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https://github.com/Roche/pyreadstat

Python package to read sas, spss and stata files into pandas data frames. It is a wrapper for the C library readstat.
https://github.com/Roche/pyreadstat

conversion pandas-dataframe python readstat sas7bdat spss stata-files

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Python package to read sas, spss and stata files into pandas data frames. It is a wrapper for the C library readstat.

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README 

        
# pyreadstat



A python package to read and write sas (sas7bdat, sas7bcat, xport), spps (sav, zsav, por) and stata (dta) data files

into/from pandas dataframes.





This module is a wrapper around the excellent [Readstat](https://github.com/WizardMac/ReadStat) C library by

[Evan Miller](https://www.evanmiller.org/). Readstat is the library used in the back of the R library

[Haven](https://github.com/tidyverse/haven),

meaning pyreadstat is a python equivalent to R Haven.



Detailed documentation on all available methods is in the

[Module documentation](https://ofajardo.github.io/pyreadstat_documentation/_build/html/index.html)



If you would like to read R RData and Rds files into python in an easy way,

take a look to [pyreadr](https://github.com/ofajardo/pyreadr), a wrapper

around the C library [librdata](https://github.com/WizardMac/librdata)



**DISCLAIMER**



**Pyreadstat is not a validated package. The results may have inaccuracies deriving from the fact most of the data formats

are not open. Do not use it for critical tasks such as reporting to the authorities. Pyreadstat is not meant to replace

the original applications in this regard.**  



## Table of Contents



* [Motivation](#motivation)

* [Dependencies](#dependencies)

* [Installation](#installation)

  + [Using pip](#using-pip)

  + [Using conda](#using-conda)

  + [From the latest sources](#from-the-latest-sources)

  + [Compiling on Windows and Mac](#compiling-on-windows-and-mac)

* [Usage](#usage)

  + [Basic Usage](#basic-usage)

    - [Reading Files](#reading-files)

    - [Writing Files](#writing-files)

  + [More reading options](#more-reading-options)

    - [Reading only the headers](#reading-only-the-headers)

    - [Reading selected columns](#reading-selected-columns)

    - [Reading files in parallel processes](#reading-files-in-parallel-processes)

    - [Reading rows in chunks](#reading-rows-in-chunks)

    - [Reading value labels](#reading-value-labels)

    - [Missing Values](#missing-values)

      + [SPSS](#spss)

      + [SAS and STATA](#sas-and-stata)

    - [Reading datetime and date columns](#reading-datetime-and-date-columns)

    - [Other options](#other-options)

  + [More writing options](#more-writing-options)

    - [File specific options](#file-specific-options)

    - [Writing value labels](#writing-value-labels)

    - [Writing user defined missing values](#writing-user-defined-missing-values)

    - [Setting variable formats](#setting-variable-formats)

    - [Variable type conversion](#variable-type-conversion)

* [Roadmap](#roadmap)

* [CD/CI and wheels](#cdci_and_wheels)

* [Known limitations](#known-limitations)

* [Python 2.7 support.](#python-27-support)

* [Change log](#change-log)

* [License](#license)

* [Contributing](#contributing)

* [People](#people)



## Motivation



The original motivation came from reading sas7bdat files in python. That is already possible using either the (pure

python) package [sas7bdat](https://pypi.org/project/sas7bdat/) or the (cythonized) method

[read_sas](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_sas.html)

from pandas. However, those methods are slow (important if you want to read several large files), do not give the

possibility to recover value labels (stored in

the file itself in the case of spss or stata, or in catalog files in sas), convert both dates and datetime variables to datetime,

and you have to specify the encoding otherwise in python 3 instead of strings you get bytes.



This package corrects those problems.



**1. Good Performance:** Here a comparison of reading a 190 Mb sas7dat file having 202 K rows 

by 70 columns with numeric, character and date-like columns using different methods. As you can see

pyreadstat is the fastest for python and matches the speeds of R Haven.



| Method | time  |

| :----- | :-----------------: |

| Python 3 - sas7bdat | 6 min |

| Python 3- pandas | 42 s |

| Python 3- pyreadstat | 7 s  |

| R - Haven | 7 s |



**2. Reading Value Labels** Neither sas7bdat and pandas.read_sas gives the possibility to read sas7bcat catalog files.

Pyreadstat can do that and also extract value labels from SPSS and STATA files.



**3. Reading dates and datetimes** sas7bdat and pandas.read_sas convert both date and datetime variables into datetime.

That means if you have a date such a '01-01-2018' it will be transformed to '01-01-2018 00:00:00' (it always inserts a

time), making it impossible

to know looking only at the data if the variable was originally a datetime (if it had a time) or not.

