Data Visualization with Python: exploring Matplotlib, Seaborn, and Bokeh for Interactive Visualizations
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
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London
BPB
2023
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| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xx, 284 Seiten Diagramme 235 mm |
| ISBN: | 9789355515384 |
Internformat
MARC
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| 264 | 1 | |a London |b BPB |c 2023 | |
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Datensatz im Suchindex
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xii □ Table of Contents 1. Understanding Data. 1 Structure. 1 Objectives. 2 What is Data?. 2 Categories of data. 3 Data attributes.9 The Purpose of data.11 Data collection. 13 Data processing. 15 Conclusion. 17 Points to remember. 17 Questions. 18 2. Data Visualization - Importance.21
Structure. 22 Objectives. 22 Introduction. 22 Data visualization. 23 Factors that influence data visualization choices.23 Content.23 Audience.24 Context. 24 Purpose.24 Dynamics. 24 Significance of visualization. 24 Data visualization identifies data trends.25 Data visualization tends to give the data a perspective. 25 Data visualization provides the correct context for the
data. 25
xiii Time is saved by data visualization. 25 A data story is told through data visualization. 26 Benefits of data visualization .26 Fast comprehension of information. 26 Correlations in relationships. 26 Trends over time.27 Frequency. 27 Examining the market. 27 Risk and reward.27 Responding to the market. 27 Simple data sharing.28 Data visualization trends. 28 Video visualization. 28 Data beyond
visuals. 29 Data democratization. 29 Data visualization is more social. 29 Real-time visualization. 30 Data storytelling.30 Data journalism is becoming more popular. 30 Mobile and social data visualization.31 Artificial intelligence and machine learning. 31 Mobile-friendly data. 32 Examples of current trends in data visualization and business intelligence. 32 Augmented analytics.32 Plug and play analytics solutions. 32 Data Quality Management. 33 Data
governance. 33 Integrated capabilities. 33 NLP Driven Analytical Queries. 33 Data-driven culture. 34
xiv Շ Clear and concise data visualization. 34 Collaborative business intelligence. 34 Mobile BI. 34 Conclusion. 35 Points to remember. 35 Questions.36 3. Data Visualization Use Cases. 37 Structure.37 Objectives. 38 Introduction. 38 Importance of use cases. 39 Various domains where data visualization is implemented. 39 National Geographic. 39 Improvado use case
examples. 40 Grafana. 43 19E.43 Wufoo (Infinity-Box). 44 Payback. 44 Associated Press. 45 IDS. 45 Lifetime Brands. 46 Informatica.46 QualiSystems. 47 How different industries and sectorsutilize data visualization. 48 Possible reasons to use data visualization . 51 Domain-wise use-case. 53 Other common data visualization use
cases. 54 Conclusion. 55 Points to remember. 55 Questions. 56
XV 4. Data Visualization Tools and Techniques. 57 Structure. 57 Objectives. 58 Introduction. 58 Different types of visualizations. 58 Bar chart. 59 Pie chart. 59 Donut chart. 60 Half donut chart and gauge chart. 61 Nested pie chart. 61 Line chart. 62 Scatter plot. 63 Cone
chart. 64 Pyramid chart.65 Funnel chart. 66 Radar triangle. 66 Radar Polygon.67 Polar chart. 68 Area chart. 69 Tree chart. 69 Flowchart. 70 Table. 71 Geospatial map and choropleth map. 71 Percentage bar. 72 Radial
wheel. 72 Concentric Circles. 73 Gantt chart. 74 Network diagram.75 Timeline. 76 Venn Diagram. 77 Histogram. 77
xvi C Mind map. 78 Dichotomous key. 79 PERT chart. 80 Box plot. 80 Heatmap. 81 Most popular data- visualization tools and techniques. 82 Tableau. 82 QlikView. 83 Microsoft Рогоег BI. 83 Data wrapper.83 Plotły. 84 Sísense.84 Excel. 85 Zoho
Analytics. 85 Other available tools and techniques. 85 Infogram. 85 Fusion charts.86 D3.js.86 High charts. 87 Fine Report. 87 Use-case of commonly used tools. 88 Used tools on Facebook. 88 Used tools in Google.88 Used tools on Twitter. 88 Used tools on Netflix. 89 Used tools by Intel. 89 Used tools by retails
companies. 90 Conclusion. 90 Points to remember. 90 Questions. 91
xvii 5. Data Visualization with Matplotlib. 93 Structure. 94 Objectives. 94 Introduction. 94 Importing Matplotlib and its documentation. 95 Understanding figure and subplots. 96 Basic plots. 104 Line plot. 105 Bar chart. 107 Histogram. 108 Scatter plot.110 Pie chart. 112 Area plot. 114 Boxplot. 115 Advanced plots.
