Data Analysis and Visualization Using Python
Analyze Data to Create Visualizations for BI Systems
Paperback Engels 2018 9781484241080Samenvatting
Look at Python from a data science point of view and learn proven techniques for data visualization as used in making critical business decisions. Starting with an introduction to data science with Python, you will take a closer look at the Python environment and get acquainted with editors such as Jupyter Notebook and Spyder. After going through a primer on Python programming, you will grasp fundamental Python programming techniques used in data science.
Moving on to data visualization, you will see how it caters to modern business needs and forms a key factor in decision-making. You will also take a look at some popular data visualization libraries in Python.
Shifting focus to data structures, you will learn the various aspects of data structures from a data science perspective. You will then work with file I/O and regular expressions in Python, followed by gathering and cleaning data. Moving on to exploring and analyzing data, you will look at advanced data structures in Python. Then, you will take a deep dive into data visualization techniques, going through a number of plotting systems in Python.
In conclusion, you will complete a detailed case study, where you'll get a chance to revisit the concepts you've covered so far.
What You Will Learn
-Use Python programming techniques for data science
-Master data collections in Python
-Create engaging visualizations for BI systems
-Deploy effective strategies for gathering and cleaning data
-Integrate the Seaborn and Matplotlib plotting systems
Who This Book Is For
Developers with basic Python programming knowledge looking to adopt key strategies for data analysis and visualizations using Python.
Specificaties
Lezersrecensies
Inhoudsopgave
1.1 What is data science?
1.2 Why Python?
1.3 Python learning resources.
1.4 Python environment and editors (Jupyter Notebook, Netbeans , etc)
1.5 The basics of the python programming
1.6 Fundamental python programming techniques
1.6.1 The Tabular data, and data formats
1.6.2 Python pandas data science library
1.6.3 Python lambdas, and the numpy library.
1.6.4 Introduce the data cleaning and manipulation techniques
1.6.5 Introduce the abstraction of the Series and DataFrame
1.6.6 Run basic inferential statistical analysis.
1.7 Exercises and answers
Chapter 2: The importance of data visualization in business intelligence
2.1 Shift from input to output data preference
2.2 Why Data visualization is important?
2.3 How is the modern business needs Data visualization?
2.4 The future of Data Visualization
2.5 How data visualization is used for Business decision making
2.6 Introduce data visualization tchniques
2.6.1 Loading libraries
2.6.2 Popular Libraries for Data Visualization in PythonMatplotlibSeabornGeoplotlib PandasPlotly
2.6.3 Introduce Plots in Python
2.7 Exercises and answers
Chapter 3: Data collections structure
3.1 Lists
3.1.1 Create lists
3.1.2 Accessing values in lists
3.1.3 Add and update lists
3.1.4 Delete list elements
3.1.5 Basic list operations
3.1.6 Indexing, slicing, and matrices
3.1.7 Built-in list functions & methods
3.1.8 List methods
3.1.9 List sorting and traversing
3.1.10 Lists and strings
3.2 Parsing lines
3.3 Aliasing
3.4 Dictionaries
3.4.1 Create dictionaries
3.4.2 Updating and accessing values in dictionary
3.4.3 Delete dictionary elements
3.4.4 Built-in dictionary functions & methods
3.5 Tuples
3.5.1 Create tuples
3.5.2 Updating tuples
3.5.3 Accessing values in tuples
3.5.4 Basic tuples operations
3.6 Series data structure
3.7 DataFrame data structure
3.8 Panel data structure
3.9 Exercises and answers
Chapter 4: File I/O processing & Regular expressions
4.1 File I/O processing
4.1.1 Screen in/out processing
4.1.2 Opening and closing files
4.1.4 Reading and writing files
4.1.5 Directories in python
4.2 Regular expressions
4.2.1 Regular expression patterns
4.2.2 Special character classes
4.2.3 Repetition cases Alternatives Anchors
4.3 Exercises and answers
Chapter 5: Data gathering and cleaning
5.1 Data cleaning
Check missing values
Handle the missing values
5.2 Read and clean csv file
5.3 Data integration
5.4 Read the json file
5.5 Reading the html file
5.6 Exercises and answers
Chapter 6: Data exploring and analysis
6.1 Series data structure
6.1.1 Create a series
6.1.2 Accessing data from series with position
6.2 DataFrame data structure
6.2.1 Create a DataFrame
6.2.2 Updating and accessing
DataFrame
Column selection
Column addition
Column deletion
Row selection
Row addition
Row deletion
6.3 Panel data structure
6.3.1 Create panel
6.3.2 Accessing data from panel with position
6.4 Data analysis
6.4.1 Statistical analysis
6.4.2 Data grouping
Iterating through groups
Aggregations
Transformations Filtration
6.5 Exercises and answers
Chapter 7: Data visualization
7.1 Direct plotting
Line plotting
Bar plotting
Pie chart
Box plotting
Histogram plotting
A scatterplot
7.2 Seaborn plotting system
Strip plotting
Boxplot
Swarmplot
Jointplot
7.3 Matplotlib plotting
Line plotting
Bar chart
Histogram plotting
Scatter plot
Stack plots
Pie chart
7.4 Exercises.
Chapter 8: Case Study
8.1 Business case
8.2 Case data gathering
8.3 Case data analysis
8.4 Case data Visualization
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