Pyreadstat transforms dates to dates and datetimes to datetimes, so that you have a better correspondence with the original

data. However, it is possible to keep the original pandas behavior and get always datetimes.



**4. Encoding** On python 3, pandas.read_sas reads all strings as bytes. If you want strings you have to specify the encoding manually.

pyreadstat read strings as str. Thas is possible because readstat extracts the original encoding and translates

to utf-8, so that you don't have to care about that anymore. However it is still possible to manually set the encoding.



In addition pyreadstat exposes the variable labels in an easy way (see later). As pandas dataframes cannot handle value

labels, you as user will have to take the decision whether to use those values or not. Pandas read_sas reads those labels,

but in order to recover them you have to work a bit harder.



Compared to R Haven, pyreadstat offers the possibility to read only the headers: Sometimes you want to take a

look to many (sas) files looking for the datasets that contain

some specific columns, and you want to do it quick. This package offers the possibility to read only the metadata making

it possible a very fast metadata scraping (Pandas read_sas can also do it if you pass the value iterator=True).

In addition it offers the capability to read sas7bcat files separately from the sas7bdat files.



More recently there has been a lot of interest from users on using pyreadstat to read SPSS sav files. After improvements

in pyreadstat 1.0.3 below some benchmarks are presented. The small file is 200K rows x 100 columns (152 Mb)

containing only numeric columns  and

the big file is 294K rows x 666 columns (1.5 Gb). There are two versions of the big file: one containing numeric

columns only and one with a mix of numeric and character. Pyreadstat gives two ways to read files: reading in

a single process using read_sav and reading it in multiple processes using read_file_multiprocessing (see later

in the readme for more information).



| Method | small  | big numeric | big mixed |

| :----- | :----: | :---------: | :-------: |

| pyreadstat read_sav | 2.3 s | 28 s | 40 s |

| pyreadstat read_file_multiprocessing | 0.8 s | 10 s | 21 s |



As you see performance degrades in pyreadstat when reading a table with both numeric and character types. This

is because numpy and pandas do not have a native type for strings but they use a generic object type which

brings a big hit in performance. The situation can be improved tough by reading files in multiple processes.



## Dependencies



The module depends on pandas, which you normally have installed if you got Anaconda (highly recommended.)



In order to compile from source you will need a C compiler (see installation).

Only if you want to do changes to the cython source code, you will need cython (normally not necessary).

If you want to compile for python 2.7 or windows, you will need cython (see python 2.7 support

later).



Readstat depends on the C library iconv to handle character encodings. On mac, the library is found on the system, but

users have sometimes reported problems. In those cases it may help to install libiconv with conda (see later, compilation

on mac). Readstat also depends on zlib; it was reported not to be installed by default on Lubuntu. If you face this problem installing the

library solves it.



## Installation



### Using pip



Probably the easiest way: from your conda, virtualenv or just base installation do:



```

pip install pyreadstat

```



If you are running on a machine without admin rights, and you want to install against your base installation you can do:



```

pip install pyreadstat --user

```



At the moment we offer pre-compiled wheels for windows, mac and

linux. Look at the [pypi webpage](https://pypi.org/project/pyreadstat/) to find out which python versions

are currently supported. If there is no pre-compiled

wheel available, pip will attempt to compile the source code.



### Using conda



The package is also available in [conda-forge](https://anaconda.org/conda-forge/pyreadstat) for windows, mac and linux

64 bit. Visit the Conda forge webpage to find out which python versions are currently supported.



In order to install:



```

conda install -c conda-forge pyreadstat

```



### From the latest sources



Download or clone the repo, open a command window and type:



```

python3 setup.py install

```



If you don't have admin privileges to the machine (for example on Bee) do:



```

python3 setup.py install --user

```



You can also install from the github repo directly (without cloning). Use the flag --user if necessary.



```

pip install git+https://github.com/Roche/pyreadstat.git

```



You need a working C compiler and cython >=3.0.0.



### Compiling on Windows and Mac



Compiling on linux is very easy, but on windows you need some extra preparation.

Some instructions are found [here](https://github.com/Roche/pyreadstat/blob/master/windows_compilation.md)



Compiling on mac is usually easy. Readstat depends however on the C library iconv to handle character encodings; while

on linux is part of gclib, on mac it is a separated shared library found on the system (h file is in /usr/include and shared

library on /usr/lib). While compiling against this usually works fine, some users have reported problems (for example

missing symbol _iconv, or libiconv version too old). In those cases it helped to install libiconv with conda:



```

conda install libiconv

```



and then recompile again (be sure to delete any cache, if using pip do pip --no-cache-dir, if using setup.py remove

the folder build, otherwise you may be installing the old compilation again).



