{"title":"PyVideo.org - data processing","link":[{"@attributes":{"href":"https:\/\/pyvideo.org\/","rel":"alternate"}},{"@attributes":{"href":"https:\/\/pyvideo.org\/feeds\/tag_data-processing.atom.xml","rel":"self"}}],"id":"https:\/\/pyvideo.org\/","updated":"2016-08-23T00:00:00+00:00","subtitle":{},"entry":[{"title":"How do I apply a function to a pandas Series or DataFrame?","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-30-apply-function.html","rel":"alternate"}},"published":"2016-08-23T00:00:00+00:00","updated":"2016-08-23T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-08-23:\/data-school\/pandas-30-apply-function.html","summary":"

Description<\/h3>

Have you ever struggled to figure out the differences between apply, map, and applymap? In this video, I'll explain when you should use each of these methods and demonstrate a few common use cases. Watch the end of the video for three important announcements!<\/p>\n

This is video 30 of \u2026<\/p>","content":"

Description<\/h3>

Have you ever struggled to figure out the differences between apply, map, and applymap? In this video, I'll explain when you should use each of these methods and demonstrate a few common use cases. Watch the end of the video for three important announcements!<\/p>\n

This is video 30 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"NumPy"}}]},{"title":"How do I create a pandas DataFrame from another object?","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-29-dummy-dataframe.html","rel":"alternate"}},"published":"2016-08-16T00:00:00+00:00","updated":"2016-08-16T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-08-16:\/data-school\/pandas-29-dummy-dataframe.html","summary":"

Description<\/h3>

Have you ever needed to create a DataFrame of "dummy" data, but without reading from a file? In this video, I'll demonstrate how to create a DataFrame from a dictionary, a list, and a NumPy array. I'll also show you how to create a new Series and attach it \u2026<\/p>","content":"

Description<\/h3>

Have you ever needed to create a DataFrame of "dummy" data, but without reading from a file? In this video, I'll demonstrate how to create a DataFrame from a dictionary, a list, and a NumPy array. I'll also show you how to create a new Series and attach it to the DataFrame.<\/p>\n

This is video 29 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"NumPy"}}]},{"title":"How do I change display options in pandas?","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-28-customize-display.html","rel":"alternate"}},"published":"2016-08-09T00:00:00+00:00","updated":"2016-08-09T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-08-09:\/data-school\/pandas-28-customize-display.html","summary":"

Description<\/h3>

Have you ever wanted to change the way your DataFrame is displayed? Perhaps you needed to see more rows or columns, or modify the formatting of numbers? In this video, I'll demonstrate how to change the settings for five common display options in pandas.<\/p>\n

This is video 28 of \u2026<\/p>","content":"

Description<\/h3>

Have you ever wanted to change the way your DataFrame is displayed? Perhaps you needed to see more rows or columns, or modify the formatting of numbers? In this video, I'll demonstrate how to change the settings for five common display options in pandas.<\/p>\n

This is video 28 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}}]},{"title":"How do I avoid a SettingWithCopyWarning in pandas?","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-27-setting-with-copy-warning.html","rel":"alternate"}},"published":"2016-08-02T00:00:00+00:00","updated":"2016-08-02T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-08-02:\/data-school\/pandas-27-setting-with-copy-warning.html","summary":"

Description<\/h3>

If you've been using pandas for a while, you've likely encountered a SettingWithCopyWarning. The proper response is to modify your code appropriately, not to turn off the warning! In this video, I'll show you two common scenarios in which this warning arises, explain why it's occurring, and then demonstrate \u2026<\/p>","content":"

Description<\/h3>

If you've been using pandas for a while, you've likely encountered a SettingWithCopyWarning. The proper response is to modify your code appropriately, not to turn off the warning! In this video, I'll show you two common scenarios in which this warning arises, explain why it's occurring, and then demonstrate how to address it.<\/p>\n

