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# Mastering Exploratory Analysis with pandas
<a href="https://www.packtpub.com/big-data-and-business-intelligence/mastering-exploratory-analysis-pandas?utm_source=github&utm_medium=repository&utm_campaign=9781789619638"><img src="https://d1ldz4te4covpm.cloudfront.net/sites/default/files/imagecache/ppv4_main_book_cover/B11946.png" alt="Mastering Exploratory Analysis with pandas" height="256px" align="right"></a>
This is the code repository for [Mastering Exploratory Analysis with pandas](https://www.packtpub.com/big-data-and-business-intelligence/mastering-exploratory-analysis-pandas?utm_source=github&utm_medium=repository&utm_campaign=9781789619638), published by Packt.
**Build an end-to-end data analysis workflow with Python**
## What is this book about?
The pandas is a Python library that lets you manipulate, transform, and analyze data. It is a popular framework for exploratory data visualization and analyzing datasets and data pipelines based on their properties.
This book covers the following exciting features:
* Learn how to read different kinds of data into pandas DataFrames for data analysis
* Manipulate, transform, and apply formulas to data imported into pandas DataFrames
* Use pandas to analyze and visualize different kinds of data to gain real-world insights
* Extract transformed data form pandas DataFrames and convert it into the formats your application expects
* Manipulate model time-series data, perform algorithmic trading, derive results on fixed and moving windows, and more
If you feel this book is for you, get your [copy](https://www.amazon.com/dp/1789619637) today!
<a href="https://www.packtpub.com/?utm_source=github&utm_medium=banner&utm_campaign=GitHubBanner"><img src="https://raw.githubusercontent.com/PacktPublishing/GitHub/master/GitHub.png"
alt="https://www.packtpub.com/" border="5" /></a>
## Instructions and Navigations
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
```
df = pd.read_csv('IMDB.csv', encoding = "ISO-8859-1")
df.head()
```
**Following is what you need for this book:**
If you are a budding data scientist looking to learn the popular pandas library, or a Python developer looking to step into the world of data analysis, this book is the ideal resource you need to get started. Some programming experience in Python will be helpful to get the most out of this course
With the following software and hardware list you can run all code files present in the book (Chapter 1-4).
### Software and Hardware List
| Chapter | Software required | OS required |
| -------- | ------------------------------------| -----------------------------------|
| 1-4 | Python 2.7x and above | Windows/Ubuntu |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. [Click here to download it](https://www.packtpub.com/sites/default/files/downloads/9781789619638_ColorImages.pdf).
### Related products <Other books you may enjoy>
* Artificial Intelligence By Example [[Packt]](https://www.packtpub.com/big-data-and-business-intelligence/artificial-intelligence-example?utm_source=github&utm_medium=repository&utm_campaign=9781788990547) [[Amazon]](https://www.amazon.com/dp/1788990544)
* Machine Learning Algorithms [[Packt]](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-algorithms?utm_source=github&utm_medium=repository&utm_campaign=9781785889622) [[Amazon]](https://www.amazon.com/dp/1785889621)
## Get to Know the Author
**Harish Garg** is a data analyst, author, and software developer who is really passionate about data science and Python. He is a graduate of Udacity's Data Analyst Nanodegree program. He has 17 years of industry experience in data analysis using Python, developing and testing enterprise and consumer software, managing projects and software teams, and creating training material and tutorials. He also worked for 11 years for Intel Security (previously McAfee, Inc.). He regularly contributes articles and tutorials on data analysis and Python. He is also active in the open data community and is a contributing member of the Data4Democracy open data initiative. He has written data analysis pieces for the Takshashila think tank.
### Suggestions and Feedback
[Click here](https://docs.google.com/forms/d/e/1FAIpQLSdy7dATC6QmEL81FIUuymZ0Wy9vH1jHkvpY57OiMeKGqib_Ow/viewform) if you have any feedback or suggestions.
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Mastering-Exploratory-Analysis-with-pandas-master.zip (39个子文件)
Mastering-Exploratory-Analysis-with-pandas-master
Chapter03
Removing columns from a pandas DataFrame.ipynb 30KB
Handling missing values in pandas.ipynb 14KB
Indexing in pandas dataframes.ipynb 75KB
Applying a function to a pandas Series or DataFrame.ipynb 35KB
Using groupby method.ipynb 452KB
data-titanic.csv 60KB
Modifying a Pandas Dataframe inplace.ipynb 30KB
data-movies-top-grossing.csv 2KB
Merging and concatenating multiple data frames into one.ipynb 21KB
Renaming columns in a pandas DataFrame.ipynb 13KB
data-zillow.csv 662KB
Handling SettingWithCopyWarning.ipynb 11KB
Work with dates and times data.ipynb 18KB
Chapter01
IMDB.html 379KB
IMDB.json 156KB
IMDB.csv 39KB
IMDB.xlsx 81KB
Working with different kinds of Datasets_updated_1.ipynb 170KB
IMDB.p 59KB
IMDB.sqlite 52KB
LICENSE 1KB
Chapter02
Sorting a pandas DataFrame or a Series.ipynb 50KB
Using string methods in pandas.ipynb 11KB
Using the axis parameter in pandas.ipynb 22KB
Using pandas Series data structure to select a subset of the data.ipynb 21KB
Changing the datatype of a Pandas Series.ipynb 10KB
Filter rows of a pandas DataFrame by column value.ipynb 19KB
Apply multiple filter criteria to a pandas DataFrame.ipynb 21KB
data-zillow.csv 662KB
Selecting multiple rows and columns from a pandas DataFrame.ipynb 29KB
README.md 5KB
Chapter04
Controlling plot aesthetics.ipynb 238KB
data_simpsons_episodes.csv 117KB
data-titanic.csv 60KB
Plotting categorical data.ipynb 239KB
data-alcohol.csv 4KB
Plotting with data aware grids.ipynb 225KB
Choosing the colors for the plots.ipynb 74KB
data-mlb-players.csv 28KB
共 39 条
- 1
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