## Usage



### Basic Usage



#### Reading files



Pass the path to a file to any of the functions provided by pyreadstat. It will return a pandas data frame and a metadata

object. 


The dataframe uses the column names. The metadata object contains the column names, column labels, number_rows,

number_columns, file label

(if any), file encoding (if applicable), notes and objects about value labels (if present). Be aware that file_label and

file_encoding may be None, not all columns may have labels, notes may not be present and there may be no value labels.



For example, in order to read a sas7bdat file:



```python

import pyreadstat



df, meta = pyreadstat.read_sas7bdat('/path/to/a/file.sas7bdat')



# done! let's see what we got

print(df.head())

print(meta.column_names)

print(meta.column_labels)

print(meta.column_names_to_labels)

print(meta.number_rows)

print(meta.number_columns)

print(meta.file_label)

print(meta.file_encoding)

# there are other metadata pieces extracted. See the documentation for more details.

```



You can replace the column names by column labels very easily (but check first that all columns have distinct labels!):



```python

# replace column names with column labels

df.columns = meta.column_labels

# to go back to column names

df.columns = meta.column_names

```



#### Writing files



Pyreadstat can write STATA (dta), SPSS (sav and zsav, por currently nor supported) and SAS (Xport, sas7bdat and sas7bcat

currently not supported) files from pandas data frames.



write functions take as first argument a pandas data frame (other data structures are not supported), as a second argument

the path to the destination file. Optionally you can also pass a file label and a list with column labels.



```python

import pandas as pd

import pyreadstat



df = pd.DataFrame([[1,2.0,"a"],[3,4.0,"b"]], columns=["v1", "v2", "v3"])

# column_labels can also be a dictionary with variable name as key and label as value

column_labels = ["Variable 1", "Variable 2", "Variable 3"]

pyreadstat.write_sav(df, "path/to/destination.sav", file_label="test", column_labels=column_labels)

```



Some special arguments are available depending on the function. write_sav can take also notes as string, wheter to

compress or not as zsav or apply row compression, variable display widths and variable measures. write_dta can take a stata version.

write_xport a name for the dataset. User defined missing values and value labels are also supported. See the

[Module documentation](https://ofajardo.github.io/pyreadstat_documentation/_build/html/index.html) for more details.



Here there is a relation of all functions available.

You can also check the [Module documentation](https://ofajardo.github.io/pyreadstat_documentation/_build/html/index.html).



| Function in this package | Purpose |

| ------------------- | ----------- |

| read_sas7dat        | read SAS sas7bdat files |

| read_xport          | read SAS Xport (XPT) files |

| read_sas7bcat       | read SAS catalog files |

| read_dta            | read STATA dta files |

| read_sav            | read SPSS sav and zsav files  |

| read_por            | read SPSS por files  |

| set_catalog_to_sas  | enrich sas dataframe with catalog formats |

| set_value_labels    | replace values by their labels |

| read_file_in_chunks | generator to read files in chunks |

| write_sav           | write SPSS sav and zsav files |

| write_por           | write SPSS Portable (POR) files |

| write_dta           | write STATA dta files |

| write_xport         | write SAS Xport (XPT) files version 8 and 5 |



### More reading options



#### Reading only the headers



All functions accept a keyword argument "metadataonly" which by default is False. If True, then no data will be read,

but still both the metadata and the dataframe will be returned. The metadata will contain all fields as usual, but

the dataframe will be emtpy, although with the correct columns names. Sometimes number_rows may be None if it was not

possible to determine the number of rows without reading the data.



```python

import pyreadstat



df, meta = pyreadstat.read_sas7bdat('/path/to/a/file.sas7bdat', metadataonly=True)

```



#### Reading selected columns



All functions accept a keyword "usecols" which should be a list of column names. Only the columns which names match those

in the list will be imported (case sensitive). This decreases memory consumption and speeds up the process. Usecols must

always be a list, even if there is only one member.



```python

import pyreadstat



df, meta = pyreadstat.read_sas7bdat('/path/to/a/file.sas7bdat', usecols=["variable1", "variable2"])



```



#### Reading files in parallel processes



A challenge when reading large files is the time consumed in the operation. In order to alleviate this

pyreadstat provides a function "read\_file\_multiprocessing" to read a file in parallel processes using

 the python multiprocessing library. As it reads the whole file in one go you need to have enough RAM for the operation. If

that is not the case look at Reading rows in chunks (next section)



Speed ups in the process will depend on a number of factors such as number of processes available, RAM, 

content of the file etc.



```python

import pyreadstat



fpath = "path/to/file.sav" 

df, meta = pyreadstat.read_file_multiprocessing(pyreadstat.read_sav, fpath, num_processes=4) 

```



num_processes is the number of workers and it defaults to 4 (or the number of cores if less than 4). You can play with it to see where you 

get the best performance. You can also get the number of all available workers like this:



```

import multiprocessing

num_processes = multiprocessing.cpu_count()

```



**Notes for Xport, Por and some defective SAV files not having the number of rows in the metadata**

1. In all Xport, Por and some defective SAV files, the number of rows cannot be determined from the metadata. In such cases,

   you can use the parameter num\_rows to be equal or larger to the number of rows in the dataset. This number can be obtained

   reading the file without multiprocessing, reading in another application, etc.



