Data Analysis with Python NumPy Pandas Masterclass

Learn NumPy & Pandas for data science, data analysis & business intelligence, with practical, hands-on Python projects!
Data Analysis with Python NumPy Pandas Masterclass
File Size :
4.22 GB
Total length :
13h 31m



Maven Analytics


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Data Analysis with Python NumPy Pandas Masterclass

What you’ll learn

Master the essentials of NumPy and Pandas, two of Python’s most powerful data analysis packages
Learn how to explore, transform, aggregate and join NumPy arrays and Pandas DataFrames
Analyze and manipulate dates and times for time intelligence and time-series analysis
Visualize raw data using plot methods and common chart options like line charts, bar charts, scatter plots and histograms
Import and export flat files, Excel workbooks and SQL database tables using Pandas
Build powerful, practical skills for modern analytics and business intelligence

Data Analysis with Python NumPy Pandas Masterclass


We’ll use Anaconda & Jupyter Notebooks (a free, user-friendly coding environment)
Familiarity with base Python is strongly recommended, but not a strict prerequisite


This is a hands-on, project-based course designed to help you master two of the most popular Python packages for data analysis: NumPy and Pandas.We’ll start with a NumPy primer to introduce arrays and array properties, practice common operations like indexing, slicing, filtering and sorting, and explore important concepts like vectorization and broadcasting.From there we’ll dive into Pandas, and focus on the essential tools and methods to explore, analyze, aggregate and transform series and dataframes. You’ll practice plotting dataframes with charts and graphs, manipulating time-series data, importing and exporting various file types, and combining dataframes using common join methods.Throughout the course you’ll play the role of Data Analyst for Maven Mega Mart, a large, multinational corporation that operates a chain of retail and grocery stores. Using the Python skills you learn throughout the course, you’ll work with members of the Maven Mega Mart team to analyze products, pricing, transactions, and more.COURSE OUTLINE:Intro to NumPy & PandasIntroduce NumPy and Pandas, two critical Python libraries that help structure data in arrays & DataFrames and contain built-in functions for data analysisPandas SeriesIntroduce Pandas Series, the Python equivalent of a column of data, and cover their basic properties, creation, manipulation, and useful functions for analysisIntro to DataFramesWork with Pandas DataFrames, the Python equivalent of an Excel or SQL table, and use them to store, manipulate, and analyze data efficientlyManipulating DataFramesAggregate & reshape data in DataFrames by grouping columns, performing aggregation calculations, and pivoting & unpivoting dataBasic Data VisualizationLearn the basics of data visualization in Pandas, and use the plot method to create & customize line charts, bar charts, scatterplots, and histogramsMID-COURSE PROJECTPut your skills to the test with a brand new dataset, and use your Python skills to analyze and evaluate a new retailer as a potential acquisition target for Maven MegaMartAnalyzing Dates & TimesLearn how to work with the datetime data type in Pandas to extract date components, group by dates, and perform time intelligence calculations like moving averagesImporting & Exporting DataRead in data from flat files and apply processing steps during import, create DataFrames by querying SQL tables, and write data back out to its sourceJoining DataFramesCombine multiple DataFrames by joining data from related fields to add new columns, and appending data with the same fields to add new rowsFINAL COURSE PROJECTPut the finishing touches on your project by joining a new table, performing time series analysis, optimizing your workflow, and writing out your resultsJoin today and get immediate, lifetime access to the following:13+ hours of high-quality videoPython & Pandas PDF ebook (350+ pages)Downloadable project files & solutionsExpert support and Q&A forum30-day Udemy satisfaction guaranteeIf you’re a data scientist, BI analyst or data engineer looking to add Pandas to your Python skill set, this course is for you.Happy learning!-Chris Bruehl (Python Expert & Lead Python Instructor, Maven Analytics)__________Looking for our full business intelligence stack? Search for “Maven Analytics” to browse our full course library, including Excel, Power BI, MySQL, Tableau and Machine Learning courses!See why our courses are among the TOP-RATED on Udemy:”Some of the BEST courses I’ve ever taken. I’ve studied several programming languages, Excel, VBA and web dev, and Maven is among the very best I’ve seen!” Russ C.”This is my fourth course from Maven Analytics and my fourth 5-star review, so I’m running out of things to say. I wish Maven was in my life earlier!” Tatsiana M.”Maven Analytics should become the new standard for all courses taught on Udemy!” Jonah M.


