datetime()

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🔹 7. Data Aggregation and Grouping

• Grouping data using groupby()

• Aggregation functions: sum(), mean(), count(), min(), max()

• Multi-level grouping

• Custom aggregation with agg()

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🔹 8. Merging, Joining, and Concatenation

• merge() for SQL-style joins (inner, outer, left, right)

• concat() for combining along axis

• join() for joining on indices

• Combining data from multiple sources

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🔹 9. Sorting and Ranking

• Sorting by values or index: sort_values(), sort_index()

• Ranking data: rank()

• Sorting with multiple columns

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🔹 10. Pivot Tables and Crosstab

• Creating pivot tables with pivot_table()

• Summarizing categorical data with pd.crosstab()

• Adding margins and aggregation functions

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🔹 11. Time Series Analysis

• Creating datetime indexes

• Resampling data: resample()

• Time-based grouping and rolling windows

• Date ranges, frequency, and offsets

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🔹 12. Visualization with Pandas

• Built-in plotting using Matplotlib backend: df.plot()

• Line plots, bar charts, histograms, scatter plots, etc.

• Customizing charts (labels, title, legend)

• Integrating Pandas with Seaborn and Matplotlib for advanced plots

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🔹 13. Advanced Data Manipulation

• MultiIndex and hierarchical indexing

• Stack and unstack

• Melt and pivot

• Window functions (rolling, expanding)

• Categorical data optimization

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🔹 14. Performance Optimization

• Using categorical data types

• Working with large datasets

• Vectorized operations vs. loops

• Memory usage: df.memory_usage()

• Using Dask or Modin for parallel processing

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🔹 15. Real-Life Applications of Pandas

• Data cleaning and preprocessing

• Exploratory data analysis (EDA)

• Feature engineering in machine learning

• Financial data analysis

• Web scraping with Pandas + BeautifulSoup/Requests

• Integration with NumPy, Matplotlib, Seaborn, Scikit-learn