**Notes for windows**



1. For this to work you must include a __name__ == "__main__" section in your script. See [this issue](#85)

for more details.



```

import pyreadstat



if __name__ == "__main__":

     df, meta = pyreadstat.read_file_multiprocessing(pyreadstat.read_sav, 'sample.sav')

```

2. If you include too many workers or you run out of RAM you main get a message about not enough page file

size. See [this issue](#87)



#### Reading rows in chunks



Reading large files with hundred of thouseds of rows can be challenging due to memory restrictions. In such cases, it may be helpful

to read the files in chunks.



Every reading function has two arguments row_limit and row_offset that help achieving this. row_offset makes to skip a number of rows before

start reading. row_limit makes to stop after a number of rows are read. Combining both you can read the file in chunks inside or outside a loop.



```python

import pyreadstat



df, meta = pyreadstat.read_sas7bdat("/path/to/file.sas7bdat", row_offset=1, row_limit=1)

# df will contain only the second row of the file

```



Pyreadstat also has a convienence function read_file_in_chunks, which returns a generator that helps you to iterate through the file in

chunks. This function takes as first argument a pyreadstat reading function and a second argument a path to a file. Optionally you can

change the size of the chunks with chunksize (default to 100000), and also add an offset and limit. You can use any keyword argument

you wish to pass to the pyreadstat reading function.



```python

import pyreadstat

fpath = "path/to/file.sas7bdat"

reader = pyreadstat.read_file_in_chunks(pyreadstat.read_sas7bdat, fpath, chunksize= 10, offset=2, limit=100, disable_datetime_conversion=True)



for df, meta in reader:

    print(df) # df will contain 10 rows except for the last one

    # do some cool calculations here for the chunk

```



For very large files it may be convienient to speed up the process by reading each chunks in parallel. For

this purpose you can pass the argument multiprocess=True. This is a combination of read_file_in_chunks and

read_file_multiprocessing. Here you can use the arguments row_offset and row_limit to start reading the

file from an offest and stop after a row_offset+row_limit. 



```python

import pyreadstat

fpath = "path/to/file.sav"

reader = pyreadstat.read_file_in_chunks(pyreadstat.read_sav, fpath, chunksize= 10000, multiprocess=True, num_processes=4)



for df, meta in reader:

    print(df) # df will contain 10000 rows except for the last one

    # do some cool calculations here for the chunk

```



**If using multiprocessing, please read the notes in the previous section regarding Xport, Por and some defective SAV files not

having the number of rows in the metadata**

 

**For Windows, please check the notes on the previous section reading files in parallel processes**



#### Reading value labels



For sas7bdat files, value labels are stored in separated sas7bcat files. You can use them in combination with the sas7bdat

or read them separately.



If you want to read them in combination with the sas7bdat files, pass the path to the sas7bcat files to the read_sas7bdat

function. The original values will be replaced by the values in the catalog.



```python

import pyreadstat



# formats_as_category is by default True, and it means the replaced values will be transformed to a pandas category column. There is also formats_as_ordered_category to get an ordered category, this by default is False.

df, meta = pyreadstat.read_sas7bdat('/path/to/a/file.sas7bdat', catalog_file='/path/to/a/file.sas7bcat', formats_as_category=True, formats_as_ordered_category=False)

```



If you prefer to read the sas7bcat file separately, you can apply the formats later with the function set_catalog_to_sas.

In this way you can have two copies of the dataframe, one with catalog and one without.



```python

import pyreadstat



# this df will have the original values

df, meta = pyreadstat.read_sas7bdat('/path/to/a/file.sas7bdat')

# read_sas7bdat returns an emtpy data frame and the catalog

df_empty, catalog = pyreadstat.read_sas7bdat('/path/to/a/file.sas7bcat')

# enrich the dataframe with the catalog

# formats_as_category is by default True, and it means the replaced values will be transformed to a pandas category column. formats_as_ordered_category is by default False meaning by default categories are not ordered.

df_enriched, meta_enriched = pyreadstat.set_catalog_to_sas(df, meta, catalog, 

                             formats_as_category=True, formats_as_ordered_category=False)

