Data Analysis with Python and Pandas

13h 36mIntermediate2024-09-30

Authors

Maven Analytics

Chris Bruehl

Course details

This hands-on, project-based course is designed to help you learn two of the most popular Python packages for data analysis: NumPy and pandas. 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, dive into pandas and focus on the essential tools and methods to explore, analyze, aggregate and transform series and DataFrames. 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, work with members of the Maven Mega Mart team to analyze products, pricing, transactions, and more.

Skills covered

pandasPythonData AnalysisProgramming LanguagesData ScienceBusiness Analysis and StrategyBusiness Software and ToolsOpen SourceSoftware DevelopmentOne-Off

Concepts

0. Introduction

01 - Course introduction
02 - Course structure and outline
03 - Introducing the course project
04 - Setting expectations
05 - Jupyter installation and launch

1. NumPy Primer

06 - pandas and NumPy intro
07 - NumPy arrays and array properties
08 - Challenge - Array basics
09 - Solution - Array basics
10 - Array creation
11 - Random number generation
12 - Challenge - Array creation
13 - Solution - Array creation
14 - Indexing and slicing arrays
15 - Challenge - Indexing and slicing arrays
16 - Solution - Indexing and slicing arrays
17 - Array operations
18 - Challenge - Array operations
19 - Solution - Array operations
20 - Filtering arrays and modifying array values
21 - The where() function
22 - Challenge - Filtering and modifying arrays
23 - Solution - Filtering and modifying arrays
24 - Array aggregation
25 - Array functions
26 - Sorting arrays
27 - Challenge - Aggregation and sorting
28 - Solution - Aggregation and sorting
29 - Vectorization
30 - Broadcasting
31 - Challenge - Bringing it all together
32 - Solution - Bringing it all together
33 - Key takeaways

2. pandas Series

34 - Series basics
35 - pandas data types and type conversion
36 - Challenge - Data types and type conversion
37 - Solution - Data types and type conversion
38 - The series index and custom indices
39 - The .iloc accessor
40 - The .loc accessor
41 - Duplicate index values and resetting the index
42 - Challenge - Accessing data and resetting the index
43 - Solution - Accessing data and resetting the index
44 - Filtering series and logical tests
45 - Sorting series
46 - Challenge - Sorting and filtering series
47 - Solution - Sorting and filtering series
48 - Numeric series operations
49 - Text series operations
50 - Challenge - Series operations
51 - Solution - Series operations
52 - Numerical series aggregation
53 - Categorical series aggregation
54 - Challenge - Series aggregation
55 - Solution - Series aggregation
56 - Missing data representation in pandas
57 - Identifying missing data
58 - Fixing missing data
59 - Challenge - Missing data
60 - Solution - Missing data
61 - Applying custom functions to series
62 - pandas where() vs. NumPy where()
63 - Challenge - apply() and where()
64 - Solution - apply() and where()
65 - Key takeaways

3. Intro to DataFrames

66 - DataFrame basics
67 - Creating a DataFrame
68 - Challenge - DataFrame basics
69 - Solution - DataFrame basics
70 - Exploring DataFrames - Heads, tails, and sample
71 - Exploring DataFrames - Info and describe
72 - Challenge - Exploring a DataFrame
73 - Solution - Exploring a DataFrame
74 - Accessing DataFrame columns
75 - Accessing DataFrame data with .iloc and .loc
76 - Challenge - Accessing DataFrame data
77 - Solution - Accessing DataFrame data
78 - Dropping columns and rows
79 - Identifying and dropping duplicates
80 - Challenge - Dropping data
81 - Solution - Dropping data
82 - Missing data
83 - Challenge - Missing data
84 - Solution - Missing data
85 - Filtering DataFrames
86 - Pro tip - The query() method
87 - Challenge - Filtering DataFrames
88 - Solution - Filtering DataFrames
89 - Sorting DataFrames
90 - Challenge - Sorting DataFrames
91 - Solution - Sorting DataFrames
92 - Renaming and reordering columns
93 - Challenge - Renaming and reordering columns
94 - Solution - Renaming and reordering columns
95 - Arithmetic and Boolean column creation
96 - Challenge - Arithmetic and Boolean columns
97 - Solution - Arithmetic and Boolean columns
98 - Pro tip - Advanced conditional columns with select()
99 - Challenge - The select() function
100 - Solution - The select() function
101 - The map() method
102 - Pro tip - Multiple column creation with assign()
103 - Challenge - map() and assign()
104 - Solution - map() and assign()
105 - The categorical data type
106 - Type conversion
107 - Pro tip - Memory usage and data types
108 - Pro tip - Downcasting numeric data types
109 - Challenge - DataFrame data types
110 - Solution - DataFrame data types
111 - Key takeaways

