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# 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)
    <ipython-input-11-5c782b38cd2f> in ?()
    ----> 1 if pd.Series([False, True, False]):
          2     print("I was true")

    ~/work/pandas/pandas/pandas/core/generic.py in ?(self)
       1497     @final
       1498     def __bool__(self) -> NoReturn:
    -> 1499         raise ValueError(
       1500             f"The truth value of a {type(self).__name__} is ambiguous. "
       1501             "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
       1502         )

    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 `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:3631, in Index.get_loc(self, key)
       3630 try:
    -> 3631     return self._engine.get_loc(casted_key)
       3632 except KeyError as err:

    File ~/work/pandas/pandas/pandas/_libs/index.pyx:172, in pandas._libs.index.IndexEngine.get_loc()

    File ~/work/pandas/pandas/pandas/_libs/index.pyx:201, in pandas._libs.index.IndexEngine.get_loc()

    File pandas/_libs/hashtable_class_helper.pxi:7697, in pandas._libs.hashtable.PyObjectHashTable.get_item()

    File pandas/_libs/hashtable_class_helper.pxi:7705, 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:14251, in DataFrame.apply(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs)
      14237         raise ValueError(f"Unknown engine '{engine}'")
      14239     op = frame_apply(
      14240         self,
      14241         func=func,
       (...)  14249         kwargs=kwargs,
      14250     )
    > 14251     return op.apply().__finalize__(self, method="apply")
      14252 elif hasattr(engine, "__pandas_udf__"):
      14253     if result_type is not None:

    File ~/work/pandas/pandas/pandas/core/apply.py:1016, in FrameApply.apply(self)
       1013 elif self.raw:
       1014     return self.apply_raw(engine=self.engine, engine_kwargs=self.engine_kwargs)
    -> 1016 return self.apply_standard()

    File ~/work/pandas/pandas/pandas/core/apply.py:1168, in FrameApply.apply_standard(self)
       1166 def apply_standard(self):
       1167     if self.engine == "python":
    -> 1168         results, res_index = self.apply_series_generator()
       1169     else:
       1170         results, res_index = self.apply_series_numba()

    File ~/work/pandas/pandas/pandas/core/apply.py:1184, in FrameApply.apply_series_generator(self)
       1181 results = {}
       1183 for i, v in enumerate(series_gen):
    -> 1184     results[i] = self.func(v, *self.args, **self.kwargs)
       1185     if isinstance(results[i], ABCSeries):
       1186         # If we have a view on v, we need to make a copy because
       1187         #  series_generator will swap out the underlying data
       1188         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:6004, in Series.pop(self, item)
       5970 def pop(self, item: Hashable) -> Any:
       5971     """
       5972     Return item and drops from series. Raise KeyError if not found.
       5973
       (...)   6002     dtype: int64
       6003     """
    -> 6004     return maybe_unbox_numpy_scalar(super().pop(item=item))

    File ~/work/pandas/pandas/pandas/core/generic.py:822, in NDFrame.pop(self, item)
        821 def pop(self, item: Hashable) -> Series | Any:
    --> 822     result = self[item]
        823     del self[item]
        825     return result

    File ~/work/pandas/pandas/pandas/core/series.py:1009, in Series.__getitem__(self, key)
       1004     key = unpack_1tuple(key)
       1006 elif key_is_scalar:
       1007     # Note: GH#50617 in 3.0 we changed int key to always be treated as
       1008     #  a label, matching DataFrame behavior.
    -> 1009     return self._get_value(key)
       1011 # Convert generator to list before going through hashable part
       1012 # (We will iterate through the generator there to check for slices)
       1013 if is_iterator(key):

    File ~/work/pandas/pandas/pandas/core/series.py:1096, in Series._get_value(self, label, takeable)
       1093     return self._values[label]
       1095 # Similar to Index.get_value, but we do not fall back to positional
    -> 1096 loc = self.index.get_loc(label)
       1098 if is_integer(loc):
       1099     return self._values[loc]

    File ~/work/pandas/pandas/pandas/core/indexes/base.py:3638, in Index.get_loc(self, key)
       3633     if isinstance(casted_key, slice) or (
       3634         isinstance(casted_key, abc.Iterable)
       3635         and any(isinstance(x, slice) for x in casted_key)
       3636     ):
       3637         raise InvalidIndexError(key) from err
    -> 3638     raise KeyError(key) from err
       3639 except TypeError:
       3640     # If we have a listlike key, _check_indexing_error will raise
       3641     #  InvalidIndexError. Otherwise we fall through and re-raise
       3642     #  the TypeError.
       3643     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 `NA` across 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
--- | ---
`floating` | no change
`object` | no change
`integer` | cast to `float64`
`boolean` | cast to `object`

### 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

- `Int8Dtype`

- `Int16Dtype`

- `Int32Dtype`

- `Int64Dtype`

- `ArrowDtype`

    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`).

## 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 [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.