From 64a0d99671970abf11b5a09fe17f4e11c30ed844 Mon Sep 17 00:00:00 2001 From: tommyod Date: Fri, 23 Feb 2018 07:34:02 +0100 Subject: [PATCH 1/2] Changed links to 2 tutorials from v0.2 to v0.1, as v0.2 did not exist for all of them --- doc/source/tutorials.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/source/tutorials.rst b/doc/source/tutorials.rst index db9385519bff2..333c03b8d537c 100644 --- a/doc/source/tutorials.rst +++ b/doc/source/tutorials.rst @@ -34,7 +34,7 @@ See `How to use this cookbook `_ It's not totally obvious how to select data from a pandas dataframe. Here we explain the basics (how to take slices and get columns) -- `Chapter 3: `_ +- `Chapter 3: `_ Here we get into serious slicing and dicing and learn how to filter dataframes in complicated ways, really fast. - `Chapter 4: `_ @@ -43,7 +43,7 @@ See `How to use this cookbook `_ Here you get to find out if it's cold in Montreal in the winter (spoiler: yes). Web scraping with pandas is fun! Here we combine dataframes. -- `Chapter 6: `_ +- `Chapter 6: `_ Strings with pandas are great. It has all these vectorized string operations and they're the best. We will turn a bunch of strings containing "Snow" into vectors of numbers in a trice. From 3f09bc32b144eb74b560e18a4804207e9046172c Mon Sep 17 00:00:00 2001 From: tommyod Date: Sat, 24 Feb 2018 08:13:20 +0100 Subject: [PATCH 2/2] Updated all pandas-tutorial links to v0.2, added chapter 9 of tutorial --- doc/source/tutorials.rst | 20 +++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) diff --git a/doc/source/tutorials.rst b/doc/source/tutorials.rst index 333c03b8d537c..0398e2892cef5 100644 --- a/doc/source/tutorials.rst +++ b/doc/source/tutorials.rst @@ -26,32 +26,34 @@ repository `_. To run the examples in th clone the GitHub repository and get IPython Notebook running. See `How to use this cookbook `_. -- `A quick tour of the IPython Notebook: `_ +- `A quick tour of the IPython Notebook: `_ Shows off IPython's awesome tab completion and magic functions. -- `Chapter 1: `_ +- `Chapter 1: `_ Reading your data into pandas is pretty much the easiest thing. Even when the encoding is wrong! -- `Chapter 2: `_ +- `Chapter 2: `_ It's not totally obvious how to select data from a pandas dataframe. Here we explain the basics (how to take slices and get columns) -- `Chapter 3: `_ +- `Chapter 3: `_ Here we get into serious slicing and dicing and learn how to filter dataframes in complicated ways, really fast. -- `Chapter 4: `_ +- `Chapter 4: `_ Groupby/aggregate is seriously my favorite thing about pandas and I use it all the time. You should probably read this. -- `Chapter 5: `_ +- `Chapter 5: `_ Here you get to find out if it's cold in Montreal in the winter (spoiler: yes). Web scraping with pandas is fun! Here we combine dataframes. -- `Chapter 6: `_ +- `Chapter 6: `_ Strings with pandas are great. It has all these vectorized string operations and they're the best. We will turn a bunch of strings containing "Snow" into vectors of numbers in a trice. -- `Chapter 7: `_ +- `Chapter 7: `_ Cleaning up messy data is never a joy, but with pandas it's easier. -- `Chapter 8: `_ +- `Chapter 8: `_ Parsing Unix timestamps is confusing at first but it turns out to be really easy. +- `Chapter 9: `_ + Reading data from SQL databases. Lessons for new pandas users