117 Saving the plots. 121 Features of Matplotlib library. 124 Conclusion. 124 Points to remember. 125 Questions.125 Annexure Installing Python. 126 Annexure Pandas. 127 Annexure Numpy. 131 6. Data Visualization with Seaborn. 135 Structure. 135 Objectives. 136 Introduction. 136 Importing seaborn and its documentation. 137 Seaborn datasets. 139 Understanding figure, axes, subplots, and palette. 140
xviii Π Figure, axes, and subplots. 140 Palettes. 144 Categorical plots. 146 Bar plot. 147 Count plot. 150 Box plot. 152 Violin plot. 154 Strip plot. 157 Swarm plot.159 Cat plot. 161 FacetGrid. 164 Other plots. 166 Scatter
plot. 166 Line plot.169 Heatmap.172 Joint plot. 174 Pair plot. 177 Distribution plot. 182 Regression plot. 185 Saving the plot. 186 Conclusion. 187 Points to remember. 188 Questions. 189 7. Data Visualization with Bokeh. 191
Structure.191 Objectives. 192 Introduction. 192 Importing Bokeh and its documentation. 194 Understanding figures, glyphs, axes, and palettes.194 Figure. 194
xix Glyphs. 196 Axes. 199 Changing the axis range. 200 Adjusting the tick marks and labels. 200 Changing the axis line color and width. 200 Formatting tick labels. 200 Palettes and colours.200 Palettes. 201 Turbo. 201 Colors. 202 Creating plots. 203 Scatter plot. 203 Line plot. 205 Bar
plot. 206 HeatMap. 208 Histogram. 210 Patch plot. 211 Area plots. 213 Stacked bar plot. 215 Creating interactive plots. 217 Creating multiple plots. 219 Saving the plot. 226 Conclusion. 228 Points to remember.228 Questions. 230 8. Exploratory Data Analysis. 231 Structure.
231 Objectives. 232 Introduction. 232 Types of EDA.232
XX Π Univariate analysis. 233 Bivariate analysis. 234 Multivariate analysis. 235 Steps to perform EDA. 237 Statistical analysis.238 Data cleaning. 242 Handling missing values. 243 Handling outliers. 244 Missing inconsistent values. 248 Data visualization. 249 Visualizationsfor Univariate EDA. 250 Visualizations for Bivariate EDA. 251 Visualizations for Multivariate EDA. 252 Feature
engineering. 253 Handling categorical values. 255 Feature scaling. 257 Standardization example on 'titanic_survival' dataset. 258 Min-Max Scaling example on 'titanic_survival՛ dataset. 260 Normalization example on 'titanic_survival' dataset. 261 Correlation analysis. 262 EDA using dataset in Python. 265 Example 1. 267 Conclusion. 271 Points to remember. 271 Questions. 273 Index 275-284 |
| adam_txt |
xii □ Table of Contents 1. Understanding Data. 1 Structure. 1 Objectives. 2 What is Data?. 2 Categories of data. 3 Data attributes.9 The Purpose of data.11 Data collection. 13 Data processing. 15 Conclusion. 17 Points to remember. 17 Questions. 18 2. Data Visualization - Importance.21
Structure. 22 Objectives. 22 Introduction. 22 Data visualization. 23 Factors that influence data visualization choices.23 Content.23 Audience.24 Context. 24 Purpose.24 Dynamics. 24 Significance of visualization. 24 Data visualization identifies data trends.25 Data visualization tends to give the data a perspective. 25 Data visualization provides the correct context for the
data. 25
xiii Time is saved by data visualization. 25 A data story is told through data visualization. 26 Benefits of data visualization .26 Fast comprehension of information. 26 Correlations in relationships. 26 Trends over time.27 Frequency. 27 Examining the market. 27 Risk and reward.27 Responding to the market. 27 Simple data sharing.28 Data visualization trends. 28 Video visualization. 28 Data beyond
visuals. 29 Data democratization. 29 Data visualization is more social. 29 Real-time visualization. 30 Data storytelling.30 Data journalism is becoming more popular. 30 Mobile and social data visualization.31 Artificial intelligence and machine learning. 31 Mobile-friendly data. 32 Examples of current trends in data visualization and business intelligence. 32 Augmented analytics.32 Plug and play analytics solutions. 32 Data Quality Management. 33 Data
