comparison_with_stata.rst

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Comparison with Stata

For potential users coming from Stata this page is meant to demonstrate how different Stata operations would be performed in pandas.

Data structures

General terminology translation

pandas	Stata
DataFrame	data set
column	variable
row	observation
groupby	bysort
NaN	.

DataFrame

A DataFrame in pandas is analogous to a Stata data set -- a two-dimensional data source with labeled columns that can be of different types. As will be shown in this document, almost any operation that can be applied to a data set in Stata can also be accomplished in pandas.

Series

A Series is the data structure that represents one column of a DataFrame. Stata doesn't have a separate data structure for a single column, but in general, working with a Series is analogous to referencing a column of a data set in Stata.

Index

Every DataFrame and Series has an Index -- labels on the rows of the data. Stata does not have an exactly analogous concept. In Stata, a data set's rows are essentially unlabeled, other than an implicit integer index that can be accessed with _n.

In pandas, if no index is specified, an integer index is also used by default (first row = 0, second row = 1, and so on). While using a labeled Index or MultiIndex can enable sophisticated analyses and is ultimately an important part of pandas to understand, for this comparison we will essentially ignore the Index and just treat the DataFrame as a collection of columns. Please see the :ref:`indexing documentation<indexing>` for much more on how to use an Index effectively.

Data input / output

Constructing a DataFrame from values

A Stata data set can be built from specified values by placing the data after an input statement and specifying the column names.

input x y
1 2
3 4
5 6
end

Reading external data

Like Stata, pandas provides utilities for reading in data from many formats. The tips data set, found within the pandas tests (csv) will be used in many of the following examples.

Stata provides import delimited to read csv data into a data set in memory. If the tips.csv file is in the current working directory, we can import it as follows.

import delimited tips.csv

The pandas method is :func:`read_csv`, which works similarly. Additionally, it will automatically download the data set if presented with a url.

.. ipython:: python

   url = (
       "https://raw.github.com/pandas-dev"
       "/pandas/master/pandas/tests/io/data/csv/tips.csv"
   )
   tips = pd.read_csv(url)
   tips

Like import delimited, :func:`read_csv` can take a number of parameters to specify how the data should be parsed. For example, if the data were instead tab delimited, did not have column names, and existed in the current working directory, the pandas command would be:

tips = pd.read_csv("tips.csv", sep="\t", header=None)

# alternatively, read_table is an alias to read_csv with tab delimiter
tips = pd.read_table("tips.csv", header=None)

pandas can also read Stata data sets in .dta format with the :func:`read_stata` function.

df = pd.read_stata("data.dta")

In addition to text/csv and Stata files, pandas supports a variety of other data formats such as Excel, SAS, HDF5, Parquet, and SQL databases. These are all read via a pd.read_* function. See the :ref:`IO documentation<io>` for more details.

Limiting output

The equivalent in Stata would be:

list in 1/5

Exporting data

The inverse of import delimited in Stata is export delimited

export delimited tips2.csv

Similarly in pandas, the opposite of read_csv is :meth:`DataFrame.to_csv`.

tips.to_csv("tips2.csv")

pandas can also export to Stata file format with the :meth:`DataFrame.to_stata` method.

tips.to_stata("tips2.dta")

Data operations

Operations on columns

In Stata, arbitrary math expressions can be used with the generate and replace commands on new or existing columns. The drop command drops the column from the data set.

replace total_bill = total_bill - 2
generate new_bill = total_bill / 2
drop new_bill

Filtering

Filtering in Stata is done with an if clause on one or more columns.

list if total_bill > 10

If/then logic

In Stata, an if clause can also be used to create new columns.

generate bucket = "low" if total_bill < 10
replace bucket = "high" if total_bill >= 10

Date functionality

Stata provides a variety of functions to do operations on date/datetime columns.

generate date1 = mdy(1, 15, 2013)
generate date2 = date("Feb152015", "MDY")

generate date1_year = year(date1)
generate date2_month = month(date2)

* shift date to beginning of next month
generate date1_next = mdy(month(date1) + 1, 1, year(date1)) if month(date1) != 12
replace date1_next = mdy(1, 1, year(date1) + 1) if month(date1) == 12
generate months_between = mofd(date2) - mofd(date1)

list date1 date2 date1_year date2_month date1_next months_between

The equivalent pandas operations are shown below. In addition to these functions, pandas supports other Time Series features not available in Stata (such as time zone handling and custom offsets) -- see the :ref:`timeseries documentation<timeseries>` for more details.