```



For SPSS and STATA files, the value labels are included in the files. You can choose to replace the values by the labels

when reading the file using the option apply_value_formats, ...



```python

import pyreadstat



# apply_value_formats is by default False, so you have to set it to True manually if you want the labels

# formats_as_category is by default True, and it means the replaced values will be transformed to a pandas category column. formats_as_ordered_category is by default False meaning by default categories are not ordered.

df, meta = pyreadstat.read_sav("/path/to/sav/file.sav", apply_value_formats=True, 

                                formats_as_category=True, formats_as_ordered_category=False)

```



... or to do it later with the function set_value_labels:



```python

import pyreadstat



# This time no value labels.

df, meta = pyreadstat.read_sav("/path/to/sav/file.sav", apply_value_formats=False)

# now let's add them to a second copy

df_enriched = pyreadstat.set_value_labels(df, meta, formats_as_category=True, formats_as_ordered_category=False)

```



Internally each variable is associated with a label set. This information is stored in meta.variable_to_label. Each

label set contains a map of the actual value in the variable to the label, this informtion is stored in

meta.variable_value_labels. By combining both you can get a dictionary of variable names to a dictionary of actual

values to labels.



For SPSS and STATA:



```python

import pyreadstat



df, meta = pyreadstat.read_sav("test_data/basic/sample.sav")

# the variables mylabl and myord are associated to the label sets labels0 and labels1 respectively

print(meta.variable_to_label)

#{'mylabl': 'labels0', 'myord': 'labels1'}



# labels0 and labels1 contain a dictionary of actual value to label

print(meta.value_labels)

#{'labels0': {1.0: 'Male', 2.0: 'Female'}, 'labels1': {1.0: 'low', 2.0: 'medium', 3.0: 'high'}}



# both things have been joined by pyreadstat for convienent use

print(meta.variable_value_labels)

#{'mylabl': {1.0: 'Male', 2.0: 'Female'}, 'myord': {1.0: 'low', 2.0: 'medium', 3.0: 'high'}}



```



SAS is very similar except that meta.variable_to_label comes from the sas7bdat file and meta.value_labels comes from the

sas7bcat file. That means if you read a sas7bdat file and a sas7bcat file togheter meta.variable_value_labels will be

filled in. If you read only the sas7bdat file only meta.variable_to_label will be available and if you read the

sas7bcat file only meta.value_labels will be available. If you read a sas7bdat file and there are no associated label

sets, SAS will assign by default the variable format as label sets.



```python

import pyreadstat



df, meta = pyreadstat.read_sas7bdat("test_data/sas_catalog/test_data_linux.sas7bdat")

meta.variable_to_label

{'SEXA': '$A', 'SEXB': '$B'}



df2, meta2 = pyreadstat.read_sas7bcat("test_data/sas_catalog/test_formats_linux.sas7bcat")

meta2.value_labels

{'$A': {'1': 'Male', '2': 'Female'}, '$B': {'2': 'Female', '1': 'Male'}}



```



#### Missing Values



There are two types of missing values: system and user defined. System are assigned by the program by default. User defined are

valid values that the user decided to give the meaning of missing in order to differentiate between several situations.For

example if one has a categorical variable representing if the person passed a test, you could have 0 for did not pass,

1 for pass, and as user defined missing variables 2 for did not show up for the test, 3 for unable to process the results,

etc.



**By default both cases are represented by NaN when

read with pyreadstat**. Notice that the only possible missing value in pandas is NaN (Not a Number) for both string and numeric

variables, date, datetime and time variables have NaT (Not a Time).



##### SPSS



In the case of SPSS sav files, the user can assign to a numeric variable either up to three discrete missing values or

one range plus one discrete missing value. As mentioned by default all of these possiblities are translated into NaN,

but one can get those original values by passing the argument user_missing=True to the read_sav function:



```python

# user set with default missing values

import pyreadstat

df, meta = pyreadstat.read_sav("/path/to/file.sav")

print(df)

>> test_passed

   1

   0

   NaN

   NaN

```



Now, reading the user defined missing values:



```python

# user set with user defined missing values

import pyreadstat

df, meta = pyreadstat.read_sav("/path/to/file.sav", user_missing=True)

print(df)

>> test_passed

   1

   0

   2

   3

```



As you see now instead o NaN the values 2 and 3 appear. In case the dataset had value labels, we could bring those in

```python

# user set with user defined missing values and labels

import pyreadstat

df, meta = pyreadstat.read_sav("/path/to/file.sav", user_missing=True, apply_value_formats=True)

print(df)

>> test_passed

   "passed"

   "not passed"

   "not shown"

   "not processed"

```



Finally, the information about what values are user missing is stored in the meta object, in the variable missing_ranges.

