analysis toolkit, Release 0.25.3 {{ header }} 10 Chapter 2. Installation CHAPTER THREE GETTING STARTED {{ header }} 3.1 Package overview pandas is a Python package providing fast, flexible, and expressive code: for col in df.columns: series = df[col] # do something with series 12 Chapter 3. Getting started pandas: powerful Python data analysis toolkit, Release 0.25.3 3.1.2 Mutability and copying of data DataFrame by passing a NumPy array, with a datetime index and labeled columns: 14 Chapter 3. Getting started pandas: powerful Python data analysis toolkit, Release 0.25.3 In [5]: dates = pd.date_range('20130101'

Release 1.0.5 Wes McKinney and the Pandas Development Team Jun 17, 2020 CONTENTS 1 Getting started 3 1.1 Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.4.4 Getting started tutorials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 1.4.5 Essential easy-to-use data structures and data analysis tools for the Python programming language. To the getting started guides To the user guide To the reference guide To the development guide CONTENTS 1 pandas:

Release 1.0.4 Wes McKinney and the Pandas Development Team May 28, 2020 CONTENTS 1 Getting started 3 1.1 Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.4.4 Getting started tutorials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 1.4.5 Essential easy-to-use data structures and data analysis tools for the Python programming language. To the getting started guides To the user guide To the reference guide To the development guide CONTENTS 1 pandas:

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Getting started 5 2.1 Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.4.4 Getting started tutorials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 2.4.5 Essential easy-to-use data structures and data analysis tools for the Python programming language. To the getting started guides To the user guide To the reference guide To the development guide CONTENTS 1 pandas:

easy-to-use data structures and data analysis tools for the Python programming language. To the getting started guides To the user guide To the reference guide To the development guide CONTENTS 1 pandas: toolkit, Release 1.0.0 42 Chapter 1. What’s new in 1.0.0 (January 29, 2020) CHAPTER TWO GETTING STARTED {{ header }} 2.1 Installation The easiest way to install pandas is to install it as part of the use pip to install those packages: conda install pip pip install django 44 Chapter 2. Getting started pandas: powerful Python data analysis toolkit, Release 1.0.0 Installing from PyPI pandas can be

analysis toolkit, Release 0.25.1 {{ header }} 40 Chapter 2. Installation CHAPTER THREE GETTING STARTED {{ header }} 3.1 Package overview pandas is a Python package providing fast, flexible, and expressive code: for col in df.columns: series = df[col] # do something with series 42 Chapter 3. Getting started pandas: powerful Python data analysis toolkit, Release 0.25.1 3.1.2 Mutability and copying of data DataFrame by passing a NumPy array, with a datetime index and labeled columns: 44 Chapter 3. Getting started pandas: powerful Python data analysis toolkit, Release 0.25.1 In [5]: dates = pd.date_range('20130101'

analysis toolkit, Release 0.25.0 {{ header }} 40 Chapter 2. Installation CHAPTER THREE GETTING STARTED {{ header }} 3.1 Package overview pandas is a Python package providing fast, flexible, and expressive code: for col in df.columns: series = df[col] # do something with series 42 Chapter 3. Getting started pandas: powerful Python data analysis toolkit, Release 0.25.0 3.1.2 Mutability and copying of data DataFrame by passing a NumPy array, with a datetime index and labeled columns: 44 Chapter 3. Getting started pandas: powerful Python data analysis toolkit, Release 0.25.0 In [5]: dates = pd.date_range('20130101'

Enthought Canopy may be worth considering. 64 Chapter 2. Installation CHAPTER THREE GETTING STARTED 3.1 Package overview pandas is a Python package providing fast, flexible, and expressive data structures data untouched. In general we like to favor immutability where sensible. 66 Chapter 3. Getting started pandas: powerful Python data analysis toolkit, Release 0.24.0 3.1.3 Getting Support The first '2013-01-03', '2013-01-04', '2013-01-05', '2013-01-06'], (continues on next page) 68 Chapter 3. Getting started pandas: powerful Python data analysis toolkit, Release 0.24.0 (continued from previous page)

Release 1.3.3 Wes McKinney and the Pandas Development Team Sep 12, 2021 CONTENTS 1 Getting started 3 1.1 Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.4.3 Getting started tutorials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.4.4 Comparison language. Getting started New to pandas? Check out the getting started guides. They contain an introduction to pandas’ main concepts and links to additional tutorials. To the getting started guides User

Release 1.3.4 Wes McKinney and the Pandas Development Team Oct 17, 2021 CONTENTS 1 Getting started 3 1.1 Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.4.3 Getting started tutorials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.4.4 Comparison language. Getting started New to pandas? Check out the getting started guides. They contain an introduction to pandas’ main concepts and links to additional tutorials. To the getting started guides User