This is video 27 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"missing data"}}]},{"title":"How do I find and remove duplicate rows in pandas?","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-26-duplicate-data.html","rel":"alternate"}},"published":"2016-07-26T00:00:00+00:00","updated":"2016-07-26T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-07-26:\/data-school\/pandas-26-duplicate-data.html","summary":"

Description<\/h3>

During the data cleaning process, you will often need to figure out whether you have duplicate data, and if so, how to deal with it. In this video, I'll demonstrate the two key methods for finding and removing duplicate rows, as well as how to modify their behavior to \u2026<\/p>","content":"

Description<\/h3>

During the data cleaning process, you will often need to figure out whether you have duplicate data, and if so, how to deal with it. In this video, I'll demonstrate the two key methods for finding and removing duplicate rows, as well as how to modify their behavior to suit your specific needs.<\/p>\n

This is video 26 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"duplicate data"}}]},{"title":"How do I work with dates and times in pandas?","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-25-dates-and-times.html","rel":"alternate"}},"published":"2016-07-19T00:00:00+00:00","updated":"2016-07-19T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-07-19:\/data-school\/pandas-25-dates-and-times.html","summary":"

Description<\/h3>

Let's say that you have dates and times in your DataFrame and you want to analyze your data by minute, month, or year. What should you do? In this video, I'll demonstrate how you can convert your data to "datetime" format, enabling you to access a ton of convenient \u2026<\/p>","content":"

Description<\/h3>

Let's say that you have dates and times in your DataFrame and you want to analyze your data by minute, month, or year. What should you do? In this video, I'll demonstrate how you can convert your data to "datetime" format, enabling you to access a ton of convenient attributes and perform datetime comparisons and mathematical operations.<\/p>\n

This is video 25 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data visualization"}}]},{"title":"How do I create dummy variables in pandas?","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-24-dummy-variables.html","rel":"alternate"}},"published":"2016-07-12T00:00:00+00:00","updated":"2016-07-12T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-07-12:\/data-school\/pandas-24-dummy-variables.html","summary":"

Description<\/h3>

If you want to include a categorical feature in your machine learning model, one common solution is to create dummy variables. In this video, I'll demonstrate three different ways you can create dummy variables from your existing DataFrame columns. I'll also show you a trick for simplifying your code \u2026<\/p>","content":"

Description<\/h3>

If you want to include a categorical feature in your machine learning model, one common solution is to create dummy variables. In this video, I'll demonstrate three different ways you can create dummy variables from your existing DataFrame columns. I'll also show you a trick for simplifying your code that was introduced in pandas 0.18.<\/p>\n

This is video 24 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"machine learning"}}]},{"title":"More of your pandas questions answered!","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-23-viewer-questions.html","rel":"alternate"}},"published":"2016-07-05T00:00:00+00:00","updated":"2016-07-05T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-07-05:\/data-school\/pandas-23-viewer-questions.html","summary":"

Description<\/h3>

In this video, I'm answering a few of the pandas questions I've received in the YouTube comments: Could you explain how to read the pandas documentation? What is the difference between ufo.isnull() and pd.isnull(ufo)? Why are DataFrame slices inclusive when using .loc, but exclusive when using \u2026<\/p>","content":"

Description<\/h3>

In this video, I'm answering a few of the pandas questions I've received in the YouTube comments: Could you explain how to read the pandas documentation? What is the difference between ufo.isnull() and pd.isnull(ufo)? Why are DataFrame slices inclusive when using .loc, but exclusive when using .iloc? How do I randomly sample rows from a DataFrame?<\/p>\n

This is video 23 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"reproducibility"}}]},{"title":"How do I use pandas with scikit-learn to create Kaggle submissions?","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-22-prepare-for-machine-learning.html","rel":"alternate"}},"published":"2016-06-28T00:00:00+00:00","updated":"2016-06-28T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-06-28:\/data-school\/pandas-22-prepare-for-machine-learning.html","summary":"

