Merge pull request #90 from madefu/master
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2
.gitignore
vendored
2
.gitignore
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@ -102,3 +102,5 @@ venv.bak/
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# mypy
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.mypy_cache/
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/.idea
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/datasets/*.bak
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@ -31,4 +31,4 @@ y_pred = regressor.predict(X_test)
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# regression evaluation
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from sklearn.metrics import r2_score
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print(r2_score(Y_test,y_pred))
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print(r2_score(Y_test, y_pred))
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13
Code/KafkaProducer.py
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Code/KafkaProducer.py
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#!/usr/bin/python
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from kafka import KafkaProducer
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kafkaHosts=["kafka01.paas.longfor.sit:9092"
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,"kafka02.paas.longfor.sit:9092"
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,"kafka03.paas.longfor.sit:9092"]
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producer = KafkaProducer(bootstrap_servers=kafkaHosts);
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for _ in range(20):
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producer.send("testapplog_plm-prototype",b"Hello....")
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producer.flush();
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Code/TestKafka.py
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Code/TestKafka.py
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#!/usr/bin/python
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from kafka import KafkaConsumer;
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kafkaHosts=["kafka01.paas.longfor.sit:9092"
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,"kafka02.paas.longfor.sit:9092"
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,"kafka03.paas.longfor.sit:9092"]
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'''
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earliest
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当各分区下有已提交的offset时,从提交的offset开始消费;无提交的offset时,从头开始消费
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latest
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当各分区下有已提交的offset时,从提交的offset开始消费;无提交的offset时,消费新产生的该分区下的数据
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none
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topic各分区都存在已提交的offset时,从offset后开始消费;只要有一个分区不存在已提交的offset,则抛出异常
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'''
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consumer = KafkaConsumer(
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bootstrap_servers=kafkaHosts,group_id='mdf_group',auto_offset_reset='latest');
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consumer.subscribe("testapplog_plm-prototype");
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for msg in consumer:
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print(msg.value)
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Code/my/Data_age_salary.csv
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Code/my/Data_age_salary.csv
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Age,Salary
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44,72000
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27,48000
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30,54000
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38,61000
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40,78000
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35,58000
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35,52000
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48,79000
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50,83000
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37,67000
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Code/my/LinerTest.py
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Code/my/LinerTest.py
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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dataset = pd.read_csv('Data_age_salary.csv');
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dataset.iloc[:1]
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BIN
Other Docs/速查手册/2018年数据科学家报告.pdf
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Other Docs/速查手册/Python数据科学速查表 - Bokeh.pdf
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Other Docs/速查手册/Python数据科学速查表 - Jupyter Notebook.pdf
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Other Docs/速查手册/Python数据科学速查表 - Keras.pdf
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Other Docs/速查手册/Python数据科学速查表 - Matplotlib 绘图.pdf
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Other Docs/速查手册/Python数据科学速查表 - Numpy 基础.pdf
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Other Docs/速查手册/Python数据科学速查表 - Pandas 基础.pdf
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Other Docs/速查手册/Python数据科学速查表 - Pandas 进阶.pdf
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Other Docs/速查手册/Python数据科学速查表 - Python 基础.pdf
