Comparison with Stata

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 10 Minutes to pandas 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.

In [1]: import pandas as pd

In [2]: import numpy as np

list in 1/5

Data Structures

General Terminology Translation

DataFrame / Series

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.

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 indexing documentation 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

A 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.

In [3]: df = pd.DataFrame({
   ...:          'x': [1, 3, 5],
   ...:          'y': [2, 4, 6]})
   ...: 

In [4]: df
Out[4]: 
   x  y
0  1  2
1  3  4
2  5  6

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 read_csv(), which works similarly. Additionally, it will automatically download the data set if presented with a url.

In [5]: url = 'https://raw.github.com/pandas-dev/pandas/master/pandas/tests/data/tips.csv'

In [6]: tips = pd.read_csv(url)

In [7]: tips.head()
Out[7]: 
   total_bill   tip     sex smoker  day    time  size
0       16.99  1.01  Female     No  Sun  Dinner     2
1       10.34  1.66    Male     No  Sun  Dinner     3
2       21.01  3.50    Male     No  Sun  Dinner     3
3       23.68  3.31    Male     No  Sun  Dinner     2
4       24.59  3.61  Female     No  Sun  Dinner     4

Like import delimited, 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 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 IO documentation for more details.

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 DataFrame.to_csv().

tips.to_csv('tips2.csv')

Pandas can also export to Stata file format with the 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

pandas provides similar vectorized operations by specifying the individual Series in the DataFrame. New columns can be assigned in the same way. The DataFrame.drop() method drops a column from the DataFrame.

In [8]: tips['total_bill'] = tips['total_bill'] - 2

In [9]: tips['new_bill'] = tips['total_bill'] / 2

In [10]: tips.head()
Out[10]: 
   total_bill   tip     sex smoker  day    time  size  new_bill
0       14.99  1.01  Female     No  Sun  Dinner     2     7.495
1        8.34  1.66    Male     No  Sun  Dinner     3     4.170
2       19.01  3.50    Male     No  Sun  Dinner     3     9.505
3       21.68  3.31    Male     No  Sun  Dinner     2    10.840
4       22.59  3.61  Female     No  Sun  Dinner     4    11.295

In [11]: tips = tips.drop('new_bill', axis=1)

Filtering

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

list if total_bill > 10

DataFrames can be filtered in multiple ways; the most intuitive of which is using boolean indexing.

In [12]: tips[tips['total_bill'] > 10].head()
Out[12]: 
   total_bill   tip     sex smoker  day    time  size
0       14.99  1.01  Female     No  Sun  Dinner     2
2       19.01  3.50    Male     No  Sun  Dinner     3
3       21.68  3.31    Male     No  Sun  Dinner     2
4       22.59  3.61  Female     No  Sun  Dinner     4
5       23.29  4.71    Male     No  Sun  Dinner     4

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

The same operation in pandas can be accomplished using the where method from numpy.

In [13]: tips['bucket'] = np.where(tips['total_bill'] < 10, 'low', 'high')

In [14]: tips.head()
Out[14]: 
   total_bill   tip     sex smoker  day    time  size bucket
0       14.99  1.01  Female     No  Sun  Dinner     2   high
1        8.34  1.66    Male     No  Sun  Dinner     3    low
2       19.01  3.50    Male     No  Sun  Dinner     3   high
3       21.68  3.31    Male     No  Sun  Dinner     2   high
4       22.59  3.61  Female     No  Sun  Dinner     4   high

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 timeseries documentation for more details.

In [15]: tips['date1'] = pd.Timestamp('2013-01-15')

In [16]: tips['date2'] = pd.Timestamp('2015-02-15')

In [17]: tips['date1_year'] = tips['date1'].dt.year

In [18]: tips['date2_month'] = tips['date2'].dt.month

In [19]: tips['date1_next'] = tips['date1'] + pd.offsets.MonthBegin()

In [20]: tips['months_between'] = (tips['date2'].dt.to_period('M') -
   ....:                           tips['date1'].dt.to_period('M'))
   ....: 

In [21]: tips[['date1','date2','date1_year','date2_month',
   ....:       'date1_next','months_between']].head()
   ....: 
Out[21]: 
       date1      date2  date1_year  date2_month date1_next months_between
0 2013-01-15 2015-02-15        2013            2 2013-02-01             25
1 2013-01-15 2015-02-15        2013            2 2013-02-01             25
2 2013-01-15 2015-02-15        2013            2 2013-02-01             25
3 2013-01-15 2015-02-15        2013            2 2013-02-01             25
4 2013-01-15 2015-02-15        2013            2 2013-02-01             25

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

The 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.

# keep
In [22]: tips[['sex', 'total_bill', 'tip']].head()
Out[22]: 
      sex  total_bill   tip
0  Female       14.99  1.01
1    Male        8.34  1.66
2    Male       19.01  3.50
3    Male       21.68  3.31
4  Female       22.59  3.61

# drop
In [23]: tips.drop('sex', axis=1).head()