Caveats and Gotchas¶
Using If/Truth Statements with pandas¶
pandas follows the numpy convention of raising an error when you try to convert something to a bool.
This happens in a if or when using the boolean operations, and, or, or not. It is not clear
what the result of
>>> if pd.Series([False, True, False]):
...
should be. Should it be True because it’s not zero-length? False because there are False values?
It is unclear, so instead, pandas raises a ValueError:
>>> if pd.Series([False, True, False]):
print("I was true")
Traceback
...
ValueError: The truth value of an array is ambiguous. Use a.empty, a.any() or a.all().
If you see that, you need to explicitly choose what you want to do with it (e.g., use any(), all() or empty).
or, you might want to compare if the pandas object is None
>>> if pd.Series([False, True, False]) is not None:
print("I was not None")
>>> I was not None
or return if any value is True.
>>> if pd.Series([False, True, False]).any():
print("I am any")
>>> I am any
To evaluate single-element pandas objects in a boolean context, use the method .bool():
In [1]: pd.Series([True]).bool()
Out[1]: True
In [2]: pd.Series([False]).bool()
Out[2]: False
In [3]: pd.DataFrame([[True]]).bool()
Out[3]: True
In [4]: pd.DataFrame([[False]]).bool()
Out[4]: False
Bitwise boolean¶
Bitwise boolean operators like == and != will return a boolean Series,
which is almost always what you want anyways.
>>> s = pd.Series(range(5))
>>> s == 4
0 False
1 False
2 False
3 False
4 True
dtype: bool
See boolean comparisons for more examples.
Using the in operator¶
Using the Python in operator on a Series tests for membership in the
index, not membership among the values.
If this behavior is surprising, keep in mind that using in on a Python
dictionary tests keys, not values, and Series are dict-like.
To test for membership in the values, use the method isin():
For DataFrames, likewise, in applies to the column axis,
testing for membership in the list of column names.
NaN, Integer NA values and NA type promotions¶
Choice of NA representation¶
For lack of NA (missing) support from the ground up in NumPy and Python in
general, we were given the difficult choice between either
- A masked array solution: an array of data and an array of boolean values indicating whether a value
- Using a special sentinel value, bit pattern, or set of sentinel values to
denote
NAacross the dtypes
For many reasons we chose the latter. After years of production use it has
proven, at least in my opinion, to be the best decision given the state of
affairs in NumPy and Python in general. The special value NaN
(Not-A-Number) is used everywhere as the NA value, and there are API
functions isnull and notnull which can be used across the dtypes to
detect NA values.
However, it comes with it a couple of trade-offs which I most certainly have not ignored.
Support for integer NA¶
In the absence of high performance NA support being built into NumPy from
the ground up, the primary casualty is the ability to represent NAs in integer
arrays. For example:
In [5]: s = pd.Series([1, 2, 3, 4, 5], index=list('abcde'))
In [6]: s
Out[6]:
a 1
b 2
c 3
d 4
e 5
dtype: int64
In [7]: s.dtype
Out[7]: dtype('int64')
In [8]: s2 = s.reindex(['a', 'b', 'c', 'f', 'u'])
In [9]: s2
Out[9]:
a 1.0
b 2.0
c 3.0
f NaN
u NaN
dtype: float64
In [10]: s2.dtype
Out[10]: dtype('float64')
This trade-off is made largely for memory and performance reasons, and also so
that the resulting Series continues to be “numeric”. One possibility is to use
dtype=object arrays instead.
NA type promotions¶
When introducing NAs into an existing Series or DataFrame via reindex or
some other means, boolean and integer types will be promoted to a different
dtype in order to store the NAs. These are summarized by this table:
| Typeclass | Promotion dtype for storing NAs |
|---|---|
floating |
no change |
object |
no change |
integer |
cast to float64 |
boolean |
cast to object |
While this may seem like a heavy trade-off, I have found very few cases where this is an issue in practice. Some explanation for the motivation here in the next section.
Why not make NumPy like R?¶
Many people have suggested that NumPy should simply emulate the NA support
present in the more domain-specific statistical programming language R. Part of the reason is the NumPy type hierarchy:
| Typeclass | Dtypes |
|---|---|
numpy.floating |
float16, float32, float64, float128 |
numpy.integer |
int8, int16, int32, int64 |
numpy.unsignedinteger |
uint8, uint16, uint32, uint64 |
numpy.object_ |
object_ |
numpy.bool_ |
bool_ |
numpy.character |
string_, unicode_ |
The R language, by contrast, only has a handful of built-in data types:
integer, numeric (floating-point), character, and
boolean. NA types are implemented by reserving special bit patterns for
each type to be used as the missing value. While doing this with the full NumPy
type hierarchy would be possible, it would be a more substantial trade-off
(especially for the 8- and 16-bit data types) and implementation undertaking.