This is a dicitonary with the key being the name of the variable, and as value a list of dictionaries, each dictionary

contains the elements "hi" and "lo" to represent the lower and upper bound of the range, however for discrete values

as in the example, both boundaries are also present although the value is the same in both cases.



```python

# user set with default missing values

import pyreadstat

df, meta = pyreadstat.read_sav("/path/to/file.sav", user_missing=True, apply_value_formats=True)

print(meta.missing_ranges)

>>> {'test_passed':[{'hi':2, 'lo':2}, {'hi':3, 'lo':3}]}

```



SPSS sav files also support up to 3 discrete user defined missing values for non numeric (character) variables.

Pyreadstat is able to read those and the behavior is the same as for discrete

numerical user defined missing values. That means those values will be

translated as NaN by default and to the correspoding string value if

user_missing is set to True. meta.missing_ranges will show the string

value as well.



If the value in

a character variable is an empty string (''), it will not be translated to NaN, but will stay as an empty string. This

is because the empty string is a valid character value in SPSS and pyreadstat preserves that property. You can convert

empty strings to nan very easily with pandas if you think it is appropiate

for your dataset.



##### SAS and STATA



In SAS the user can assign values from .A to .Z and ._ as user defined missing values. In Stata values from

.a to .z. As in SPSS, those are normally translated to

NaN. However, using user_missing=True with read_sas7bdat or read_dta

will produce values from A to Z and _ for SAS and a to z for dta. In addition a variable

missing_user_values will appear in the metadata object, being a list with those values that are user defined missing

values.



```python

import pyreadstat



df, meta = pyreadstat.read_sas7bdat("/path/to/file.sas7bdat", user_missing=True)



df, meta = pyreadstat.read_dta("/path/to/file.dta", user_missing=True)



print(meta.missing_user_values)



```



The user may also assign a label to user defined missing values. In such

case passing the corresponding sas7bcat file to read_sas7bdat or using

the option apply_value_formats to read_dta will show those labels instead

of the user defined missing value.



```python

import pyreadstat



df, meta = pyreadstat.read_sas7bdat("/path/to/file.sas7bdat", catalog_file="/path/to/file.sas7bcat", user_missing=True)



df, meta = pyreadstat.read_dta("/path/to/file.dta", user_missing=True, apply_value_formats=True)



```



Empty strings are still transtaled as empty strings and not as NaN.



The information about what values are user missing is stored in the meta object, in the variable missing_user_values.

This is a list listing all user defined missing values.



User defined missing values are currently not supported for file types other than sas7bdat,

sas7bcat and dta.



#### Reading datetime and date columns



SAS, SPSS and STATA represent datetime, date and other similar concepts as a numeric column and then applies a 

display format on top. Roughly speaking, internally there are two possible representations: one for concepts with a day or lower 

granularity (date, week, quarter, year, etc.) and those with a higher granularity than a day (datetime, time, hour, etc).

The first group is suceptible to be converted to a python date object and the second to a python datetime object. 



Pyreadstat attempts to read columns with datetime, date and time formats that are convertible

to python datetime, date and time objects automatically. However there are other formats that are not fully convertible to

any of these formats, for example SAS "YEAR" (displaying only the year), "MMYY" (displaying only month and year), etc.

Because there are too many of these formats and these keep changing, it is not possible to implement a rule for each of

those, therefore these columns are not transformed and the user will obtain a numeric column. 



In order to cope with this issue, there are two options for each reader function: extra\_datetime\_formats and

 extra\_date\_formats that allow the user to

pass these datetime or date formats, to transform the numeric values into datetime or date python objects. Then, the user

can format those columns appropiately; for example extracting the year only to an integer column in the case of 'YEAR' or

formatting it to a string 'YYYY-MM' in the case of 'MMYY'. The choice between datetime or date format depends on the granularity

of the data as explained above.



This arguments are also useful in the case you have a valid datetime, date or time format that is currently not recognized in pyreadstat.

In those cases, feel free to file an issue to ask those to be added to the list, in the meantime you can use these arguments to do

the conversion.



```python

import pyreadstat



df, meta = pyreadstat.read_sas7bdat('/path/to/a/file.sas7bdat', extra_date_formats=["YEAR", "MMYY"])

```



#### Other options



You can set the encoding of the original file manually. The encoding must be a [iconv-compatible encoding](https://gist.github.com/hakre/4188459).

