English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 216 lectures (13h 20m) | 4.01 GB
Learn NumPy & Pandas for data science, data analysis & business intelligence, with practical, hands-on Python projects!
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 & Pandas
Introduce NumPy and Pandas, two critical Python libraries that help structure data in arrays & DataFrames and contain built-in functions for data analysis
Pandas Series
Introduce Pandas Series, the Python equivalent of a column of data, and cover their basic properties, creation, manipulation, and useful functions for analysis
Intro to DataFrames
Work with Pandas DataFrames, the Python equivalent of an Excel or SQL table, and use them to store, manipulate, and analyze data efficiently
Manipulating DataFrames
Aggregate & reshape data in DataFrames by grouping columns, performing aggregation calculations, and pivoting & unpivoting data
Basic Data Visualization
Learn the basics of data visualization in Pandas, and use the plot method to create & customize line charts, bar charts, scatterplots, and histograms
MID-COURSE PROJECT
Put 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 MegaMart
Analyzing Dates & Times
Learn how to work with the datetime data type in Pandas to extract date components, group by dates, and perform time intelligence calculations like moving averages
Importing & Exporting Data
Read in data from flat files and apply processing steps during import, create DataFrames by querying SQL tables, and write data back out to its source
Joining DataFrames
Combine multiple DataFrames by joining data from related fields to add new columns, and appending data with the same fields to add new rows
FINAL COURSE PROJECT
Put the finishing touches on your project by joining a new table, performing time series analysis, optimizing your workflow, and writing out your results
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
Table of Contents
Getting Started
1 Course Structure & Outline
2 READ ME Important Notes for New Students
3 DOWNLOAD Course Resources
4 Introducing the Course Project
5 Setting Expectations
6 Jupyter Installation & Launch
NumPy Primer
7 Pandas & NumPy Intro
8 Numpy Arrays & Array Properties
9 ASSIGNMENT Array Basics
10 SOLUTION Array Basics
11 Array Creation
12 Random Number Generation
13 ASSIGNMENT Array Creation
14 SOLUTION Array Creation
15 Indexing & Slicing Arrays
16 ASSIGNMENT Indexing & Slicing Arrays
17 SOLUTION Indexing & Slicing Arrays
18 Array Operations
19 ASSIGNMENT Array Operations
20 SOLUTION Array Operations
21 Filtering Arrays & Modifying Array Values
22 The Where Function
23 ASSIGNMENT Filtering & Modifying Arrays
24 SOLUTION Filtering & Modifying Arrays
25 Array Aggregation
26 Array Functions
27 Sorting Arrays
28 ASSIGNMENT Aggregation & Sorting
29 SOLUTION Aggregation & Sorting
30 Vectorization
31 Broadcasting
32 ASSIGNMENT Bringing it all together
33 SOLUTION Bringing it all together
34 Key Takeaways
Pandas Series
35 Series Basics
36 Pandas Data Types & Type Conversion
37 ASSIGNMENT Data Types & Type Conversion
38 SOLUTION Data Types & Type Conversion
39 The Series Index & Custom Indices
40 The .iloc Accessor
41 The .loc Accessor
42 Duplicate Index Values & Resetting The Index
43 ASSIGNMENT Accessing Data & Resetting The Index
44 SOLUTION Accessing Data & Resetting The Index
45 Filtering Series & Logical Tests
46 Sorting Series
47 ASSIGNMENT Sorting & Filtering Series
48 SOLUTION Sorting & Filtering Series
49 Numeric Series Operations
50 Text Series Operations
51 ASSIGNMENT Series Operations
52 SOLUTION Series Operations
53 Numerical Series Aggregation
54 Categorical Series Aggregation
55 ASSIGNMENT Series Aggregation
56 SOLUTION Series Aggregation
57 Missing Data Representation in Pandas
58 Identifying Missing Data
59 Fixing Missing Data
60 ASSIGNMENT Missing Data
61 SOLUTION Missing Data
62 Applying Custom Functions to Series
63 Pandas Where (vs. NumPy Where)
64 ASSIGNMENT Apply & Where
65 SOLUTION Apply & Where
66 Key Takeaways
Intro to DataFrames
67 DataFrame Basics
68 Creating a DataFrame
69 ASSIGNMENT DataFrame Basics
70 SOLUTION DataFrame Basics
71 Exploring DataFrames Heads, Tails & Sample
72 Exploring DataFrames Info & Describe
73 ASSIGNMENT Exploring a DataFrame
74 SOLUTION Exploring a DataFrame
75 Accessing DataFrame Columns
76 Accessing DataFrame Data with .iloc & .loc