governance. 33 Integrated capabilities. 33 NLP Driven Analytical Queries. 33 Data-driven culture. 34
xiv Շ Clear and concise data visualization. 34 Collaborative business intelligence. 34 Mobile BI. 34 Conclusion. 35 Points to remember. 35 Questions.36 3. Data Visualization Use Cases. 37 Structure.37 Objectives. 38 Introduction. 38 Importance of use cases. 39 Various domains where data visualization is implemented. 39 National Geographic. 39 Improvado use case
examples. 40 Grafana. 43 19E.43 Wufoo (Infinity-Box). 44 Payback. 44 Associated Press. 45 IDS. 45 Lifetime Brands. 46 Informatica.46 QualiSystems. 47 How different industries and sectorsutilize data visualization. 48 Possible reasons to use data visualization . 51 Domain-wise use-case. 53 Other common data visualization use
cases. 54 Conclusion. 55 Points to remember. 55 Questions. 56
XV 4. Data Visualization Tools and Techniques. 57 Structure. 57 Objectives. 58 Introduction. 58 Different types of visualizations. 58 Bar chart. 59 Pie chart. 59 Donut chart. 60 Half donut chart and gauge chart. 61 Nested pie chart. 61 Line chart. 62 Scatter plot. 63 Cone
chart. 64 Pyramid chart.65 Funnel chart. 66 Radar triangle. 66 Radar Polygon.67 Polar chart. 68 Area chart. 69 Tree chart. 69 Flowchart. 70 Table. 71 Geospatial map and choropleth map. 71 Percentage bar. 72 Radial
wheel. 72 Concentric Circles. 73 Gantt chart. 74 Network diagram.75 Timeline. 76 Venn Diagram. 77 Histogram. 77
xvi C Mind map. 78 Dichotomous key. 79 PERT chart. 80 Box plot. 80 Heatmap. 81 Most popular data- visualization tools and techniques. 82 Tableau. 82 QlikView. 83 Microsoft Рогоег BI. 83 Data wrapper.83 Plotły. 84 Sísense.84 Excel. 85 Zoho
Analytics. 85 Other available tools and techniques. 85 Infogram. 85 Fusion charts.86 D3.js.86 High charts. 87 Fine Report. 87 Use-case of commonly used tools. 88 Used tools on Facebook. 88 Used tools in Google.88 Used tools on Twitter. 88 Used tools on Netflix. 89 Used tools by Intel. 89 Used tools by retails
companies. 90 Conclusion. 90 Points to remember. 90 Questions. 91
xvii 5. Data Visualization with Matplotlib. 93 Structure. 94 Objectives. 94 Introduction. 94 Importing Matplotlib and its documentation. 95 Understanding figure and subplots. 96 Basic plots. 104 Line plot. 105 Bar chart. 107 Histogram. 108 Scatter plot.110 Pie chart. 112 Area plot. 114 Boxplot. 115 Advanced plots.
117 Saving the plots. 121 Features of Matplotlib library. 124 Conclusion. 124 Points to remember. 125 Questions.125 Annexure Installing Python. 126 Annexure Pandas. 127 Annexure Numpy. 131 6. Data Visualization with Seaborn. 135 Structure. 135 Objectives. 136 Introduction. 136 Importing seaborn and its documentation. 137 Seaborn datasets. 139 Understanding figure, axes, subplots, and palette. 140
xviii Π Figure, axes, and subplots. 140 Palettes. 144 Categorical plots. 146 Bar plot. 147 Count plot. 150 Box plot. 152 Violin plot. 154 Strip plot. 157 Swarm plot.159 Cat plot. 161 FacetGrid. 164 Other plots. 166 Scatter
plot. 166 Line plot.169 Heatmap.172 Joint plot. 174 Pair plot. 177 Distribution plot. 182 Regression plot. 185 Saving the plot. 186 Conclusion. 187 Points to remember. 188 Questions. 189 7. Data Visualization with Bokeh. 191
Structure.191 Objectives. 192 Introduction. 192 Importing Bokeh and its documentation. 194 Understanding figures, glyphs, axes, and palettes.194 Figure. 194
xix Glyphs. 196 Axes. 199 Changing the axis range. 200 Adjusting the tick marks and labels. 200 Changing the axis line color and width. 200 Formatting tick labels. 200 Palettes and colours.200 Palettes. 201 Turbo. 201 Colors. 202 Creating plots. 203 Scatter plot. 203 Line plot. 205 Bar
plot. 206 HeatMap. 208 Histogram. 210 Patch plot. 211 Area plots. 213 Stacked bar plot. 215 Creating interactive plots. 217 Creating multiple plots. 219 Saving the plot. 226 Conclusion. 228 Points to remember.228 Questions. 230 8. Exploratory Data Analysis. 231 Structure.
