Frequently Asked Questions (FAQ)#
DataFrame memory usage#
The memory usage of a DataFrame (including the index) is shown when calling
the info(). A configuration option, display.memory_usage
(see the list of options), specifies if the
DataFrame memory usage will be displayed when invoking the info()
method.
For example, the memory usage of the DataFrame below is shown
when calling info():
In [1]: dtypes = [
...: "int64",
...: "float64",
...: "datetime64[ns]",
...: "timedelta64[ns]",
...: "complex128",
...: "object",
...: "bool",
...: ]
...:
In [2]: n = 5000
In [3]: data = {t: np.random.randint(100, size=n).astype(t) for t in dtypes}
In [4]: df = pd.DataFrame(data)
In [5]: df["categorical"] = df["object"].astype("category")
In [6]: df.info()
<class 'pandas.DataFrame'>
RangeIndex: 5000 entries, 0 to 4999
Data columns (total 8 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 int64 5000 non-null int64
1 float64 5000 non-null float64
2 datetime64[ns] 5000 non-null datetime64[ns]
3 timedelta64[ns] 5000 non-null timedelta64[ns]
4 complex128 5000 non-null complex128
5 object 5000 non-null object
6 bool 5000 non-null bool
7 categorical 5000 non-null category
dtypes: bool(1), category(1), complex128(1), datetime64[ns](1), float64(1), int64(1), object(1), timedelta64[ns](1)
memory usage: 284.1+ KB
The + symbol indicates that the true memory usage could be higher, because
pandas does not count the memory used by values in columns with
dtype=object.
Passing memory_usage='deep' will enable a more accurate memory usage report,
accounting for the full usage of the contained objects. This is optional
as it can be expensive to do this deeper introspection.
In [7]: df.info(memory_usage="deep")
<class 'pandas.DataFrame'>
RangeIndex: 5000 entries, 0 to 4999
Data columns (total 8 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 int64 5000 non-null int64
1 float64 5000 non-null float64
2 datetime64[ns] 5000 non-null datetime64[ns]
3 timedelta64[ns] 5000 non-null timedelta64[ns]
4 complex128 5000 non-null complex128
5 object 5000 non-null object
6 bool 5000 non-null bool
7 categorical 5000 non-null category
dtypes: bool(1), category(1), complex128(1), datetime64[ns](1), float64(1), int64(1), object(1), timedelta64[ns](1)
memory usage: 420.8 KB
By default the display option is set to True but can be explicitly
overridden by passing the memory_usage argument when invoking info().
The memory usage of each column can be found by calling the
memory_usage() method. This returns a Series with an index
represented by column names and memory usage of each column shown in bytes. For
the DataFrame above, the memory usage of each column and the total memory
usage can be found with the memory_usage() method:
In [8]: df.memory_usage()
Out[8]:
Index 132
int64 40000
float64 40000
datetime64[ns] 40000
timedelta64[ns] 40000
complex128 80000
object 40000
bool 5000
categorical 5800
dtype: int64
# total memory usage of dataframe
In [9]: df.memory_usage().sum()
Out[9]: np.int64(290932)
By default the memory usage of the DataFrame index is shown in the
returned Series, the memory usage of the index can be suppressed by passing
the index=False argument:
In [10]: df.memory_usage(index=False)
Out[10]:
int64 40000
float64 40000
datetime64[ns] 40000
timedelta64[ns] 40000
complex128 80000
object 40000
bool 5000
categorical 5800
dtype: int64
The memory usage displayed by the info() method utilizes the
memory_usage() method to determine the memory usage of a
DataFrame while also formatting the output in human-readable units (base-2
representation; i.e. 1KB = 1024 bytes).
See also Categorical Memory Usage.
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 an if-statement or when using the
boolean operations: and, or, and not. It is not clear what the result
of the following code should be:
>>> if pd.Series([False, True, False]):
... pass
Should it be True because it’s not zero-length, or False because there
are False values? It is unclear, so instead, pandas raises a ValueError:
In [11]: if pd.Series([False, True, False]):
....: print("I was true")
....:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[11], line 1
----> 1 if pd.Series([False, True, False]):
2 print("I was true")
File ~/work/pandas/pandas/pandas/core/generic.py:1561, in NDFrame.__bool__(self)
1559 @final
1560 def __bool__(self) -> NoReturn:
-> 1561 raise ValueError(
1562 f"The truth value of a {type(self).__name__} is ambiguous. "
1563 "Use a.empty, a.item(), a.any() or a.all()."
