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

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

Data Structures

General Terminology Translation

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

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_next  months_between
0 2013-01-15 2015-02-15       ...        2013-02-01              25
1 2013-01-15 2015-02-15       ...        2013-02-01              25
2 2013-01-15 2015-02-15       ...        2013-02-01              25
3 2013-01-15 2015-02-15       ...        2013-02-01              25
4 2013-01-15 2015-02-15       ...        2013-02-01              25

[5 rows x 6 columns]

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()
Out[23]: 
   total_bill   tip smoker  day    time  size
0       14.99  1.01     No  Sun  Dinner     2
1        8.34  1.66     No  Sun  Dinner     3
2       19.01  3.50     No  Sun  Dinner     3
3       21.68  3.31     No  Sun  Dinner     2
4       22.59  3.61     No  Sun  Dinner     4

# rename
In [24]: tips.rename(columns={'total_bill': 'total_bill_2'}).head()
Out[24]: 
   total_bill_2   tip     sex smoker  day    time  size
0         14.99  1.01  Female     No  Sun  Dinner     2
1          8.34  1.66    Male     No  Sun  Dinner     3
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

Sorting by Values

Sorting in Stata is accomplished via sort

sort sex total_bill

pandas objects have a DataFrame.sort_values() method, which takes a list of columns to sort by.

In [25]: tips = tips.sort_values(['sex', 'total_bill'])

In [26]: tips.head()
Out[26]: 
     total_bill   tip     sex smoker   day    time  size
67         1.07  1.00  Female    Yes   Sat  Dinner     1
92         3.75  1.00  Female    Yes   Fri  Dinner     2
111        5.25  1.00  Female     No   Sat  Dinner     1
145        6.35  1.50  Female     No  Thur   Lunch     2
135        6.51  1.25  Female     No  Thur   Lunch     2

String Processing

Finding Length of String

Stata determines the length of a character string with the strlen() and 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.

In [27]: tips['time'].str.len().head()
Out[27]: 
67     6
92     6
111    6
145    5
135    5
Name: time, dtype: int64

In [28]: tips['time'].str.rstrip().str.len().head()
Out[28]: 
67     6
92     6
111    6
145    5
135    5
Name: time, dtype: int64

Finding Position of Substring

Stata determines the position of a character in a string with the 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 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.

In [29]: tips['sex'].str.find("ale").head()
Out[29]: 
67     3
92     3
111    3
145    3
135    3
Name: sex, dtype: int64

Extracting Substring by Position

Stata extracts a substring from a string based on its position with the 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.

In [30]: tips['sex'].str[0:1].head()
Out[30]: 
67     F
92     F
111    F
145    F
135    F
Name: sex, dtype: object

Extracting nth Word

The Stata 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.

In [31]: firstlast = pd.DataFrame({'string': ['John Smith', 'Jane Cook']})

In [32]: firstlast['First_Name'] = firstlast['string'].str.split(" ", expand=True)[0]

In [33]: firstlast['Last_Name'] = firstlast['string'].str.rsplit(" ", expand=True)[0]

In [34]: firstlast
Out[34]: 
       string First_Name Last_Name
0  John Smith       John      John
1   Jane Cook       Jane      Jane

Changing Case

The Stata strupper(), strlower(), strproper(), ustrupper(), ustrlower(), and 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

The equivalent Python functions are upper, lower, and title.

In [35]: firstlast = pd.DataFrame({'string': ['John Smith', 'Jane Cook']})

In [36]: firstlast['upper'] = firstlast['string'].str.upper()

In [37]: firstlast['lower'] = firstlast['string'].str.lower()

In [38]: firstlast['title'] = firstlast['string'].str.title()

In [39]: firstlast
Out[39]: 
       string       upper       lower       title
0  John Smith  JOHN SMITH  john smith  John Smith
1   Jane Cook   JANE COOK   jane cook   Jane Cook

Merging

The following tables will be used in the merge examples

In [40]: df1 = pd.DataFrame({'key': ['A', 'B', 'C', 'D'],
   ....:                     'value': np.random.randn(4)})
   ....: 

In [41]: df1
Out[41]: 
  key     value
0   A  0.885906
1   B  0.794848
2   C -0.943848
3   D  0.328609

In [42]: df2 = pd.DataFrame({'key': ['B', 'D', 'D', 'E'],
   ....:                      'value': np.random.randn(4)})
   ....: 

In [43]: df2
Out[43]: 
  key     value
0   B -1.634931
1   D  2.197567
2   D  0.054695
3   E  0.283297

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

pandas DataFrames have a DataFrame.merge() method, which provides similar functionality. Note that different join types are accomplished via the how keyword.

