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一、seaborn

1 Introduction
2.as
3. Website

Second, the data is loaded

1. own data
2.Seaborn sample data set (snsn.load_dataset ())

一、seaborn

1 Introduction

seaborn is matplotlib extension and expansion, if you know matplotlib, you will have mastered most of Seaborn.

seaborn particular visualized as the target data.

Matplotlib with the greatest difficulty is the default of various parameters, while seaborn completely avoids this problem.

2.as

import seaborn as sns

3. Website

http://seaborn.pydata.org/examples/index.html

http://seaborn.pydata.org/api.html

Second, the data is loaded

1. own data

Load Pandas DataFrame.

2.Seaborn sample data set (snsn.load_dataset ())

September 8, 2018 13:33:16 silly spat Reads: 2408

When we believe in learning GroupBy, or PivotTable, you are likely to encounter similar to the following line of code:

import seaborn as sns



planets = sns.load_dataset('planets')

Then you can find planets have stored the data, then the data is in the end come from?

We look load_dataset the docstring:

In [54]: sns.load_dataset??



Signature: sns.load_dataset(name, cache=True, data_home=None, **kws)



Source:



def load_dataset(name, cache=True, data_home=None, **kws):



    """Load a dataset from the online repository (requires internet).







    Parameters



    ----------



    name : str



        Name of the dataset (`name`.csv on



        https://github.com/mwaskom/seaborn-data).  You can obtain list of



        available datasets using :func:`get_dataset_names`



    cache : boolean, optional



        If True, then cache data locally and use the cache on subsequent calls



    data_home : string, optional



        The directory in which to cache data. By default, uses ~/seaborn-data/



    kws : dict, optional



        Passed to pandas.read_csv







    """

We can see the first line of the docstring illustrates this function is to load the data set from the online repository (requires internet).

URL: https://github.com/mwaskom/seaborn-data

sns.heatmap()

square = Ture, forced the box, but the data is too long will huddle together

square = False, automatically adjusting the length of more rectangular data, not crowded together

reference:

https://www.jianshu.com/p/5ff47c7d0cc9

python library of seaborn library