Section 1: Getting Started

Lecture 1 Course Structure & Outline

Lecture 2 READ ME: Important Notes for New Students

Lecture 3 DOWNLOAD: Course Resources

Lecture 4 Introducing the Course Project

Lecture 5 Setting Expectations

Lecture 6 Jupyter Installation & Launch

Section 2: NumPy Primer

Lecture 7 Pandas & NumPy Intro

Lecture 8 Numpy Arrays & Array Properties

Lecture 9 ASSIGNMENT: Array Basics

Lecture 10 Array Creation

Lecture 11 SOLUTION: Array Basics

Lecture 12 Random Number Generation

Lecture 13 ASSIGNMENT: Array Creation

Lecture 14 SOLUTION: Array Creation

Lecture 15 Indexing & Slicing Arrays

Lecture 16 ASSIGNMENT: Indexing & Slicing Arrays

Lecture 17 SOLUTION: Indexing & Slicing Arrays

Lecture 18 Array Operations

Lecture 19 ASSIGNMENT: Array Operations

Lecture 20 SOLUTION: Array Operations

Lecture 21 Filtering Arrays & Modifying Array Values

Lecture 22 The Where Function

Lecture 23 ASSIGNMENT: Filtering & Modifying Arrays

Lecture 24 SOLUTION: Filtering & Modifying Arrays

Lecture 25 Array Aggregation

Lecture 26 Array Functions

Lecture 27 Sorting Arrays

Lecture 28 ASSIGNMENT: Aggregation & Sorting

Lecture 29 SOLUTION: Aggregation & Sorting

Lecture 30 Vectorization

Lecture 31 Broadcasting

Lecture 32 ASSIGNMENT: Bringing it all together

Lecture 33 SOLUTION: Bringing it all together

Lecture 34 Key Takeaways

Section 3: Pandas Series

Lecture 35 Series Basics

Lecture 36 Pandas Data Types & Type Conversion

Lecture 37 ASSIGNMENT: Data Types & Type Conversion

Lecture 38 SOLUTION: Data Types & Type Conversion

Lecture 39 The Series Index & Custom Indices

Lecture 40 The .iloc Accessor

Lecture 41 The .loc Accessor

Lecture 42 Duplicate Index Values & Resetting The Index

Lecture 43 ASSIGNMENT: Accessing Data & Resetting The Index

Lecture 44 SOLUTION: Accessing Data & Resetting The Index

Lecture 45 Filtering Series & Logical Tests

Lecture 46 Sorting Series

Lecture 47 ASSIGNMENT: Sorting & Filtering Series

Lecture 48 SOLUTION: Sorting & Filtering Series

Lecture 49 Numeric Series Operations

Lecture 50 Text Series Operations

Lecture 51 ASSIGNMENT: Series Operations

Lecture 52 SOLUTION: Series Operations

Lecture 53 Numerical Series Aggregation

Lecture 54 Categorical Series Aggregation

Lecture 55 ASSIGNMENT: Series Aggregation

Lecture 56 SOLUTION: Series Aggregation

Lecture 57 Missing Data Representation in Pandas

Lecture 58 Identifying Missing Data

Lecture 59 Fixing Missing Data

Lecture 60 ASSIGNMENT: Missing Data

Lecture 61 SOLUTION: Missing Data

Lecture 62 Applying Custom Functions to Series

Lecture 63 Pandas Where (vs. NumPy Where)