1564 )
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.item(), a.any() or a.all().
You need to explicitly choose what you want to do with the DataFrame, e.g.
use any(), all() or empty().
Alternatively, you might want to compare if the pandas object is None:
In [12]: if pd.Series([False, True, False]) is not None:
....: print("I was not None")
....:
I was not None
Below is how to check if any of the values are True:
In [13]: if pd.Series([False, True, False]).any():
....: print("I am any")
....:
I am any
Bitwise Boolean#
Bitwise boolean operators like == and != return a boolean Series
which performs an element-wise comparison when compared to a scalar.
In [14]: s = pd.Series(range(5))
In [15]: s == 4
Out[15]:
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.
In [16]: s = pd.Series(range(5), index=list("abcde"))
In [17]: 2 in s
Out[17]: False
In [18]: 'b' in s
Out[18]: True
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():
In [19]: s.isin([2])
Out[19]:
a False
b False
c True
d False
e False
dtype: bool
In [20]: s.isin([2]).any()
Out[20]: np.True_
For DataFrame, likewise, in applies to the column axis,
testing for membership in the list of column names.
Mutating with User Defined Function (UDF) methods#
This section applies to pandas methods that take a UDF. In particular, the methods
DataFrame.apply(), DataFrame.aggregate(), DataFrame.transform(), and
DataFrame.filter().
It is a general rule in programming that one should not mutate a container while it is being iterated over. Mutation will invalidate the iterator, causing unexpected behavior. Consider the example:
In [21]: values = [0, 1, 2, 3, 4, 5]
In [22]: n_removed = 0
In [23]: for k, value in enumerate(values):
....: idx = k - n_removed
....: if value % 2 == 1:
....: del values[idx]
....: n_removed += 1
....: else:
....: values[idx] = value + 1
....:
In [24]: values
Out[24]: [1, 4, 5]
One probably would have expected that the result would be [1, 3, 5].
When using a pandas method that takes a UDF, internally pandas is often
iterating over the
DataFrame or other pandas object. Therefore, if the UDF mutates (changes)
the DataFrame, unexpected behavior can arise.
Here is a similar example with DataFrame.apply():
In [25]: def f(s):
....: s.pop("a")
....: return s
....:
In [26]: df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
In [27]: df.apply(f, axis="columns")
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
File ~/work/pandas/pandas/pandas/core/indexes/base.py:3803, in Index.get_loc(self, key)
3802 try:
-> 3803 return self._engine.get_loc(casted_key)
3804 except KeyError as err:
File pandas/_libs/index.pyx:172, in pandas._libs.index.IndexEngine.get_loc()
--> 172 'Could not get source, probably due dynamically evaluated source code.'
File pandas/_libs/index.pyx:201, in pandas._libs.index.IndexEngine.get_loc()
--> 201 'Could not get source, probably due dynamically evaluated source code.'
File pandas/_libs/hashtable_class_helper.pxi:7726, in pandas._libs.hashtable.PyObjectHashTable.get_item()
-> 7726 'Could not get source, probably due dynamically evaluated source code.'
File pandas/_libs/hashtable_class_helper.pxi:7734, in pandas._libs.hashtable.PyObjectHashTable.get_item()
-> 7734 'Could not get source, probably due dynamically evaluated source code.'
