# Intro to Data Structures

We’ll start with a quick, non-comprehensive overview of the fundamental data structures in pandas to get you started. The fundamental behavior about data types, indexing, and axis labeling / alignment apply across all of the objects. To get started, import numpy and load pandas into your namespace:

    In [1]: import numpy as np

    In [2]: import pandas as pd

Here is a basic tenet to keep in mind: data alignment is intrinsic. The link between labels and data will not be broken unless done so explicitly by you.

We’ll give a brief intro to the data structures, then consider all of the broad categories of functionality and methods in separate sections.

## Series

`Series` is a one-dimensional labeled array capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.). The axis labels are collectively referred to as the index. The basic method to create a Series is to call:

    >>> s = pd.Series(data, index=index)

Here, `data` can be many different things:

> - a Python dict
> - an ndarray
> - a scalar value (like 5)

The passed index is a list of axis labels. Thus, this separates into a few cases depending on what data is:

From ndarray

If `data` is an ndarray, index must be the same length as data. If no index is passed, one will be created having values `[0, ..., len(data) - 1]`.

    In [3]: s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])

    In [4]: s
    Out[4]:
    a    0.4691
    b   -0.2829
    c   -1.5091
    d   -1.1356
    e    1.2121
    dtype: float64

    In [5]: s.index
    Out[5]: Index([u'a', u'b', u'c', u'd', u'e'], dtype='object')

    In [6]: pd.Series(np.random.randn(5))
    Out[6]:
    0   -0.1732
    1    0.1192
    2   -1.0442
    3   -0.8618
    4   -2.1046
    dtype: float64

Note

pandas supports non-unique index values. If an operation that does not support duplicate index values is attempted, an exception will be raised at that time. The reason for being lazy is nearly all performance-based (there are many instances in computations, like parts of GroupBy, where the index is not used).

From dict

If `data` is a dict, if index is passed the values in data corresponding to the labels in the index will be pulled out. Otherwise, an index will be constructed from the sorted keys of the dict, if possible.

    In [7]: d = {'a' : 0., 'b' : 1., 'c' : 2.}

    In [8]: pd.Series(d)
    Out[8]:
    a    0.0
    b    1.0
    c    2.0
    dtype: float64

    In [9]: pd.Series(d, index=['b', 'c', 'd', 'a'])
    Out[9]:
    b    1.0
    c    2.0
    d    NaN
    a    0.0
    dtype: float64

Note

NaN (not a number) is the standard missing data marker used in pandas

From scalar value If `data` is a scalar value, an index must be provided. The value will be repeated to match the length of index

    In [10]: pd.Series(5., index=['a', 'b', 'c', 'd', 'e'])
    Out[10]:
    a    5.0
    b    5.0
    c    5.0
    d    5.0
    e    5.0
    dtype: float64

### Series is ndarray-like

`Series` acts very similarly to a `ndarray`, and is a valid argument to most NumPy functions. However, things like slicing also slice the index.

    In [11]: s[0]
    Out[11]: 0.46911229990718628

    In [12]: s[:3]
    Out[12]:
    a    0.4691
    b   -0.2829
    c   -1.5091
    dtype: float64

    In [13]: s[s > s.median()]
    Out[13]:
    a    0.4691
    e    1.2121
    dtype: float64

    In [14]: s[[4, 3, 1]]
    Out[14]:
    e    1.2121
    d   -1.1356
    b   -0.2829
    dtype: float64

    In [15]: np.exp(s)
    Out[15]:
    a    1.5986
    b    0.7536
    c    0.2211
    d    0.3212
    e    3.3606
    dtype: float64

We will address array-based indexing in a separate section.

### Series is dict-like

A Series is like a fixed-size dict in that you can get and set values by index label:

    In [16]: s['a']
    Out[16]: 0.46911229990718628

    In [17]: s['e'] = 12.

    In [18]: s
    Out[18]:
    a     0.4691
    b    -0.2829
    c    -1.5091
    d    -1.1356
    e    12.0000
    dtype: float64

    In [19]: 'e' in s
    Out[19]: True

    In [20]: 'f' in s
    Out[20]: False

If a label is not contained, an exception is raised:

    >>> s['f']
    KeyError: 'f'

Using the `get` method, a missing label will return None or specified default:

    In [21]: s.get('f')

    In [22]: s.get('f', np.nan)
    Out[22]: nan

See also the section on attribute access.

