Data Analysis and Visualization Using Python: Analyze Data to Create Visualizations for Bi Systems (Paperback)

Chapter 1: Introduction to data science with python 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 Python Matplotlib Seaborn Geoplotlib Pandas Plotly 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 4.1.3 The file object attributes 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