Frequently Asked Questions (FAQ) — pandas 3.0.0.dev0+2094.g7595ed503d documentation (original) (raw)

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 theDataFrame 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), datetime64ns, float64(1), int64(1), object(1), timedelta64ns 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 withdtype=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), datetime64ns, float64(1), int64(1), object(1), timedelta64ns memory usage: 420.6 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 thememory_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 128 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(290928)

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 thememory_usage() method to determine the memory usage of aDataFrame 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) in ?() ----> 1 if pd.Series([False, True, False]): 2 print("I was true")

~/work/pandas/pandas/pandas/core/generic.py in ?(self) 1499 @final 1500 def bool(self) -> NoReturn: -> 1501 raise ValueError( 1502 f"The truth value of a {type(self).name} is ambiguous. " 1503 "Use a.empty, a.bool(), a.item(), a.any() or a.all()." 1504 )

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), 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 Serieswhich 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 theindex, 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 methodsDataFrame.apply(), DataFrame.aggregate(), DataFrame.transform(), andDataFrame.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 theDataFrame 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:3588, in Index.get_loc(self, key) 3587 try: -> 3588 return self._engine.get_loc(casted_key) 3589 except KeyError as err:

File index.pyx:168, in pandas._libs.index.IndexEngine.get_loc()

File index.pyx:197, in pandas._libs.index.IndexEngine.get_loc()

File pandas/_libs/hashtable_class_helper.pxi:7668, in pandas._libs.hashtable.PyObjectHashTable.get_item()

File pandas/_libs/hashtable_class_helper.pxi:7676, in pandas._libs.hashtable.PyObjectHashTable.get_item()

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:10503, in DataFrame.apply(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs) 10489 raise ValueError(f"Unknown engine '{engine}'") 10491 op = frame_apply( 10492 self, 10493 func=func, (...) 10501 kwargs=kwargs, 10502 )

10503 return op.apply().finalize(self, method="apply") 10504 elif hasattr(engine, "pandas_udf"): 10505 if result_type is not None:

File ~/work/pandas/pandas/pandas/core/apply.py:1007, in FrameApply.apply(self) 1004 elif self.raw: 1005 return self.apply_raw(engine=self.engine, engine_kwargs=self.engine_kwargs) -> 1007 return self.apply_standard()

File ~/work/pandas/pandas/pandas/core/apply.py:1159, in FrameApply.apply_standard(self) 1157 def apply_standard(self): 1158 if self.engine == "python": -> 1159 results, res_index = self.apply_series_generator() 1160 else: 1161 results, res_index = self.apply_series_numba()

File ~/work/pandas/pandas/pandas/core/apply.py:1175, in FrameApply.apply_series_generator(self) 1172 results = {} 1174 for i, v in enumerate(series_gen): -> 1175 results[i] = self.func(v, *self.args, **self.kwargs) 1176 if isinstance(results[i], ABCSeries): 1177 # If we have a view on v, we need to make a copy because 1178 # series_generator will swap out the underlying data 1179 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:5144, in Series.pop(self, item) 5113 def pop(self, item: Hashable) -> Any: 5114 """ 5115 Return item and drops from series. Raise KeyError if not found. 5116 (...) 5142 dtype: int64 5143 """ -> 5144 return super().pop(item=item)

File ~/work/pandas/pandas/pandas/core/generic.py:840, in NDFrame.pop(self, item) 839 def pop(self, item: Hashable) -> Series | Any: --> 840 result = self[item] 841 del self[item] 843 return result

File ~/work/pandas/pandas/pandas/core/series.py:949, in Series.getitem(self, key) 944 key = unpack_1tuple(key) 946 elif key_is_scalar: 947 # Note: GH#50617 in 3.0 we changed int key to always be treated as 948 # a label, matching DataFrame behavior. --> 949 return self._get_value(key) 951 # Convert generator to list before going through hashable part 952 # (We will iterate through the generator there to check for slices) 953 if is_iterator(key):

File ~/work/pandas/pandas/pandas/core/series.py:1036, in Series._get_value(self, label, takeable) 1033 return self._values[label] 1035 # Similar to Index.get_value, but we do not fall back to positional -> 1036 loc = self.index.get_loc(label) 1038 if is_integer(loc): 1039 return self._values[loc]

File ~/work/pandas/pandas/pandas/core/indexes/base.py:3595, in Index.get_loc(self, key) 3590 if isinstance(casted_key, slice) or ( 3591 isinstance(casted_key, abc.Iterable) 3592 and any(isinstance(x, slice) for x in casted_key) 3593 ): 3594 raise InvalidIndexError(key) from err -> 3595 raise KeyError(key) from err 3596 except TypeError: 3597 # If we have a listlike key, _check_indexing_error will raise 3598 # InvalidIndexError. Otherwise we fall through and re-raise 3599 # the TypeError. 3600 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:

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 viareindex() 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:

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 u 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 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 theNumPy type hierarchy.

The R language, by contrast, only has a handful of built-in data types:integer, numeric (floating-point), character, andboolean. 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 Int64Dtypeor PyArrow types (ArrowDtype).

Differences with NumPy#

For Series and DataFrame objects, var() normalizes byN-1 to produce unbiased estimates of the population variance, while NumPy’snumpy.var() normalizes by N, which measures the variance of the sample. Note thatcov() 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 ofDataFrame objects shared among threads, we recommend holding locks inside the threads where the data copying occurs.

See this linkfor 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 DataFrameconstructors using something similar to the following:

In [49]: x = np.array(list(range(10)), ">i4") # big endian

In [50]: newx = x.byteswap().view(x.dtype.newbyteorder()) # force native byteorder

In [51]: s = pd.Series(newx)

See the NumPy documentation on byte order for more details.