KeyError: 'a'
The above exception was the direct cause of the following exception:
KeyError Traceback (most recent call last)
Cell In[27], line 1
----> 1 df.apply(f, axis="columns")
File ~/work/pandas/pandas/pandas/core/frame.py:14878, in DataFrame.apply(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs)
14874 engine_kwargs=engine_kwargs,
14875 args=args,
14876 kwargs=kwargs,
14877 )
> 14878 return op.apply().__finalize__(self, method="apply")
14879 elif hasattr(engine, "__pandas_udf__"):
14880 if result_type is not None:
14881 raise NotImplementedError(
File ~/work/pandas/pandas/pandas/core/apply.py:1061, in FrameApply.apply(self)
1058 elif self.raw:
1059 return self.apply_raw(engine=self.engine, engine_kwargs=self.engine_kwargs)
-> 1061 return self.apply_standard()
File ~/work/pandas/pandas/pandas/core/apply.py:1281, in FrameApply.apply_standard(self)
1279 def apply_standard(self):
1280 if self.engine == "python":
-> 1281 results, res_index = self.apply_series_generator()
1282 else:
1283 results, res_index = self.apply_series_numba()
File ~/work/pandas/pandas/pandas/core/apply.py:1297, in FrameApply.apply_series_generator(self)
1294 results = {}
1296 for i, v in enumerate(series_gen):
-> 1297 results[i] = self.func(v, *self.args, **self.kwargs)
1298 if isinstance(results[i], ABCSeries):
1299 # If we have a view on v, we need to make a copy because
1300 # series_generator will swap out the underlying data
1301 results[i] = results[i].copy(deep=False)
Cell In[25], line 2, in f(s)
1 def f(s):
----> 2 s.pop("a")
3 return s
File ~/work/pandas/pandas/pandas/core/series.py:6233, in Series.pop(self, item)
6229 1 2
6230 2 3
6231 dtype: int64
6232 """
-> 6233 return maybe_unbox_numpy_scalar(super().pop(item=item), dtype=self.dtype)
File ~/work/pandas/pandas/pandas/core/generic.py:839, in NDFrame.pop(self, item)
838 def pop(self, item: Hashable) -> Series | Any:
--> 839 result = self[item]
840 del self[item]
841
842 return result
File ~/work/pandas/pandas/pandas/core/series.py:1064, in Series.__getitem__(self, key)
1060
1061 elif key_is_scalar:
1062 # Note: GH#50617 in 3.0 we changed int key to always be treated as
1063 # a label, matching DataFrame behavior.
-> 1064 return self._get_value(key)
1065
1066 # Convert generator to list before going through hashable part
1067 # (We will iterate through the generator there to check for slices)
File ~/work/pandas/pandas/pandas/core/series.py:1151, in Series._get_value(self, label, takeable)
1147 if takeable:
1148 return self._values[label]
1149
1150 # Similar to Index.get_value, but we do not fall back to positional
-> 1151 loc = self.index.get_loc(label)
1152
1153 if is_integer(loc):
1154 return self._values[loc]
File ~/work/pandas/pandas/pandas/core/indexes/base.py:3810, in Index.get_loc(self, key)
3805 if isinstance(casted_key, slice) or (
3806 isinstance(casted_key, abc.Iterable)
3807 and any(isinstance(x, slice) for x in casted_key)
3808 ):
3809 raise InvalidIndexError(key) from err
-> 3810 raise KeyError(key) from err
3811 except TypeError:
3812 # If we have a listlike key, _check_indexing_error will raise
3813 # InvalidIndexError. Otherwise we fall through and re-raise
3814 # the TypeError.
3815 self._check_indexing_error(key)
KeyError: 'a'
To resolve this issue, one can make a copy so that the mutation does not apply to the container being iterated over.
In [28]: values = [0, 1, 2, 3, 4, 5]
In [29]: n_removed = 0
In [30]: for k, value in enumerate(values.copy()):
....: idx = k - n_removed
....: if value % 2 == 1:
....: del values[idx]
....: n_removed += 1
....: else:
....: values[idx] = value + 1
....:
In [31]: values
Out[31]: [1, 3, 5]
In [32]: def f(s):
....: s = s.copy()
....: s.pop("a")
....: return s
....:
In [33]: df = pd.DataFrame({"a": [1, 2, 3], 'b': [4, 5, 6]})
In [34]: df.apply(f, axis="columns")
Out[34]:
b
0 4
1 5
2 6
Missing value representation for NumPy types#
np.nan as the NA representation for NumPy types#
For lack of NA (missing) support from the ground up in NumPy and Python in
general, NA could have been represented with:
A masked array solution: an array of data and an array of boolean values indicating whether a value is there or is missing.
