PANDAS Mac OS
Filtering Rows with Pandas query: Example 5. Starting with Pandas 1.0.0. Query function has expanded the functionalities of using backtick quoting for more than only spaces. In the simplest use case backticks quoted variable is useful for column names with spaces in it. For example, if we have data frame with column ‘C C’ with space. 1:07 can just click on the proper operating system and get your download options. 1:12 So, for the Mac OS X computers, we have two options here. 1:16 We have Python 3.5 and Python 2.7 right below. Specifically what gave me trouble, was displaying the styled pandas html in an outlook email, as it just wouldn't render properly with the css mess that pandas was producing. Iterate over the dict and generate the html there by simply wrapping keys/values in the tags that you need, adding classes etc. And concatenate this all into one string.
Pandas is an data analysis module for the Python programming language. It is open-source and BSD-licensed.
Pandas is used in a wide range of fields including academia, finance, economics, statistics, analytics, etc.
Related course:Data Analysis with Python Pandas
Install Pandas
The Pandas module isn’t bundled with Python, so you can manually install the module with pip.
Linux
If you use Linux, you can use one of the commands below to install pandas.
For Ubuntu Users
For Fedora Users
Windows
For Windows uses, you can do the following:
pip is likely in:
Apple Mac OS X
Pandas Mac Os Catalina
To install pandas on Mac OS X, first install python.
If Python is not installed,
Then install pandas with pip.
Pandas Mac Os 11
Related course:Data Analysis with Python Pandas
Pandas offer many ways to select rows from a dataframe. One of the commonly used approach to filter rows of a dataframe is to use the indexing in multiple ways. For example, one can use label based indexing with loc function.
As Jake VanderPlas nicely explains, introducing query() function
While these abstractions are efficient and effective for many common use cases, they often rely on the creation of temporary intermediate objects, which can cause undue overhead in computational time and memory use.
Not just that, often this involve slightly messier code with a lot of repetition. A simpler alternative in Pandas to select or filter rows dataframe with specified condition is to use query function Pandas.
In this post, we will see multiple examples of using query function in Pandas to select or filter rows of Pandas data frame based values of columns.
Let us first load Pandas.
Let us load gapminder dataset to work through examples of using query() to filter rows.
Filtering Rows of Pandas Dataframe – the usual way
Let us say we want to subset the gapminder dataframe such that we want all rows whose country value is United States. We can use Pandas indexing to subset the gapminder dataframe for United States as follows. Here we first create a boolean series and use it to filter the dataframe.
And we would get
Filtering Rows of Pandas Dataframe by variable using query() function
In the above example, we can see that we have to create an intermediate boolean variable and also have to repeat “gapminder” two times.
Filtering Rows with Pandas query(): Example 1
A cleaner approach to filter Pandas dataframe is to use Pandas query() function and select rows. The way to query() function to filter rows is to specify the condition within quotes inside query().
And we would get the same answer as above.
Filtering Rows with Pandas query(): Example 2
In the above query() example we used string to select rows of a dataframe. We can also use it to select based on numerical values. For example, to select rows for year 1952, we can write
And we would get a new dataframe for the year 1952.
Filtering Rows with Pandas query() multiple conditions: Example 3
Similarly, we use boolean operators to combine multiple conditions. For example, if want to select rows corresponding to US for the year greater than 1996,
And we would get
Filtering Rows with Pandas query() Multiple Conditions: Example 4
We can also use query() to check for matches with a list of values corresponding to a column. Here we use in operator to check for equality.
And we would get
Filtering Rows with Pandas query(): Example 5
Starting with Pandas 1.0.0. query() function has expanded the functionalities of using backtick quoting for more than only spaces. In the simplest use case backticks quoted variable is useful for column names with spaces in it. For example, if we have data frame with column ‘C C’ with space
We can use query function with backticks quoting as shown in Pandas documentation.