This is absolutely necessary if you are handling old xport files with

non-ascii characters. Those files do not have stamped the encoding in the

file itself, therefore the encoding must be set manually.



```python

import pyreadstat



df, meta = pyreadstat.read_sas7bdat('/path/to/a/file.sas7bdat', encoding="LATIN1")

```



You can preserve the original pandas behavior regarding dates (meaning dates are converted to pandas datetime) with the

dates_as_pandas_datetime option



```python

import pyreadstat



df, meta = pyreadstat.read_sas7bdat('/path/to/a/file.sas7bdat', dates_as_pandas_datetime=True)

```



You can get a dictionary of numpy arrays instead of a pandas dataframe when reading any file format.

In order to do that, set the parameter output_format='dict' (default is 'pandas'). This is useful if

you want to transform the data to some other format different to pandas, as transforming the data to pandas is a costly

process both in terms of speed and memory. Here for example an efficient way to transform the data to a polars dataframe:



```python

import pyreadstat

import polars



dicdata, meta = pyreadstat.read_sav('/path/to/a/file.sav', output_format='dict')

df = polars.DataFrame(dicdata)

```



For more information, please check the [Module documentation](https://ofajardo.github.io/pyreadstat_documentation/_build/html/index.html).



### More writing options



#### File specific options



Some special arguments are available depending on the function. write_sav can take also notes as string, wheter to

compress or not as zsav or apply row compression, variable display widths and variable measures. write_dta can take a stata version.

write_xport a name for the dataset. See the

[Module documentation](https://ofajardo.github.io/pyreadstat_documentation/_build/html/index.html) for more details.



#### Writing value labels



The argument variable_value_labels can be passed to write_sav and write_dta to write value labels. This argument must be a

dictionary where keys are variable names (names must match column names in the pandas data frame). Values are another dictionary where

keys are the value present in the dataframe and values are the labels (strings).



```python

import pandas as pd

import pyreadstat

df = pd.DataFrame([[1,1],[2,2],[1,3]], columns=['mylabl', 'myord'])

variable_value_labels = {'mylabl': {1: 'Male', 2: 'Female'}, 'myord': {1: 'low', 2: 'medium', 3: 'high'}}

path = "/path/to/somefile.sav"

pyreadstat.write_sav(df, path, variable_value_labels=variable_value_labels)

```



#### Writing user defined missing values



##### SPSS



The argument missing_ranges can be passed to write_sav to write user defined missing values.

This argument be a dictionary with keys as variable names matching variable

names in the dataframe. The values must be a list. Each element in that list can either be

either a discrete numeric or string value (max 3 per variable) or a dictionary with keys 'hi' and 'lo' to

indicate the upper and lower range for numeric values (max 1 range value + 1 discrete value per

variable). hi and lo may also be the same value in which case it will be interpreted as a discrete

missing value. For this to be effective, values in the dataframe must be the same as reported here and not NaN.



```python

import pandas as pd

import pyreadstat

df = pd.DataFrame([["a",1],["c",2],["c",3]], columns=['mychar', 'myord'])

missing_ranges = {'mychar':['a'], 'myord': [{'hi':2, 'lo':1}]}

path = "/path/to/somefile.sav"

pyreadstat.write_sav(df, path, missing_ranges=missing_ranges)

```



##### STATA



The argument missing_user_values can be passed to write_dta to write user defined missing values only for numeric variables.

This argument be a dictionary with keys as variable names matching variable

names in the dataframe. The values must be a list of missing values, valid values are single character strings

between a and z. Optionally a value label can also be attached to those missing values using variable_value_labels.



```python

import pandas as pd

import pyreadstat

df = pd.DataFrame([["a", 1],[2.2, 2],[3.3, "b"]], columns=['Var1', 'Var2'])

variable_value_labels = {'Var1':{'a':'a missing value'}

missing_ranges = {'Var1':['a'], 'Var2': ['b']}

path = "/path/to/somefile.sav"

pyreadstat.write_sav(df, path, missing_ranges=missing_ranges, variable_value_labels=variable_value_labels)

```



#### Setting variable formats



Numeric types in SPSS, SAS and STATA can have formats that affect how those values are displayed to the user

in the application. Pyreadstat automatically sets the formatting in some cases, as for example when translating

dates or datetimes (which in SPSS/SAS/STATA are just numbers with a special format). The user can however specify custom formats

for their columns with the argument "variable_format", which is

a dictionary with the column name as key and a string with the format as values:



```python

import pandas as pd

import pyreadstat



path = "path/where/to/write/file.sav"

df = pd.DataFrame({'restricted':[1023, 10], 'integer':[1,2]})

formats = {'restricted':'N4', 'integer':'F1.0'}

pyreadstat.write_sav(df, path, variable_format=formats)

```



The appropiate formats to use are beyond the scope of this documentation. Probably you want to read a file

produced in the original application and use meta.original_value_formats to get the formats. Otherwise look

for the documentation of the original application.