Selection of columns

Stata provides keywords to select, drop, and rename columns.

keep sex total_bill tip

drop sex

rename total_bill total_bill_2

Sorting by values

Sorting in Stata is accomplished via sort

sort sex total_bill

String processing

Finding length of string

Stata determines the length of a character string with the :func:`strlen` and :func:`ustrlen` functions for ASCII and Unicode strings, respectively.

generate strlen_time = strlen(time)
generate ustrlen_time = ustrlen(time)

Finding position of substring

Stata determines the position of a character in a string with the :func:`strpos` function. This takes the string defined by the first argument and searches for the first position of the substring you supply as the second argument.

generate str_position = strpos(sex, "ale")

Extracting substring by position

Stata extracts a substring from a string based on its position with the :func:`substr` function.

generate short_sex = substr(sex, 1, 1)

Extracting nth word

The Stata :func:`word` function returns the nth word from a string. The first argument is the string you want to parse and the second argument specifies which word you want to extract.

clear
input str20 string
"John Smith"
"Jane Cook"
end

generate first_name = word(name, 1)
generate last_name = word(name, -1)

Changing case

The Stata :func:`strupper`, :func:`strlower`, :func:`strproper`, :func:`ustrupper`, :func:`ustrlower`, and :func:`ustrtitle` functions change the case of ASCII and Unicode strings, respectively.

clear
input str20 string
"John Smith"
"Jane Cook"
end

generate upper = strupper(string)
generate lower = strlower(string)
generate title = strproper(string)
list

Merging

In Stata, to perform a merge, one data set must be in memory and the other must be referenced as a file name on disk. In contrast, Python must have both DataFrames already in memory.

By default, Stata performs an outer join, where all observations from both data sets are left in memory after the merge. One can keep only observations from the initial data set, the merged data set, or the intersection of the two by using the values created in the _merge variable.

* First create df2 and save to disk
clear
input str1 key
B
D
D
E
end
generate value = rnormal()
save df2.dta

* Now create df1 in memory
clear
input str1 key
A
B
C
D
end
generate value = rnormal()

preserve

* Left join
merge 1:n key using df2.dta
keep if _merge == 1

* Right join
restore, preserve
merge 1:n key using df2.dta
keep if _merge == 2

* Inner join
restore, preserve
merge 1:n key using df2.dta
keep if _merge == 3

* Outer join
restore
merge 1:n key using df2.dta

Missing data

Both pandas and Stata have a representation for missing data.

One difference is that missing data cannot be compared to its sentinel value. For example, in Stata you could do this to filter missing values.

* Keep missing values
list if value_x == .
* Keep non-missing values
list if value_x != .

GroupBy

Aggregation

Stata's collapse can be used to group by one or more key variables and compute aggregations on numeric columns.

collapse (sum) total_bill tip, by(sex smoker)

Transformation

In Stata, if the group aggregations need to be used with the original data set, one would usually use bysort with :func:`egen`. For example, to subtract the mean for each observation by smoker group.

bysort sex smoker: egen group_bill = mean(total_bill)
generate adj_total_bill = total_bill - group_bill

By group processing

In addition to aggregation, pandas groupby can be used to replicate most other bysort processing from Stata. For example, the following example lists the first observation in the current sort order by sex/smoker group.

bysort sex smoker: list if _n == 1

In pandas this would be written as:

.. ipython:: python

   tips.groupby(["sex", "smoker"]).first()

Other considerations

Disk vs memory

pandas and Stata both operate exclusively in memory. This means that the size of data able to be loaded in pandas is limited by your machine's memory. If out of core processing is needed, one possibility is the dask.dataframe library, which provides a subset of pandas functionality for an on-disk DataFrame.