Anaconda if you decide (just delete that folder). 2.2.2 Installing with Miniconda The previous section outlined how to get pandas installed as part of the Anaconda distribution. However this approach import numpy as np In [2]: import pandas as pd 3.2.1 Object creation See the Data Structure Intro section. Creating a Series by passing a list of values, letting pandas create a default integer index: the rest of the attributes have been truncated for brevity. 3.2.2 Viewing data See the Basics section. Here is how to view the top and bottom rows of the frame: In [13]: df.head() Out[13]: A B C D

replace() now allows regular expressions on contained Series with object dtype. See the examples section in the regular docs Replacing via String Expression For example you can do In [28]: df = DataFrame({’a’: should pay close attention to. There is a new section in the documentation, 10 Minutes to Pandas, primarily geared to new users. There is a new section in the documentation, Cookbook, a collection of 11.0, these methods may be deprecated in future versions. • irow • icol • iget_value See the section Selection by Position for substitutes. 1.2.3 Dtypes Numeric dtypes will propagate and can coexist

method. • Suggested tutorials in new Tutorials section. • Our pandas ecosystem is growing, We now feature related projects in a new Pandas Ecosystem section. • Much work has been taking place on improving improving the docs, and a new Contributing section has been added. • Even though it may only be of interest to devs, we <3 our new CI status page: ScatterCI. 3 pandas: powerful Python data analysis toolkit replace() now allows regular expressions on contained Series with object dtype. See the examples section in the regular docs Replacing via String Expression For example you can do In [28]: df = DataFrame({’a’:

improvements in plotting functions, including: hexbin, area and pie plots, see Here. – Performance doc section on I/O operations, See Here • Other Enhancements • API Changes • Text Parsing API Changes • Groupby method. • Suggested tutorials in new Tutorials section. • Our pandas ecosystem is growing, We now feature related projects in a new Pandas Ecosystem section. • Much work has been taking place on improving improving the docs, and a new Contributing section has been added. • Even though it may only be of interest to devs, we <3 our new CI status page: ScatterCI. Warning: 0.13.1 fixes a bug that was caused by

a 0.753 b 0.215 c 1.177 d 0.523 e 0.182 We will address array-based indexing in a separate section. 5.1.2 Series is dict-like A Series is alike a fixed-size dict in that you can get and set values other column name provided). Missing Data Much more will be said on this topic in the Missing data section. To construct a DataFrame with missing data, use np.nan for those values which are missing. Alternatively label-based indexing and slicing, see the section on indexing. We will address the fundamentals of reindexing / conforming to new sets of lables in the section on reindexing. 32 Chapter 5. Intro to Data

a 0.753 b 0.215 c 1.177 d 0.523 e 0.182 We will address array-based indexing in a separate section. 5.1.2 Series is dict-like A Series is alike a fixed-size dict in that you can get and set values other column name provided). Missing Data Much more will be said on this topic in the Missing data section. To construct a DataFrame with missing data, use np.nan for those values which are missing. Alternatively label-based indexing and slicing, see the section on indexing. We will address the fundamentals of reindexing / conforming to new sets of lables in the section on reindexing. 32 Chapter 5. Intro to Data

a 0.753 b 0.215 c 1.177 d 0.523 e 0.182 We will address array-based indexing in a separate section. 5.1.2 Series is dict-like A Series is alike a fixed-size dict in that you can get and set values other column name provided). Missing Data Much more will be said on this topic in the Missing data section. To construct a DataFrame with missing data, use np.nan for those values which are missing. Alternatively label-based indexing and slicing, see the section on indexing. We will address the fundamentals of reindexing / conforming to new sets of lables in the section on reindexing. 36 Chapter 5. Intro to Data

Highlights include: • Support for a CategoricalIndex, a category based index, see here • New section on how-to-contribute to pandas, see here • Revised “Merge, join, and concatenate” documentation – Support for ignoring full line comments in the read_csv() text parser. – New documentation section on Options and Settings. – Lots of bug fixes. • Enhancements • API Changes • Performance Improvements improvements in plotting functions, including: hexbin, area and pie plots, see Here. – Performance doc section on I/O operations, See Here • Other Enhancements • API Changes • Text Parsing API Changes • Groupby

– Support for ignoring full line comments in the read_csv() text parser. – New documentation section on Options and Settings. – Lots of bug fixes. • Enhancements • API Changes • Performance Improvements improvements in plotting functions, including: hexbin, area and pie plots, see Here. – Performance doc section on I/O operations, See Here • Other Enhancements • API Changes • Text Parsing API Changes • Groupby method. • Suggested tutorials in new Tutorials section. • Our pandas ecosystem is growing, We now feature related projects in a new Pandas Ecosystem section. • Much work has been taking place on improving

delete Anaconda if you decide (just delete that folder). Installing with Miniconda The previous section outlined how to get pandas installed as part of the Anaconda distribution. However this approach [1]: import numpy as np In [2]: import pandas as pd Object creation See the Data Structure Intro section. Creating a Series by passing a list of values, letting pandas create a default integer index: started pandas: powerful Python data analysis toolkit, Release 1.0.5 Viewing data See the Basics section. Here is how to view the top and bottom rows of the frame: In [13]: df.head() Out[13]: A B C D