### Vectorized operations and label alignment with Series

When doing data analysis, as with raw NumPy arrays looping through Series value-by-value is usually not necessary. Series can also be passed into most NumPy methods expecting an ndarray.

    In [23]: s + s
    Out[23]:
    a     0.9382
    b    -0.5657
    c    -3.0181
    d    -2.2713
    e    24.0000
    dtype: float64

    In [24]: s * 2
    Out[24]:
    a     0.9382
    b    -0.5657
    c    -3.0181
    d    -2.2713
    e    24.0000
    dtype: float64

    In [25]: np.exp(s)
    Out[25]:
    a         1.5986
    b         0.7536
    c         0.2211
    d         0.3212
    e    162754.7914
    dtype: float64

A key difference between Series and ndarray is that operations between Series automatically align the data based on label. Thus, you can write computations without giving consideration to whether the Series involved have the same labels.

    In [26]: s[1:] + s[:-1]
    Out[26]:
    a       NaN
    b   -0.5657
    c   -3.0181
    d   -2.2713
    e       NaN
    dtype: float64

The result of an operation between unaligned Series will have the union of the indexes involved. If a label is not found in one Series or the other, the result will be marked as missing `NaN`. Being able to write code without doing any explicit data alignment grants immense freedom and flexibility in interactive data analysis and research. The integrated data alignment features of the pandas data structures set pandas apart from the majority of related tools for working with labeled data.

Note

In general, we chose to make the default result of operations between differently indexed objects yield the union of the indexes in order to avoid loss of information. Having an index label, though the data is missing, is typically important information as part of a computation. You of course have the option of dropping labels with missing data via the dropna function.

### Name attribute

Series can also have a `name` attribute:

    In [27]: s = pd.Series(np.random.randn(5), name='something')

    In [28]: s
    Out[28]:
    0   -0.4949
    1    1.0718
    2    0.7216
    3   -0.7068
    4   -1.0396
    Name: something, dtype: float64

    In [29]: s.name
    Out[29]: 'something'

The Series `name` will be assigned automatically in many cases, in particular when taking 1D slices of DataFrame as you will see below.

New in version 0.18.0.

You can rename a Series with the `pandas.Series.rename()` method.

    In [30]: s2 = s.rename("different")

    In [31]: s2.name
    Out[31]: 'different'

Note that `s` and `s2` refer to different objects.

## DataFrame

DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. You can think of it like a spreadsheet or SQL table, or a dict of Series objects. It is generally the most commonly used pandas object. Like Series, DataFrame accepts many different kinds of input:

> - Dict of 1D ndarrays, lists, dicts, or Series
> - 2-D numpy.ndarray
> - Structured or record ndarray
> - A `Series`
> - Another `DataFrame`

Along with the data, you can optionally pass index (row labels) and columns (column labels) arguments. If you pass an index and / or columns, you are guaranteeing the index and / or columns of the resulting DataFrame. Thus, a dict of Series plus a specific index will discard all data not matching up to the passed index.

If axis labels are not passed, they will be constructed from the input data based on common sense rules.

### From dict of Series or dicts

The result index will be the union of the indexes of the various Series. If there are any nested dicts, these will be first converted to Series. If no columns are passed, the columns will be the sorted list of dict keys.

    In [32]: d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),
       ....:      'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}
       ....:

    In [33]: df = pd.DataFrame(d)

    In [34]: df
    Out[34]:
       one  two
    a  1.0  1.0
    b  2.0  2.0
    c  3.0  3.0
    d  NaN  4.0

    In [35]: pd.DataFrame(d, index=['d', 'b', 'a'])
    Out[35]:
       one  two
    d  NaN  4.0
    b  2.0  2.0
    a  1.0  1.0

    In [36]: pd.DataFrame(d, index=['d', 'b', 'a'], columns=['two', 'three'])
    Out[36]:
       two three
    d  4.0   NaN
    b  2.0   NaN
    a  1.0   NaN

The row and column labels can be accessed respectively by accessing the index and columns attributes:

Note

When a particular set of columns is passed along with a dict of data, the passed columns override the keys in the dict.

    In [37]: df.index
    Out[37]: Index([u'a', u'b', u'c', u'd'], dtype='object')

    In [38]: df.columns
    Out[38]: Index([u'one', u'two'], dtype='object')

### From dict of ndarrays / lists

The ndarrays must all be the same length. If an index is passed, it must clearly also be the same length as the arrays. If no index is passed, the result will be `range(n)`, where `n` is the array length.

