# Frequently Asked Questions (FAQ)

## DataFrame memory usage

The memory usage of a dataframe (including the index) is shown when accessing the `info` method of a dataframe. A configuration option, `display.memory_usage` (see Options and Settings), 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 `df.info()`:

    In [1]: dtypes = ['int64', 'float64', 'datetime64[ns]', 'timedelta64[ns]',
       ...:           'complex128', 'object', 'bool']
       ...:

    In [2]: n = 5000

    In [3]: data = dict([ (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):
    bool               5000 non-null bool
    complex128         5000 non-null complex128
    datetime64[ns]     5000 non-null datetime64[ns]
    float64            5000 non-null float64
    int64              5000 non-null int64
    object             5000 non-null object
    timedelta64[ns]    5000 non-null timedelta64[ns]
    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: 289.1+ 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`.

New in version 0.17.1.

Passing `memory_usage='deep'` will enable a more accurate memory usage report, that accounts 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):
    bool               5000 non-null bool
    complex128         5000 non-null complex128
    datetime64[ns]     5000 non-null datetime64[ns]
    float64            5000 non-null float64
    int64              5000 non-null int64
    object             5000 non-null object
    timedelta64[ns]    5000 non-null timedelta64[ns]
    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: 406.2 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 of the dataframe can be found with the memory_usage method:

    In [8]: df.memory_usage()
    Out[8]:
    Index                 72
    bool                5000
    complex128         80000
    datetime64[ns]     40000
    float64            40000
    int64              40000
    object             40000
    timedelta64[ns]    40000
    categorical        10920
    dtype: int64

    # total memory usage of dataframe
    In [9]: df.memory_usage().sum()
    Out[9]: 295992

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]:
    bool                5000
    complex128         80000
    datetime64[ns]     40000
    float64            40000
    int64              40000
    object             40000
    timedelta64[ns]    40000
    categorical        10920
    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 a `if` or when using the boolean operations, `and`, `or`, or `not`. It is not clear what the result of

    >>> if pd.Series([False, True, False]):
         ...

should be. Should it be `True` because it’s not zero-length? `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().

If you see that, you need to explicitly choose what you want to do with it (e.g., use any(), all() or empty). or, 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

or return if `any` value is `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.

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()`:

For DataFrames, likewise, `in` applies to the column axis, testing for membership in the list of column names.

## `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 [15]: s = pd.Series([1, 2, 3, 4, 5], index=list('abcde'))

    In [16]: s
    Out[16]:
    a    1
    b    2
    c    3
    d    4
    e    5
    dtype: int64

    In [17]: s.dtype
    Out[17]: dtype('int64')

    In [18]: s2 = s.reindex(['a', 'b', 'c', 'f', 'u'])

    In [19]: s2
    Out[19]:
    a    1.0
    b    2.0
    c    3.0
    f    NaN
    u    NaN
    dtype: float64

    In [20]: s2.dtype
    Out[20]: dtype('float64')

This trade-off is made largely for memory and performance reasons, and also so that the resulting Series continues to be “numeric”. One possibility is to use `dtype=object` arrays instead.

### `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. These are summarized by 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 `DataFrame.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/DataFrame/Panel constructors using something similar to the following:

    In [21]: x = np.array(list(range(10)), '>i4') # big endian

    In [22]: newx = x.byteswap().newbyteorder() # force native byteorder

    In [23]: s = pd.Series(newx)

See the NumPy documentation on byte order for more details.

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