231 Objectives. 232 Introduction. 232 Types of EDA.232
XX Π Univariate analysis. 233 Bivariate analysis. 234 Multivariate analysis. 235 Steps to perform EDA. 237 Statistical analysis.238 Data cleaning. 242 Handling missing values. 243 Handling outliers. 244 Missing inconsistent values. 248 Data visualization. 249 Visualizationsfor Univariate EDA. 250 Visualizations for Bivariate EDA. 251 Visualizations for Multivariate EDA. 252 Feature
engineering. 253 Handling categorical values. 255 Feature scaling. 257 Standardization example on 'titanic_survival' dataset. 258 Min-Max Scaling example on 'titanic_survival՛ dataset. 260 Normalization example on 'titanic_survival' dataset. 261 Correlation analysis. 262 EDA using dataset in Python. 265 Example 1. 267 Conclusion. 271 Points to remember. 271 Questions. 273 Index 275-284 |
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| id | DE-604.BV049313104 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T22:41:27Z |
| indexdate | 2024-09-20T04:26:09Z |
| institution | BVB |
| isbn | 9789355515384 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034574187 |
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| publishDate | 2023 |
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| publishDateSort | 2023 |
| publisher | BPB |
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| spelling | Pooja Verfasser aut Data Visualization with Python exploring Matplotlib, Seaborn, and Bokeh for Interactive Visualizations Dr. Pooja London BPB 2023 © 2023 xx, 284 Seiten Diagramme 235 mm txt rdacontent n rdamedia nc rdacarrier Explorative Datenanalyse (DE-588)4128896-8 gnd rswk-swf Visualisierung (DE-588)4188417-6 gnd rswk-swf Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Daten (DE-588)4135391-2 gnd rswk-swf Daten (DE-588)4135391-2 s Visualisierung (DE-588)4188417-6 s Python Programmiersprache (DE-588)4434275-5 s DE-604 Explorative Datenanalyse (DE-588)4128896-8 s Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034574187&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Pooja Data Visualization with Python exploring Matplotlib, Seaborn, and Bokeh for Interactive Visualizations Explorative Datenanalyse (DE-588)4128896-8 gnd Visualisierung (DE-588)4188417-6 gnd Python Programmiersprache (DE-588)4434275-5 gnd Daten (DE-588)4135391-2 gnd |
| subject_GND | (DE-588)4128896-8 (DE-588)4188417-6 (DE-588)4434275-5 (DE-588)4135391-2 |
| title | Data Visualization with Python exploring Matplotlib, Seaborn, and Bokeh for Interactive Visualizations |
| title_auth | Data Visualization with Python exploring Matplotlib, Seaborn, and Bokeh for Interactive Visualizations |
| title_exact_search | Data Visualization with Python exploring Matplotlib, Seaborn, and Bokeh for Interactive Visualizations |
| title_exact_search_txtP | Data Visualization with Python exploring Matplotlib, Seaborn, and Bokeh for Interactive Visualizations |
| title_full | Data Visualization with Python exploring Matplotlib, Seaborn, and Bokeh for Interactive Visualizations Dr. Pooja |
| title_fullStr | Data Visualization with Python exploring Matplotlib, Seaborn, and Bokeh for Interactive Visualizations Dr. Pooja |
| title_full_unstemmed | Data Visualization with Python exploring Matplotlib, Seaborn, and Bokeh for Interactive Visualizations Dr. Pooja |
| title_short | Data Visualization with Python |
| title_sort | data visualization with python exploring matplotlib seaborn and bokeh for interactive visualizations |
| title_sub | exploring Matplotlib, Seaborn, and Bokeh for Interactive Visualizations |
| topic | Explorative Datenanalyse (DE-588)4128896-8 gnd Visualisierung (DE-588)4188417-6 gnd Python Programmiersprache (DE-588)4434275-5 gnd Daten (DE-588)4135391-2 gnd |
| topic_facet | Explorative Datenanalyse Visualisierung Python Programmiersprache Daten |
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| work_keys_str_mv | AT pooja datavisualizationwithpythonexploringmatplotlibseabornandbokehforinteractivevisualizations |