Description<\/h3>

Have you been using scikit-learn for machine learning, and wondering whether pandas could help you to prepare your data and export your predictions? In this video, I'll demonstrate the simplest way to integrate pandas into your machine learning workflow, and will create a submission for Kaggle's Titanic competition in \u2026<\/p>","content":"

Description<\/h3>

Have you been using scikit-learn for machine learning, and wondering whether pandas could help you to prepare your data and export your predictions? In this video, I'll demonstrate the simplest way to integrate pandas into your machine learning workflow, and will create a submission for Kaggle's Titanic competition in just a few lines of code!<\/p>\n

This is video 22 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"scikit-learn"}},{"@attributes":{"term":"machine learning"}}]},{"title":"How do I make my pandas DataFrame smaller and faster?","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-21-reduce-dataframe-size.html","rel":"alternate"}},"published":"2016-06-21T00:00:00+00:00","updated":"2016-06-21T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-06-21:\/data-school\/pandas-21-reduce-dataframe-size.html","summary":"

Description<\/h3>

Are you working with a large dataset in pandas, and wondering if you can reduce its memory footprint or improve its efficiency? In this video, I'll show you how to do exactly that in one line of code using the "category" data type, introduced in pandas 0.15. I'll \u2026<\/p>","content":"

Description<\/h3>

Are you working with a large dataset in pandas, and wondering if you can reduce its memory footprint or improve its efficiency? In this video, I'll show you how to do exactly that in one line of code using the "category" data type, introduced in pandas 0.15. I'll explain how it works, and how to know when you shouldn't use it.<\/p>\n

This is video 21 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}}]},{"title":"When should I use the \"inplace\" parameter in pandas?","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-20-inplace-parameter.html","rel":"alternate"}},"published":"2016-06-14T00:00:00+00:00","updated":"2016-06-14T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-06-14:\/data-school\/pandas-20-inplace-parameter.html","summary":"

Description<\/h3>

We've used the "inplace" parameter many times during this video series, but what exactly does it do, and when should you use it? In this video, I'll explain how "inplace" affects methods such as "drop" and "dropna", and why it is always False by default.<\/p>\n

This is video 20 \u2026<\/p>","content":"

Description<\/h3>

We've used the "inplace" parameter many times during this video series, but what exactly does it do, and when should you use it? In this video, I'll explain how "inplace" affects methods such as "drop" and "dropna", and why it is always False by default.<\/p>\n

This is video 20 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"missing data"}}]},{"title":"How do I select multiple rows and columns from a pandas DataFrame?","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-19-select-dataframe-rows-and-columns.html","rel":"alternate"}},"published":"2016-06-07T00:00:00+00:00","updated":"2016-06-07T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-06-07:\/data-school\/pandas-19-select-dataframe-rows-and-columns.html","summary":"

Description<\/h3>

Have you ever been confused about the "right" way to select rows and columns from a DataFrame? pandas gives you an incredible number of options for doing so, but in this video, I'll outline the current best practices for row and column selection using the loc, iloc, and ix \u2026<\/p>","content":"

Description<\/h3>

Have you ever been confused about the "right" way to select rows and columns from a DataFrame? pandas gives you an incredible number of options for doing so, but in this video, I'll outline the current best practices for row and column selection using the loc, iloc, and ix methods.<\/p>\n

This is video 19 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}}]},{"title":"What do I need to know about the pandas index? (Part 2)","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-18-index-part-2.html","rel":"alternate"}},"published":"2016-06-02T00:00:00+00:00","updated":"2016-06-02T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-06-02:\/data-school\/pandas-18-index-part-2.html","summary":"

Description<\/h3>

In part two of our discussion of the index, we'll switch our focus from the DataFrame index to the Series index. After discussing index-based selection and sorting, I'll demonstrate how automatic index alignment during mathematical operations and concatenation enables us to easily work with incomplete data in pandas.<\/p>\n

This \u2026<\/p>","content":"

Description<\/h3>

In part two of our discussion of the index, we'll switch our focus from the DataFrame index to the Series index. After discussing index-based selection and sorting, I'll demonstrate how automatic index alignment during mathematical operations and concatenation enables us to easily work with incomplete data in pandas.<\/p>\n

