Advanced Data Analysis & Wrangling with Python Pandas

Advanced Data Analysis & Wrangling with Python Pandas, available at $89.99, has an average rating of 4.6, with 121 lectures, based on 21 reviews, and has 146 subscribers.

You will learn about Learn Python pandas package for advanced data analysis and wrangling Data Frames & Series Input and Output into Pandas Data selection and filtering Sort, count, unique, duplicated values Handling missing values Data Aggregation Data Transformation apply, map Complex Groupby (Split-Apply-Combine) Vectorized string manipulation Vectorized date/time manipulation reshape and pivot Joins/Merge Rolling Windows Operations Data Visualization Stock Market Case Study This course is ideal for individuals who are Data Analysts & Data Scientists or Anyone who is interested in series data manipulation and wrangling in Python or Researchers in all fields or Business analysts and marketing researchers It is particularly useful for Data Analysts & Data Scientists or Anyone who is interested in series data manipulation and wrangling in Python or Researchers in all fields or Business analysts and marketing researchers.

Enroll now: Advanced Data Analysis & Wrangling with Python Pandas

Summary

Title: Advanced Data Analysis & Wrangling with Python Pandas

Price: $89.99

Average Rating: 4.6

Number of Lectures: 121

Number of Published Lectures: 121

Number of Curriculum Items: 121

Number of Published Curriculum Objects: 121

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Learn Python pandas package for advanced data analysis and wrangling
Data Frames & Series
Input and Output into Pandas
Data selection and filtering
Sort, count, unique, duplicated values
Handling missing values
Data Aggregation
Data Transformation
apply, map
Complex Groupby (Split-Apply-Combine)
Vectorized string manipulation
Vectorized date/time manipulation
reshape and pivot
Joins/Merge
Rolling Windows Operations
Data Visualization
Stock Market Case Study

Who Should Attend

Data Analysts & Data Scientists
Anyone who is interested in series data manipulation and wrangling in Python
Researchers in all fields
Business analysts and marketing researchers

Target Audiences

Data Analysts & Data Scientists
Anyone who is interested in series data manipulation and wrangling in Python
Researchers in all fields
Business analysts and marketing researchers

This course of the Fantastic Python Series is an advanced course on data manipulation and wrangling with the pandas package in Python. Pandas is one of the most important packages in the Python eco-system and it is where most data scientists spend 80% of their time on. It is essential to have a deep and complete understanding of how pandas work to conduct analysis more effectively and efficiently.

This course offers a complete guide on all areas of Pandas functionalities, from the foundamentals, all the way to highly advanced and complex skills such as rolling windows and time series resampling. It will teach data scientists from all fields, including IT, business, finance, etc, how data manipulation and wrangling is done effectively in pandas and how to avoid potential pitfalls (“Gotchas”).

The advanced parts of this course is particularly helpful for those analysts/scientists who work with time series data (and panel data) as the pandas offers an extensive array of features for time series calculations. So finance professionals and physists will find it especially relevant to their field of work.

This course is proceeds from the foundations of data series and data frame, and then proceeds to intermediate level data manipulations, and eventually dive deep into advanced data wrangling topics such as complex groupby operations, sophisticated joins/merges and reshaping from wide format to long and vice versa.

Finally, a stock market case study is offered as a capstone for this entire course. This case study will draw together most, if not all, areas of knowledge of pandas and analyze real-world financial data.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: What makes this course different?

Lecture 3: What is pandas?

Lecture 4: Course content and structure

Chapter 2: Installation of Python, Pandas and Jupyter Notebook

Lecture 1: Install Python and Pandas

Lecture 2: Install Jupyter Notebook/Lab

Chapter 3: Series in Pandas

Lecture 1: Pandas vs NumPy

Lecture 2: Basics of series

Lecture 3: Advanced series operations

Chapter 4: DataFrame: An Introduction

Lecture 1: Data Frames: Basics

Lecture 2: Data Frame: basics operations and Gotchas!

