# 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`’s memory usage will be displayed when invoking the `df.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.core.frame.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: 288.2+ 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.core.frame.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: 424.7 KB

By default the display option is set to `True` but can be explicitly overridden by passing the `memory_usage` argument when invoking `df.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                128
    int64              40000
    float64            40000
    datetime64[ns]     40000
    timedelta64[ns]    40000
    complex128         80000
    object             40000
    bool                5000
    categorical         9968
    dtype: int64

    # total memory usage of dataframe
    In [9]: df.memory_usage().sum()
    Out[9]: 295096

By default the memory usage of the `DataFrame`’s 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         9968
    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`:

    >>> 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().

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`:

    >>> 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`:

    >>> 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 [11]: pd.Series([True]).bool()
    Out[11]: True

    In [12]: pd.Series([False]).bool()
    Out[12]: False

    In [13]: pd.DataFrame([[True]]).bool()
    Out[13]: True

    In [14]: pd.DataFrame([[False]]).bool()
    Out[14]: False

### Bitwise boolean

Bitwise boolean operators like `==` and `!=` 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.

    In [15]: s = pd.Series(range(5), index=list("abcde"))

    In [16]: 2 in s
    Out[16]: False

    In [17]: 'b' in s
    Out[17]: 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 [18]: s.isin([2])
    Out[18]:
    a    False
    b    False
    c     True
    d    False
    e    False
    dtype: bool

    In [19]: s.isin([2]).any()
    Out[19]: True

For `DataFrames`, 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 `.apply`, `.aggregate`, `.transform`, and `.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 [20]: values = [0, 1, 2, 3, 4, 5]

    In [21]: n_removed = 0

    In [22]: 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 [23]: values
    Out[23]: [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 [24]: def f(s):
       ....:     s.pop("a")
       ....:     return s
       ....:

    In [25]: df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})

    In [26]: try:
       ....:     df.apply(f, axis="columns")
       ....: except Exception as err:
       ....:     print(repr(err))
       ....:
    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 [27]: values = [0, 1, 2, 3, 4, 5]

    In [28]: n_removed = 0

    In [29]: 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 [30]: values
    Out[30]: [1, 3, 5]

    In [31]: def f(s):
       ....:     s = s.copy()
       ....:     s.pop("a")
       ....:     return s
       ....:

    In [32]: df = pd.DataFrame({"a": [1, 2, 3], 'b': [4, 5, 6]})

    In [33]: df.apply(f, axis="columns")
    Out[33]:
       b
    0  4
    1  5
    2  6

## `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 is there or is missing.

- Using a special sentinel value, bit pattern, or set of sentinel values to denote `NA` across 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 `isna` and `notna` 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 [34]: s = pd.Series([1, 2, 3, 4, 5], index=list("abcde"))

    In [35]: s
    Out[35]:
    a    1
    b    2
    c    3
    d    4
    e    5
    dtype: int64

    In [36]: s.dtype
    Out[36]: dtype('int64')

    In [37]: s2 = s.reindex(["a", "b", "c", "f", "u"])

    In [38]: s2
    Out[38]:
    a    1.0
    b    2.0
    c    3.0
    f    NaN
    u    NaN
    dtype: float64

    In [39]: s2.dtype
    Out[39]: 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

- `Int8Dtype`

- `Int16Dtype`

- `Int32Dtype`

- `Int64Dtype`

    In [40]: s_int = pd.Series([1, 2, 3, 4, 5], index=list("abcde"), dtype=pd.Int64Dtype())

    In [41]: s_int
    Out[41]:
    a    1
    b    2
    c    3
    d    4
    e    5
    dtype: Int64

    In [42]: s_int.dtype
    Out[42]: Int64Dtype()

    In [43]: s2_int = s_int.reindex(["a", "b", "c", "f", "u"])

    In [44]: s2_int
    Out[44]:
    a       1
    b       2
    c       3
    f    <NA>
    u    <NA>
    dtype: Int64

    In [45]: s2_int.dtype
    Out[45]: Int64Dtype()

See Nullable integer data type for more.

### `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. 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`

While this may seem like a heavy trade-off, I have found very few cases where this is an issue in practice i.e. storing values greater than 2**53. Some explanation for the motivation is 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.

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

    In [47]: newx = x.byteswap().newbyteorder()  # force native byteorder

    In [48]: s = pd.Series(newx)

See the NumPy documentation on byte order for more details.