seaborn package

This notebook is based on https://qiita.com/hik0107/items/3dc541158fceb3156ee0

seaborn is a python package for data viaualization. It is a wrapper of matplotlib. matplotlib has many features, but it requires many parameter to use. seaborn is included in Anaconda.



In [1]:

    
# setup

import numpy as np
import pandas as pd
import seaborn as sns

x = np.random.normal(size=100) #create random data of numpy array

titanic = sns.load_dataset("titanic") ## Titanic servivor's data from kaggle
tips = sns.load_dataset("tips")  ## 
iris = sns.load_dataset("iris")  ##



In [2]:

    
# seaborn use Pandas dataframe

print('data type is:', type(tips))
tips.head()









    



data type is: <class 'pandas.core.frame.DataFrame'>






    Out[2]:







  
    
      
      total_bill
      tip
      sex
      smoker
      day
      time
      size
    
  
  
    
      0
      16.99
      1.01
      Female
      No
      Sun
      Dinner
      2
    
    
      1
      10.34
      1.66
      Male
      No
      Sun
      Dinner
      3
    
    
      2
      21.01
      3.50
      Male
      No
      Sun
      Dinner
      3
    
    
      3
      23.68
      3.31
      Male
      No
      Sun
      Dinner
      2
    
    
      4
      24.59
      3.61
      Female
      No
      Sun
      Dinner
      4



In [3]:

    
# Histgram. change arguments and see what happens

# sns.distplot(x, kde=False, rug=False, bins=10)
sns.distplot(x, kde=True, rug=False, bins=20)









    



/home/kato/Miniconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval






    Out[3]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f8758514d30>



In [4]:

    
# Scatter plot

sns.jointplot('sepal_width', 'petal_length', data=iris)









    Out[4]:





<seaborn.axisgrid.JointGrid at 0x7f875816bf28>



In [5]:

    
# Scatter plot of variable combination

# sns.pairplot(iris)
sns.pairplot(iris, hue='species')









    Out[5]:





<seaborn.axisgrid.PairGrid at 0x7f8758115e48>



In [6]:

    
features = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']
sns.heatmap(iris[features].corr(), center=0, cmap='coolwarm', annot=True)









    Out[6]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f8757ac4b38>



In [7]:

    
# Distribution (stripplot)

sns.stripplot(x="day", y="total_bill", data=tips)









    Out[7]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f8755a0a8d0>



In [8]:

    
# separate lunch and dinner

sns.stripplot(x="day", y="total_bill", data=tips, hue='time')









    Out[8]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f87559dac18>



In [9]:

    
sns.boxplot(x="size", y="tip", data=tips.sort_values('size'))









    Out[9]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f87559aac18>

Color palette management

Please refer http://seaborn.pydata.org/tutorial/color_palettes.html



In [10]:

    
flatui = ["#9b59b6", "#3498db", "#95a5a6", "#e74c3c", "#34495e", "#2ecc71"]

sns.palplot(sns.color_palette(flatui))
sns.set_palette(flatui)



In [11]:

    
# Histgram (barplot)

# survivors ratio
sns.barplot(x='sex', y='survived', data=titanic, hue='class')









    Out[11]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f87558a08d0>



In [12]:

    
# total survivors

titanic_grpby = titanic.groupby( ['sex', 'class'])
titanic_data_for_graph = titanic_grpby['survived'].aggregate(sum).reset_index()

sns.barplot(x='sex', y='survived', hue= 'class', data=titanic_data_for_graph)









    Out[12]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f87557f6860>



In [13]:

    
# count number of data (countplot)

sns.countplot(x='sex', hue='embarked', data=titanic, palette='Greens_d')









    Out[13]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f875578e198>



In [ ]:

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4