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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)
    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:1547, in NDFrame.__bool__(self)
       1545     @final
       1546     def __bool__(self) -> NoReturn:
    -> 1547         raise ValueError(
       1548             f"The truth value of a {type(self).__name__} is ambiguous. "
       1549             "Use a.empty, a.item(), a.any() or a.all()."
       1550         )

    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:3792, in Index.get_loc(self, key)
       3791 try:
    -> 3792     return self._engine.get_loc(casted_key)
       3793 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:14821, in DataFrame.apply(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs)
      14817                 engine_kwargs=engine_kwargs,
      14818                 args=args,
      14819                 kwargs=kwargs,
      14820             )
    > 14821             return op.apply().__finalize__(self, method="apply")
      14822         elif hasattr(engine, "__pandas_udf__"):
      14823             if result_type is not None:
      14824                 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:6211, in Series.pop(self, item)
       6207         1    2
       6208         2    3
       6209         dtype: int64
       6210         """
    -> 6211         return maybe_unbox_numpy_scalar(super().pop(item=item), dtype=self.dtype)

    File ~/work/pandas/pandas/pandas/core/generic.py:825, in NDFrame.pop(self, item)
        824     def pop(self, item: Hashable) -> Series | Any:
    --> 825         result = self[item]
        826         del self[item]
        827
        828         return result

    File ~/work/pandas/pandas/pandas/core/series.py:1063, in Series.__getitem__(self, key)
       1059
       1060         elif key_is_scalar:
       1061             # Note: GH#50617 in 3.0 we changed int key to always be treated as
       1062             #  a label, matching DataFrame behavior.
    -> 1063             return self._get_value(key)
       1064
       1065         # Convert generator to list before going through hashable part
       1066         # (We will iterate through the generator there to check for slices)

    File ~/work/pandas/pandas/pandas/core/series.py:1150, in Series._get_value(self, label, takeable)
       1146         if takeable:
       1147             return self._values[label]
       1148
       1149         # Similar to Index.get_value, but we do not fall back to positional
    -> 1150         loc = self.index.get_loc(label)
       1151
       1152         if is_integer(loc):
       1153             return self._values[loc]

    File ~/work/pandas/pandas/pandas/core/indexes/base.py:3799, in Index.get_loc(self, key)
       3794     if isinstance(casted_key, slice) or (
       3795         isinstance(casted_key, abc.Iterable)
       3796         and any(isinstance(x, slice) for x in casted_key)
       3797     ):
       3798         raise InvalidIndexError(key) from err
    -> 3799     raise KeyError(key) from err
       3800 except TypeError:
       3801     # If we have a listlike key, _check_indexing_error will raise
       3802     #  InvalidIndexError. Otherwise we fall through and re-raise
       3803     #  the TypeError.
       3804     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`).

## 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