An alternate approach is that of using masked arrays. A masked array is an
array of data with an associated boolean mask denoting whether each value
should be considered NA or not. I am personally not in love with this
approach as I feel that overall it places a fairly heavy burden on the user and
the library implementer. Additionally, it exacts a fairly high performance cost
when working with numerical data compared with the simple approach of using
NaN. Thus, I have chosen the Pythonic “practicality beats purity” approach
and traded integer NA capability for a much simpler approach of using a
special value in float and object arrays to denote NA, and promoting
integer arrays to floating when NAs must be introduced.
Integer indexing¶
Label-based indexing with integer axis labels is a thorny topic. It has been
discussed heavily on mailing lists and among various members of the scientific
Python community. In pandas, our general viewpoint is that labels matter more
than integer locations. Therefore, with an integer axis index only
label-based indexing is possible with the standard tools like .ix. The
following code will generate exceptions:
s = pd.Series(range(5))
s[-1]
df = pd.DataFrame(np.random.randn(5, 4))
df
df.ix[-2:]
This deliberate decision was made to prevent ambiguities and subtle bugs (many users reported finding bugs when the API change was made to stop “falling back” on position-based indexing).
Label-based slicing conventions¶
Non-monotonic indexes require exact matches¶
If the index of a Series or DataFrame is monotonically increasing or decreasing, then the bounds
of a label-based slice can be outside the range of the index, much like slice indexing a
normal Python list. Monotonicity of an index can be tested with the is_monotonic_increasing and
is_monotonic_decreasing attributes.
In [11]: df = pd.DataFrame(index=[2,3,3,4,5], columns=['data'], data=range(5))
In [12]: df.index.is_monotonic_increasing
Out[12]: True
# no rows 0 or 1, but still returns rows 2, 3 (both of them), and 4:
In [13]: df.loc[0:4, :]
Out[13]:
data
2 0
3 1
3 2
4 3
# slice is are outside the index, so empty DataFrame is returned
In [14]: df.loc[13:15, :]
Out[14]:
Empty DataFrame
Columns: [data]
Index: []
On the other hand, if the index is not monotonic, then both slice bounds must be unique members of the index.
In [15]: df = pd.DataFrame(index=[2,3,1,4,3,5], columns=['data'], data=range(6))
In [16]: df.index.is_monotonic_increasing
Out[16]: False
# OK because 2 and 4 are in the index
In [17]: df.loc[2:4, :]
Out[17]:
data
2 0
3 1
1 2
4 3
# 0 is not in the index
In [9]: df.loc[0:4, :]
KeyError: 0
# 3 is not a unique label
In [11]: df.loc[2:3, :]
KeyError: 'Cannot get right slice bound for non-unique label: 3'
Endpoints are inclusive¶
Compared with standard Python sequence slicing in which the slice endpoint is not inclusive, label-based slicing in pandas is inclusive. The primary reason for this is that it is often not possible to easily determine the “successor” or next element after a particular label in an index. For example, consider the following Series:
In [18]: s = pd.Series(np.random.randn(6), index=list('abcdef'))
In [19]: s
Out[19]:
a 1.544821
b -1.708552
c 1.545458
d -0.735738
e -0.649091
f -0.403878
dtype: float64
Suppose we wished to slice from c to e, using integers this would be
In [20]: s[2:5]
Out[20]:
c 1.545458
d -0.735738
e -0.649091
dtype: float64
However, if you only had c and e, determining the next element in the
index can be somewhat complicated. For example, the following does not work:
s.ix['c':'e'+1]
A very common use case is to limit a time series to start and end at two specific dates. To enable this, we made the design design to make label-based slicing include both endpoints:
In [21]: s.ix['c':'e']
Out[21]:
c 1.545458
d -0.735738
e -0.649091
dtype: float64
This is most definitely a “practicality beats purity” sort of thing, but it is something to watch out for if you expect label-based slicing to behave exactly in the way that standard Python integer slicing works.
Miscellaneous indexing gotchas¶
Reindex versus ix gotchas¶
Many users will find themselves using the ix indexing capabilities as a
concise means of selecting data from a pandas object:
In [22]: df = pd.DataFrame(np.random.randn(6, 4), columns=['one', 'two', 'three', 'four'],
....: index=list('abcdef'))
....:
In [23]: df
Out[23]:
one two three four
a -2.474932 0.975891 -0.204206 0.452707
b 3.478418 -0.591538 -0.508560 0.047946
c -0.170009 -1.615606 -0.894382 1.334681
d -0.418002 -0.690649 0.128522 0.429260
e 1.207515 -1.308877 -0.548792 -1.520879
f 1.153696 0.609378 -0.825763 0.218223
In [24]: df.ix[['b', 'c', 'e']]
Out[24]:
one two three four
b 3.478418 -0.591538 -0.508560 0.047946
c -0.170009 -1.615606 -0.894382 1.334681
e 1.207515 -1.308877 -0.548792 -1.520879
This is, of course, completely equivalent in this case to using the
reindex method:
In [25]: df.reindex(['b', 'c', 'e'])
Out[25]:
one two three four
b 3.478418 -0.591538 -0.508560 0.047946
c -0.170009 -1.615606 -0.894382 1.334681
e 1.207515 -1.308877 -0.548792 -1.520879
Some might conclude that ix and reindex are 100% equivalent based on
this. This is indeed true except in the case of integer indexing. For
example, the above operation could alternately have been expressed as:
In [26]: df.ix[[1, 2, 4]]
Out[26]:
one two three four
b 3.478418 -0.591538 -0.508560 0.047946
c -0.170009 -1.615606 -0.894382 1.334681
e 1.207515 -1.308877 -0.548792 -1.520879
If you pass [1, 2, 4] to reindex you will get another thing entirely:
In [27]: df.reindex([1, 2, 4])
Out[27]:
one two three four
1 NaN NaN NaN NaN
2 NaN NaN NaN NaN
4 NaN NaN NaN NaN
So it’s important to remember that reindex is strict label indexing
only. This can lead to some potentially surprising results in pathological
cases where an index contains, say, both integers and strings:
In [28]: s = pd.Series([1, 2, 3], index=['a', 0, 1])
In [29]: s
Out[29]:
a 1
0 2
1 3
dtype: int64
In [30]: s.ix[[0, 1]]
Out[30]:
0 2
1 3
dtype: int64
In [31]: s.reindex([0, 1])
Out[31]:
0 2
1 3
dtype: int64
Because the index in this case does not contain solely integers, ix falls
back on integer indexing. By contrast, reindex only looks for the values
passed in the index, thus finding the integers 0 and 1. While it would
be possible to insert some logic to check whether a passed sequence is all
contained in the index, that logic would exact a very high cost in large data
sets.
Reindex potentially changes underlying Series dtype¶
The use of reindex_like can potentially change the dtype of a Series.
In [32]: series = pd.Series([1, 2, 3])
In [33]: x = pd.Series([True])
In [34]: x.dtype
Out[34]: dtype('bool')
In [35]: x = pd.Series([True]).reindex_like(series)
In [36]: x.dtype
Out[36]: dtype('O')
This is because reindex_like silently inserts NaNs and the dtype
changes accordingly. This can cause some issues when using numpy ufuncs
such as numpy.logical_and.
See the this old issue for a more detailed discussion.
