├── Datasets ├── dataset.md └── heart.csv ├── README.md ├── .gitignore ├── 10. Interactive Visualizations.ipynb ├── LICENSE └── 04. Pie Charts.ipynb /Datasets/dataset.md: -------------------------------------------------------------------------------- 1 | ,m 2 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Data-Visualizations 2 | 3 | Data Visualizations is emerging as one of the most essential skills in almost all of the IT and Non IT Background Sectors and Jobs. Using Data Visualizations to make wiser decisions which could land the Business to make bigger profits and understand the root cause and behavioral analysis of people and customers associated to it. In this Repository I have deeply discussed about Line Plots, Bar plots, Scatter Plots, and Pie Charts, Apart from that I have Discussed scientific plots, 3d plots, animated plots, interactive plots to visualize any kind of business problem and that too of any complexity. 4 | 5 | Data Visualizations using Python Data Visualization Library is the easiest way to perform Data Visualizations, and with the use of Dash, we can also, build up Dynamic and Interactive Live Dashboards without the help of Front end or backend javascript. 6 | 7 | In this Tutorial, I have discussed all the Important and prominent Charts and their variations so that anybody can understand it easily. I have covered Matplotlib, seaborn, Plotly, Plotly-express, bubbly, ipywidgets. All the Tutorials are built upon JUpyter Notebooks, and the Datasets used for these Notebooks are already uploaded in a folder named **Datasets**. 8 | 9 | 10 | ![alt text](https://www.boostlabs.com/wp-content/uploads/2019/09/10-types-of-data-visualization.jpg) 11 | 12 | ## Contents 13 | * 1. Line Plots 14 | * 2. Bar Plots 15 | * 3. Pie Charts 16 | * 4. Scatter Charts 17 | * 5. Improving the Visibility of Charts 18 | * 6. Introduction to seaborn 19 | * 7. Introduction to Plotly 20 | * 8. Introduction to Plotly Express 21 | * 9. Introduction to Animated Visualizations 22 | * 10. Introduction to Interactive Visualizations 23 | 24 | If any doubts or suggestions, please leave a message for me on linkedin. 25 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /Datasets/heart.csv: -------------------------------------------------------------------------------- 1 | age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal,target 2 | 63,1,3,145,233,1,0,150,0,2.3,0,0,1,1 3 | 37,1,2,130,250,0,1,187,0,3.5,0,0,2,1 4 | 41,0,1,130,204,0,0,172,0,1.4,2,0,2,1 5 | 56,1,1,120,236,0,1,178,0,0.8,2,0,2,1 6 | 57,0,0,120,354,0,1,163,1,0.6,2,0,2,1 7 | 57,1,0,140,192,0,1,148,0,0.4,1,0,1,1 8 | 56,0,1,140,294,0,0,153,0,1.3,1,0,2,1 9 | 44,1,1,120,263,0,1,173,0,0,2,0,3,1 10 | 52,1,2,172,199,1,1,162,0,0.5,2,0,3,1 11 | 57,1,2,150,168,0,1,174,0,1.6,2,0,2,1 12 | 54,1,0,140,239,0,1,160,0,1.2,2,0,2,1 13 | 48,0,2,130,275,0,1,139,0,0.2,2,0,2,1 14 | 49,1,1,130,266,0,1,171,0,0.6,2,0,2,1 15 | 64,1,3,110,211,0,0,144,1,1.8,1,0,2,1 16 | 58,0,3,150,283,1,0,162,0,1,2,0,2,1 17 | 50,0,2,120,219,0,1,158,0,1.6,1,0,2,1 18 | 58,0,2,120,340,0,1,172,0,0,2,0,2,1 19 | 66,0,3,150,226,0,1,114,0,2.6,0,0,2,1 20 | 43,1,0,150,247,0,1,171,0,1.5,2,0,2,1 21 | 69,0,3,140,239,0,1,151,0,1.8,2,2,2,1 22 | 59,1,0,135,234,0,1,161,0,0.5,1,0,3,1 23 | 44,1,2,130,233,0,1,179,1,0.4,2,0,2,1 24 | 42,1,0,140,226,0,1,178,0,0,2,0,2,1 25 | 61,1,2,150,243,1,1,137,1,1,1,0,2,1 26 | 40,1,3,140,199,0,1,178,1,1.4,2,0,3,1 27 | 71,0,1,160,302,0,1,162,0,0.4,2,2,2,1 28 | 59,1,2,150,212,1,1,157,0,1.6,2,0,2,1 29 | 51,1,2,110,175,0,1,123,0,0.6,2,0,2,1 30 | 65,0,2,140,417,1,0,157,0,0.8,2,1,2,1 31 | 53,1,2,130,197,1,0,152,0,1.2,0,0,2,1 32 | 41,0,1,105,198,0,1,168,0,0,2,1,2,1 33 | 65,1,0,120,177,0,1,140,0,0.4,2,0,3,1 34 | 44,1,1,130,219,0,0,188,0,0,2,0,2,1 35 | 54,1,2,125,273,0,0,152,0,0.5,0,1,2,1 36 | 51,1,3,125,213,0,0,125,1,1.4,2,1,2,1 37 | 46,0,2,142,177,0,0,160,1,1.4,0,0,2,1 38 | 54,0,2,135,304,1,1,170,0,0,2,0,2,1 39 | 54,1,2,150,232,0,0,165,0,1.6,2,0,3,1 40 | 65,0,2,155,269,0,1,148,0,0.8,2,0,2,1 41 | 65,0,2,160,360,0,0,151,0,0.8,2,0,2,1 42 | 51,0,2,140,308,0,0,142,0,1.5,2,1,2,1 43 | 48,1,1,130,245,0,0,180,0,0.2,1,0,2,1 44 | 45,1,0,104,208,0,0,148,1,3,1,0,2,1 45 | 53,0,0,130,264,0,0,143,0,0.4,1,0,2,1 46 | 39,1,2,140,321,0,0,182,0,0,2,0,2,1 47 | 52,1,1,120,325,0,1,172,0,0.2,2,0,2,1 48 | 44,1,2,140,235,0,0,180,0,0,2,0,2,1 49 | 47,1,2,138,257,0,0,156,0,0,2,0,2,1 50 | 53,0,2,128,216,0,0,115,0,0,2,0,0,1 51 | 53,0,0,138,234,0,0,160,0,0,2,0,2,1 52 | 51,0,2,130,256,0,0,149,0,0.5,2,0,2,1 53 | 66,1,0,120,302,0,0,151,0,0.4,1,0,2,1 54 | 62,1,2,130,231,0,1,146,0,1.8,1,3,3,1 55 | 44,0,2,108,141,0,1,175,0,0.6,1,0,2,1 56 | 63,0,2,135,252,0,0,172,0,0,2,0,2,1 57 | 52,1,1,134,201,0,1,158,0,0.8,2,1,2,1 58 | 48,1,0,122,222,0,0,186,0,0,2,0,2,1 59 | 45,1,0,115,260,0,0,185,0,0,2,0,2,1 60 | 34,1,3,118,182,0,0,174,0,0,2,0,2,1 61 | 