Lecture 3: Data Frame: computations and new columns

Lecture 4: Useful data frame methods

Lecture 5: Add and drop columns

Chapter 5: Read and Write Data Files

Lecture 1: Overview of Data File Formats

Lecture 2: How to Read CSV files

Lecture 3: Read CSV Files with Date/Time Columns

Lecture 4: Dataset with headers and footers (Fama-French)

Lecture 5: How to write to CSV files

Lecture 6: How to read and write Parquet files

Lecture 7: How to read and write tab-deliminated and other formats

Lecture 8: How to read and write JSON from the web

Chapter 6: Data Selection and Filtering

Lecture 1: Basic data selection in data frames

Lecture 2: Gotchas!

Lecture 3: The .loc selector

Lecture 4: How to conditionally modify rows using .loc selector

Lecture 5: The .iloc selector

Lecture 6: Reset the index

Lecture 7: Filter rows with logical conditions

Lecture 8: Chaining complex operations in pandas

Chapter 7: Sorting, Counting, Uniquing and Dealing with Duplicated Values

Lecture 1: Sort by a single column

Lecture 2: Sort by multiple columns

Lecture 3: Counting rows & values

Lecture 4: Finding unique values

Lecture 5: Duplicated values: part 1

Lecture 6: Duplicated values: part 2

Chapter 8: Missing Values Handling

Lecture 1: How to find missing values

Lecture 2: Missing value propogation

Lecture 3: How to fill missing values: basics

Lecture 4: How to forward and backward fill missing values in a time-series

Lecture 5: How to fill missing values with averages

Lecture 6: How to use the replace method to good effect

Lecture 7: How to interpolate missing values in a time-series

Chapter 9: Aggregation

Lecture 1: Aggregation vs. transformation

Lecture 2: Aggregation basics

Lecture 3: Multiple statistics for multiple columns at once

Lecture 4: Specific statistics for specific columns at once

Lecture 5: idxmax and idxmin

Lecture 6: Pandas build-in aggregation functions

Lecture 7: Pandas statistic functions

Lecture 8: User Defined Functions (UDF) for aggregation

Chapter 10: Transformation

Lecture 1: Basics of transformation

Lecture 2: Time series transform: lag, shift, diff and pct_change

Lecture 3: The transform( ) function itself

Lecture 4: User Defined Functions (UDF) for transformation

Chapter 11: Apply, Map and Lambda Functions

Lecture 1: Apply

Lecture 2: Map

Lecture 3: Lambda Functions

Chapter 12: Mid-course talk

Lecture 1: Study tips

Chapter 13: Groupby Operations

Lecture 1: Introduction to the "Split-Apply-Combine" Strategy in data analytics

Lecture 2: Groupby: basics

Lecture 3: Aggregation/statistics by group

Lecture 4: The "agg" function and California restaurants

Lecture 5: Transformation by group & stock prices

Lecture 6: Caveat on transformation by group

Chapter 14: Vectorized String Manipulations

Lecture 1: String data types in pandas, concatenate & change cases

Lecture 2: Split strings

Lecture 3: Replace, strip, pad, zerofill strings

Lecture 4: Removing prefix/suffix, string slicing, length & count

Chapter 15: Vectorized Data & Time Manipulations

Lecture 1: How pandas store date and time?

Lecture 2: The time stamp

Lecture 3: Frequencies: Part 1

Lecture 4: Frequences: Part 2

Lecture 5: The .dt accessor magic

Lecture 6: Date & time calculations: Absolute Time Delta

Lecture 7: More sensible date & time calculations: Offsets

Lecture 8: Date/Time resampling: the basics

Lecture 9: Date/Time resampling: by group

Chapter 16: Reshaping Data and Pivot Tables

Lecture 1: Reshape from long to wide formats: pivot( )

Lecture 2: Reshape/pivot from long to wide with multiple columns

Lecture 3: Excel-style pivot tables

Lecture 4: Differences between pivot( ) and pivot_table( )

Lecture 5: Reshape from wide to long format: melt( )

Lecture 6: Financial ratios case study

Instructors

Richard Wang
Professor and Entrepreneur

Rating Distribution

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2 stars: 1 votes
3 stars: 0 votes
4 stars: 6 votes
5 stars: 14 votes

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