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For potential users coming from Stata this page is meant to demonstrate how different Stata operations would be performed in pandas.
If you're new to pandas, you might want to first read through :ref:`10 Minutes to pandas<10min>` to familiarize yourself with the library.
As is customary, we import pandas and NumPy as follows. This means that we can refer to the
libraries as pd and np, respectively, for the rest of the document.
.. ipython:: python
import pandas as pd
import numpy as np
Note
Throughout this tutorial, the pandas DataFrame will be displayed by calling
df.head(), which displays the first N (default 5) rows of the DataFrame.
This is often used in interactive work (e.g. Jupyter notebook or terminal) -- the equivalent in Stata would be:
list in 1/5| pandas | Stata |
|---|---|
DataFrame |
data set |
| column | variable |
| row | observation |
| groupby | bysort |
NaN |
. |
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.
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.
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.
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
endA pandas DataFrame can be constructed in many different ways,
but for a small number of values, it is often convenient to specify it as
a Python dictionary, where the keys are the column names
and the values are the data.
.. ipython:: python
df = pd.DataFrame({'x': [1, 3, 5], 'y': [2, 4, 6]})
df
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.csvThe 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/data/tips.csv')
tips = pd.read_csv(url)
tips.head()
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.
The inverse of import delimited in Stata is export delimited
export delimited tips2.csvSimilarly 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')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_billpandas provides similar vectorized operations by
specifying the individual Series in the DataFrame.
New columns can be assigned in the same way. The :meth:`DataFrame.drop` method
drops a column from the DataFrame.
.. ipython:: python
tips['total_bill'] = tips['total_bill'] - 2
tips['new_bill'] = tips['total_bill'] / 2
tips.head()
tips = tips.drop('new_bill', axis=1)
Filtering in Stata is done with an if clause on one or more columns.
list if total_bill > 10DataFrames can be filtered in multiple ways; the most intuitive of which is using :ref:`boolean indexing <indexing.boolean>`.
.. ipython:: python tips[tips['total_bill'] > 10].head()
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 >= 10The same operation in pandas can be accomplished using
the where method from numpy.
.. ipython:: python tips['bucket'] = np.where(tips['total_bill'] < 10, 'low', 'high') tips.head()
.. ipython:: python
:suppress:
tips = tips.drop('bucket', axis=1)
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_betweenThe 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.
.. ipython:: python
tips['date1'] = pd.Timestamp('2013-01-15')
tips['date2'] = pd.Timestamp('2015-02-15')
tips['date1_year'] = tips['date1'].dt.year
tips['date2_month'] = tips['date2'].dt.month
tips['date1_next'] = tips['date1'] + pd.offsets.MonthBegin()
tips['months_between'] = (tips['date2'].dt.to_period('M')
- tips['date1'].dt.to_period('M'))
tips[['date1', 'date2', 'date1_year', 'date2_month', 'date1_next',
'months_between']].head()
.. ipython:: python
:suppress:
tips = tips.drop(['date1', 'date2', 'date1_year', 'date2_month',
'date1_next', 'months_between'], axis=1)
Stata provides keywords to select, drop, and rename columns.
keep sex total_bill tip
drop sex
rename total_bill total_bill_2The same operations are expressed in pandas below. Note that in contrast to Stata, these operations do not happen in place. To make these changes persist, assign the operation back to a variable.
.. ipython:: python
# keep
tips[['sex', 'total_bill', 'tip']].head()
# drop
tips.drop('sex', axis=1).head()
# rename
tips.rename(columns={'total_bill': 'total_bill_2'}).head()
Sorting in Stata is accomplished via sort
sort sex total_billpandas objects have a :meth:`DataFrame.sort_values` method, which takes a list of columns to sort by.
.. ipython:: python tips = tips.sort_values(['sex', 'total_bill']) tips.head()
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)Python determines the length of a character string with the len function.
In Python 3, all strings are Unicode strings. len includes trailing blanks.
Use len and rstrip to exclude trailing blanks.