Using a special sentinel value, bit pattern, or set of sentinel values to denote
NAacross the dtypes.
The special value np.nan (Not-A-Number) was chosen as the NA value for NumPy types, and there are API
functions like DataFrame.isna() and DataFrame.notna() which can be used across the dtypes to
detect NA values. However, this choice has a downside of coercing missing integer data as float types as
shown in Support for integer NA.
NA type promotions for NumPy types#
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. The promotions are
summarized in this table:
Typeclass |
Promotion dtype for storing NAs |
|---|---|
|
no change |
|
no change |
|
cast to |
|
cast to |
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 [35]: s = pd.Series([1, 2, 3, 4, 5], index=list("abcde"))
In [36]: s
Out[36]:
a 1
b 2
c 3
d 4
e 5
dtype: int64
In [37]: s.dtype
Out[37]: dtype('int64')
In [38]: s2 = s.reindex(["a", "b", "c", "f", "u"])
In [39]: s2
Out[39]:
a 1.0
b 2.0
c 3.0
f NaN
u NaN
dtype: float64
In [40]: s2.dtype
Out[40]: dtype('float64')
This trade-off is made largely for memory and performance reasons, and also so
that the resulting Series continues to be “numeric”.
If you need to represent integers with possibly missing values, use one of the nullable-integer extension dtypes provided by pandas or pyarrow
In [41]: s_int = pd.Series([1, 2, 3, 4, 5], index=list("abcde"), dtype=pd.Int64Dtype())
In [42]: s_int
Out[42]:
a 1
b 2
c 3
d 4
e 5
dtype: Int64
In [43]: s_int.dtype
Out[43]: Int64Dtype()
In [44]: s2_int = s_int.reindex(["a", "b", "c", "f", "u"])
In [45]: s2_int
Out[45]:
a 1
b 2
c 3
f <NA>
u <NA>
dtype: Int64
In [46]: s2_int.dtype
Out[46]: Int64Dtype()
In [47]: s_int_pa = pd.Series([1, 2, None], dtype="int64[pyarrow]")
In [48]: s_int_pa
Out[48]:
0 1
1 2
2 <NA>
dtype: int64[pyarrow]
See Nullable integer data type and PyArrow Functionality for more.
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.
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.
However, R NA semantics are now available by using masked NumPy types such as Int64Dtype
or PyArrow types (ArrowDtype).
Integer overflow#
pandas uses NumPy’s fixed-width integer types (int64 by default) rather than
Python’s arbitrary-precision integers. This means integer arithmetic can silently
overflow without raising an error:
In [49]: series = pd.Series([406, 372, 496, 41, 63, 118, 311, 271, 431, 95, 57, 52])
In [50]: series.dtype
Out[50]: dtype('int64')
The product of these integers exceeds the range of int64, so the result wraps
around silently:
In [51]: series.prod()
Out[51]: np.int64(3321523566636606464)
Compare this to pure Python, which uses arbitrary-precision integers and gives the correct result:
In [52]: python_product = 1
In [53]: for val in [406, 372, 496, 41, 63, 118, 311, 271, 431, 95, 57, 52]:
....: python_product *= val
....:
In [54]: python_product
Out[54]: 233542442569297099243299840
This behavior comes from NumPy and affects all integer arithmetic operations
(addition, subtraction, multiplication, etc.), not just prod(). See the
NumPy documentation on overflow errors
for more details.
If your computation may exceed int64 range, you can convert to float64 first
(at the cost of precision for very large values) or use Python’s built-in integers
via object dtype:
In [55]: series.astype("float64").prod()
Out[55]: np.float64(2.3354244256929707e+26)
In [56]: series.astype("object").prod()
Out[56]: 233542442569297099243299840
NumPy compatibility kwargs#
Many pandas methods accept additional keyword arguments (often passed as **kwargs) to ensure compatibility with NumPy’s API. When a pandas object is passed to a NumPy function (for example, np.sum(df)), NumPy will often pass its own arguments (such as out or keepdims) to the pandas method. To prevent errors, pandas accepts these keywords, if passed with their default value.