57,0,0,128,303,0,0,159,0,0,2,1,2,1 62 | 71,0,2,110,265,1,0,130,0,0,2,1,2,1 63 | 54,1,1,108,309,0,1,156,0,0,2,0,3,1 64 | 52,1,3,118,186,0,0,190,0,0,1,0,1,1 65 | 41,1,1,135,203,0,1,132,0,0,1,0,1,1 66 | 58,1,2,140,211,1,0,165,0,0,2,0,2,1 67 | 35,0,0,138,183,0,1,182,0,1.4,2,0,2,1 68 | 51,1,2,100,222,0,1,143,1,1.2,1,0,2,1 69 | 45,0,1,130,234,0,0,175,0,0.6,1,0,2,1 70 | 44,1,1,120,220,0,1,170,0,0,2,0,2,1 71 | 62,0,0,124,209,0,1,163,0,0,2,0,2,1 72 | 54,1,2,120,258,0,0,147,0,0.4,1,0,3,1 73 | 51,1,2,94,227,0,1,154,1,0,2,1,3,1 74 | 29,1,1,130,204,0,0,202,0,0,2,0,2,1 75 | 51,1,0,140,261,0,0,186,1,0,2,0,2,1 76 | 43,0,2,122,213,0,1,165,0,0.2,1,0,2,1 77 | 55,0,1,135,250,0,0,161,0,1.4,1,0,2,1 78 | 51,1,2,125,245,1,0,166,0,2.4,1,0,2,1 79 | 59,1,1,140,221,0,1,164,1,0,2,0,2,1 80 | 52,1,1,128,205,1,1,184,0,0,2,0,2,1 81 | 58,1,2,105,240,0,0,154,1,0.6,1,0,3,1 82 | 41,1,2,112,250,0,1,179,0,0,2,0,2,1 83 | 45,1,1,128,308,0,0,170,0,0,2,0,2,1 84 | 60,0,2,102,318,0,1,160,0,0,2,1,2,1 85 | 52,1,3,152,298,1,1,178,0,1.2,1,0,3,1 86 | 42,0,0,102,265,0,0,122,0,0.6,1,0,2,1 87 | 67,0,2,115,564,0,0,160,0,1.6,1,0,3,1 88 | 68,1,2,118,277,0,1,151,0,1,2,1,3,1 89 | 46,1,1,101,197,1,1,156,0,0,2,0,3,1 90 | 54,0,2,110,214,0,1,158,0,1.6,1,0,2,1 91 | 58,0,0,100,248,0,0,122,0,1,1,0,2,1 92 | 48,1,2,124,255,1,1,175,0,0,2,2,2,1 93 | 57,1,0,132,207,0,1,168,1,0,2,0,3,1 94 | 52,1,2,138,223,0,1,169,0,0,2,4,2,1 95 | 54,0,1,132,288,1,0,159,1,0,2,1,2,1 96 | 45,0,1,112,160,0,1,138,0,0,1,0,2,1 97 | 53,1,0,142,226,0,0,111,1,0,2,0,3,1 98 | 62,0,0,140,394,0,0,157,0,1.2,1,0,2,1 99 | 52,1,0,108,233,1,1,147,0,0.1,2,3,3,1 100 | 43,1,2,130,315,0,1,162,0,1.9,2,1,2,1 101 | 53,1,2,130,246,1,0,173,0,0,2,3,2,1 102 | 42,1,3,148,244,0,0,178,0,0.8,2,2,2,1 103 | 59,1,3,178,270,0,0,145,0,4.2,0,0,3,1 104 | 63,0,1,140,195,0,1,179,0,0,2,2,2,1 105 | 42,1,2,120,240,1,1,194,0,0.8,0,0,3,1 106 | 50,1,2,129,196,0,1,163,0,0,2,0,2,1 107 | 68,0,2,120,211,0,0,115,0,1.5,1,0,2,1 108 | 69,1,3,160,234,1,0,131,0,0.1,1,1,2,1 109 | 45,0,0,138,236,0,0,152,1,0.2,1,0,2,1 110 | 50,0,1,120,244,0,1,162,0,1.1,2,0,2,1 111 | 50,0,0,110,254,0,0,159,0,0,2,0,2,1 112 | 64,0,0,180,325,0,1,154,1,0,2,0,2,1 113 | 57,1,2,150,126,1,1,173,0,0.2,2,1,3,1 114 | 64,0,2,140,313,0,1,133,0,0.2,2,0,3,1 115 | 43,1,0,110,211,0,1,161,0,0,2,0,3,1 116 | 55,1,1,130,262,0,1,155,0,0,2,0,2,1 117 | 37,0,2,120,215,0,1,170,0,0,2,0,2,1 118 | 41,1,2,130,214,0,0,168,0,2,1,0,2,1 119 | 56,1,3,120,193,0,0,162,0,1.9,1,0,3,1 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67,1,0,160,286,0,0,108,1,1.5,1,3,2,0 168 | 67,1,0,120,229,0,0,129,1,2.6,1,2,3,0 169 | 62,0,0,140,268,0,0,160,0,3.6,0,2,2,0 170 | 63,1,0,130,254,0,0,147,0,1.4,1,1,3,0 171 | 53,1,0,140,203,1,0,155,1,3.1,0,0,3,0 172 | 56,1,2,130,256,1,0,142,1,0.6,1,1,1,0 173 | 48,1,1,110,229,0,1,168,0,1,0,0,3,0 174 | 58,1,1,120,284,0,0,160,0,1.8,1,0,2,0 175 | 58,1,2,132,224,0,0,173,0,3.2,2,2,3,0 176 | 60,1,0,130,206,0,0,132,1,2.4,1,2,3,0 177 | 40,1,0,110,167,0,0,114,1,2,1,0,3,0 178 | 60,1,0,117,230,1,1,160,1,1.4,2,2,3,0 179 | 64,1,2,140,335,0,1,158,0,0,2,0,2,0 180 | 43,1,0,120,177,0,0,120,1,2.5,1,0,3,0 181 | 57,1,0,150,276,0,0,112,1,0.6,1,1,1,0 182 | 55,1,0,132,353,0,1,132,1,1.2,1,1,3,0 183 | 65,0,0,150,225,0,0,114,0,1,1,3,3,0 184 | 61,0,0,130,330,0,0,169,0,0,2,0,2,0 185 | 58,1,2,112,230,0,0,165,0,2.5,1,1,3,0 186 | 50,1,0,150,243,0,0,128,0,2.6,1,0,3,0 187 | 44,1,0,112,290,0,0,153,0,0,2,1,2,0 188 | 60,1,0,130,253,0,1,144,1,1.4,2,1,3,0 189 | 54,1,0,124,266,0,0,109,1,2.2,1,1,3,0 190 | 50,1,2,140,233,0,1,163,0,0.6,1,1,3,0 191 | 41,1,0,110,172,0,0,158,0,0,2,0,3,0 192 | 51,0,0,130,305,0,1,142,1,1.2,1,0,3,0 193 | 58,1,0,128,216,0,0,131,1,2.2,1,3,3,0 194 | 54,1,0,120,188,0,1,113,0,1.4,1,1,3,0 195 | 60,1,0,145,282,0,0,142,1,2.8,1,2,3,0 196 | 60,1,2,140,185,0,0,155,0,3,1,0,2,0 197 | 59,1,0,170,326,0,0,140,1,3.4,0,0,3,0 198 | 46,1,2,150,231,0,1,147,0,3.6,1,0,2,0 199 | 67,1,0,125,254,1,1,163,0,0.2,1,2,3,0 200 | 62,1,0,120,267,0,1,99,1,1.8,1,2,3,0 201 | 65,1,0,110,248,0,0,158,0,0.6,2,2,1,0 202 | 44,1,0,110,197,0,0,177,0,0,2,1,2,0 203 | 60,1,0,125,258,0,0,141,1,2.8,1,1,3,0 204 | 58,1,0,150,270,0,0,111,1,0.8,2,0,3,0 205 | 68,1,2,180,274,1,0,150,1,1.6,1,0,3,0 206 | 62,0,0,160,164,0,0,145,0,6.2,0,3,3,0 207 | 52,1,0,128,255,0,1,161,1,0,2,1,3,0 208 | 59,1,0,110,239,0,0,142,1,1.2,1,1,3,0 209 | 60,0,0,150,258,0,0,157,0,2.6,1,2,3,0 210 | 49,1,2,120,188,0,1,139,0,2,1,3,3,0 211 | 59,1,0,140,177,0,1,162,1,0,2,1,3,0 212 | 57,1,2,128,229,0,0,150,0,0.4,1,1,3,0 213 | 61,1,0,120,260,0,1,140,1,3.6,1,1,3,0 214 | 39,1,0,118,219,0,1,140,0,1.2,1,0,3,0 215 | 61,0,0,145,307,0,0,146,1,1,1,0,3,0 216 | 56,1,0,125,249,1,0,144,1,1.2,1,1,2,0 217 | 43,0,0,132,341,1,0,136,1,3,1,0,3,0 218 | 62,0,2,130,263,0,1,97,0,1.2,1,1,3,0 219 | 63,1,0,130,330,1,0,132,1,1.8,2,3,3,0 220 | 65,1,0,135,254,0,0,127,0,2.8,1,1,3,0 221 | 48,1,0,130,256,1,0,150,1,0,2,2,3,0 222 | 63,0,0,150,407,0,0,154,0,4,1,3,3,0 223 | 55,1,0,140,217,0,1,111,1,5.6,0,0,3,0 224 | 65,1,3,138,282,1,0,174,0,1.4,1,1,2,0 225 | 56,0,0,200,288,1,0,133,1,4,0,2,3,0 226 | 54,1,0,110,239,0,1,126,1,2.8,1,1,3,0 227 | 70,1,0,145,174,0,1,125,1,2.6,0,0,3,0 228 | 62,1,1,120,281,0,0,103,0,1.4,1,1,3,0 229 | 35,1,0,120,198,0,1,130,1,1.6,1,0,3,0 230 | 59,1,3,170,288,0,0,159,0,0.2,1,0,3,0 231 | 64,1,2,125,309,0,1,131,1,1.8,1,0,3,0 232 | 47,1,2,108,243,0,1,152,0,0,2,0,2,0 233 | 57,1,0,165,289,1,0,124,0,1,1,3,3,0 234 | 55,1,0,160,289,0,0,145,1,0.8,1,1,3,0 235 | 64,1,0,120,246,0,0,96,1,2.2,0,1,2,0 236 | 70,1,0,130,322,0,0,109,0,2.4,1,3,2,0 237 | 51,1,0,140,299,0,1,173,1,1.6,2,0,3,0 238 | 58,1,0,125,300,0,0,171,0,0,2,2,3,0 239 | 60,1,0,140,293,0,0,170,0,1.2,1,2,3,0 240 | 77,1,0,125,304,0,0,162,1,0,2,3,2,0 241 | 35,1,0,126,282,0,0,156,1,0,2,0,3,0 242 | 70,1,2,160,269,0,1,112,1,2.9,1,1,3,0 243 | 59,0,0,174,249,0,1,143,1,0,1,0,2,0 244 | 64,1,0,145,212,0,0,132,0,2,1,2,1,0 245 | 57,1,0,152,274,0,1,88,1,1.2,1,1,3,0 246 | 56,1,0,132,184,0,0,105,1,2.1,1,1,1,0 247 | 48,1,0,124,274,0,0,166,0,0.5,1,0,3,0 248 | 56,0,0,134,409,0,0,150,1,1.9,1,2,3,0 249 | 66,1,1,160,246,0,1,120,1,0,1,3,1,0 250 | 54,1,1,192,283,0,0,195,0,0,2,1,3,0 251 | 69,1,2,140,254,0,0,146,0,2,1,3,3,0 252 | 51,1,0,140,298,0,1,122,1,4.2,1,3,3,0 253 | 43,1,0,132,247,1,0,143,1,0.1,1,4,3,0 254 | 62,0,0,138,294,1,1,106,0,1.9,1,3,2,0 255 | 67,1,0,100,299,0,0,125,1,0.9,1,2,2,0 256 | 59,1,3,160,273,0,0,125,0,0,2,0,2,0 257 | 45,1,0,142,309,0,0,147,1,0,1,3,3,0 258 | 58,1,0,128,259,0,0,130,1,3,1,2,3,0 259 | 50,1,0,144,200,0,0,126,1,0.9,1,0,3,0 260 | 62,0,0,150,244,0,1,154,1,1.4,1,0,2,0 261 | 38,1,3,120,231,0,1,182,1,3.8,1,0,3,0 262 | 66,0,0,178,228,1,1,165,1,1,1,2,3,0 263 | 52,1,0,112,230,0,1,160,0,0,2,1,2,0 264 | 53,1,0,123,282,0,1,95,1,2,1,2,3,0 265 | 63,0,0,108,269,0,1,169,1,1.8,1,2,2,0 266 | 54,1,0,110,206,0,0,108,1,0,1,1,2,0 267 | 66,1,0,112,212,0,0,132,1,0.1,2,1,2,0 268 | 55,0,0,180,327,0,2,117,1,3.4,1,0,2,0 269 | 49,1,2,118,149,0,0,126,0,0.8,2,3,2,0 270 | 54,1,0,122,286,0,0,116,1,3.2,1,2,2,0 271 | 56,1,0,130,283,1,0,103,1,1.6,0,0,3,0 272 | 46,1,0,120,249,0,0,144,0,0.8,2,0,3,0 273 | 61,1,3,134,234,0,1,145,0,2.6,1,2,2,0 274 | 67,1,0,120,237,0,1,71,0,1,1,0,2,0 275 | 58,1,0,100,234,0,1,156,0,0.1,2,1,3,0 276 | 47,1,0,110,275,0,0,118,1,1,1,1,2,0 277 | 52,1,0,125,212,0,1,168,0,1,2,2,3,0 278 | 58,1,0,146,218,0,1,105,0,2,1,1,3,0 279 | 57,1,1,124,261,0,1,141,0,0.3,2,0,3,0 280 | 58,0,1,136,319,1,0,152,0,0,2,2,2,0 281 | 61,1,0,138,166,0,0,125,1,3.6,1,1,2,0 282 | 42,1,0,136,315,0,1,125,1,1.8,1,0,1,0 283 | 52,1,0,128,204,1,1,156,1,1,1,0,0,0 284 | 59,1,2,126,218,1,1,134,0,2.2,1,1,1,0 285 | 40,1,0,152,223,0,1,181,0,0,2,0,3,0 286 | 61,1,0,140,207,0,0,138,1,1.9,2,1,3,0 287 | 46,1,0,140,311,0,1,120,1,1.8,1,2,3,0 288 | 59,1,3,134,204,0,1,162,0,0.8,2,2,2,0 289 | 57,1,1,154,232,0,0,164,0,0,2,1,2,0 290 | 57,1,0,110,335,0,1,143,1,3,1,1,3,0 291 | 55,0,0,128,205,0,2,130,1,2,1,1,3,0 292 | 61,1,0,148,203,0,1,161,0,0,2,1,3,0 293 | 58,1,0,114,318,0,2,140,0,4.4,0,3,1,0 294 | 58,0,0,170,225,1,0,146,1,2.8,1,2,1,0 295 | 67,1,2,152,212,0,0,150,0,0.8,1,0,3,0 296 | 44,1,0,120,169,0,1,144,1,2.8,0,0,1,0 297 | 63,1,0,140,187,0,0,144,1,4,2,2,3,0 298 | 63,0,0,124,197,0,1,136,1,0,1,0,2,0 299 | 59,1,0,164,176,1,0,90,0,1,1,2,1,0 300 | 57,0,0,140,241,0,1,123,1,0.2,1,0,3,0 301 | 45,1,3,110,264,0,1,132,0,1.2,1,0,3,0 302 | 68,1,0,144,193,1,1,141,0,3.4,1,2,3,0 303 | 57,1,0,130,131,0,1,115,1,1.2,1,1,3,0 304 | 57,0,1,130,236,0,0,174,0,0,1,1,2,0 305 | -------------------------------------------------------------------------------- /10. Interactive Visualizations.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "data": { 10 | "text/html": [ 11 | " \n", 26 | " " 27 | ] 28 | }, 29 | "metadata": {}, 30 | "output_type": "display_data" 31 | } 32 | ], 33 | "source": [ 34 | "# lets import basic python libraries\n", 35 | "import numpy as np\n", 36 | "import pandas as pd\n", 37 | "import datetime\n", 38 | "\n", 39 | "# for advanced data visualizations\n", 40 | "import plotly\n", 41 | "import plotly.graph_objects as go\n", 42 | "import plotly.offline as py\n", 43 | "from plotly.offline import init_notebook_mode, iplot, plot\n", 44 | "import plotly.graph_objs as go\n", 45 | "from plotly import tools\n", 46 | "init_notebook_mode(connected = True)\n", 47 | "\n", 48 | "# for jupyter notebook widgets\n", 49 | "import ipywidgets as widgets\n", 50 | "from ipywidgets import interact\n", 51 | "from ipywidgets import interact_manual" 52 | ] 53 | }, 54 | { 55 | "cell_type": "code", 56 | "execution_count": 2, 57 | "metadata": {}, 58 | "outputs": [ 59 | { 60 | "data": { 61 | "text/plain": [ 62 | "(336776, 16)" 63 | ] 64 | }, 65 | "execution_count": 2, 66 | "metadata": {}, 67 | "output_type": "execute_result" 68 | } 69 | ], 70 | "source": [ 71 | "# lets read the dataset\n", 72 | "data = pd.read_csv('https://raw.githubusercontent.com/yankev/testing/master/datasets/nycflights.csv')\n", 73 | "data = data.drop(data.columns[[0]], axis=1)\n", 74 | "\n", 75 | "# lets check the shape of data\n", 76 | "data.shape" 77 | ] 78 | }, 79 | { 80 | "cell_type": "code", 81 | "execution_count": 3, 82 | "metadata": {}, 83 | "outputs": [ 84 | { 85 | "data": { 86 | "application/vnd.jupyter.widget-view+json": { 87 | "model_id": "745cca45335447788a5a6d51059d0a50" 88 | }, 89 | "text/html": [ 90 | "
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yearmonthdaydep_timedep_delayarr_timearr_delaycarriertailnumflightorigindestair_timedistancehourminute
0201311517.02.0830.011.0UAN142281545EWRIAH227.014005.017.0
1201311533.04.0850.020.0UAN242111714LGAIAH227.014165.033.0
2201311542.02.0923.033.0AAN619AA1141JFKMIA160.010895.042.0
3201311544.0-1.01004.0-18.0B6N804JB725JFKBQN183.015765.044.0
4201311554.0-6.0812.0-25.0DLN668DN461LGAATL116.07625.054.0
\n", 224 | "
" 225 | ], 226 | "text/plain": [ 227 | " year month day dep_time dep_delay arr_time arr_delay carrier tailnum \\\n", 228 | "0 2013 1 1 517.0 2.0 830.0 11.0 UA N14228 \n", 229 | "1 2013 1 1 533.0 4.0 850.0 20.0 UA N24211 \n", 230 | "2 2013 1 1 542.0 2.0 923.0 33.0 AA N619AA \n", 231 | "3 2013 1 1 544.0 -1.0 1004.0 -18.0 B6 N804JB \n", 232 | "4 2013 1 1 554.0 -6.0 812.0 -25.0 DL N668DN \n", 233 | "\n", 234 | " flight origin dest air_time distance hour minute \n", 235 | "0 1545 EWR IAH 227.0 1400 5.0 17.0 \n", 236 | "1 1714 LGA IAH 227.0 1416 5.0 33.0 \n", 237 | "2 1141 JFK MIA 160.0 1089 5.0 42.0 \n", 238 | "3 725 JFK BQN 183.0 1576 5.0 44.0 \n", 239 | "4 461 LGA ATL 116.0 762 5.0 54.0 " 240 | ] 241 | }, 242 | "metadata": {}, 243 | "output_type": "display_data" 244 | } 245 | ], 246 | "source": [ 247 | "# lets check the head of the dataset\n", 248 | "data.head()" 249 | ] 250 | }, 251 | { 252 | "cell_type": "code", 253 | "execution_count": 4, 254 | "metadata": {}, 255 | "outputs": [ 256 | { 257 | "data": { 258 | "application/vnd.jupyter.widget-view+json": { 259 | "model_id": "19a20b1c8a914257a1ebe1baaf364e4c", 260 | "version_major": 2, 261 | "version_minor": 0 262 | }, 263 | "text/plain": [ 264 | "interactive(children=(Dropdown(description='column1', options=('year', 'month', 'day', 'dep_time', 'dep_delay'…" 265 | ] 266 | }, 267 | "metadata": {}, 268 | "output_type": "display_data" 269 | } 270 | ], 271 | "source": [ 272 | "@interact\n", 273 | "def correlations(column1 = list(data.select_dtypes('number').columns),\n", 274 | " column2 = list(data.select_dtypes('number').columns[1:])):\n", 275 | " print(f\"Correlations: \", {data[column1].corr(data[column2])})\n" 276 | ] 277 | }, 278 | { 279 | "cell_type": "code", 280 | "execution_count": 5, 281 | "metadata": { 282 | "scrolled": true 283 | }, 284 | "outputs": [ 285 | { 286 | "data": { 287 | "application/vnd.jupyter.widget-view+json": { 288 | "model_id": "78df7a62d92d4b77b0515e43b35c9572", 289 | "version_major": 2, 290 | "version_minor": 0 291 | }, 292 | "text/plain": [ 293 | "interactive(children=(Text(value='dep_delay', description='column'), FloatSlider(value=60.0, description='x', …" 294 | ] 295 | }, 296 | "metadata": {}, 297 | "output_type": "display_data" 298 | } 299 | ], 300 | "source": [ 301 | "\n", 302 | "pd.set_option('max_rows', 30000)\n", 303 | "\n", 304 | "@interact\n", 305 | "def show_articles_more_than(column='dep_delay', x=60.0):\n", 306 | " return data.loc[data[column] > x]" 307 | ] 308 | }, 309 | { 310 | "cell_type": "code", 311 | "execution_count": 12, 312 | "metadata": {}, 313 | "outputs": [], 314 | "source": [ 315 | "month = widgets.IntSlider(\n", 316 | " value=1.0,\n", 317 | " min=1.0,\n", 318 | " max=12.0,\n", 319 | " step=1.0,\n", 320 | " description='Month:',\n", 321 | " continuous_update=False\n", 322 | ")\n", 323 | "\n", 324 | "use_date = widgets.Checkbox(\n", 325 | " description='Date: ',\n", 326 | " value=True,\n", 327 | ")\n", 328 | "\n", 329 | "container = widgets.HBox(children=[use_date, month])\n", 330 | "\n", 331 | "textbox = widgets.Dropdown(\n", 332 | " description='Airline: ',\n", 333 | " value='DL',\n", 334 | " options=data['carrier'].unique().tolist()\n", 335 | ")\n", 336 | "\n", 337 | "origin = widgets.Dropdown(\n", 338 | " options=list(data['origin'].unique()),\n", 339 | " value='LGA',\n", 340 | " description='Origin Airport:',\n", 341 | ")\n", 342 | "\n", 343 | "\n", 344 | "# Assign an empty figure widget with two traces\n", 345 | "trace1 = go.Histogram(x=data['arr_delay'], opacity=0.75, name='Arrival Delays')\n", 346 | "trace2 = go.Histogram(x=data['dep_delay'], opacity=0.75, name='Departure Delays')\n", 347 | "g = go.FigureWidget(data=[trace1, trace2],\n", 348 | " layout=go.Layout(\n", 349 | " title=dict(\n", 350 | " text='NYC FlightDatabase'\n", 351 | " ),\n", 352 | " barmode='overlay'\n", 353 | " ))" 354 | ] 