.. ipython:: python tips['time'].str.len().head() tips['time'].str.rstrip().str.len().head()
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")Python determines the position of a character in a string with the
:func:`find` function. find searches for the first position of the
substring. If the substring is found, the function returns its
position. Keep in mind that Python indexes are zero-based and
the function will return -1 if it fails to find the substring.
.. ipython:: python
tips['sex'].str.find("ale").head()
Stata extracts a substring from a string based on its position with the :func:`substr` function.
generate short_sex = substr(sex, 1, 1)With pandas you can use [] notation to extract a substring
from a string by position locations. Keep in mind that Python
indexes are zero-based.
.. ipython:: python tips['sex'].str[0:1].head()
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)Python extracts a substring from a string based on its text by using regular expressions. There are much more powerful approaches, but this just shows a simple approach.
.. ipython:: python
firstlast = pd.DataFrame({'string': ['John Smith', 'Jane Cook']})
firstlast['First_Name'] = firstlast['string'].str.split(" ", expand=True)[0]
firstlast['Last_Name'] = firstlast['string'].str.rsplit(" ", expand=True)[0]
firstlast
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)
listThe equivalent Python functions are upper, lower, and title.
.. ipython:: python
firstlast = pd.DataFrame({'string': ['John Smith', 'Jane Cook']})
firstlast['upper'] = firstlast['string'].str.upper()
firstlast['lower'] = firstlast['string'].str.lower()
firstlast['title'] = firstlast['string'].str.title()
firstlast
The following tables will be used in the merge examples
.. ipython:: python
df1 = pd.DataFrame({'key': ['A', 'B', 'C', 'D'],
'value': np.random.randn(4)})
df1
df2 = pd.DataFrame({'key': ['B', 'D', 'D', 'E'],
'value': np.random.randn(4)})
df2
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.dtapandas DataFrames have a :meth:`DataFrame.merge` method, which provides
similar functionality. Note that different join
types are accomplished via the how keyword.
.. ipython:: python inner_join = df1.merge(df2, on=['key'], how='inner') inner_join left_join = df1.merge(df2, on=['key'], how='left') left_join right_join = df1.merge(df2, on=['key'], how='right') right_join outer_join = df1.merge(df2, on=['key'], how='outer') outer_join
Like Stata, pandas has a representation for missing data -- the
special float value NaN (not a number). Many of the semantics
are the same; for example missing data propagates through numeric
operations, and is ignored by default for aggregations.
.. ipython:: python outer_join outer_join['value_x'] + outer_join['value_y'] outer_join['value_x'].sum()
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 != .This doesn't work in pandas. Instead, the :func:`pd.isna` or :func:`pd.notna` functions should be used for comparisons.
.. ipython:: python outer_join[pd.isna(outer_join['value_x'])] outer_join[pd.notna(outer_join['value_x'])]
Pandas also provides a variety of methods to work with missing data -- some of which would be challenging to express in Stata. For example, there are methods to drop all rows with any missing values, replacing missing values with a specified value, like the mean, or forward filling from previous rows. See the :ref:`missing data documentation<missing_data>` for more.
.. ipython:: python # Drop rows with any missing value outer_join.dropna() # Fill forwards outer_join.fillna(method='ffill') # Impute missing values with the mean outer_join['value_x'].fillna(outer_join['value_x'].mean())
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)pandas provides a flexible groupby mechanism that
allows similar aggregations. See the :ref:`groupby documentation<groupby>`
for more details and examples.
.. ipython:: python tips_summed = tips.groupby(['sex', 'smoker'])['total_bill', 'tip'].sum() tips_summed.head()
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_billpandas groubpy provides a transform mechanism that allows
these type of operations to be succinctly expressed in one
operation.
.. ipython:: python
gb = tips.groupby('smoker')['total_bill']
tips['adj_total_bill'] = tips['total_bill'] - gb.transform('mean')
tips.head()
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 == 1In pandas this would be written as:
.. ipython:: python tips.groupby(['sex', 'smoker']).first()
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.