In [44]: inner_join = df1.merge(df2, on=['key'], how='inner')

In [45]: inner_join
Out[45]: 
  key   value_x   value_y
0   B  0.794848 -1.634931
1   D  0.328609  2.197567
2   D  0.328609  0.054695

In [46]: left_join = df1.merge(df2, on=['key'], how='left')

In [47]: left_join
Out[47]: 
  key   value_x   value_y
0   A  0.885906       NaN
1   B  0.794848 -1.634931
2   C -0.943848       NaN
3   D  0.328609  2.197567
4   D  0.328609  0.054695

In [48]: right_join = df1.merge(df2, on=['key'], how='right')

In [49]: right_join
Out[49]: 
  key   value_x   value_y
0   B  0.794848 -1.634931
1   D  0.328609  2.197567
2   D  0.328609  0.054695
3   E       NaN  0.283297

In [50]: outer_join = df1.merge(df2, on=['key'], how='outer')

In [51]: outer_join
Out[51]: 
  key   value_x   value_y
0   A  0.885906       NaN
1   B  0.794848 -1.634931
2   C -0.943848       NaN
3   D  0.328609  2.197567
4   D  0.328609  0.054695
5   E       NaN  0.283297

Missing Data

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.

In [52]: outer_join
Out[52]: 
  key   value_x   value_y
0   A  0.885906       NaN
1   B  0.794848 -1.634931
2   C -0.943848       NaN
3   D  0.328609  2.197567
4   D  0.328609  0.054695
5   E       NaN  0.283297

In [53]: outer_join['value_x'] + outer_join['value_y']
Out[53]: 
0         NaN
1   -0.840083
2         NaN
3    2.526176
4    0.383304
5         NaN
dtype: float64

In [54]: outer_join['value_x'].sum()
Out[54]: 1.3941243310085349

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 pd.isna() or pd.notna() functions should be used for comparisons.

In [55]: outer_join[pd.isna(outer_join['value_x'])]
Out[55]: 
  key  value_x   value_y
5   E      NaN  0.283297

In [56]: outer_join[pd.notna(outer_join['value_x'])]
Out[56]: 
  key   value_x   value_y
0   A  0.885906       NaN
1   B  0.794848 -1.634931
2   C -0.943848       NaN
3   D  0.328609  2.197567
4   D  0.328609  0.054695

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 missing data documentation for more.

# Drop rows with any missing value
In [57]: outer_join.dropna()
Out[57]: 
  key   value_x   value_y
1   B  0.794848 -1.634931
3   D  0.328609  2.197567
4   D  0.328609  0.054695

# Fill forwards
In [58]: outer_join.fillna(method='ffill')
Out[58]: 
  key   value_x   value_y
0   A  0.885906       NaN
1   B  0.794848 -1.634931
2   C -0.943848 -1.634931
3   D  0.328609  2.197567
4   D  0.328609  0.054695
5   E  0.328609  0.283297

# Impute missing values with the mean
In [59]: outer_join['value_x'].fillna(outer_join['value_x'].mean())
Out[59]: 
0    0.885906
1    0.794848
2   -0.943848
3    0.328609
4    0.328609
5    0.278825
Name: value_x, dtype: float64

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)

pandas provides a flexible groupby mechanism that allows similar aggregations. See the groupby documentation for more details and examples.

In [60]: tips_summed = tips.groupby(['sex', 'smoker'])['total_bill', 'tip'].sum()

In [61]: tips_summed.head()
Out[61]: 
               total_bill     tip
sex    smoker                    
Female No          869.68  149.77
       Yes         527.27   96.74
Male   No         1725.75  302.00
       Yes        1217.07  183.07

Transformation

In Stata, if the group aggregations need to be used with the original data set, one would usually use bysort with 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

pandas groubpy provides a transform mechanism that allows these type of operations to be succinctly expressed in one operation.

In [62]: gb = tips.groupby('smoker')['total_bill']

In [63]: tips['adj_total_bill'] = tips['total_bill'] - gb.transform('mean')

In [64]: tips.head()
Out[64]: 
     total_bill   tip     sex      ...          time size adj_total_bill
67         1.07  1.00  Female      ...        Dinner    1     -17.686344
92         3.75  1.00  Female      ...        Dinner    2     -15.006344
111        5.25  1.00  Female      ...        Dinner    1     -11.938278
145        6.35  1.50  Female      ...         Lunch    2     -10.838278
135        6.51  1.25  Female      ...         Lunch    2     -10.678278

[5 rows x 8 columns]

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:

In [65]: tips.groupby(['sex','smoker']).first()
Out[65]: 
               total_bill   tip      ...       size adj_total_bill
sex    smoker                        ...                          
Female No            5.25  1.00      ...          1     -11.938278
       Yes           1.07  1.00      ...          1     -17.686344
Male   No            5.51  2.00      ...          2     -11.678278
       Yes           5.25  5.15      ...          2     -13.506344

[4 rows x 6 columns]

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.

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