355 | }, 356 | { 357 | "cell_type": "code", 358 | "execution_count": 13, 359 | "metadata": {}, 360 | "outputs": [], 361 | "source": [ 362 | "def validate():\n", 363 | " if origin.value in data['origin'].unique() and textbox.value in data['carrier'].unique():\n", 364 | " return True\n", 365 | " else:\n", 366 | " return False\n", 367 | "\n", 368 | "\n", 369 | "def response(change):\n", 370 | " if validate():\n", 371 | " if use_date.value:\n", 372 | " filter_list = [i and j and k for i, j, k in\n", 373 | " zip(data['month'] == month.value, data['carrier'] == textbox.value,\n", 374 | " data['origin'] == origin.value)]\n", 375 | " temp_df = data[filter_list]\n", 376 | "\n", 377 | " else:\n", 378 | " filter_list = [i and j for i, j in\n", 379 | " zip(data['carrier'] == 'DL', data['origin'] == origin.value)]\n", 380 | " temp_df = data[filter_list]\n", 381 | " x1 = temp_df['arr_delay']\n", 382 | " x2 = temp_df['dep_delay']\n", 383 | " with g.batch_update():\n", 384 | " g.data[0].x = x1\n", 385 | " g.data[1].x = x2\n", 386 | " g.layout.barmode = 'overlay'\n", 387 | " g.layout.xaxis.title = 'Delay in Minutes'\n", 388 | " g.layout.yaxis.title = 'Number of Delays'\n", 389 | "\n", 390 | "\n", 391 | "origin.observe(response, names=\"value\")\n", 392 | "textbox.observe(response, names=\"value\")\n", 393 | "month.observe(response, names=\"value\")\n", 394 | "use_date.observe(response, names=\"value\")" 395 | ] 396 | }, 397 | { 398 | "cell_type": "code", 399 | "execution_count": 14, 400 | "metadata": {}, 401 | "outputs": [ 402 | { 403 | "data": { 404 | "application/vnd.jupyter.widget-view+json": { 405 | "model_id": "54e57c4ba6fa40148ae304d27b719413", 406 | "version_major": 2, 407 | "version_minor": 0 408 | }, 409 | "text/plain": [ 410 | "VBox(children=(HBox(children=(Checkbox(value=True, description='Date: '), IntSlider(value=1, continuous_update…" 411 | ] 412 | }, 413 | "metadata": {}, 414 | "output_type": "display_data" 415 | } 416 | ], 417 | "source": [ 418 | "container2 = widgets.HBox([origin, textbox])\n", 419 | "widgets.VBox([container,\n", 420 | " container2,\n", 421 | " g])" 422 | ] 423 | }, 424 | { 425 | "cell_type": "code", 426 | "execution_count": 18, 427 | "metadata": {}, 428 | "outputs": [ 429 | { 430 | "data": { 431 | "application/vnd.jupyter.widget-view+json": { 432 | "model_id": "4efc2d7d9dba46f8b6bf44dddb34d0ce", 433 | "version_major": 2, 434 | "version_minor": 0 435 | }, 436 | "text/plain": [ 437 | "interactive(children=(Dropdown(description='x', options=('year', 'month', 'day', 'dep_time', 'dep_delay', 'arr…" 438 | ] 439 | }, 440 | "metadata": {}, 441 | "output_type": "display_data" 442 | } 443 | ], 444 | "source": [ 445 | "\n", 446 | "@interact_manual\n", 447 | "def scatter_plot(x = list(data.select_dtypes('number').columns),\n", 448 | " y = list(data.select_dtypes('number').columns)[1:],\n", 449 | " ):\n", 450 | " data.plot(kind = 'scatter',\n", 451 | " x = x,\n", 452 | " y = y,\n", 453 | " )" 454 | ] 455 | }, 456 | { 457 | "cell_type": "code", 458 | "execution_count": null, 459 | "metadata": {}, 460 | "outputs": [], 461 | "source": [] 462 | } 463 | ], 464 | "metadata": { 465 | "kernelspec": { 466 | "display_name": "Python 3", 467 | "language": "python", 468 | "name": "python3" 469 | }, 470 | "language_info": { 471 | "codemirror_mode": { 472 | "name": "ipython", 473 | "version": 3 474 | }, 475 | "file_extension": ".py", 476 | "mimetype": "text/x-python", 477 | "name": "python", 478 | "nbconvert_exporter": "python", 479 | "pygments_lexer": "ipython3", 480 | "version": "3.6.8" 481 | } 482 | }, 483 | "nbformat": 4, 484 | "nbformat_minor": 4 485 | } 486 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, 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Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /04. Pie Charts.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Pie Charts\n", 8 | "\n", 9 | "* Pie Charts are one of the most popular Charts used in the domain of Data Science and Business Intelligence.\n", 10 | "* Pie Charts are beautiful and provide users to visualize data very easily.\n", 11 | "* Pie Charts are can be used to Visualize ```Univariate``` and ```Bivariate Data```.\n", 12 | "\n", 13 | "**In this Tutorial, we will learn,**\n", 14 | " * Making a simple Pie Chart.\n", 15 | " * Using other parameters of pie charts, to make it look more attractive.\n", 16 | " * Making ```Donut Charts```.\n", 17 | " \n" 18 | ] 19 | }, 20 | { 21 | "cell_type": "code", 22 | "execution_count": 2, 23 | "metadata": {}, 24 | "outputs": [], 25 | "source": [ 26 | "# importing basic libraries\n", 27 | "import numpy as np\n", 28 | "import pandas as pd\n", 29 | "\n", 30 | "# importing matplot for data visualization\n", 31 | "import matplotlib.pyplot as plt" 32 | ] 33 | }, 34 | { 35 | "cell_type": "code", 36 | "execution_count": 3, 37 | "metadata": {}, 38 | "outputs": [ 39 | { 40 | "data": { 41 | "text/plain": [ 42 | "(1470, 35)" 43 | ] 44 | }, 45 | "execution_count": 3, 46 | "metadata": {}, 47 | "output_type": "execute_result" 48 | } 49 | ], 50 | "source": [ 51 | "# lets import a dataset to work with\n", 52 | "data = pd.read_csv('employee.csv')\n", 53 | "\n", 54 | "# lets also check the shape of the dataset\n", 55 | "data.shape" 56 | ] 57 | }, 58 | { 59 | "cell_type": "code", 60 | "execution_count": 5, 61 | "metadata": {}, 62 | "outputs": [ 63 | { 64 | "data": { 65 | "text/html": [ 66 | "
\n", 67 | "\n", 80 | "\n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | " \n", 184 | " \n", 185 | " \n", 186 | " \n", 187 | " \n", 188 | " \n", 189 | " \n", 190 | " \n", 191 | " \n", 192 | " \n", 193 | " \n", 194 | " \n", 195 | " \n", 196 | " \n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | " \n", 242 | " \n", 243 | " \n", 244 | " \n", 245 | " \n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | " \n", 252 | " \n", 253 | " \n", 254 | " \n", 255 | " \n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | " \n", 261 | " \n", 262 | " \n", 263 | " \n", 264 | " \n", 265 | " \n", 266 | " \n", 267 | " \n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | " \n", 290 | " \n", 291 | " \n", 292 | " \n", 293 | " \n", 294 | " \n", 295 | " \n", 296 | " \n", 297 | " \n", 298 | " \n", 299 | " \n", 300 | " \n", 301 | " \n", 302 | " \n", 303 | " \n", 304 | " \n", 305 | " \n", 306 | " \n", 307 | " \n", 308 | " \n", 309 | " \n", 310 | " \n", 311 | " \n", 312 | " \n", 313 | "
AgeAttritionBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountEmployeeNumberEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
041YesTravel_Rarely1102Sales12Life Sciences112Female9432Sales Executive4Single5993194798YYes11318008016405
149NoTravel_Frequently279Research & Development81Life Sciences123Male6122Research Scientist2Married5130249071YNo2344801103310717
237YesTravel_Rarely1373Research & Development22Other144Male9221Laboratory Technician3Single209023966YYes15328007330000
333NoTravel_Frequently1392Research & Development34Life Sciences154Female5631Research Scientist3Married2909231591YYes11338008338730
427NoTravel_Rarely591Research & Development21Medical171Male4031Laboratory Technician2Married3468166329YNo12348016332222
\n", 314 | "
" 315 | ], 316 | "text/plain": [ 317 | " Age Attrition BusinessTravel DailyRate Department \\\n", 318 | "0 41 Yes Travel_Rarely 1102 Sales \n", 319 | "1 49 No Travel_Frequently 279 Research & Development \n", 320 | "2 37 Yes Travel_Rarely 1373 Research & Development \n", 321 | "3 33 No Travel_Frequently 1392 Research & Development \n", 322 | "4 27 No Travel_Rarely 591 Research & Development \n", 323 | "\n", 324 | " DistanceFromHome Education EducationField EmployeeCount EmployeeNumber \\\n", 325 | "0 1 2 Life Sciences 1 1 \n", 326 | "1 8 1 Life Sciences 1 2 \n", 327 | "2 2 2 Other 1 4 \n", 328 | "3 3 4 Life Sciences 1 5 \n", 329 | "4 2 1 Medical 1 7 \n", 330 | "\n", 331 | " EnvironmentSatisfaction Gender HourlyRate JobInvolvement JobLevel \\\n", 332 | "0 2 Female 94 3 2 \n", 333 | "1 3 Male 61 2 2 \n", 334 | "2 4 Male 92 2 1 \n", 335 | "3 4 Female 56 3 1 \n", 336 | "4 1 Male 40 3 1 \n", 337 | "\n", 338 | " JobRole JobSatisfaction MaritalStatus MonthlyIncome \\\n", 339 | "0 Sales Executive 4 Single 5993 \n", 340 | "1 Research Scientist 2 Married 5130 \n", 341 | "2 Laboratory Technician 3 Single 2090 \n", 342 | "3 Research Scientist 3 Married 2909 \n", 343 | "4 Laboratory Technician 2 Married 3468 \n", 344 | "\n", 345 | " MonthlyRate NumCompaniesWorked Over18 OverTime PercentSalaryHike \\\n", 346 | "0 19479 8 Y Yes 11 \n", 347 | "1 24907 1 Y No 23 \n", 348 | "2 2396 6 Y Yes 15 \n", 349 | "3 23159 1 Y Yes 11 \n", 350 | "4 16632 9 Y No 12 \n", 351 | "\n", 352 | " PerformanceRating RelationshipSatisfaction StandardHours \\\n", 353 | "0 3 1 80 \n", 354 | "1 4 4 80 \n", 355 | "2 3 2 80 \n", 356 | "3 3 3 80 \n", 357 | "4 3 4 80 \n", 358 | "\n", 359 | " StockOptionLevel TotalWorkingYears TrainingTimesLastYear \\\n", 360 | "0 0 8 0 \n", 361 | "1 1 10 3 \n", 362 | "2 0 7 3 \n", 363 | "3 0 8 3 \n", 364 | "4 1 6 3 \n", 365 | "\n", 366 | " WorkLifeBalance YearsAtCompany YearsInCurrentRole \\\n", 367 | "0 1 6 4 \n", 368 | "1 3 10 7 \n", 369 | "2 3 0 0 \n", 370 | "3 3 8 7 \n", 371 | "4 3 2 2 \n", 372 | "\n", 373 | " YearsSinceLastPromotion YearsWithCurrManager \n", 374 | "0 0 5 \n", 375 | "1 1 7 \n", 376 | "2 0 0 \n", 377 | "3 3 0 \n", 378 | "4 2 2 " 379 | ] 380 | }, 381 | "execution_count": 5, 382 | "metadata": {}, 383 | "output_type": "execute_result" 384 | } 385 | ], 386 | "source": [ 387 | "# lets also check the structure of the dataset\n", 388 | "\n", 389 | "pd.set_option('max_columns', 300)\n", 390 | "data.head()" 391 | ] 392 | }, 393 | { 394 | "cell_type": "code", 395 | "execution_count": 5, 396 | "metadata": {}, 397 | "outputs": [ 398 | { 399 | "name": "stdout", 400 | "output_type": "stream", 401 | "text": [ 402 | "Help on function pie in module matplotlib.pyplot:\n", 403 | "\n", 404 | "pie(x, explode=None, labels=None, colors=None, autopct=None, pctdistance=0.6, shadow=False, labeldistance=1.1, startangle=None, radius=None, counterclock=True, wedgeprops=None, textprops=None, center=(0, 0), frame=False, rotatelabels=False, *, data=None)\n", 405 | " Plot a pie chart.