##### SPSS



In the case of SPSS we have some presets for some formats:

* restricted_integer: with leading zeros, equivalent to N + variable width (e.g N4)

* integer: Numeric with no decimal places, equivalent to F + variable width + ".0" (0 decimal positions). A 

  pandas column of type integer will also be translated into this format automatically.



```python

import pandas as pd

import pyreadstat



path = "path/where/to/write/file.sav"

df = pd.DataFrame({'restricted':[1023, 10], 'integer':[1,2]})

formats = {'restricted':'restricted_integer', 'integer':'integer'}

pyreadstat.write_sav(df, path, variable_format=formats)

```



There is some information about the possible formats [here](https://www.gnu.org/software/pspp/pspp-dev/html_node/Variable-Record.html).



#### Variable type conversion



The following rules are used in order to convert from pandas/numpy/python types to the target file types:



| Python Type         | Converted Type    |

| ------------------- | --------- |

| np.int32 or lower   | integer (stata), numeric (spss, sas) |

| int, np.int64, np.float  | double (stata), numeric (spss, sas)   |

| str                 | character |

| bool                | integer (stata), numeric (spss, sas) |

| datetime, date, time | numeric with datetime/date/time formatting |

| category            | depends on the original dtype |

| any other object    | character |

| column all missing  | integer (stata), numeric (spss, sas)   |

| column with mixed types | character |



Columns with mixed types are translated to character. This does not apply to column

cotaining np.nan, where the missing values are correctly translated. It also does not apply to columns with

user defined missing values in stata/sas where characters (a to z, A to Z, \_) will be recorded as numeric.



## Roadmap



* Include latest releases from Readstat as they come out.



## CD/CI and Wheels



A CD/CI pipeline producing the wheels is available [here](https://github.com/ofajardo/pyreadstat_wheels4). Contributions

are welcome.



## Known limitations



pyreadstat builds on top of Readstat and therefore inherits its limitations. Currently those include:



* Cannot write SAS sas7bdat. Those files can be written but not read in

SAS and therefore are not supported in pyreadstat. (see [here](https://github.com/WizardMac/ReadStat/issues/98))



Converting data types from foreign applications into python some times also bring some limitations:



* Pyreadstat transforms date, datetime and time like variables which are internally represented in the original application as

 numbers to python datetime objects. Python datetime objects are however limited in the range of dates they can represent

 (for example the max year is 10,000), while in other applications it is possible (although probably an error in the data)

 to have very high or very low dates. In this cases pyreadstat would raise an error:



 ```

 OverflowError: date value out of range

 ```



  The workaround is to deal with this include using the keyword argument disable_datetime_conversion so that you will

  get numbers instead of datetime objects or skipping reading such columns with the argument usecols.



## Python 2.7 support.



As version 1.2.3 Python 2.7 is not supported. In previous versions it was possible to compile it for 

mac and linux but not for windows, but no wheels were provided. In linux and mac it will fail if

 the path file contains non-ascii characters.



## Change log



A log with the changes for each version can be found [here](https://github.com/Roche/pyreadstat/blob/master/change_log.md)



## License



pyreadstat is distributed under Apache 2.0 license. Readstat is distributed under MIT license. See the License file for

more information.



## Contributing



Contributions are welcome! Those include corrections to the documentation, bugs reporting, testing,

and of course code pull requests. For code pull requests please

consider opening an issue explaining what you plan to do, so that we can get aligned before you start investing time on

it (this also avoids duplicated efforts).



The ReadStat code in this repo (under the subfolder src) is coming from the main Readstat trunk and should not be

modified in order to

keep full compatibility with the original. In that way improvements in ReadStat can be taken here with almost

no effort. If you would like to propose new features involving changes in the ReadStat code, please submit a

pull request to ReadStat first.



## People



[Otto Fajardo](https://github.com/ofajardo) - author, maintainer



[Matthew Brett](http://matthew.dynevor.org/) - contributor [python wheels](https://github.com/MacPython/pyreadstat-wheels)



[Jonathon Love](https://jona.thon.love/) - contributor: open files with international characters. Function to open files for writing.



[Clemens Brunner](https://github.com/cbrnr) - integration with pandas.read_spss



[Thomas Grainger](https://github.com/graingert) - corrections and suggestions to source code



[benjello](https://github.com/benjello), [maxwell8888](https://github.com/maxwell8888), [drcjar](https://github.com/drcjar), [labenech](https://github.com/labenech): improvements to documentation



[alchemyst](https://github.com/alchemyst): improvements to docstrings



[bmwiedemann](https://github.com/bmwiedemann), [toddrme2178 ](https://github.com/toddrme2178), [Martin Thorsen Ranang](https://github.com/mtr): improvements to source code