This is video 18 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"missing data"}}]},{"title":"What do I need to know about the pandas index? (Part 1)","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-17-index-part-1.html","rel":"alternate"}},"published":"2016-05-31T00:00:00+00:00","updated":"2016-05-31T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-05-31:\/data-school\/pandas-17-index-part-1.html","summary":"

Description<\/h3>

The DataFrame index is core to the functionality of pandas, yet it's confusing to many users. In this video, I'll explain what the index is used for and why you might want to store your data in the index. I'll also demonstrate how to set and reset the index \u2026<\/p>","content":"

Description<\/h3>

The DataFrame index is core to the functionality of pandas, yet it's confusing to many users. In this video, I'll explain what the index is used for and why you might want to store your data in the index. I'll also demonstrate how to set and reset the index, and show how that affects the DataFrame's shape and contents.<\/p>\n

This is video 17 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}}]},{"title":"How do I handle missing values in pandas?","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-16-missing-values.html","rel":"alternate"}},"published":"2016-05-26T00:00:00+00:00","updated":"2016-05-26T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-05-26:\/data-school\/pandas-16-missing-values.html","summary":"

Description<\/h3>

Most datasets contain "missing values", meaning that the data is incomplete. Deciding how to handle missing values can be challenging! In this video, I'll cover all of the basics: how missing values are represented in pandas, how to locate them, and options for how to drop them or fill \u2026<\/p>","content":"

Description<\/h3>

Most datasets contain "missing values", meaning that the data is incomplete. Deciding how to handle missing values can be challenging! In this video, I'll cover all of the basics: how missing values are represented in pandas, how to locate them, and options for how to drop them or fill them in.<\/p>\n

This is video 16 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"missing data"}}]},{"title":"How do I explore a pandas Series?","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-15-explore-series.html","rel":"alternate"}},"published":"2016-05-24T00:00:00+00:00","updated":"2016-05-24T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-05-24:\/data-school\/pandas-15-explore-series.html","summary":"

Description<\/h3>

When you start working with a new dataset, how should you go about exploring it? In this video, I'll demonstrate some of the basic tools in pandas for exploring both numeric and non-numeric data. I'll also show you how to create simple visualizations in a single line of code \u2026<\/p>","content":"

Description<\/h3>

When you start working with a new dataset, how should you go about exploring it? In this video, I'll demonstrate some of the basic tools in pandas for exploring both numeric and non-numeric data. I'll also show you how to create simple visualizations in a single line of code!<\/p>\n

This is video 15 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data visualization"}}]},{"title":"When should I use a \"groupby\" in pandas?","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-14-analyze-data-by-category.html","rel":"alternate"}},"published":"2016-05-19T00:00:00+00:00","updated":"2016-05-19T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-05-19:\/data-school\/pandas-14-analyze-data-by-category.html","summary":"

Description<\/h3>

The pandas "groupby" method allows you to split a DataFrame into groups, apply a function to each group independently, and then combine the results back together. This is called the "split-apply-combine" pattern, and is a powerful tool for analyzing data across different categories. In this video, I'll explain when \u2026<\/p>","content":"

Description<\/h3>

The pandas "groupby" method allows you to split a DataFrame into groups, apply a function to each group independently, and then combine the results back together. This is called the "split-apply-combine" pattern, and is a powerful tool for analyzing data across different categories. In this video, I'll explain when you should use a groupby and then demonstrate its flexibility using four different examples.<\/p>\n

This is video 14 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data visualization"}}]},{"title":"How do I change the data type of a pandas Series?","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-13-change-data-type-of-series.html","rel":"alternate"}},"published":"2016-05-17T00:00:00+00:00","updated":"2016-05-17T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-05-17:\/data-school\/pandas-13-change-data-type-of-series.html","summary":"

