1/29/2024 0 Comments Matlab vs python vs stata![]() dta files.īest practice: don’t save data in Excel files.ĭf = df.drop(df.filter(like='varstem*').columns, axis=1)ĭf = df. You can find more on (frequentist) regressions in Regression.īest practice: don’t do this bring the data to you!ĭf = pd.read_stata(‘dtafile’, columns=varlist)īest practice: don’t save data in. For the econometrics examples, you will need import trics as mt or other package imports as specified below. Remember that you need to import pandas as pd before running any of the examples that use pd. This article, formerly known as The Popularity of Data Analysis Software, presents various ways of measuring software popularity or market share for advanced analytics. We will use placeholders like varname for Stata variables and df for the Python equivalent. ![]() It’s not meant to be exhaustive but it should give you a flavour of the syntax differences and, in some cases, I’ve pointed out where to find further information.įollowing Daniel’s treatment, the Stata-to-Python translations assume that, in Python, you have a pandas DataFrame called df. ![]() Some of the econometrics examples below use Daniel M Sullivan’s econtools package, but you could also use statsmodels. What follows is a giant table of translations between Stata code and Python’s pandas(panel-data-analysis) package. Support for doing regressions is perhaps less good than Stata, and certainly a bit more verbose-but you can still do pretty much every standard operation you can think of. Regardless of Python not being a programming language solely dedicated to data analysis, it really does have first class support for data analysis via its pandas package. This causes the first major notational differences in Python, you need to specify which dataframe you want to perform an operation on, in addition to which column (or row, or entry).įinally, Python and its data analysis packages are free. In Python, you can have as many DataFrames as you like in action at once. In Python, variables can be anything, even functions! But most data analysis in Python is done using dataframes, which are objects that are somewhat similar to a single dataset in Stata. There is greater competition for each command in Python because it does many more things.Īnother difference is that, in Stata, there is one dataset in memory that is represented as matrix where each column is a “variable” with a unique name. What this means in practice is that sometimes the notation to do this or that operation in Python (or any other general purpose programming language) is less concise than in Stata. The biggest difference between Python and Stata is that Python is a fully-fledged programming language, which means it can do lots of things, while Stata is really just for data analysis. This chapter has benefitted enormously from Daniel M. ![]()
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