\n", 406 | " \n", 407 | " Make a pie chart of array *x*. The fractional area of each wedge is\n", 408 | " given by ``x/sum(x)``. If ``sum(x) < 1``, then the values of *x* give\n", 409 | " the fractional area directly and the array will not be normalized. The\n", 410 | " resulting pie will have an empty wedge of size ``1 - sum(x)``.\n", 411 | " \n", 412 | " The wedges are plotted counterclockwise, by default starting from the\n", 413 | " x-axis.\n", 414 | " \n", 415 | " Parameters\n", 416 | " ----------\n", 417 | " x : array-like\n", 418 | " The wedge sizes.\n", 419 | " \n", 420 | " explode : array-like, optional, default: None\n", 421 | " If not *None*, is a ``len(x)`` array which specifies the fraction\n", 422 | " of the radius with which to offset each wedge.\n", 423 | " \n", 424 | " labels : list, optional, default: None\n", 425 | " A sequence of strings providing the labels for each wedge\n", 426 | " \n", 427 | " colors : array-like, optional, default: None\n", 428 | " A sequence of matplotlib color args through which the pie chart\n", 429 | " will cycle. If *None*, will use the colors in the currently\n", 430 | " active cycle.\n", 431 | " \n", 432 | " autopct : None (default), string, or function, optional\n", 433 | " If not *None*, is a string or function used to label the wedges\n", 434 | " with their numeric value. The label will be placed inside the\n", 435 | " wedge. If it is a format string, the label will be ``fmt%pct``.\n", 436 | " If it is a function, it will be called.\n", 437 | " \n", 438 | " pctdistance : float, optional, default: 0.6\n", 439 | " The ratio between the center of each pie slice and the start of\n", 440 | " the text generated by *autopct*. Ignored if *autopct* is *None*.\n", 441 | " \n", 442 | " shadow : bool, optional, default: False\n", 443 | " Draw a shadow beneath the pie.\n", 444 | " \n", 445 | " labeldistance : float or None, optional, default: 1.1\n", 446 | " The radial distance at which the pie labels are drawn.\n", 447 | " If set to ``None``, label are not drawn, but are stored for use in\n", 448 | " ``legend()``\n", 449 | " \n", 450 | " startangle : float, optional, default: None\n", 451 | " If not *None*, rotates the start of the pie chart by *angle*\n", 452 | " degrees counterclockwise from the x-axis.\n", 453 | " \n", 454 | " radius : float, optional, default: None\n", 455 | " The radius of the pie, if *radius* is *None* it will be set to 1.\n", 456 | " \n", 457 | " counterclock : bool, optional, default: True\n", 458 | " Specify fractions direction, clockwise or counterclockwise.\n", 459 | " \n", 460 | " wedgeprops : dict, optional, default: None\n", 461 | " Dict of arguments passed to the wedge objects making the pie.\n", 462 | " For example, you can pass in ``wedgeprops = {'linewidth': 3}``\n", 463 | " to set the width of the wedge border lines equal to 3.\n", 464 | " For more details, look at the doc/arguments of the wedge object.\n", 465 | " By default ``clip_on=False``.\n", 466 | " \n", 467 | " textprops : dict, optional, default: None\n", 468 | " Dict of arguments to pass to the text objects.\n", 469 | " \n", 470 | " center : list of float, optional, default: (0, 0)\n", 471 | " Center position of the chart. Takes value (0, 0) or is a sequence\n", 472 | " of 2 scalars.\n", 473 | " \n", 474 | " frame : bool, optional, default: False\n", 475 | " Plot axes frame with the chart if true.\n", 476 | " \n", 477 | " rotatelabels : bool, optional, default: False\n", 478 | " Rotate each label to the angle of the corresponding slice if true.\n", 479 | " \n", 480 | " Returns\n", 481 | " -------\n", 482 | " patches : list\n", 483 | " A sequence of :class:`matplotlib.patches.Wedge` instances\n", 484 | " \n", 485 | " texts : list\n", 486 | " A list of the label :class:`matplotlib.text.Text` instances.\n", 487 | " \n", 488 | " autotexts : list\n", 489 | " A list of :class:`~matplotlib.text.Text` instances for the numeric\n", 490 | " labels. This will only be returned if the parameter *autopct* is\n", 491 | " not *None*.\n", 492 | " \n", 493 | " Notes\n", 494 | " -----\n", 495 | " The pie chart will probably look best if the figure and axes are\n", 496 | " square, or the Axes aspect is equal.\n", 497 | " This method sets the aspect ratio of the axis to \"equal\".\n", 498 | " The axes aspect ratio can be controlled with `Axes.set_aspect`.