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Other Docs/速查手册/Python数据科学速查表 - SciPy.pdf
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Other Docs/速查手册/Python数据科学速查表 - Scikit-Learn.pdf
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Other Docs/速查手册/Python数据科学速查表 - Seaborn.pdf
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Other Docs/速查手册/Python数据科学速查表 - Spark RDD 基础.pdf
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Other Docs/速查手册/Python数据科学速查表 - Spark SQL 基础.pdf
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Other Docs/速查手册/Python数据科学速查表 - 导入数据.pdf
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64
Other Docs/速查手册/README.md
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@ -0,0 +1,64 @@
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# 14张速查表,带你玩转 Python 数据科学
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译自 DataCamp 的速查表,有兴趣的朋友可以在这里查看[英文原版](https://www.datacamp.com/community/data-science-cheatsheets)。
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欢迎扫描二维码关注我的 **呆鸟的Python数据分析** 公众号,虽然现在内容还比较少,但我会不断增加的。
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一、[Python 基础系列](https://www.jianshu.com/p/4574d95755db)
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* [Python数据科学速查表 - Python 基础](https://github.com/jaystone776/python-data-science-cheatsheet/blob/master/Python数据科学速查表%20-%20Python%20基础.pdf)
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* [Python数据科学速查表 - 导入数据](https://github.com/jaystone776/python-data-science-cheatsheet/blob/master/Python数据科学速查表%20-%20导入数据.pdf)
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* [Python数据科学速查表 - Jupyter Notebook](https://github.com/jaystone776/python-data-science-cheatsheet/blob/master/Python%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6%E9%80%9F%E6%9F%A5%E8%A1%A8%20-%20Jupyter%20Notebook.pdf)
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二、[数据处理系列(Numpy、Pandas 及 SciPy)](https://www.jianshu.com/p/8d51642dfa26)
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* [Python数据科学速查表 - Numpy 基础](https://github.com/jaystone776/python-data-science-cheatsheet/blob/master/Python数据科学速查表%20-%20Numpy%20基础.pdf)
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* [Python数据科学速查表 - Pandas 基础](https://github.com/jaystone776/python-data-science-cheatsheet/blob/master/Python数据科学速查表%20-%20Pandas%20基础.pdf)
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* [Python数据科学速查表 - Pandas 进阶](https://github.com/jaystone776/python-data-science-cheatsheet/blob/master/Python数据科学速查表%20-%20Pandas%20进阶.pdf)
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* [Python数据科学速查表 - SciPy](https://github.com/jaystone776/python-data-science-cheatsheet/blob/master/Python%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6%E9%80%9F%E6%9F%A5%E8%A1%A8%20-%20SciPy.pdf)
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三、[可视化系列(Matplotlib、Bokeh、Seaborn)](https://www.jianshu.com/p/7e186d43d7f1)
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* [Python数据科学速查表 - Matplotlib](https://github.com/jaystone776/python-data-science-cheatsheet/blob/master/Python数据科学速查表%20-%20Matplotlib%20绘图.pdf)
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* [Python数据科学速查表 - Bokeh](https://github.com/jaystone776/python-data-science-cheatsheet/blob/master/Python数据科学速查表%20-%20Bokeh.pdf)
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* [Python数据科学速查表 - Seaborn](https://github.com/jaystone776/python-data-science-cheatsheet/blob/master/Python%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6%E9%80%9F%E6%9F%A5%E8%A1%A8%20-%20Seaborn.pdf)
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四、[机器学习系列(Keras、Scikit-learn)](https://www.jianshu.com/p/cba49ff5fc97)
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* [Python数据科学速查表 - Keras](https://github.com/jaystone776/python-data-science-cheatsheet/blob/master/Python数据科学速查表%20-%20Keras.pdf)
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* [Python数据科学速查表 - Scikit-learn](https://github.com/jaystone776/python-data-science-cheatsheet/blob/master/Python%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6%E9%80%9F%E6%9F%A5%E8%A1%A8%20-%20Scikit-Learn.pdf)
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五、[PySpark系列(SQL与RDD)](https://www.jianshu.com/p/7dea578c56d8)
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* [Python数据科学速查表 - Spark SQL 基础](https://github.com/jaystone776/python-data-science-cheatsheet/blob/master/Python%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6%E9%80%9F%E6%9F%A5%E8%A1%A8%20-%20Spark%20SQL%20%E5%9F%BA%E7%A1%80.pdf)
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* [Python数据科学速查表 - Spark RDD 基础](https://github.com/jaystone776/python-data-science-cheatsheet/blob/master/Python%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6%E9%80%9F%E6%9F%A5%E8%A1%A8%20-%20Spark%20RDD%20%E5%9F%BA%E7%A1%80.pdf)
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如果喜欢本文,敬请关注我的简书专题 **[呆鸟的Python数据分析](https://www.jianshu.com/c/38980843c0f2)**
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感谢**天善智能**的**Python爱好者社区**公众号一直以来对我的支持,这里也大力推荐,是我学习入门 Python 数据分析入门的引路者,欢迎关注!
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@ -24,3 +24,6 @@ Hours,Scores
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3.8,35
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6.9,76
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7.8,86
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2.1,93
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2.2,93
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2.5,93
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