Dive into a comprehensive 4.5-hour tutorial series on the Pandas Python library, essential for data science and analytics. Learn from the basics of DataFrame creation to advanced techniques like handling missing data, grouping, merging, and pivoting. Explore time series analysis, including working with DatetimeIndex, resampling, and timezone handling. Master memory optimization tips for large datasets, and discover how to read and write data from various sources, including Excel, CSV, and databases. Suitable for those with basic Python knowledge, this series equips you with the skills to efficiently manipulate and analyze data using Pandas.
Overview
Syllabus
Python Pandas Tutorial 1. What is Pandas python? Introduction and Installation.
Python Pandas Tutorial 2: Dataframe Basics.
Python Pandas Tutorial 3: Different Ways Of Creating DataFrame.
Python Pandas Tutorial 4: Read Write Excel CSV File.
Python Pandas Tutorial 5: Handle Missing Data: fillna, dropna, interpolate.
Python Pandas Tutorial 6. Handle Missing Data: replace function.
Python Pandas Tutorial 7. Group By (Split Apply Combine).
Python Pandas Tutorial 8. Concat Dataframes.
Python Pandas Tutorial 9. Merge Dataframes.
Python Pandas Tutorial 10. Pivot table.
Python Pandas Tutorial 11. Reshape dataframe using melt.
Python Pandas Tutorial 12. Stack Unstack.
Python Pandas Tutorial 13. Crosstab.
Python Pandas Tutorial 14: Read Write Data From Database (read_sql, to_sql).
Pandas Time Series Analysis Part 1: DatetimeIndex and Resample.
Pandas Time Series Analysis Part 2: date_range.
Pandas Time Series Analysis 3: Holidays.
Pandas Time Series Analysis 4: to_datetime.
Pandas Time Series Analysis 5: Period and PeriodIndex.
Pandas Time Series Analysis 6: Timezone Handling.
Pandas Time Series Analysis 6: Shifting and Lagging.
Python Pandas Tutorial 15. Handle Large Datasets In Pandas | Memory Optimization Tips For Pandas.
Taught by
codebasics