Lecture 64 ASSIGNMENT: Apply & Where

Lecture 65 SOLUTION: Apply & Where

Lecture 66 Key Takeaways

Section 4: Intro to DataFrames

Lecture 67 DataFrame Basics

Lecture 68 Creating a DataFrame

Lecture 69 ASSIGNMENT: DataFrame Basics

Lecture 70 SOLUTION: DataFrame Basics

Lecture 71 Exploring DataFrames: Heads, Tails & Sample

Lecture 72 Exploring DataFrames: Info & Describe

Lecture 73 ASSIGNMENT: Exploring a DataFrame

Lecture 74 SOLUTION: Exploring a DataFrame

Lecture 75 Accessing DataFrame Columns

Lecture 76 Accessing DataFrame Data with .iloc & .loc

Lecture 77 ASSIGNMENT: Accessing DataFrame Data

Lecture 78 SOLUTION: Accessing DataFrame Data

Lecture 79 Dropping Columns & Rows

Lecture 80 Identifying & Dropping Duplicates

Lecture 81 ASSIGNMENT: Dropping Data

Lecture 82 SOLUTION: Dropping Data

Lecture 83 Missing Data

Lecture 84 ASSIGNMENT: Missing Data

Lecture 85 SOLUTION: Missing Data

Lecture 86 Filtering DataFrames

Lecture 87 PRO TIP: The Query Method

Lecture 88 ASSIGNMENT: Filtering DataFrames

Lecture 89 SOLUTION: Filtering DataFrames

Lecture 90 Sorting DataFrames

Lecture 91 ASSIGNMENT: Sorting DataFrames

Lecture 92 SOLUTION: Sorting DataFrames

Lecture 93 Renaming & Reordering Columns

Lecture 94 ASSIGNMENT: Renaming & Reordering Columns

Lecture 95 SOLUTION: Renaming & Reordering Columns

Lecture 96 Arithmetic & Boolean Column Creation

Lecture 97 ASSIGNMENT: Arithmetic & Boolean Columns

Lecture 98 SOLUTION: Arithmetic & Boolean Columns

Lecture 99 PRO TIP: Advanced Conditional Columns with Select

Lecture 100 ASSIGNMENT: The Select Function

Lecture 101 SOLUTION: The Select Function

Lecture 102 The Map Method

Lecture 103 PRO TIP: Multiple Column Creation with Assign

Lecture 104 ASSIGNMENT: Map & Assign

Lecture 105 SOLUTION: Map & Assign

Lecture 106 The Categorical Data Type

Lecture 107 Type Conversion

Lecture 108 PRO TIP: Memory Usage & DataTypes

Lecture 109 PRO TIP: Downcasting Numeric Data Types

Lecture 110 ASSIGNMENT: DataFrame DataTypes

Lecture 111 SOLUTION: DataFrame DataTypes

Lecture 112 Key Takeways

Section 5: Aggregating & Reshaping DataFrames

Lecture 113 Basic Aggregations

Lecture 114 The Groupby Method

Lecture 115 ASSIGNMENT: Groupby

Lecture 116 SOLUTION: Groupby

Lecture 117 Grouping By Multiple Columns

Lecture 118 ASSIGNMENT: Grouping By Multiple Columns

Lecture 119 SOLUTION: Grouping By Multiple Columns

Lecture 120 Multi-Index DataFrames

Lecture 121 Modifying Multi-Indices

Lecture 122 ASSIGNMENT: Multi-Index DataFrames

Lecture 123 SOLUTION: Multi-Index DataFrames

Lecture 124 The Agg Method & Named Aggregations

Lecture 125 ASSIGNMENT: The Agg Method

Lecture 126 SOLUTION: The Agg Method

Lecture 127 PRO TIP: Transforming DataFrames

Lecture 128 ASSIGNMENT: Transforming a DataFrame

Lecture 129 SOLUTION: Transforming a DataFrame

Lecture 130 Pivot Tables in Pandas

Lecture 131 Multiple Aggregation Pivot Tables

Lecture 132 PRO TIP: Pivot Table Heatmaps

Lecture 133 Melting DataFrames

Lecture 134 ASSIGNMENT: Pivot & Melt

Lecture 135 SOLUTION: Pivot & Melt

Lecture 136 Key Takeaways

Section 6: Basic Data Visualization in Python

Lecture 137 The matplotlib API & The .plot() Method

Lecture 138 ASSIGNMENT: Basic Line Chart

Lecture 139 SOLUTION: Basic Line Chart

Lecture 140 Chart Titles

Lecture 141 Chart Colors

Lecture 142 Line Styles

Lecture 143 Chart Legends & Gridlines

Lecture 144 Chart Styles

Lecture 145 ASSIGNMENT: Stylized Line Chart

Lecture 146 SOLUTION: Stylized Line Chart

Lecture 147 Subplots & Figure Size

Lecture 148 ASSIGNMENT: Subplots

Lecture 149 SOLUTION: Subplots

Lecture 150 Bar Charts

Lecture 151 Grouped & Stacked Bar Charts

Lecture 152 ASSIGNMENT: Bar Charts

Lecture 153 SOLUTION: Bar Charts

Lecture 154 Pie Charts & Scatterplots

Lecture 155 ASSIGNMENT: Scatterplots

Lecture 156 SOLUTION: Scatterplots

Lecture 157 Histograms

Lecture 158 ASSIGNMENT: Histograms

Lecture 159 SOLUTION: Histograms

Lecture 160 Saving Plots & Further Exploration

Lecture 161 Key Takeaways


Lecture 162 Mid-Course Project Intro

Lecture 163 SOLUTION: Mid-Course Project

Section 8: Analyzing Dates & Times

Lecture 164 Times in Python and Pandas

Lecture 165 Converting To Datetimes

Lecture 166 Formatting Dates

Lecture 167 Date & Time Parts

Lecture 168 ASSIGNMENT: Pandas Datetime Basics

Lecture 169 SOLUTION: Pandas Datetime Basics

Lecture 170 Time Deltas & Arithmetic

Lecture 171 ASSIGNMENT: Time Deltas

Lecture 172 SOLUTION: Time Deltas

Lecture 173 Time Series Indices

Lecture 174 Missing Time Series Data

Lecture 175 ASSIGNMENT: Missing Time Series Data

Lecture 176 SOLUTION: Missing Time Series Data

Lecture 177 Shifting Time Series

Lecture 178 PRO TIP: DIFF()

Lecture 179 ASSIGNMENT: Shift & Diff

Lecture 180 SOLUTION: Shift & Diff

Lecture 181 Aggregation & Resampling

Lecture 182 ASSIGNMENT: Resampling

Lecture 183 SOLUTION: Resampling

Lecture 184 Rolling Aggregations

Lecture 185 ASSIGNMENT: Rolling Aggregations

Lecture 186 SOLUTION: Rolling Aggregations

Lecture 187 Key Takeaways

Section 9: Importing & Exporting Data

Lecture 188 Preprocessing with read_csv

Lecture 189 Column Selection

Lecture 190 Row Selection & Missing Values

Lecture 191 Parsing Dates & Data Types

Lecture 192 PRO TIP: Converters

Lecture 193 ASSIGNMENT: Importing Data

Lecture 194 SOLUTION: Importing Data

Lecture 195 Importing from Text & Excel Files

Lecture 196 Exporting to Flat Files

Lecture 197 ASSIGNMENT: Importing & Exporting Excel Data

Lecture 198 SOLUTION: Importing & Exporting Excel Data

Lecture 199 Working With SQL Databases

Lecture 200 Other Supported File Formats

Lecture 201 Key Takeaways

Section 10: Joining DataFrames

Lecture 202 Why Multiple Tables

Lecture 203 Appending DataFrames

Lecture 204 ASSIGNMENT: Appending DataFrames

Lecture 205 SOLUTION: Appending DataFrames

Lecture 206 Joining DataFrames

Lecture 207 Join Types

Lecture 208 Inner Joins

Lecture 209 Left Joins

Lecture 210 ASSIGNMENT: Joining DataFrames

Lecture 211 SOLUTION: Joining DataFrames

Lecture 212 The Join Method

Lecture 213 Key Takeaways


Lecture 214 Final Project Intro

Lecture 215 SOLUTION: Final Project

Section 12: BONUS LESSON

Lecture 216 BONUS LESSON

Analysts or BI professionals looking to learn data analysis with NumPy and Pandas,Aspiring data scientists who want to build or strengthen their Python skills,Anyone interested in learning one of the most popular open source programming languages in the world,Students looking to learn powerful, practical skills with unique, hands-on projects and course demos

Course Information:

Udemy | English | 13h 31m | 4.22 GB
Created by: Maven Analytics

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