10. Python 101 - Data Visualization with Seaborn - Skeleton.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"10. Python 101 - Data Visualization with Seaborn - Skeleton.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyOo+JgUFs/Palp2HgXNXPpm"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"WnhpHVK2bH3s","colab_type":"text"},"source":["# 1. SEABRON SCATTERPLOT & COUNTPLOT"]},{"cell_type":"code","metadata":{"id":"uTkfgtjBn6ek","colab_type":"code","colab":{}},"source":["# Seaborn is a visualization library that sits on top of matplotlib\n","# Seaborn offers enhanced features compared to matplotlib\n","# https://seaborn.pydata.org/examples/index.html\n","# import libraries \n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"8bbCI59uHICY","colab_type":"code","colab":{}},"source":["# np.C_ class object translates slice objects to concatenation along the second axis.\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"B8cfhCibHS7A","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"AnTgR6-OHRFK","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"bUQkwM8IHtlp","colab_type":"code","colab":{}},"source":["# Import Cancer data drom the Sklearn library\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Y5w4VqKCHvox","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"tZEMoIXPoiqI","colab_type":"code","colab":{}},"source":["# Create a dataFrame named df_cancer with input/output data\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"BCBZU_VQoqIz","colab_type":"code","colab":{}},"source":["# Check out the head of the dataframe\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"8XkZE6jCdfBS","colab_type":"code","colab":{}},"source":["# Check out the tail of the dataframe\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"atb9g7nGo1Zf","colab_type":"code","colab":{}},"source":["# Plot scatter plot between mean area and mean smoothness\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"rc3p4fBso1XA","colab_type":"code","colab":{}},"source":["# Let's print out countplot to know how many samples belong to class #0 and #1\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"RIpzECOpcFa5","colab_type":"text"},"source":["**MINI CHALLENGE #1:**\n","- **Plot the scatterplot between the mean radius and mean area. Comment on the plot** \n"]},{"cell_type":"code","metadata":{"id":"14dehTx3dNAw","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Al9AW2YLbezU","colab_type":"text"},"source":["# 2. SEABORN PAIRPLOT, DISPLOT, AND HEATMAPS/CORRELATIONS"]},{"cell_type":"code","metadata":{"id":"DYsVoE4ko1Up","colab_type":"code","colab":{}},"source":["# Plot the pairplot\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"u_UzA1GFo1cK","colab_type":"code","colab":{}},"source":["# Strong correlation between the mean radius and mean perimeter, mean area and mean primeter\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"m01UPXL9pGsM","colab_type":"code","colab":{}},"source":["# plot the distplot \n","# Displot combines matplotlib histogram function with kdeplot() (Kernel density estimate)\n","# KDE is used to plot the Probability Density of a continuous variable. \n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"BlEUElj_gGrm","colab_type":"text"},"source":["**MINI CHALLENGE #2:**\n","- **Plot two separate distplot for each target class #0 and target class #1**\n"]},{"cell_type":"code","metadata":{"id":"JPrD3KlngIcb","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"gOSi69b9ROSn","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"2CPA2hTOROV0","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"t5ntc3muROYh","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"9QwPrO3qRObJ","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"_o555HjYbtlP","colab_type":"text"},"source":["# EXCELLENT JOB!"]},{"cell_type":"markdown","metadata":{"id":"-iXHoC8PbwSg","colab_type":"text"},"source":["# MINI CHALLENGE SOLUTIONS:"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"GTN9KMR7dIWt"},"source":["**MINI CHALLENGE #1 SOLUTION:**\n","- **Plot the scatterplot between the mean radius and mean area. Comment on the plot** \n"]},{"cell_type":"code","metadata":{"colab_type":"code","id":"jo00_TzudIWw","colab":{}},"source":["sns.scatterplot(x = 'mean radius', y = 'mean area', hue = 'target', data = df_cancer)\n","# As mean radius increases, mean area increases \n","# class #0 generally has larger mean radius and mean area compared to class #1"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"f5qD9kzIeQZE","colab_type":"text"},"source":["**MINI CHALLENGE #2 SOLUTION:**\n","- **Plot two separate distplot for each target class #0 and target class #1**\n"]},{"cell_type":"code","metadata":{"id":"YX0FkgQGfLbC","colab_type":"code","colab":{}},"source":["class_0_df = df_cancer[ df_cancer['target']==0 ]\n","class_1_df = df_cancer[ df_cancer['target']==1 ]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"D1DTuyaIfX-e","colab_type":"code","colab":{}},"source":["class_0_df"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"cftPJApPfaUm","colab_type":"code","colab":{}},"source":["class_1_df"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"hN10mhlzefSO","colab_type":"code","colab":{}},"source":["# Plot the distplot for both classes\n","plt.figure(figsize=(10, 7))\n","sns.distplot(class_0_df['mean radius'], bins = 25, color = 'blue')\n","sns.distplot(class_1_df['mean radius'], bins = 25, color = 'red')\n","plt.grid()"],"execution_count":null,"outputs":[]}]}