    In [39]: d = {'one' : [1., 2., 3., 4.],
       ....:      'two' : [4., 3., 2., 1.]}
       ....:

    In [40]: pd.DataFrame(d)
    Out[40]:
       one  two
    0  1.0  4.0
    1  2.0  3.0
    2  3.0  2.0
    3  4.0  1.0

    In [41]: pd.DataFrame(d, index=['a', 'b', 'c', 'd'])
    Out[41]:
       one  two
    a  1.0  4.0
    b  2.0  3.0
    c  3.0  2.0
    d  4.0  1.0

### From structured or record array

This case is handled identically to a dict of arrays.

    In [42]: data = np.zeros((2,), dtype=[('A', 'i4'),('B', 'f4'),('C', 'a10')])

    In [43]: data[:] = [(1,2.,'Hello'), (2,3.,"World")]

    In [44]: pd.DataFrame(data)
    Out[44]:
       A    B      C
    0  1  2.0  Hello
    1  2  3.0  World

    In [45]: pd.DataFrame(data, index=['first', 'second'])
    Out[45]:
            A    B      C
    first   1  2.0  Hello
    second  2  3.0  World

    In [46]: pd.DataFrame(data, columns=['C', 'A', 'B'])
    Out[46]:
           C  A    B
    0  Hello  1  2.0
    1  World  2  3.0

Note

DataFrame is not intended to work exactly like a 2-dimensional NumPy ndarray.

### From a list of dicts

    In [47]: data2 = [{'a': 1, 'b': 2}, {'a': 5, 'b': 10, 'c': 20}]

    In [48]: pd.DataFrame(data2)
    Out[48]:
       a   b     c
    0  1   2   NaN
    1  5  10  20.0

    In [49]: pd.DataFrame(data2, index=['first', 'second'])
    Out[49]:
            a   b     c
    first   1   2   NaN
    second  5  10  20.0

    In [50]: pd.DataFrame(data2, columns=['a', 'b'])
    Out[50]:
       a   b
    0  1   2
    1  5  10

### From a dict of tuples

You can automatically create a multi-indexed frame by passing a tuples dictionary

    In [51]: pd.DataFrame({('a', 'b'): {('A', 'B'): 1, ('A', 'C'): 2},
       ....:               ('a', 'a'): {('A', 'C'): 3, ('A', 'B'): 4},
       ....:               ('a', 'c'): {('A', 'B'): 5, ('A', 'C'): 6},
       ....:               ('b', 'a'): {('A', 'C'): 7, ('A', 'B'): 8},
       ....:               ('b', 'b'): {('A', 'D'): 9, ('A', 'B'): 10}})
       ....:
    Out[51]:
           a              b
           a    b    c    a     b
    A B  4.0  1.0  5.0  8.0  10.0
      C  3.0  2.0  6.0  7.0   NaN
      D  NaN  NaN  NaN  NaN   9.0

### From a Series

The result will be a DataFrame with the same index as the input Series, and with one column whose name is the original name of the Series (only if no other column name provided).

Missing Data

Much more will be said on this topic in the Missing data section. To construct a DataFrame with missing data, use `np.nan` for those values which are missing. Alternatively, you may pass a `numpy.MaskedArray` as the data argument to the DataFrame constructor, and its masked entries will be considered missing.

### Alternate Constructors

DataFrame.from_dict

`DataFrame.from_dict` takes a dict of dicts or a dict of array-like sequences and returns a DataFrame. It operates like the `DataFrame` constructor except for the `orient` parameter which is `'columns'` by default, but which can be set to `'index'` in order to use the dict keys as row labels.

DataFrame.from_records

`DataFrame.from_records` takes a list of tuples or an ndarray with structured dtype. Works analogously to the normal `DataFrame` constructor, except that index maybe be a specific field of the structured dtype to use as the index. For example:

    In [52]: data
    Out[52]:
    array([(1,  2., 'Hello'), (2,  3., 'World')],
          dtype=[('A', '<i4'), ('B', '<f4'), ('C', 'S10')])

    In [53]: pd.DataFrame.from_records(data, index='C')
    Out[53]:
           A    B
    C
    Hello  1  2.0
    World  2  3.0

DataFrame.from_items

`DataFrame.from_items` works analogously to the form of the `dict` constructor that takes a sequence of `(key, value)` pairs, where the keys are column (or row, in the case of `orient='index'`) names, and the value are the column values (or row values). This can be useful for constructing a DataFrame with the columns in a particular order without having to pass an explicit list of columns:

    In [54]: pd.DataFrame.from_items([('A', [1, 2, 3]), ('B', [4, 5, 6])])
    Out[54]:
       A  B
    0  1  4
    1  2  5
    2  3  6

If you pass `orient='index'`, the keys will be the row labels. But in this case you must also pass the desired column names:

    In [55]: pd.DataFrame.from_items([('A', [1, 2, 3]), ('B', [4, 5, 6])],
       ....:                         orient='index', columns=['one', 'two', 'three'])
       ....:
    Out[55]:
       one  two  three
    A    1    2      3
    B    4    5      6

### Column selection, addition, deletion

You can treat a DataFrame semantically like a dict of like-indexed Series objects. Getting, setting, and deleting columns works with the same syntax as the analogous dict operations:

    In [56]: df['one']
    Out[56]:
    a    1.0
    b    2.0
    c    3.0
    d    NaN
    Name: one, dtype: float64

    In [57]: df['three'] = df['one'] * df['two']

    In [58]: df['flag'] = df['one'] > 2

    In [59]: df
    Out[59]:
       one  two  three   flag
    a  1.0  1.0    1.0  False
    b  2.0  2.0    4.0  False
    c  3.0  3.0    9.0   True
    d  NaN  4.0    NaN  False

Columns can be deleted or popped like with a dict:

    In [60]: del df['two']

    In [61]: three = df.pop('three')

    In [62]: df
    Out[62]:
       one   flag
    a  1.0  False
    b  2.0  False
    c  3.0   True
    d  NaN  False

When inserting a scalar value, it will naturally be propagated to fill the column:

    In [63]: df['foo'] = 'bar'

    In [64]: df
    Out[64]:
       one   flag  foo
    a  1.0  False  bar
    b  2.0  False  bar
    c  3.0   True  bar
    d  NaN  False  bar

When inserting a Series that does not have the same index as the DataFrame, it will be conformed to the DataFrame’s index:

    In [65]: df['one_trunc'] = df['one'][:2]

    In [66]: df
    Out[66]:
       one   flag  foo  one_trunc
    a  1.0  False  bar        1.0
    b  2.0  False  bar        2.0
    c  3.0   True  bar        NaN
    d  NaN  False  bar        NaN

You can insert raw ndarrays but their length must match the length of the DataFrame’s index.

By default, columns get inserted at the end. The `insert` function is available to insert at a particular location in the columns:

    In [67]: df.insert(1, 'bar', df['one'])

    In [68]: df
    Out[68]:
       one  bar   flag  foo  one_trunc
    a  1.0  1.0  False  bar        1.0
    b  2.0  2.0  False  bar        2.0
    c  3.0  3.0   True  bar        NaN
    d  NaN  NaN  False  bar        NaN

### Assigning New Columns in Method Chains

Inspired by dplyr’s `mutate` verb, DataFrame has an `assign()` method that allows you to easily create new columns that are potentially derived from existing columns.

    In [69]: iris = pd.read_csv('data/iris.data')

    In [70]: iris.head()
    Out[70]:
       SepalLength  SepalWidth  PetalLength  PetalWidth         Name
    0          5.1         3.5          1.4         0.2  Iris-setosa
    1          4.9         3.0          1.4         0.2  Iris-setosa
    2          4.7         3.2          1.3         0.2  Iris-setosa
    3          4.6         3.1          1.5         0.2  Iris-setosa
    4          5.0         3.6          1.4         0.2  Iris-setosa

    In [71]: (iris.assign(sepal_ratio = iris['SepalWidth'] / iris['SepalLength'])
       ....:      .head())
       ....:
    Out[71]:
       SepalLength  SepalWidth  PetalLength  PetalWidth         Name  sepal_ratio
    0          5.1         3.5          1.4         0.2  Iris-setosa       0.6863
    1          4.9         3.0          1.4         0.2  Iris-setosa       0.6122
    2          4.7         3.2          1.3         0.2  Iris-setosa       0.6809
    3          4.6         3.1          1.5         0.2  Iris-setosa       0.6739
    4          5.0         3.6          1.4         0.2  Iris-setosa       0.7200

Above was an example of inserting a precomputed value. We can also pass in a function of one argument to be evalutated on the DataFrame being assigned to.

