Simplifying Administrative Tasks with Python
Quickly and easily complete tasks that could have otherwise be quite time-consuming and tedious with this Python code.
Python
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Simplifying Administrative Tasks with Python
I’ve recently had to do some basic administrative tasks with Excel files. Using Python, I was able to quickly and easily complete tasks that could have otherwise been quite time-consuming and tedious. The examples below illustrate how Python can be used to improve these kinds of processes.
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Multiple Python Positional Parameters - *args
You may have seen *args in the help documentation for Python functions and methods. This post explains what this parameter is, how to use functions that include an *args parameter and how to include *args in your own custom functions.
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What are Python *args?
You may have seen *args in the help documentation for Python functions and methods. This post explains what this parameter is, how to use functions that include an *args parameter and how to include *args in your own custom functions.
Read More >
Python Magic Commands in Jupyter
Python users who work in Jupyter computational notebooks have access to an extra collection of commands – known as magic commands. Magic commands are an enhancement of the interactive IPython kernel (used by Jupyter) and make performing common tasks easier than using Python code alone.
Read More >
Python Magic Commands in Jupyter
Python users who work in Jupyter computational notebooks have access to an extra collection of commands – known as magic commands. Magic commands are an enhancement of the interactive IPython kernel (used by Jupyter) and make performing common tasks easier than using Python code alone.
Read More >
Data Frames and DataFrames
The data frame (or DataFrame) is an essential tool for anyone using R or Python to analyse data. They are rectangular or tabular data structures, that hold rows and columns of data, similar to tables in a database or an Excel spreadsheet.
Read More >
Data Frames and DataFrames
The data frame (or DataFrame) is an essential tool for anyone using R or Python to analyse data. They are rectangular or tabular data structures, that hold rows and columns of data, similar to tables in a database or an Excel spreadsheet.
Read More >
The pandas DataFrame groupby() method
The pandas groupby() is a versatile DataFrame method that enables to users to quickly and efficiently separate datasets into groups by a chosen variable. It can be used to report summary statistics for each group, loop over groups and to separate data into subsets.
Read More >
The pandas DataFrame groupby() method
The pandas groupby() is a versatile DataFrame method that enables to users to quickly and efficiently separate datasets into groups by a chosen variable. It can be used to report summary statistics for each group, loop over groups and to separate data into subsets.
Read More >
Code-Based Tools for Analysing Data
We have recently been training a growing number of professionals in R and Python as they migrate from other code-based analytical packages such as MATLAB, Stata, SAS and SPSS. This post outlines some of the reasons for using code-based tools and why organisations are increasingly using Python and R for their data analysis needs.
Read More >
Code-Based Tools for Analysing Data
We have recently been training a growing number of professionals in R and Python as they migrate from other code-based analytical packages such as MATLAB, Stata, SAS and SPSS. This post outlines some of the reasons for using code-based tools and why organisations are increasingly using Python and R for their data analysis needs.
Read More >
Increase Productivity with Python & R
Both R and Python have a huge range of functionality available for working with data. Investing a few weeks to learn to use a programming language will enable you achieve big productivity gains.
Read More >
Increase Productivity with Python & R
Both R and Python have a huge range of functionality available for working with data. Investing a few weeks to learn to use a programming language will enable you achieve big productivity gains.
Read More >
Topics
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Simplifying Administrative Tasks with Python
I’ve recently had to do some basic administrative tasks with Excel files. Using Python, I was able to quickly and easily complete tasks that could have otherwise been quite time-consuming and tedious. The examples below illustrate how Python can be used to improve these kinds of processes.
Multiple Python Positional Parameters - *args
You may have seen *args in the help documentation for Python functions and methods. This post explains what this parameter is, how to use functions that include an *args parameter and how to include *args in your own custom functions.
Read More >
What are Python *args?
You may have seen *args in the help documentation for Python functions and methods. This post explains what this parameter is, how to use functions that include an *args parameter and how to include *args in your own custom functions.
Read More >
Python Magic Commands in Jupyter
Python users who work in Jupyter computational notebooks have access to an extra collection of commands – known as magic commands. Magic commands are an enhancement of the interactive IPython kernel (used by Jupyter) and make performing common tasks easier than using Python code alone.
Read More >
Python Magic Commands in Jupyter
Python users who work in Jupyter computational notebooks have access to an extra collection of commands – known as magic commands. Magic commands are an enhancement of the interactive IPython kernel (used by Jupyter) and make performing common tasks easier than using Python code alone.
Read More >
Data Frames and DataFrames
The data frame (or DataFrame) is an essential tool for anyone using R or Python to analyse data. They are rectangular or tabular data structures, that hold rows and columns of data, similar to tables in a database or an Excel spreadsheet.
Read More >
Data Frames and DataFrames
The data frame (or DataFrame) is an essential tool for anyone using R or Python to analyse data. They are rectangular or tabular data structures, that hold rows and columns of data, similar to tables in a database or an Excel spreadsheet.
Read More >
The pandas DataFrame groupby() method
The pandas groupby() is a versatile DataFrame method that enables to users to quickly and efficiently separate datasets into groups by a chosen variable. It can be used to report summary statistics for each group, loop over groups and to separate data into subsets.
Read More >
The pandas DataFrame groupby() method
The pandas groupby() is a versatile DataFrame method that enables to users to quickly and efficiently separate datasets into groups by a chosen variable. It can be used to report summary statistics for each group, loop over groups and to separate data into subsets.
Read More >
Code-Based Tools for Analysing Data
We have recently been training a growing number of professionals in R and Python as they migrate from other code-based analytical packages such as MATLAB, Stata, SAS and SPSS. This post outlines some of the reasons for using code-based tools and why organisations are increasingly using Python and R for their data analysis needs.
Read More >
Code-Based Tools for Analysing Data
We have recently been training a growing number of professionals in R and Python as they migrate from other code-based analytical packages such as MATLAB, Stata, SAS and SPSS. This post outlines some of the reasons for using code-based tools and why organisations are increasingly using Python and R for their data analysis needs.
Read More >
Increase Productivity with Python & R
Both R and Python have a huge range of functionality available for working with data. Investing a few weeks to learn to use a programming language will enable you achieve big productivity gains.
Read More >
Increase Productivity with Python & R
Both R and Python have a huge range of functionality available for working with data. Investing a few weeks to learn to use a programming language will enable you achieve big productivity gains.
Read More >
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