77 ASSIGNMENT Accessing DataFrame Data
78 SOLUTION Accessing DataFrame Data
79 Dropping Columns & Rows
80 Identifying & Dropping Duplicates
81 ASSIGNMENT Dropping Data
82 SOLUTION Dropping Data
83 Missing Data
84 ASSIGNMENT Missing Data
85 SOLUTION Missing Data
86 Filtering DataFrames
87 PRO TIP The Query Method
88 ASSIGNMENT Filtering DataFrames
89 SOLUTION Filtering DataFrames
90 Sorting DataFrames
91 ASSIGNMENT Sorting DataFrames
92 SOLUTION Sorting DataFrames
93 Renaming & Reordering Columns
94 ASSIGNMENT Renaming & Reordering Columns
95 SOLUTION Renaming & Reordering Columns
96 Arithmetic & Boolean Column Creation
97 ASSIGNMENT Arithmetic & Boolean Columns
98 SOLUTION Arithmetic & Boolean Columns
99 PRO TIP Advanced Conditional Columns with Select
100 ASSIGNMENT The Select Function
101 SOLUTION The Select Function
102 The Map Method
103 PRO TIP Multiple Column Creation with Assign
104 ASSIGNMENT Map & Assign
105 SOLUTION Map & Assign
106 The Categorical Data Type
107 Type Conversion
108 PRO TIP Memory Usage & DataTypes
109 PRO TIP Downcasting Numeric Data Types
110 ASSIGNMENT DataFrame DataTypes
111 SOLUTION DataFrame DataTypes
112 Key Takeways
Aggregating & Reshaping DataFrames
113 Basic Aggregations
114 The Groupby Method
115 ASSIGNMENT Groupby
116 SOLUTION Groupby
117 Grouping By Multiple Columns
118 ASSIGNMENT Grouping By Multiple Columns
119 SOLUTION Grouping By Multiple Columns
120 Multi-Index DataFrames
121 Modifying Multi-Indices
122 ASSIGNMENT Multi-Index DataFrames
123 SOLUTION Multi-Index DataFrames
124 The Agg Method & Named Aggregations
125 ASSIGNMENT The Agg Method
126 SOLUTION The Agg Method
127 PRO TIP Transforming DataFrames
128 ASSIGNMENT Transforming a DataFrame
129 SOLUTION Transforming a DataFrame
130 Pivot Tables in Pandas
131 Multiple Aggregation Pivot Tables
132 PRO TIP Pivot Table Heatmaps
133 Melting DataFrames
134 ASSIGNMENT Pivot & Melt
135 SOLUTION Pivot & Melt
136 Key Takeaways
Basic Data Visualization in Python
137 The matplotlib API & The .plot() Method
138 ASSIGNMENT Basic Line Chart
139 SOLUTION Basic Line Chart
140 Chart Titles
141 Chart Colors
142 Line Styles
143 Chart Legends & Gridlines
144 Chart Styles
145 ASSIGNMENT Stylized Line Chart
146 SOLUTION Stylized Line Chart
147 Subplots & Figure Size
148 ASSIGNMENT Subplots
149 SOLUTION Subplots
150 Bar Charts
151 Grouped & Stacked Bar Charts
152 ASSIGNMENT Bar Charts
153 SOLUTION Bar Charts
154 Pie Charts & Scatterplots
155 ASSIGNMENT Scatterplots
156 SOLUTION Scatterplots
157 Histograms
158 ASSIGNMENT Histograms
159 SOLUTION Histograms
160 Saving Plots & Further Exploration
161 Key Takeaways
MID-COURSE PROJECT
162 Mid-Course Project Intro
163 SOLUTION Mid-Course Project
Analyzing Dates & Times
164 Times in Python and Pandas
165 Converting To Datetimes
166 Formatting Dates
167 Date & Time Parts
168 ASSIGNMENT Pandas Datetime Basics
169 SOLUTION Pandas Datetime Basics
170 Time Deltas & Arithmetic
171 ASSIGNMENT Time Deltas
172 SOLUTION Time Deltas
173 Time Series Indices
174 Missing Time Series Data
175 ASSIGNMENT Missing Time Series Data
176 SOLUTION Missing Time Series Data
177 Shifting Time Series
178 PRO TIP DIFF()
179 ASSIGNMENT Shift & Diff
180 SOLUTION Shift & Diff
181 Aggregation & Resampling
182 ASSIGNMENT Resampling
183 SOLUTION Resampling
184 Rolling Aggregations
185 ASSIGNMENT Rolling Aggregations
186 SOLUTION Rolling Aggregations
187 Key Takeaways
Importing & Exporting Data
188 Preprocessing with read csv
189 Column Selection
190 Row Selection & Missing Values
191 Parsing Dates & Data Types
192 PRO TIP Converters
193 ASSIGNMENT Importing Data
194 SOLUTION Importing Data
195 Importing from Text & Excel Files
196 Exporting to Flat Files
197 ASSIGNMENT Importing & Exporting Excel Data
198 SOLUTION Importing & Exporting Excel Data
199 Working With SQL Databases
200 Other Supported File Formats
201 Key Takeaways
Joining DataFrames
202 Why Multiple Tables
203 Appending DataFrames
204 ASSIGNMENT Appending DataFrames
205 SOLUTION Appending DataFrames
206 Joining DataFrames
207 Join Types
208 Inner Joins
209 Left Joins
210 ASSIGNMENT Joining DataFrames
211 SOLUTION Joining DataFrames
212 The Join Method
213 Key Takeaways
FINAL COURSE PROJECT
214 Final Project Intro
215 SOLUTION Final Project
BONUS LESSON
216 BONUS LESSON
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