Parsing Dates from Text Files¶
When parsing multiple text file columns into a single date column, the new date column is prepended to the data and then index_col specification is indexed off of the new set of columns rather than the original ones:
In [37]: print(open('tmp.csv').read())
KORD,19990127, 19:00:00, 18:56:00, 0.8100
KORD,19990127, 20:00:00, 19:56:00, 0.0100
KORD,19990127, 21:00:00, 20:56:00, -0.5900
KORD,19990127, 21:00:00, 21:18:00, -0.9900
KORD,19990127, 22:00:00, 21:56:00, -0.5900
KORD,19990127, 23:00:00, 22:56:00, -0.5900
In [38]: date_spec = {'nominal': [1, 2], 'actual': [1, 3]}
In [39]: df = pd.read_csv('tmp.csv', header=None,
....: parse_dates=date_spec,
....: keep_date_col=True,
....: index_col=0)
....:
# index_col=0 refers to the combined column "nominal" and not the original
# first column of 'KORD' strings
In [40]: df
Out[40]:
actual 0 1 2 3 \
nominal
1999-01-27 19:00:00 1999-01-27 18:56:00 KORD 19990127 19:00:00 18:56:00
1999-01-27 20:00:00 1999-01-27 19:56:00 KORD 19990127 20:00:00 19:56:00
1999-01-27 21:00:00 1999-01-27 20:56:00 KORD 19990127 21:00:00 20:56:00
1999-01-27 21:00:00 1999-01-27 21:18:00 KORD 19990127 21:00:00 21:18:00
1999-01-27 22:00:00 1999-01-27 21:56:00 KORD 19990127 22:00:00 21:56:00
1999-01-27 23:00:00 1999-01-27 22:56:00 KORD 19990127 23:00:00 22:56:00
4
nominal
1999-01-27 19:00:00 0.81
1999-01-27 20:00:00 0.01
1999-01-27 21:00:00 -0.59
1999-01-27 21:00:00 -0.99
1999-01-27 22:00:00 -0.59
1999-01-27 23:00:00 -0.59
Differences with NumPy¶
For Series and DataFrame objects, var normalizes by N-1 to produce
unbiased estimates of the sample variance, while NumPy’s var normalizes
by N, which measures the variance of the sample. Note that cov
normalizes by N-1 in both pandas and NumPy.
Thread-safety¶
As of pandas 0.11, pandas is not 100% thread safe. The known issues relate to
the DataFrame.copy method. If you are doing a lot of copying of DataFrame
objects shared among threads, we recommend holding locks inside the threads
where the data copying occurs.
See this link for more information.
HTML Table Parsing¶
There are some versioning issues surrounding the libraries that are used to
parse HTML tables in the top-level pandas io function read_html.
Issues with lxml
- Benefits
- Drawbacks
- lxml does not make any guarantees about the results of its parse unless it is given strictly valid markup.
- In light of the above, we have chosen to allow you, the user, to use the lxml backend, but this backend will use html5lib if lxml fails to parse
- It is therefore highly recommended that you install both BeautifulSoup4 and html5lib, so that you will still get a valid result (provided everything else is valid) even if lxml fails.
Issues with BeautifulSoup4 using lxml as a backend
- The above issues hold here as well since BeautifulSoup4 is essentially just a wrapper around a parser backend.
Issues with BeautifulSoup4 using html5lib as a backend
- Benefits
- html5lib is far more lenient than lxml and consequently deals with real-life markup in a much saner way rather than just, e.g., dropping an element without notifying you.
- html5lib generates valid HTML5 markup from invalid markup automatically. This is extremely important for parsing HTML tables, since it guarantees a valid document. However, that does NOT mean that it is “correct”, since the process of fixing markup does not have a single definition.
- html5lib is pure Python and requires no additional build steps beyond its own installation.
- Drawbacks
- The biggest drawback to using html5lib is that it is slow as molasses. However consider the fact that many tables on the web are not big enough for the parsing algorithm runtime to matter. It is more likely that the bottleneck will be in the process of reading the raw text from the URL over the web, i.e., IO (input-output). For very large tables, this might not be true.
Issues with using Anaconda
- Anaconda ships with lxml version 3.2.0; the following workaround for Anaconda was successfully used to deal with the versioning issues surrounding lxml and BeautifulSoup4.
Note
Unless you have both:
- A strong restriction on the upper bound of the runtime of some code that incorporates
read_html()- Complete knowledge that the HTML you will be parsing will be 100% valid at all times
then you should install html5lib and things will work swimmingly without you having to muck around with conda. If you want the best of both worlds then install both html5lib and lxml. If you do install lxml then you need to perform the following commands to ensure that lxml will work correctly:
# remove the included version conda remove lxml # install the latest version of lxml pip install 'git+git://github.com/lxml/lxml.git' # install the latest version of beautifulsoup4 pip install 'bzr+lp:beautifulsoup'Note that you need bzr and git installed to perform the last two operations.
Byte-Ordering Issues¶
Occasionally you may have to deal with data that were created on a machine with a different byte order than the one on which you are running Python. A common symptom of this issue is an error like
Traceback
...
ValueError: Big-endian buffer not supported on little-endian compiler
To deal with this issue you should convert the underlying NumPy array to the native system byte order before passing it to Series/DataFrame/Panel constructors using something similar to the following:
In [41]: x = np.array(list(range(10)), '>i4') # big endian
In [42]: newx = x.byteswap().newbyteorder() # force native byteorder
In [43]: s = pd.Series(newx)
See the NumPy documentation on byte order for more details.