Description<\/h3>

Have you ever tried to do math with a pandas Series that you thought was numeric, but it turned out that your numbers were stored as strings? In this video, I'll demonstrate two different ways to change the data type of a Series so that you can fix incorrect \u2026<\/p>","content":"

Description<\/h3>

Have you ever tried to do math with a pandas Series that you thought was numeric, but it turned out that your numbers were stored as strings? In this video, I'll demonstrate two different ways to change the data type of a Series so that you can fix incorrect data types. I'll also show you the easiest way to convert a boolean Series to integers, which is useful for creating dummy\/indicator variables for machine learning.<\/p>\n

This is video 13 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}}]},{"title":"How do I use string methods in pandas?","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-12-string-methods.html","rel":"alternate"}},"published":"2016-05-12T00:00:00+00:00","updated":"2016-05-12T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-05-12:\/data-school\/pandas-12-string-methods.html","summary":"

Description<\/h3>

pandas includes powerful string manipulation capabilities that you can easily apply to any Series of strings. In this video, I'll show you how to access string methods in pandas (along with a few examples), and then end with two bonus tips to help you maximize your efficiency.<\/p>\n

This is \u2026<\/p>","content":"

Description<\/h3>

pandas includes powerful string manipulation capabilities that you can easily apply to any Series of strings. In this video, I'll show you how to access string methods in pandas (along with a few examples), and then end with two bonus tips to help you maximize your efficiency.<\/p>\n

This is video 12 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"regular expressions"}},{"@attributes":{"term":"string processing"}}]},{"title":"How do I use the \"axis\" parameter in pandas?","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-11-dataframe-axis.html","rel":"alternate"}},"published":"2016-05-10T00:00:00+00:00","updated":"2016-05-10T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-05-10:\/data-school\/pandas-11-dataframe-axis.html","summary":"

Description<\/h3>

When performing operations on a pandas DataFrame, such as dropping columns or calculating row means, it is often necessary to specify the "axis". But what exactly is an axis? In this video, I'll help you to build a mental model for understanding the axis parameter so that you will \u2026<\/p>","content":"

Description<\/h3>

When performing operations on a pandas DataFrame, such as dropping columns or calculating row means, it is often necessary to specify the "axis". But what exactly is an axis? In this video, I'll help you to build a mental model for understanding the axis parameter so that you will know when and how to use it.<\/p>\n

This is video 11 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}}]},{"title":"Your pandas questions answered!","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-10-viewer-questions.html","rel":"alternate"}},"published":"2016-05-05T00:00:00+00:00","updated":"2016-05-05T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-05-05:\/data-school\/pandas-10-viewer-questions.html","summary":"

Description<\/h3>

In this video, I'm answering a few of the pandas questions I've received in the YouTube comments: When reading from a file, how do I read in only a subset of the columns or rows? How do I iterate through a Series or a DataFrame? How do I drop \u2026<\/p>","content":"

Description<\/h3>

In this video, I'm answering a few of the pandas questions I've received in the YouTube comments: When reading from a file, how do I read in only a subset of the columns or rows? How do I iterate through a Series or a DataFrame? How do I drop all non-numeric columns from a DataFrame? How do I know whether I should pass an argument as a string or a list?<\/p>\n

This is video 10 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}}]},{"title":"How do I apply multiple filter criteria to a pandas DataFrame?","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-09-multiple-filter-criteria.html","rel":"alternate"}},"published":"2016-05-03T00:00:00+00:00","updated":"2016-05-03T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-05-03:\/data-school\/pandas-09-multiple-filter-criteria.html","summary":"

Description<\/h3>

Let's say that you want to filter the rows of a DataFrame by multiple conditions. In this video, I'll demonstrate how to do this using two different logical operators. I'll also explain the special rules in pandas for combining filter criteria, and end with a trick for simplifying chained \u2026<\/p>","content":"

Description<\/h3>

Let's say that you want to filter the rows of a DataFrame by multiple conditions. In this video, I'll demonstrate how to do this using two different logical operators. I'll also explain the special rules in pandas for combining filter criteria, and end with a trick for simplifying chained conditions!<\/p>\n