    In [72]: iris.assign(sepal_ratio = lambda x: (x['SepalWidth'] /
       ....:                                      x['SepalLength'])).head()
       ....:
    Out[72]:
       SepalLength  SepalWidth  PetalLength  PetalWidth         Name  sepal_ratio
    0          5.1         3.5          1.4         0.2  Iris-setosa       0.6863
    1          4.9         3.0          1.4         0.2  Iris-setosa       0.6122
    2          4.7         3.2          1.3         0.2  Iris-setosa       0.6809
    3          4.6         3.1          1.5         0.2  Iris-setosa       0.6739
    4          5.0         3.6          1.4         0.2  Iris-setosa       0.7200

`assign` always returns a copy of the data, leaving the original DataFrame untouched.

Passing a callable, as opposed to an actual value to be inserted, is useful when you don’t have a reference to the DataFrame at hand. This is common when using `assign` in chains of operations. For example, we can limit the DataFrame to just those observations with a Sepal Length greater than 5, calculate the ratio, and plot:

    In [73]: (iris.query('SepalLength > 5')
       ....:      .assign(SepalRatio = lambda x: x.SepalWidth / x.SepalLength,
       ....:              PetalRatio = lambda x: x.PetalWidth / x.PetalLength)
       ....:      .plot(kind='scatter', x='SepalRatio', y='PetalRatio'))
       ....:
    Out[73]: <matplotlib.axes._subplots.AxesSubplot at 0x7faad4e0ef90>

Since a function is passed in, the function is computed on the DataFrame being assigned to. Importantly, this is the DataFrame that’s been filtered to those rows with sepal length greater than 5. The filtering happens first, and then the ratio calculations. This is an example where we didn’t have a reference to the filtered DataFrame available.

The function signature for `assign` is simply `**kwargs`. The keys are the column names for the new fields, and the values are either a value to be inserted (for example, a `Series` or NumPy array), or a function of one argument to be called on the `DataFrame`. A copy of the original DataFrame is returned, with the new values inserted.

Warning

Since the function signature of `assign` is `**kwargs`, a dictionary, the order of the new columns in the resulting DataFrame cannot be guaranteed to match the order you pass in. To make things predictable, items are inserted alphabetically (by key) at the end of the DataFrame.

All expressions are computed first, and then assigned. So you can’t refer to another column being assigned in the same call to `assign`. For example:

>     In [74]: # Don't do this, bad reference to `C`
>             df.assign(C = lambda x: x['A'] + x['B'],
>                       D = lambda x: x['A'] + x['C'])
>     In [2]: # Instead, break it into two assigns
>             (df.assign(C = lambda x: x['A'] + x['B'])
>                .assign(D = lambda x: x['A'] + x['C']))

### Indexing / Selection

The basics of indexing are as follows:

Operation | Syntax | Result
--- | --- | ---
Select column | `df[col]` | Series
Select row by label | `df.loc[label]` | Series
Select row by integer location | `df.iloc[loc]` | Series
Slice rows | `df[5:10]` | DataFrame
Select rows by boolean vector | `df[bool_vec]` | DataFrame

Row selection, for example, returns a Series whose index is the columns of the DataFrame:

    In [75]: df.loc['b']
    Out[75]:
    one              2
    bar              2
    flag         False
    foo            bar
    one_trunc        2
    Name: b, dtype: object

    In [76]: df.iloc[2]
    Out[76]:
    one             3
    bar             3
    flag         True
    foo           bar
    one_trunc     NaN
    Name: c, dtype: object

For a more exhaustive treatment of more sophisticated label-based indexing and slicing, see the section on indexing. We will address the fundamentals of reindexing / conforming to new sets of labels in the section on reindexing.

### Data alignment and arithmetic

Data alignment between DataFrame objects automatically align on both the columns and the index (row labels). Again, the resulting object will have the union of the column and row labels.

    In [77]: df = pd.DataFrame(np.random.randn(10, 4), columns=['A', 'B', 'C', 'D'])

    In [78]: df2 = pd.DataFrame(np.random.randn(7, 3), columns=['A', 'B', 'C'])

    In [79]: df + df2
    Out[79]:
            A       B       C   D
    0  0.0457 -0.0141  1.3809 NaN
    1 -0.9554 -1.5010  0.0372 NaN
    2 -0.6627  1.5348 -0.8597 NaN
    3 -2.4529  1.2373 -0.1337 NaN
    4  1.4145  1.9517 -2.3204 NaN
    5 -0.4949 -1.6497 -1.0846 NaN
    6 -1.0476 -0.7486 -0.8055 NaN
    7     NaN     NaN     NaN NaN
    8     NaN     NaN     NaN NaN
    9     NaN     NaN     NaN NaN

