pydata ecosystem基于python的数据分析生态系统

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Agenda

Data Science ecosystem

Data Wrangling

Data Analysis

Data Visualization

3 Real Case Demo

Bigger Data Consideration

Spark Data Frame Demo

1.

Data Science Process

Data Collection

Databases

Applications

3rdpart data

Data Wrangling

Enrichment

ETL/Blending

Data

Intergration

Data Analysis

insights

Statistics

Visualization

Modeling

2.

Data Wrangling

Data scientists spend 80% of their time convert data into a usable form.

Clean data:handle messy or missed data

Transform and Extract data

Merge,Join and Reshape data

Time series Resampling

3.Data Analysis

Interactive Data Exploration

Rich visualzation

Satistical Modeling

4.python vs R

TIOBE Index

5.Pros and Cons

R+visualization = perfect match

R,Lingua Franca of Statistics(develop by Statistics)

R is slow

Python is multi-purpose language

Python is challenger for either visualization or essential R packages replacement

6.PyData Ecosystem

Fundamental Libs

numpy\scipy

AdvancedLibs

pandas\sympy\Scikit-lean\xray\Blaze

7.Numpy

High performance N-Arrary operation lib

高性能多维

8.pands

打包

9.Blaze

High-level user interface for databases and array computing systems

10.Spark

11.DataFrame

12.matplotlib

13.seaborn

14.Bokeh

15.IPython