Published 3/2023MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 1.48 GB | Duration: 4h 46m

NumPy and Pandas for Data Analysis and Financial Applications, Examples in Trading Market Analysis

What you'll learn

Data manipulation: working with data, filter, sort, and transform large datasets

Data analysis: perform a wide range of data analysis tasks, including aggregating data, perfog statistical calculations

Data visualization: create a variety of visualizations to help understand data and communicate findings

Data wrangling: cleaning and preparing data for analysis, handling missing data, merge datasets, and reshape data

Requirements

Python basics, for loops, condition statements, python containers; lists, sets, tuples and dictionnaries.

Description

This online course is designed to equip you with the skills and knowledge needed to efficiently and effectively manipulate and analyze data using two powerful Python libraries: Pandas and NumPy.In this course, you will start by learning the fundamentals of data wrangling, including the different types of data and data cleaning techniques. You will then dive into the NumPy library, exploring its powerful features for working with N-dimensional arrays and universal functions.Next, you will explore the Pandas library, which offers powerful tools for data manipulation, including data structures and data frame manipulation. You will learn how to use advanced Pandas functions, manipulate and series data, and read and write data with Pandas.Throughout the course, you will engage in hands-on exercises and practice problems to reinforce your learning and build your skills. By the end of the course, you will be able to effectively wrangle and analyze data using Pandas and NumPy, and create compelling data visualizations using these tools.Whether you're a data analyst, data scientist, or data enthusiast, this course will give you the skills you need to take your data wrangling and analysis to the next level.Content Table:Lesson 1: Introduction to Data WranglingLesson 2: Introduction to NumPyLesson 3: Data structure in PandasLesson 4: Pandas DataFrame ManipulationLesson 5: Advanced Pandas FunctionsLesson 6: and Series in PandasLesson 7: Reading and Writing Data with PandasLesson 8: Data Visualization with PandasPractice Exercises

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: NumPy or Numerical Python

Lecture 2 NumPy Installation

Lecture 3 NumPy Basic Functions

Lecture 4 NumPy Slicing

Lecture 5 NumPy Multidimentional Arrays

Lecture 6 NumPy DTypes

Lecture 7 NumPy Structured Arrays

Lecture 8 NumPy Reading And Writing Data Files

Lecture 9 NumPy Arithmetic Operations

Lecture 10 NumPy Logical Operations

Lecture 11 NumPy Array Broadcasting

Lecture 12 NumPy Conditional Indexing

Section 3: NumPy Exercises

Lecture 13 Exercises And Solutions

Lecture 14 Exercise 1

Lecture 15 Exercise 2

Lecture 16 Exercise 3

Lecture 17 Exercise 4

Lecture 18 Exercise 5

Lecture 19 Exercise 6

Section 4: Data Structure in Pandas

Lecture 20 Pandas Series

Lecture 21 Series Missing Values

Lecture 22 Applying Functions to Series

Lecture 23 Pandas DataFrames

Section 5: DataFrame Manipulation

Lecture 24 Columns And Indexes In Pandas

Lecture 25 Accessing DataFrames With Loc[] and iLoc[]

Lecture 26 Accessing Scalars/Values In DataFrames at[] And iat[]

Lecture 27 Filling And Replacing Values In DataFrames

Lecture 28 Arithmetic Operations On DataFrames

Lecture 29 Concatenating DataFrames

Lecture 30 Meg And Joining DataFrames

Section 6: Advanced Pandas Function

Lecture 31 Recap And Planning This Lesson

Lecture 32 Pivot Tables

Lecture 33 GroupBy In DataFrames

Lecture 34 Binning Values And The Cut Function

Lecture 35 MultiLevel Indexing In DataFrames

Lecture 36 Filling Missing Values

Section 7: and Series in Pandas

Lecture 37 Date In Python

Lecture 38 Zones And Deltas In Python

Lecture 39 Rolling And Shift Functions

Section 8: Reading and Writing Data with Pandas

Lecture 40 Reading And Writing Files With Pandas

Section 9: Data Visualization with Pandas

Lecture 41 Plotting Graphs Bars And Histograms

Lecture 42 Boxplots

Lecture 43 Area Plots

Lecture 44 Scatter Points

Lecture 45 Pie Charts

Lecture 46 Conclusion

Section 10: Pandas Exercises

Lecture 47 Pandas Exercises

Lecture 48 Exercise 1 Financial Data Analysis

Lecture 49 Exercise 2 Stacked BarPlots In Pandas

Lecture 50 Exercise 3 Dinner With Friends

Lecture 51 Exercise 4 Oil spill in water: Data cleaning example

Lecture 52 Exercise 5 Financial Trading Analysis/Prediction

Lecture 53 Exercise 6 Financial Trading: analyzing the engulfing candles

Bner in Python building Data Science skills for real world applications

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