\n", 499 | " \n", 500 | " .. note::\n", 501 | " In addition to the above described arguments, this function can take a\n", 502 | " **data** keyword argument. If such a **data** argument is given, the\n", 503 | " following arguments are replaced by **data[]**:\n", 504 | " \n", 505 | " * All arguments with the following names: 'colors', 'explode', 'labels', 'x'.\n", 506 | " \n", 507 | " Objects passed as **data** must support item access (``data[]``) and\n", 508 | " membership test (`` in data``).\n", 509 | "\n" 510 | ] 511 | } 512 | ], 513 | "source": [ 514 | "# lets check the documentaion of the pie chart in matplotlib\n", 515 | "help(plt.pie)" 516 | ] 517 | }, 518 | { 519 | "cell_type": "markdown", 520 | "metadata": {}, 521 | "source": [ 522 | "### Making a Simple Pie Chart\n" 523 | ] 524 | }, 525 | { 526 | "cell_type": "code", 527 | "execution_count": 24, 528 | "metadata": {}, 529 | "outputs": [ 530 | { 531 | "name": "stdout", 532 | "output_type": "stream", 533 | "text": [ 534 | "Help on function value_counts in module pandas.core.algorithms:\n", 535 | "\n", 536 | "value_counts(values, sort=True, ascending=False, normalize=False, bins=None, dropna=True)\n", 537 | " Compute a histogram of the counts of non-null values.\n", 538 | " \n", 539 | " Parameters\n", 540 | " ----------\n", 541 | " values : ndarray (1-d)\n", 542 | " sort : boolean, default True\n", 543 | " Sort by values\n", 544 | " ascending : boolean, default False\n", 545 | " Sort in ascending order\n", 546 | " normalize: boolean, default False\n", 547 | " If True then compute a relative histogram\n", 548 | " bins : integer, optional\n", 549 | " Rather than count values, group them into half-open bins,\n", 550 | " convenience for pd.cut, only works with numeric data\n", 551 | " dropna : boolean, default True\n", 552 | " Don't include counts of NaN\n", 553 | " \n", 554 | " Returns\n", 555 | " -------\n", 556 | " value_counts : Series\n", 557 | "\n" 558 | ] 559 | } 560 | ], 561 | "source": [ 562 | "# lets check the documentataion of the value_counts function\n", 563 | "help(pd.value_counts)" 564 | ] 565 | }, 566 | { 567 | "cell_type": "code", 568 | "execution_count": 25, 569 | "metadata": {}, 570 | "outputs": [ 571 | { 572 | "data": { 573 | "image/png": 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\n", 574 | "text/plain": [ 575 | "
" 576 | ] 577 | }, 578 | "metadata": {}, 579 | "output_type": "display_data" 580 | } 581 | ], 582 | "source": [ 583 | "# lets make a simple pie Chart\n", 584 | "\n", 585 | "# we will the Business Travel Column\n", 586 | "plt.pie(data['BusinessTravel'].value_counts(), labels = data['BusinessTravel'].value_counts().index)\n", 587 | "plt.show()" 588 | ] 589 | }, 590 | { 591 | "cell_type": "markdown", 592 | "metadata": {}, 593 | "source": [ 594 | "### Making a Donut Chart " 595 | ] 596 | }, 597 | { 598 | "cell_type": "code", 599 | "execution_count": 5, 600 | "metadata": {}, 601 | "outputs": [ 602 | { 603 | "data": { 604 | "image/png": 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\n", 605 | "text/plain": [ 606 | "
" 607 | ] 608 | }, 609 | "metadata": {}, 610 | "output_type": "display_data" 611 | } 612 | ], 613 | "source": [ 614 | "# we will the Business Travel Column\n", 615 | "plt.pie(data['BusinessTravel'].value_counts(), labels = data['BusinessTravel'].value_counts().index)\n", 616 | "\n", 617 | "# lets draw a white circle in the middle of the pie chart\n", 618 | "my_circle=plt.Circle( (0,0), 0.7, color='white')\n", 619 | "\n", 620 | "# adding the circle in the plot\n", 621 | "p=plt.gcf()\n", 622 | "p.gca().add_artist(my_circle)\n", 623 | "\n", 624 | "plt.show()" 625 | ] 626 | }, 627 | { 628 | "cell_type": "code", 629 | "execution_count": null, 630 | "metadata": {}, 631 | "outputs": [], 632 | "source": [ 633 | "**When to Use Pie Chart**\n", 634 | "\n", 635 | "* It is Advisable to Use Pie Charts or Donut Charts only to display and explore the Univariate Attributes\n", 636 | "* We Should use Pie Charts only when the attributes have some Categories that is when the categories are having only two to five categories in them.\n", 637 | "* If we use Pie charts to display many categories, It may look dir" 638 | ] 639 | } 640 | ], 641 | "metadata": { 642 | "kernelspec": { 643 | "display_name": "Python 3", 644 | "language": "python", 645 | "name": "python3" 646 | }, 647 | "language_info": { 648 | "codemirror_mode": { 649 | "name": "ipython", 650 | "version": 3 651 | }, 652 | "file_extension": ".py", 653 | "mimetype": "text/x-python", 654 | "name": "python", 655 | "nbconvert_exporter": "python", 656 | "pygments_lexer": "ipython3", 657 | "version": "3.6.8" 658 | } 659 | }, 660 | "nbformat": 4, 661 | "nbformat_minor": 4 662 | } 663 | --------------------------------------------------------------------------------