This is video 9 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}}]},{"title":"How do I filter rows of a pandas DataFrame by column value?","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-08-filter-dataframe-rows.html","rel":"alternate"}},"published":"2016-04-28T00:00:00+00:00","updated":"2016-04-28T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-04-28:\/data-school\/pandas-08-filter-dataframe-rows.html","summary":"

Description<\/h3>

Let's say that you only want to display the rows of a DataFrame which have a certain column value. How would you do it? pandas makes it easy, but the notation can be confusing and thus difficult to remember. In this video, I'll work up to the solution step-by-step \u2026<\/p>","content":"

Description<\/h3>

Let's say that you only want to display the rows of a DataFrame which have a certain column value. How would you do it? pandas makes it easy, but the notation can be confusing and thus difficult to remember. In this video, I'll work up to the solution step-by-step using regular Python code so that you can truly understand the logic behind pandas filtering notation.<\/p>\n

This is video 8 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}}]},{"title":"How do I sort a pandas DataFrame or a Series?","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-07-sort-dataframe-or-series.html","rel":"alternate"}},"published":"2016-04-26T00:00:00+00:00","updated":"2016-04-26T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-04-26:\/data-school\/pandas-07-sort-dataframe-or-series.html","summary":"

Description<\/h3>

pandas allows you to sort a DataFrame by one of its columns (known as a "Series"), and also allows you to sort a Series alone. The sorting API changed in pandas version 0.17, so in this video, I'll demonstrate both the "old way" and the "new way" to \u2026<\/p>","content":"

Description<\/h3>

pandas allows you to sort a DataFrame by one of its columns (known as a "Series"), and also allows you to sort a Series alone. The sorting API changed in pandas version 0.17, so in this video, I'll demonstrate both the "old way" and the "new way" to sort. I'll also show you how to sort a DataFrame by multiple columns at once!<\/p>\n

This is video 7 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}}]},{"title":"How do I remove columns from a pandas DataFrame?","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-06-remove-dataframe-column.html","rel":"alternate"}},"published":"2016-04-21T00:00:00+00:00","updated":"2016-04-21T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-04-21:\/data-school\/pandas-06-remove-dataframe-column.html","summary":"

Description<\/h3>

If you have DataFrame columns that you're never going to use, you may want to remove them entirely in order to focus on the columns that you do use. In this video, I'll show you how to remove columns (and rows), and will briefly explain the meaning of the \u2026<\/p>","content":"

Description<\/h3>

If you have DataFrame columns that you're never going to use, you may want to remove them entirely in order to focus on the columns that you do use. In this video, I'll show you how to remove columns (and rows), and will briefly explain the meaning of the "axis" and "inplace" parameters.<\/p>\n

This is video 6 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}}]},{"title":"How do I rename columns in a pandas DataFrame?","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-05-rename-dataframe-column.html","rel":"alternate"}},"published":"2016-04-19T00:00:00+00:00","updated":"2016-04-19T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-04-19:\/data-school\/pandas-05-rename-dataframe-column.html","summary":"

Description<\/h3>

You will often want to rename the columns of a DataFrame so that their names are descriptive, easy to type, and don't contain any spaces. In this video, I'll demonstrate three different strategies for renaming columns so that you can choose the best strategy to fit your particular situation \u2026<\/p>","content":"

Description<\/h3>

You will often want to rename the columns of a DataFrame so that their names are descriptive, easy to type, and don't contain any spaces. In this video, I'll demonstrate three different strategies for renaming columns so that you can choose the best strategy to fit your particular situation.<\/p>\n

This is video 5 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}}]},{"title":"Why do some pandas commands end with parentheses (and others don't)?","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-04-methods-and-attributes.html","rel":"alternate"}},"published":"2016-04-14T00:00:00+00:00","updated":"2016-04-14T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-04-14:\/data-school\/pandas-04-methods-and-attributes.html","summary":"

