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Pandas Tutorial - Data Analysis in Python

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Overview

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This course covers the Pandas python library, widely used in data science and analytics. It is designed for learners with basic python knowledge and no prior experience with Pandas. By the end of the course, students will be able to create and manipulate DataFrames, handle missing data, perform group-by operations, merge and concatenate DataFrames, work with time series data, and optimize memory usage when working with large datasets. The teaching method includes tutorials on various Pandas functions and techniques, along with hands-on exercises and examples. The intended audience for this course includes beginners in Python looking to enhance their data analysis skills using Pandas.

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

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