4. Aggregating and Reshaping DataFrames

112 - Basic aggregations
113 - The groupby() method
114 - Challenge - groupby()
115 - Solution - groupby()
116 - Grouping by multiple columns
117 - Challenge - Grouping by multiple columns
118 - Solution - Grouping by multiple columns
119 - MultiIndex DataFrames
120 - Modifying a MultiIndex
121 - Challenge - MultiIndex DataFrames
122 - Solution - MultiIndex DataFrames
123 - The agg() method and named aggregations
124 - Challenge - The agg() method
125 - Solution - The agg() method
126 - Pro tip - Transforming DataFrames
127 - Challenge - Transforming a DataFrame
128 - Solution - Transforming a DataFrame
129 - Pivot tables in pandas
130 - Multiple aggregation pivot tables
131 - Pro tip - Pivot table heatmaps
132 - Melting DataFrames
133 - Challenge - pivot() and melt()
134 - Solution - pivot() and melt()
135 - Key takeaways

5. Basic Data Visualization

136 - The Matplotlib API and the plot() method
137 - Challenge - Basic line chart
138 - Solution - Basic line chart
139 - Chart titles
140 - Chart colors
141 - Line styles
142 - Chart legends and gridlines
143 - Chart styles
144 - Challenge - Stylized line chart
145 - Solution - Stylized line chart
146 - Subplots and figure size
147 - Challenge - Subplots
148 - Solution - Subplots
149 - Bar charts
150 - Grouped and stacked bar charts
151 - Challenge - Bar charts
152 - Solution - Bar charts
153 - Pie charts and scatterplots
154 - Challenge - Scatterplots
155 - Solution - Scatterplots
156 - Histograms
157 - Challenge - Histograms
158 - Solution - Histograms
159 - Saving plots and further exploration
160 - Key takeaways

6. Mid-Course Project

161 - Mid-course project intro
162 - Solution - Mid-course project

7. Analyzing Dates and Times

163 - Times in Python and pandas
164 - Converting to datetimes
165 - Formatting dates
166 - Date and time parts
167 - Challenge - pandas datetime basics
168 - Solution - pandas datetime basics
169 - Time deltas and arithmetic
170 - Challenge - Time deltas
171 - Solution - Time deltas
172 - Time series indices
173 - Missing time series data
174 - Challenge - Missing time series data
175 - Solution - Missing time series data
176 - Shifting time series
177 - Pro tip - diff()
178 - Challenge - shift() and diff()
179 - Solution - shift() and diff()
180 - Aggregation and resampling
181 - Challenge - Resampling
182 - Solution - Resampling
183 - Rolling aggregations
184 - Challenge - Rolling aggregations
185 - Solution - Rolling aggregations
186 - Key takeaways

8. Importing and Exporting Data

187 - Preprocessing with read csv()
188 - Column selection
189 - Row selection and missing values
190 - Parsing dates and data types
191 - Pro tip - Converters
192 - Challenge - Importing data
193 - Solution - Importing data
194 - Importing from text and Excel files
195 - Exporting to flat files
196 - Challenge - Importing and exporting Excel data
197 - Solution - Importing and exporting Excel data
198 - Working with SQL databases
199 - Other supported file formats
200 - Key takeaways

9. Joining DataFrames

201 - Why use multiple tables
202 - Appending DataFrames
203 - Challenge - Appending DataFrames
204 - Solution - Appending DataFrames
205 - Joining DataFrames
206 - Join types
207 - Inner joins
208 - Left joins
209 - Challenge - Joining DataFrames
210 - Solution - Joining DataFrames
211 - The join() method
212 - Key takeaways

10. Final Course Project

213 - Final project intro
214 - Solution - Final project