Options and Settings in Pandas

Introduction

Before learning about the options and settings in Pandas, let us first get a brief introduction to the Pandas module.

Pandas library is an open-source (free to use) library that is built on top of another very useful Python library i.e., NumPy library. Pandas is an open-source package (or library) that provides us with highly optimized data structures and data analysis tools. Pandas library is widely used in the field of data science, machine learning, and data analytics as it simplifies data importing and data analysis.

Pandas Python package offers us a wide variety of data structures and operations that helps in easy manipulation (add, update, delete) of numerical data as well as the time series. The prime reason for the Pandas package's popularity is its easy importing feature and easy data analyzing data feature. Pandas module is quite fast and comes in very handy because of its high performance and productivity.

Now a question comes to our mind what are the options and settings in Pandas? Well, the options and settings in Pandas are used to manipulate the data present in the DataFrames. Let us take a scenario to understand the need for options and settings in Pandas in more detail.

There may be situations in which the data loaded into the DataFrame may have a lot of truncated values, or maybe there some columns that are replaced with ellipsis, or there may be some floating point precision that is hard to be read. So to deal with such scenarios, we have options API. Refer to the next section for more details about the Options API provided by the Pandas module.

What is the Options API in Pandas

As we have discussed above, to deal with unreadable data and to customize the various aspects like behavior, data, analysis, etc. we use the Options API provided by the Pandas module. In simpler terms, we can say that the Options API helps us customize and configure the dataframes' global behaviors like data, behavior, etc. By using the Options API, we can directly use the get and set options as attributes of the top-level options attribute.

The Options API provides us 5 functions to deal with the data: get_option(), set_option(), reset_option(), describe_option(), option_context(). We will be learning about them in detail in the next section. These functions, along with various other features of the Options API, help us to control how the data of the DataFrames are displayed.

Methods in Options and Settings in Pandas

So far, we have discussed the use case of options and settings in Pandas as well as their need. Let us now learn about the various associated functions with some examples for more clarity.

i) get_option()

The get_option() function is used to get the value of the single function. So in situations where we want to find out the maximum number of rows of the DataFrame, we can use the get_option() function. This function provides us with the default value of any parameter in Pandas. We can only see the value but cannot set or change the value (for the setting, we have another function that we will discuss next). The get_option() function takes a single parameter.

For example, let us gather the default maximum number of rows or columns provided by the Pandas module using the display.max_rows and display.max_columns parameters.

Output:

ii) set_option()

The set_option() function is used to set the value of the single function. This function helps us to set the default value of any parameter to any desired value. The get_option() function takes two parameters, the first one is the name of the parameter, and the second one is the value of the parameter.

Changing Common Display Settings

Now if we want to change the maximum number of rows or columns of the DataFrame, we can use the set_option() function.

For example, let us change the default maximum number of rows or columns provided by the Pandas module using the display.max_rows and display.max_columns parameters.

Output:

iii) reset_option()

The reset_option() function is used to reset the value of the single function. So in situations where we want to reset the maximum number of rows of the DataFrame, we can use the reset_option() function. The reset_option() function takes a single argument i.e., the parameter that has to be set to its default value.

For example, let us first change the default maximum number of rows and columns provided by the Pandas module using the display.max_rows and display.max_columns parameters. After that, we will use the reset_option() function to set back the value to the original.

Output:

iv) describe_option()

The describe_option() function is used to print the entire description of any parameter. For example, let us print the description of the display.max_columns parameters.

Output:

v) option_context()

The option_context() parameter is used to set the optional parameter in with the statement temporarily. The values of the options are restored automatically when we exit the code block. In simpler terms, we can say this function is used to invoke a Pandas option which will be active for the cope of the block only.

For example, let us change the maximum_row parameter.

Output:

Errors & Exceptions

We can use the above functions along with the regexp pattern as an argument to match the unambiguous substring.

If we only provide a small regex that matches multiple parameters, then we get Error OptionError: 'Pattern matched multiple keys'.

For example, if we only provide max as a parameter, then the Python interpreter and pandas module cannot rectify which parameter is to be used. We have display.max_colwidth, display.max_rows, and display.max_columns having max in their names, so only providing max will be an issue. Let us see the error for more clarity.

Output: