pandas docstring guide#
About docstrings and standards#
A Python docstring is a string used to document a Python module, class, function or method, so programmers can understand what it does without having to read the details of the implementation.
Also, it is a common practice to generate online (html) documentation automatically from docstrings. Sphinx serves this purpose.
The next example gives an idea of what a docstring looks like:
def add(num1, num2):
"""
Add up two integer numbers.
This function simply wraps the ``+`` operator, and does not
do anything interesting, except for illustrating what
the docstring of a very simple function looks like.
Parameters
----------
num1 : int
First number to add.
num2 : int
Second number to add.
Returns
-------
int
The sum of ``num1`` and ``num2``.
See Also
--------
subtract : Subtract one integer from another.
Examples
--------
>>> add(2, 2)
4
>>> add(25, 0)
25
>>> add(10, -10)
0
"""
return num1 + num2
Some standards regarding docstrings exist, which make them easier to read, and allow them be easily exported to other formats such as html or pdf.
The first conventions every Python docstring should follow are defined in PEP-257.
As PEP-257 is quite broad, other more specific standards also exist. In the case of pandas, the NumPy docstring convention is followed. These conventions are explained in this document:
numpydoc is a Sphinx extension to support the NumPy docstring convention.
The standard uses reStructuredText (reST). reStructuredText is a markup language that allows encoding styles in plain text files. Documentation about reStructuredText can be found in:
pandas has some helpers for sharing docstrings between related classes, see Sharing docstrings.
The rest of this document will summarize all the above guidelines, and will provide additional conventions specific to the pandas project.
Writing a docstring#
General rules#
Docstrings must be defined with three double-quotes. No blank lines should be left before or after the docstring. The text starts in the next line after the opening quotes. The closing quotes have their own line (meaning that they are not at the end of the last sentence).
On rare occasions reST styles like bold text or italics will be used in docstrings, but is it common to have inline code, which is presented between backticks. The following are considered inline code:
The name of a parameter
Python code, a module, function, built-in, type, literal… (e.g.
os,list,numpy.abs,datetime.date,True)A pandas class (in the form
:class:`pandas.Series`)A pandas method (in the form
:meth:`pandas.Series.sum`)A pandas function (in the form
:func:`pandas.to_datetime`)
Note
To display only the last component of the linked class, method or
function, prefix it with ~. For example, :class:`~pandas.Series`
will link to pandas.Series but only display the last part, Series
as the link text. See Sphinx cross-referencing syntax
for details.
Good:
def add_values(arr):
"""
Add the values in ``arr``.
This is equivalent to Python ``sum`` of :meth:`pandas.Series.sum`.
Some sections are omitted here for simplicity.
"""
return sum(arr)
Bad:
def func():
"""Some function.
With several mistakes in the docstring.
It has a blank line after the signature ``def func():``.
The text 'Some function' should go in the line after the
opening quotes of the docstring, not in the same line.
There is a blank line between the docstring and the first line
of code ``foo = 1``.
The closing quotes should be in the next line, not in this one."""
foo = 1
bar = 2
return foo + bar
Section 1: short summary#
The short summary is a single sentence that expresses what the function does in a concise way.
The short summary must start with a capital letter, end with a dot, and fit in a single line. It needs to express what the object does without providing details. For functions and methods, the short summary must start with an infinitive verb.
Good:
def astype(dtype):
"""
Cast Series type.
This section will provide further details.
"""
pass
Bad:
def astype(dtype):
"""
Casts Series type.
Verb in third-person of the present simple, should be infinitive.
"""
pass
def astype(dtype):
"""
Method to cast Series type.
Does not start with verb.
"""
pass
def astype(dtype):
"""
Cast Series type
Missing dot at the end.
"""
pass
def astype(dtype):
"""
Cast Series type from its current type to the new type defined in
the parameter dtype.
Summary is too verbose and doesn't fit in a single line.
"""
pass
Section 2: extended summary#
The extended summary provides details on what the function does. It should not go into the details of the parameters, or discuss implementation notes, which go in other sections.
A blank line is left between the short summary and the extended summary. Every paragraph in the extended summary ends with a dot.
The extended summary should provide details on why the function is useful and their use cases, if it is not too generic.
def unstack():
"""
Pivot a row index to columns.
When using a MultiIndex, a level can be pivoted so each value in
the index becomes a column. This is especially useful when a subindex
is repeated for the main index, and data is easier to visualize as a
pivot table.
The index level will be automatically removed from the index when added
as columns.
"""
pass
Section 3: parameters#
The details of the parameters will be added in this section. This section has the title “Parameters”, followed by a line with a hyphen under each letter of the word “Parameters”. A blank line is left before the section title, but not after, and not between the line with the word “Parameters” and the one with the hyphens.
After the title, each parameter in the signature must be documented, including
*args and **kwargs, but not self.
The parameters are defined by their name, followed by a space, a colon, another
space, and the type (or types). Note that the space between the name and the
colon is important. Types are not defined for *args and **kwargs, but must
be defined for all other parameters. After the parameter definition, it is
required to have a line with the parameter description, which is indented, and
can have multiple lines. The description must start with a capital letter, and
finish with a dot.
For keyword arguments with a default value, the default will be listed after a comma at the end of the type. The exact form of the type in this case will be “int, default 0”. In some cases it may be useful to explain what the default argument means, which can be added after a comma “int, default -1, meaning all cpus”.
In cases where the default value is None, meaning that the value will not be
used. Instead of "str, default None", it is preferred to write "str, optional".
When None is a value being used, we will keep the form “str, default None”.
For example, in df.to_csv(compression=None), None is not a value being used,
but means that compression is optional, and no compression is being used if not
provided. In this case we will use "str, optional". Only in cases like
func(value=None) and None is being used in the same way as 0 or foo
would be used, then we will specify “str, int or None, default None”.
Good:
class Series:
def plot(self, kind, color='blue', **kwargs):
"""
Generate a plot.
Render the data in the Series as a matplotlib plot of the
specified kind.
Parameters
----------
kind : str
Kind of matplotlib plot.
color : str, default 'blue'
Color name or rgb code.
**kwargs
These parameters will be passed to the matplotlib plotting
function.
"""
pass
Bad:
class Series:
def plot(self, kind, **kwargs):
"""
Generate a plot.
Render the data in the Series as a matplotlib plot of the
specified kind.
Note the blank line between the parameters title and the first
parameter. Also, note that after the name of the parameter ``kind``
and before the colon, a space is missing.
Also, note that the parameter descriptions do not start with a
capital letter, and do not finish with a dot.
Finally, the ``**kwargs`` parameter is missing.
Parameters
----------
kind: str
kind of matplotlib plot
"""
pass
Parameter types#
When specifying the parameter types, Python built-in data types can be used directly (the Python type is preferred to the more verbose string, integer, boolean, etc):
int
float
str
bool
For complex types, define the subtypes. For dict and tuple, as more than
one type is present, we use the brackets to help read the type (curly brackets
for dict and normal brackets for tuple):
list of int
dict of {str : int}
tuple of (str, int, int)
tuple of (str,)
set of str
In case where there are just a set of values allowed, list them in curly brackets and separated by commas (followed by a space). If the values are ordinal and they have an order, list them in this order. Otherwise, list the default value first, if there is one:
{0, 10, 25}
{‘simple’, ‘advanced’}
{‘low’, ‘medium’, ‘high’}
{‘cat’, ‘dog’, ‘bird’}
If the type is defined in a Python module, the module must be specified:
datetime.date
datetime.datetime
decimal.Decimal
If the type is in a package, the module must be also specified:
numpy.ndarray
scipy.sparse.coo_matrix
If the type is a pandas type, also specify pandas except for Series and DataFrame:
Series
DataFrame
pandas.Index
pandas.Categorical
pandas.arrays.SparseArray
If the exact type is not relevant, but must be compatible with a NumPy array, array-like can be specified. If Any type that can be iterated is accepted, iterable can be used:
array-like
iterable
If more than one type is accepted, separate them by commas, except the last two types, that need to be separated by the word ‘or’:
int or float
float, decimal.Decimal or None
str or list of str
If None is one of the accepted values, it always needs to be the last in
the list.
For axis, the convention is to use something like:
axis : {0 or ‘index’, 1 or ‘columns’, None}, default None
Section 4: returns or yields#
If the method returns a value, it will be documented in this section. Also if the method yields its output.
The title of the section will be defined in the same way as the “Parameters”. With the names “Returns” or “Yields” followed by a line with as many hyphens as the letters in the preceding word.
The documentation of the return is also similar to the parameters. But in this case, no name will be provided, unless the method returns or yields more than one value (a tuple of values).
The types for “Returns” and “Yields” are the same as the ones for the “Parameters”. Also, the description must finish with a dot.
For example, with a single value:
def sample():
"""
Generate and return a random number.
The value is sampled from a continuous uniform distribution between
0 and 1.
Returns
-------
float
Random number generated.
"""
return np.random.random()
With more than one value:
import string
def random_letters():
"""
Generate and return a sequence of random letters.
The length of the returned string is also random, and is also
returned.
Returns
-------
length : int
Length of the returned string.
letters : str
String of random letters.
"""
length = np.random.randint(1, 10)
letters = ''.join(np.random.choice(string.ascii_lowercase)
for i in range(length))
return length, letters
If the method yields its value:
def sample_values():
"""
Generate an infinite sequence of random numbers.
The values are sampled from a continuous uniform distribution between
0 and 1.
Yields
------
float
Random number generated.
"""
while True:
yield np.random.random()
Section 5: see also#
This section is used to let users know about pandas functionality related to the one being documented. In rare cases, if no related methods or functions can be found at all, this section can be skipped.
An obvious example would be the head() and tail() methods. As tail() does
the equivalent as head() but at the end of the Series or DataFrame
instead of at the beginning, it is good to let the users know about it.
To give an intuition on what can be considered related, here there are some examples:
locandiloc, as they do the same, but in one case providing indices and in the other positionsmaxandmin, as they do the oppositeiterrows,itertuplesanditems, as it is easy that a user looking for the method to iterate over columns ends up in the method to iterate over rows, and vice-versafillnaanddropna, as both methods are used to handle missing valuesread_csvandto_csv, as they are complementarymergeandjoin, as one is a generalization of the otherastypeandpandas.to_datetime, as users may be reading the documentation ofastypeto know how to cast as a date, and the way to do it is withpandas.to_datetimewhereis related tonumpy.where, as its functionality is based on it
When deciding what is related, you should mainly use your common sense and think about what can be useful for the users reading the documentation, especially the less experienced ones.
When relating to other libraries (mainly numpy), use the name of the module
first (not an alias like np). If the function is in a module which is not
the main one, like scipy.sparse, list the full module (e.g.
scipy.sparse.coo_matrix).
This section has a header, “See Also” (note the capital S and A), followed by the line with hyphens and preceded by a blank line.
After the header, we will add a line for each related method or function, followed by a space, a colon, another space, and a short description that illustrates what this method or function does, why is it relevant in this context, and what the key differences are between the documented function and the one being referenced. The description must also end with a dot.
Note that in “Returns” and “Yields”, the description is located on the line after the type. In this section, however, it is located on the same line, with a colon in between. If the description does not fit on the same line, it can continue onto other lines which must be further indented.
For example:
class Series:
def head(self):
"""
Return the first 5 elements of the Series.
This function is mainly useful to preview the values of the
Series without displaying the whole of it.
Returns
-------
Series
Subset of the original series with the 5 first values.
See Also
--------
Series.tail : Return the last 5 elements of the Series.
Series.iloc : Return a slice of the elements in the Series,
which can also be used to return the first or last n.
"""
return self.iloc[:5]
Section 6: notes#
This is an optional section used for notes about the implementation of the algorithm, or to document technical aspects of the function behavior.
Feel free to skip it, unless you are familiar with the implementation of the algorithm, or you discover some counter-intuitive behavior while writing the examples for the function.
This section follows the same format as the extended summary section.
Section 7: examples#
This is one of the most important sections of a docstring, despite being placed in the last position, as often people understand concepts better by example than through accurate explanations.
Examples in docstrings, besides illustrating the usage of the function or method, must be valid Python code, that returns the given output in a deterministic way, and that can be copied and run by users.
Examples are presented as a session in the Python terminal. >>> is used to
present code. ... is used for code continuing from the previous line.
Output is presented immediately after the last line of code generating the
output (no blank lines in between). Comments describing the examples can
be added with blank lines before and after them.
The way to present examples is as follows:
Import required libraries (except
numpyandpandas)Create the data required for the example
Show a very basic example that gives an idea of the most common use case
Add examples with explanations that illustrate how the parameters can be used for extended functionality
A simple example could be:
class Series:
def head(self, n=5):
"""
Return the first elements of the Series.
This function is mainly useful to preview the values of the
Series without displaying all of it.
Parameters
----------
n : int
Number of values to return.
Return
------
pandas.Series
Subset of the original series with the n first values.
See Also
--------
tail : Return the last n elements of the Series.
Examples
--------
>>> ser = pd.Series(['Ant', 'Bear', 'Cow', 'Dog', 'Falcon',
... 'Lion', 'Monkey', 'Rabbit', 'Zebra'])
>>> ser.head()
0 Ant
1 Bear
2 Cow
3 Dog
4 Falcon
dtype: object
With the ``n`` parameter, we can change the number of returned rows:
>>> ser.head(n=3)
0 Ant
1 Bear
2 Cow
dtype: object
"""
return self.iloc[:n]
The examples should be as concise as possible. In cases where the complexity of
the function requires long examples, is recommended to use blocks with headers
in bold. Use double star ** to make a text bold, like in **this example**.
Conventions for the examples#
Code in examples is assumed to always start with these two lines which are not shown:
import numpy as np
import pandas as pd
Any other module used in the examples must be explicitly imported, one per line (as recommended in PEP 8#imports) and avoiding aliases. Avoid excessive imports, but if needed, imports from the standard library go first, followed by third-party libraries (like matplotlib).
When illustrating examples with a single Series use the name ser, and if
illustrating with a single DataFrame use the name df. For indices,
idx is the preferred name. If a set of homogeneous Series or
DataFrame is used, name them ser1, ser2, ser3… or df1,
df2, df3… If the data is not homogeneous, and more than one structure
is needed, name them with something meaningful, for example df_main and
df_to_join.
Data used in the example should be as compact as possible. The number of rows
is recommended to be around 4, but make it a number that makes sense for the
specific example. For example in the head method, it requires to be higher
than 5, to show the example with the default values. If doing the mean, we
could use something like [1, 2, 3], so it is easy to see that the value
returned is the mean.
For more complex examples (grouping for example), avoid using data without interpretation, like a matrix of random numbers with columns A, B, C, D… And instead use a meaningful example, which makes it easier to understand the concept. Unless required by the example, use names of animals, to keep examples consistent. And numerical properties of them.
When calling the method, keywords arguments head(n=3) are preferred to
positional arguments head(3).
Good:
class Series:
def mean(self):
"""
Compute the mean of the input.
Examples
--------
>>> ser = pd.Series([1, 2, 3])
>>> ser.mean()
2
"""
pass
def fillna(self, value):
"""
Replace missing values by ``value``.
Examples
--------
>>> ser = pd.Series([1, np.nan, 3])
>>> ser.fillna(0)
[1, 0, 3]
"""
pass
def groupby_mean(self):
"""
Group by index and return mean.
Examples
--------
>>> ser = pd.Series([380., 370., 24., 26],
... name='max_speed',
... index=['falcon', 'falcon', 'parrot', 'parrot'])
>>> ser.groupby_mean()
index
falcon 375.0
parrot 25.0
Name: max_speed, dtype: float64
"""
pass
def contains(self, pattern, case_sensitive=True, na=numpy.nan):
"""
Return whether each value contains ``pattern``.
In this case, we are illustrating how to use sections, even
if the example is simple enough and does not require them.
Examples
--------
>>> ser = pd.Series('Antelope', 'Lion', 'Zebra', np.nan)
>>> ser.contains(pattern='a')
0 False
1 False
2 True
3 NaN
dtype: bool
**Case sensitivity**
With ``case_sensitive`` set to ``False`` we can match ``a`` with both
``a`` and ``A``:
>>> s.contains(pattern='a', case_sensitive=False)
0 True
1 False
2 True
3 NaN
dtype: bool
**Missing values**
We can fill missing values in the output using the ``na`` parameter:
>>> ser.contains(pattern='a', na=False)
0 False
1 False
2 True
3 False
dtype: bool
"""
pass
Bad:
def method(foo=None, bar=None):
"""
A sample DataFrame method.
Do not import NumPy and pandas.
Try to use meaningful data, when it makes the example easier
to understand.
Try to avoid positional arguments like in ``df.method(1)``. They
can be all right if previously defined with a meaningful name,
like in ``present_value(interest_rate)``, but avoid them otherwise.
When presenting the behavior with different parameters, do not place
all the calls one next to the other. Instead, add a short sentence
explaining what the example shows.
Examples
--------
>>> import numpy as np
>>> import pandas as pd
>>> df = pd.DataFrame(np.random.randn(3, 3),
... columns=('a', 'b', 'c'))
>>> df.method(1)
21
>>> df.method(bar=14)
123
"""
pass
Tips for getting your examples pass the doctests#
Getting the examples pass the doctests in the validation script can sometimes be tricky. Here are some attention points:
Import all needed libraries (except for pandas and NumPy, those are already imported as
import pandas as pdandimport numpy as np) and define all variables you use in the example.Try to avoid using random data. However random data might be OK in some cases, like if the function you are documenting deals with probability distributions, or if the amount of data needed to make the function result meaningful is too much, such that creating it manually is very cumbersome. In those cases, always use a fixed random seed to make the generated examples predictable. Example:
>>> np.random.seed(42) >>> df = pd.DataFrame({'normal': np.random.normal(100, 5, 20)})
If you have a code snippet that wraps multiple lines, you need to use ‘…’ on the continued lines:
>>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], index=['a', 'b', 'c'], ... columns=['A', 'B'])
If you want to show a case where an exception is raised, you can do:
>>> pd.to_datetime(["712-01-01"]) Traceback (most recent call last): OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 712-01-01 00:00:00
It is essential to include the “Traceback (most recent call last):”, but for the actual error only the error name is sufficient.
If there is a small part of the result that can vary (e.g. a hash in an object representation), you can use
...to represent this part.If you want to show that
s.plot()returns a matplotlib AxesSubplot object, this will fail the doctest>>> s.plot() <matplotlib.axes._subplots.AxesSubplot at 0x7efd0c0b0690>
However, you can do (notice the comment that needs to be added)
>>> s.plot() <matplotlib.axes._subplots.AxesSubplot at ...>
Plots in examples#
There are some methods in pandas returning plots. To render the plots generated
by the examples in the documentation, the .. plot:: directive exists.
To use it, place the next code after the “Examples” header as shown below. The plot will be generated automatically when building the documentation.
class Series:
def plot(self):
"""
Generate a plot with the ``Series`` data.
Examples
--------
.. plot::
:context: close-figs
>>> ser = pd.Series([1, 2, 3])
>>> ser.plot()
"""
pass