https://github.com/panashemuriro/data

https://github.com/panashemuriro/data

Last synced: 21 days ago
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• Host: GitHub
• URL: https://github.com/panashemuriro/data
• Owner: panasheMuriro
• Created: 2024-12-12T19:16:44.000Z (25 days ago)
• Default Branch: main
• Last Pushed: 2024-12-16T23:52:38.000Z (21 days ago)
• Last Synced: 2024-12-17T00:48:12.272Z (21 days ago)
• Language: Python
• Size: 75.2 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Pandas Learning Path

## Beginner Level

1. Introduction to pandas

    - ✅ Understand what pandas is and its use cases.

    - ✅ Install pandas: pip install pandas.

2. Basic Data Structures

    - ✅ Series: Learn about 1D labeled arrays.

    - ✅ Creating, accessing, and modifying Series.

    - ✅ DataFrame: Learn about 2D labeled data structures.

    - ✅ Creating DataFrames from dictionaries, lists, NumPy arrays, and CSV/Excel files.

    - ✅ Accessing rows, columns, and individual elements (.loc, .iloc).

3. Basic Operations

    - Data inspection (.head(), .tail(), .info(), .describe()).

    - Data selection ([], .loc, .iloc).

    - Adding and deleting columns.

    - Basic descriptive statistics (.mean(), .sum(), .max(), .min()).

4. Data Cleaning

    - Handling missing values (.isna(), .fillna(), .dropna()).

    - Renaming columns and indices (.rename()).

    - Changing data types (.astype()).

## Intermediate Level

5. Data Manipulation

    - Filtering rows and columns.

    - Sorting (.sort_values(), .sort_index()).

    - Grouping data (.groupby()).

    - Aggregations (.agg(), .apply()).

    - Pivot tables (.pivot_table()).

6. Data Merging

    - Concatenation (pd.concat()).

    - Merging and joining (pd.merge(), .join()).

    - Reshaping data (.melt(), .stack(), .unstack()).

7. Time Series Analysis

    - Working with date and time data.

    - Parsing dates (pd.to_datetime()).

    - Indexing with time-series data.

    - Resampling (.resample()).

8. Data Visualization with pandas

    - Simple plotting with .plot().

    - Using pandas with Matplotlib or Seaborn for advanced visualizations.

## Advanced Level

9. Performance Optimization

    - Using vectorized operations instead of loops.

    - Working with large datasets (chunking, Dask, or modin.pandas).

    - Profiling performance (%%time, memory_usage()).

10. Advanced Indexing

    - MultiIndex (hierarchical indexing).

    - Slicing and dicing with MultiIndex.

11. Custom Functions

    - Applying functions with .apply(), .applymap().

    - Lambda functions for quick calculations.

12. Data Exporting

    - Saving DataFrames to files (CSV, Excel, JSON, SQL).

13. Real-World Applications

    - Data wrangling (cleaning and preprocessing).

    - Exploratory Data Analysis (EDA).

    - Integration with SQL databases (.read_sql(), .to_sql()).

14. Pandas with Machine Learning

    - Preparing datasets for machine learning models.

    - Feature engineering using pandas.