While pandas historically ignored many of these additional arguments, the library now strictly validates them (i.e. ensuring they are only passed with their default values) or deprecates them where they are not necessary for NumPy compatibility. If you pass an argument that pandas does not implement, it will raise an error or emit a deprecation warning. You should only rely on the arguments explicitly documented in the pandas method signature.
Differences with NumPy#
For Series and DataFrame objects, var() normalizes by
N-1 to produce unbiased estimates of the population variance, while NumPy’s
numpy.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#
pandas is not 100% thread safe. The known issues relate to
the 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.
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 or DataFrame
constructors using something similar to the following:
In [57]: x = np.array(list(range(10)), ">i4") # big endian
In [58]: newx = x.byteswap().view(x.dtype.newbyteorder()) # force native byteorder
In [59]: s = pd.Series(newx)
See the NumPy documentation on byte order for more details.
Storing lists inside a DataFrame or Series#
It is possible to store Python lists (or other collections) in the cells of
a DataFrame or Series, but it is best avoided. A column containing
lists has object dtype: each cell holds a reference to a separate Python
object, so the column uses far more memory than a native-dtype column and no
operation on it can use pandas’ vectorized code paths. Because lists are not
hashable, such a column also cannot be used as a group or merge key, and
methods that need to hash the values, like drop_duplicates(),
raise a TypeError.
In [60]: df = pd.DataFrame(
....: {
....: "name": ["A.J. Price"] * 3,
....: "opponent": ["76ers", "blazers", "bobcats"],
....: }
....: )
....:
In [61]: df["nearest_neighbors"] = [["Zach LaVine", "Jeremy Lin", "Nate Robinson"]] * 3
In [62]: df
Out[62]:
name opponent nearest_neighbors
0 A.J. Price 76ers [Zach LaVine, Jeremy Lin, Nate Robinson]
1 A.J. Price blazers [Zach LaVine, Jeremy Lin, Nate Robinson]
2 A.J. Price bobcats [Zach LaVine, Jeremy Lin, Nate Robinson]
In [63]: df.dtypes
Out[63]:
name str
opponent str
nearest_neighbors object
dtype: object
Instead, prefer a “long” format, with one row per list element. Existing
list-valued cells can be converted with explode():
In [64]: exploded = df.explode("nearest_neighbors")
In [65]: exploded
Out[65]:
name opponent nearest_neighbors
0 A.J. Price 76ers Zach LaVine
0 A.J. Price 76ers Jeremy Lin
0 A.J. Price 76ers Nate Robinson
1 A.J. Price blazers Zach LaVine
1 A.J. Price blazers Jeremy Lin
1 A.J. Price blazers Nate Robinson
2 A.J. Price bobcats Zach LaVine
2 A.J. Price bobcats Jeremy Lin
2 A.J. Price bobcats Nate Robinson
In [66]: exploded.dtypes
Out[66]:
name str
opponent str
nearest_neighbors str
dtype: object
Each element now occupies its own row in a natively-typed column, and the
usual vectorized operations, grouping, and joining all apply. If a nested
presentation is needed at the end of a computation, the lists can be
rebuilt with agg(list):
In [67]: exploded.groupby(["name", "opponent"], sort=False)["nearest_neighbors"].agg(list)
Out[67]:
name opponent
A.J. Price 76ers [Zach LaVine, Jeremy Lin, Nate Robinson]
blazers [Zach LaVine, Jeremy Lin, Nate Robinson]
bobcats [Zach LaVine, Jeremy Lin, Nate Robinson]
Name: nearest_neighbors, dtype: object
For columns of delimited strings, split directly into columns or indicator
variables with .str methods rather than creating lists as an
intermediate step:
In [68]: ser = pd.Series(["a|b", "b", "a|c"])
In [69]: ser.str.split("|", expand=True)
Out[69]:
0 1
0 a b
1 b NaN
2 a c
In [70]: ser.str.get_dummies(sep="|")
Out[70]:
a b c
0 1 1 0
1 0 1 0
2 1 0 1