Description<\/h3>

To access most of the functionality in pandas, you have to call the methods and attributes of DataFrame and Series objects. In this video, I'll discuss some common methods and attributes, and show you how to tell the difference between them. (Hint: It's all about the parentheses!)<\/p>\n

This is \u2026<\/p>","content":"

Description<\/h3>

To access most of the functionality in pandas, you have to call the methods and attributes of DataFrame and Series objects. In this video, I'll discuss some common methods and attributes, and show you how to tell the difference between them. (Hint: It's all about the parentheses!)<\/p>\n

This is video 4 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}}]},{"title":"How do I select a pandas Series from a DataFrame?","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-03-select-series-from-dataframe.html","rel":"alternate"}},"published":"2016-04-12T00:00:00+00:00","updated":"2016-04-12T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-04-12:\/data-school\/pandas-03-select-series-from-dataframe.html","summary":"

Description<\/h3>

DataFrames and Series are the two main object types in pandas for data storage: a DataFrame is like a table, and each column of the table is called a Series. You will often select a Series in order to analyze or manipulate it. In this video, I'll show you \u2026<\/p>","content":"

Description<\/h3>

DataFrames and Series are the two main object types in pandas for data storage: a DataFrame is like a table, and each column of the table is called a Series. You will often select a Series in order to analyze or manipulate it. In this video, I'll show you how to select a Series using "bracket notation" and "dot notation", and will discuss the limitations of dot notation. I'll also demonstrate how to create a new Series in a DataFrame.<\/p>\n

This is video 3 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}}]},{"title":"What is pandas? (Introduction to the Q&A series)","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-01-introduction.html","rel":"alternate"}},"published":"2016-04-07T00:00:00+00:00","updated":"2016-04-07T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-04-07:\/data-school\/pandas-01-introduction.html","summary":"

Description<\/h3>

pandas is a full-featured Python library for data analysis, manipulation, and visualization. This video series is for anyone who wants to work with data in Python, regardless of whether you are brand new to pandas or have some experience.<\/p>\n

This is video 1 of 30 in the series, Easier \u2026<\/a><\/p>","content":"

Description<\/h3>

pandas is a full-featured Python library for data analysis, manipulation, and visualization. This video series is for anyone who wants to work with data in Python, regardless of whether you are brand new to pandas or have some experience.<\/p>\n

This is video 1 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}}]},{"title":"How do I read a tabular data file into pandas?","link":{"@attributes":{"href":"https:\/\/pyvideo.org\/data-school\/pandas-02-read-tabular-data-file.html","rel":"alternate"}},"published":"2016-04-07T00:00:00+00:00","updated":"2016-04-07T00:00:00+00:00","author":{"name":"Kevin Markham"},"id":"tag:pyvideo.org,2016-04-07:\/data-school\/pandas-02-read-tabular-data-file.html","summary":"

Description<\/h3>

"Tabular data" is just data that has been formatted as a table, with rows and columns (like a spreadsheet). You can easily read a tabular data file into pandas, even directly from a URL! In this video, I'll walk you through how to do that, including how to modify \u2026<\/p>","content":"

Description<\/h3>

"Tabular data" is just data that has been formatted as a table, with rows and columns (like a spreadsheet). You can easily read a tabular data file into pandas, even directly from a URL! In this video, I'll walk you through how to do that, including how to modify some of the default arguments of the read_table function to solve common problems.<\/p>\n

This is video 2 of 30 in the series, Easier data analysis in Python with pandas<\/a>. The notebook and datasets shown in the video are available on GitHub<\/a>.<\/p>\n","category":[{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"data science"}},{"@attributes":{"term":"data analysis"}},{"@attributes":{"term":"data wrangling"}},{"@attributes":{"term":"data processing"}},{"@attributes":{"term":"pandas"}},{"@attributes":{"term":"tutorial"}},{"@attributes":{"term":"Data School"}},{"@attributes":{"term":"csv"}}]}]}