Using Seaborn To Visualize A pandas Dataframe
Preliminaries
import pandas as pd
%matplotlib inline
import random
import matplotlib.pyplot as plt
import seaborn as snsdf = pd.DataFrame()
df['x'] = random.sample(range(1, 100), 25)
df['y'] = random.sample(range(1, 100), 25)df.head()| x | y | |
|---|---|---|
| 0 | 18 | 25 |
| 1 | 42 | 67 |
| 2 | 52 | 77 |
| 3 | 4 | 34 |
| 4 | 14 | 69 |
Scatterplot
sns.lmplot('x', 'y', data=df, fit_reg=False)<seaborn.axisgrid.FacetGrid at 0x114563b00>
Density Plot
sns.kdeplot(df.y)<matplotlib.axes._subplots.AxesSubplot at 0x113ea2ef0>
sns.kdeplot(df.y, df.x)<matplotlib.axes._subplots.AxesSubplot at 0x113d7fef0>
sns.distplot(df.x)<matplotlib.axes._subplots.AxesSubplot at 0x114294160>
Histogram
plt.hist(df.x, alpha=.3)
sns.rugplot(df.x);
Boxplot
sns.boxplot([df.y, df.x])<matplotlib.axes._subplots.AxesSubplot at 0x1142b8b38>
Violin Plot
sns.violinplot([df.y, df.x])<matplotlib.axes._subplots.AxesSubplot at 0x114444a58>
Heatmap
sns.heatmap([df.y, df.x], annot=True, fmt="d")<matplotlib.axes._subplots.AxesSubplot at 0x114530c88>
Clustermap
sns.clustermap(df)<seaborn.matrix.ClusterGrid at 0x116f313c8>
Python Example for Beginners
Two Machine Learning Fields
There are two sides to machine learning:
- Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
- Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.
Data Science Resources: Data Science Recipes and Applied Machine Learning Recipes
Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!
Latest end-to-end Learn by Coding Recipes in Project-Based Learning:
Applied Statistics with R for Beginners and Business Professionals
Data Science and Machine Learning Projects in Python: Tabular Data Analytics
Data Science and Machine Learning Projects in R: Tabular Data Analytics
Python Machine Learning & Data Science Recipes: Learn by Coding
R Machine Learning & Data Science Recipes: Learn by Coding
Comparing Different Machine Learning Algorithms in Python for Classification (FREE)
Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.