From ee86ad18b02bd4bf0076a42812ca80ba1580d328 Mon Sep 17 00:00:00 2001 From: Keith Date: Thu, 17 Aug 2017 01:47:42 -0600 Subject: [PATCH] nitial --- nb/0_introduction.ipynb | 172 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 172 insertions(+) create mode 100644 nb/0_introduction.ipynb diff --git a/nb/0_introduction.ipynb b/nb/0_introduction.ipynb new file mode 100644 index 0000000..8fc8a80 --- /dev/null +++ b/nb/0_introduction.ipynb @@ -0,0 +1,172 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "toc": "true" + }, + "source": [ + "# Table of Contents\n", + "

1  Pandas
1.1  Why Pandas?
1.2  How Pandas?
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** NAVIGATION **\n", + "\n", + "**Got Pandas? _Practical Data Wrangling with Pandas_**\n", + "\n", + "* **Introduction**\n", + "1. [Data Structures](./0_data_structures.ipynb)\n", + "2. [Importing Data](./1_importing_data.ipynb)\n", + "3. [Manipulating DataFrames](./2_dataframe_operations.ipynb)\n", + "4. [Wrap Up](3_wrapping_up.ipynb)\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**NOTEBOOK OBJECTIVES**\n", + "\n", + "In this notebook we'll:\n", + "\n", + "* explore the purpose of Pandas,\n", + "* understand where Pandas fits in the scientific data analysis ecosystem,\n", + "* understand installation options." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pandas\n", + "\n", + "Pandas is a fantastic library, and if you don't _Got Pandas?_ ... perhaps it is time you do.\n", + "\n", + "Pandas is a fast and built on top of [NumPy](http://www.numpy.org/) with dependencies on [statsmodel](http://www.statsmodels.org/stable/index.html), so if you have familiarity with NumPy, Pandas might be what you've always wanted and never knew you did!\n", + "\n", + "For readers who are familiar with R and considering Python, Pandas may be the right tool to make the transition smoothly as the core DataFrame structure in Pandas is modeled after that of R's `data.frame`.\n", + "\n", + "\n", + "Pandas has many strengths but here are a few that might pique your interests:\n", + "\n", + "* flexible, consistent data import and export from a wide array of sources, including SQL, CSV, EXCEL, etc.\n", + "* tabular / matrix data representation with heterogeneous labeled or unlabeled columns\n", + "* intuitive handling of missing data \n", + "* import and conversion of data to / from NumPy\n", + "* sophisticated slicing, indexing and subsetting of data\n", + "* support for hierarchical labeling of data\n", + "* support for time series data, including time/date conversion, moving windows, etc.\n", + "* and much more ...\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + "picture\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Why Pandas?\n", + "\n", + "Pandas has become known as the go-to library in the Python data science stack. With its strong support for importing various data formats, it can be the _first tool_ you might use to work with, manipulate, convert, reorganize and prepare data for analysis.\n", + "\n", + "Pandas is not a replacement for NumPy, but rather a supplement to it. With its sophisticated indexing, it becomes a more powerful way to access and prepare data for analysis in NumPy, and in many cases it will become a necessary compliment to the features already provided by NumPy.\n", + "\n", + "Pandas brings the fun back into data engineering, and once mastered is one of many tools that will be required for doing high quality data analysis in Python.\n", + "\n", + "Everything you'd every want to know about Python can be found :\n", + "\n", + "* [pandas.pydata.org](http://pandas.pydata.org): go here for complete, up-to-date documentation on the latest and greatest of Pandas\n", + "* [github.com/pandas-dev/pandas](http://github.com/pandas-dev/pandas): if you want to browse source code for the project\n", + "\n", + "There are also many great tutorials around the web and in the blogosphere." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How Pandas?\n", + "\n", + "Pandas can be installed in Python 2 and Python 3, though it is recommended to use Python 3 as Python 2 will soon lose support and updates.\n", + "\n", + "Pandas can be installed from a variety of mechanisms.\n", + "\n", + "If you've installed [Anaconda](https://www.continuum.io/what-is-anaconda) then you need do nothing -- Pandas is installed by default in the conda stack.\n", + "\n", + "If you want, you can install Pandas via [binaries from Pypi](http://pypi.python.org/pypi/pandas) or you can install via `pip`:\n", + "\n", + "```bash\n", + "pip install pandas\n", + "```\n", + "\n", + "should get you going.\n", + "\n", + "For more about installation, please see:\n", + "\n", + "* [complete Pandas installation documentation on pydata.org](http://pandas.pydata.org/pandas-docs/stable/install.html)\n", + "Ξ" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [conda root]", + "language": "python", + "name": "conda-root-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + }, + "toc": { + "colors": { + "hover_highlight": "#DAA520", + "navigate_num": "#000000", + "navigate_text": "#333333", + "running_highlight": "#FF0000", + "selected_highlight": "#FFD700", + "sidebar_border": "#EEEEEE", + "wrapper_background": "#FFFFFF" + }, + "moveMenuLeft": true, + "nav_menu": { + "height": "67px", + "width": "251px" + }, + "navigate_menu": true, + "number_sections": false, + "sideBar": true, + "threshold": 4, + "toc_cell": true, + "toc_section_display": "block", + "toc_window_display": false, + "widenNotebook": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}