When doing an operation between DataFrame and Series, the default behavior is to align the Series index on the DataFrame columns, thus broadcasting row-wise. For example:

    In [80]: df - df.iloc[0]
    Out[80]:
            A       B       C       D
    0  0.0000  0.0000  0.0000  0.0000
    1 -1.3593 -0.2487 -0.4534 -1.7547
    2  0.2531  0.8297  0.0100 -1.9912
    3 -1.3111  0.0543 -1.7249 -1.6205
    4  0.5730  1.5007 -0.6761  1.3673
    5 -1.7412  0.7820 -1.2416 -2.0531
    6 -1.2408 -0.8696 -0.1533  0.0004
    7 -0.7439  0.4110 -0.9296 -0.2824
    8 -1.1949  1.3207  0.2382 -1.4826
    9  2.2938  1.8562  0.7733 -1.4465

In the special case of working with time series data, and the DataFrame index also contains dates, the broadcasting will be column-wise:

    In [81]: index = pd.date_range('1/1/2000', periods=8)

    In [82]: df = pd.DataFrame(np.random.randn(8, 3), index=index, columns=list('ABC'))

    In [83]: df
    Out[83]:
                     A       B       C
    2000-01-01 -1.2268  0.7698 -1.2812
    2000-01-02 -0.7277 -0.1213 -0.0979
    2000-01-03  0.6958  0.3417  0.9597
    2000-01-04 -1.1103 -0.6200  0.1497
    2000-01-05 -0.7323  0.6877  0.1764
    2000-01-06  0.4033 -0.1550  0.3016
    2000-01-07 -2.1799 -1.3698 -0.9542
    2000-01-08  1.4627 -1.7432 -0.8266

    In [84]: type(df['A'])
    Out[84]: pandas.core.series.Series

    In [85]: df - df['A']
    Out[85]:
                2000-01-01 00:00:00  2000-01-02 00:00:00  2000-01-03 00:00:00  \
    2000-01-01                  NaN                  NaN                  NaN
    2000-01-02                  NaN                  NaN                  NaN
    2000-01-03                  NaN                  NaN                  NaN
    2000-01-04                  NaN                  NaN                  NaN
    2000-01-05                  NaN                  NaN                  NaN
    2000-01-06                  NaN                  NaN                  NaN
    2000-01-07                  NaN                  NaN                  NaN
    2000-01-08                  NaN                  NaN                  NaN

                2000-01-04 00:00:00 ...  2000-01-08 00:00:00   A   B   C
    2000-01-01                  NaN ...                  NaN NaN NaN NaN
    2000-01-02                  NaN ...                  NaN NaN NaN NaN
    2000-01-03                  NaN ...                  NaN NaN NaN NaN
    2000-01-04                  NaN ...                  NaN NaN NaN NaN
    2000-01-05                  NaN ...                  NaN NaN NaN NaN
    2000-01-06                  NaN ...                  NaN NaN NaN NaN
    2000-01-07                  NaN ...                  NaN NaN NaN NaN
    2000-01-08                  NaN ...                  NaN NaN NaN NaN

    [8 rows x 11 columns]

Warning

    df - df['A']

is now deprecated and will be removed in a future release. The preferred way to replicate this behavior is

    df.sub(df['A'], axis=0)

For explicit control over the matching and broadcasting behavior, see the section on flexible binary operations.

Operations with scalars are just as you would expect:

    In [86]: df * 5 + 2
    Out[86]:
                     A       B       C
    2000-01-01 -4.1341  5.8490 -4.4062
    2000-01-02 -1.6385  1.3935  1.5106
    2000-01-03  5.4789  3.7087  6.7986
    2000-01-04 -3.5517 -1.0999  2.7487
    2000-01-05 -1.6617  5.4387  2.8822
    2000-01-06  4.0165  1.2252  3.5081
    2000-01-07 -8.8993 -4.8492 -2.7710
    2000-01-08  9.3135 -6.7158 -2.1330

    In [87]: 1 / df
    Out[87]:
                     A       B        C
    2000-01-01 -0.8151  1.2990  -0.7805
    2000-01-02 -1.3742 -8.2436 -10.2163
    2000-01-03  1.4372  2.9262   1.0420
    2000-01-04 -0.9006 -1.6130   6.6779
    2000-01-05 -1.3655  1.4540   5.6675
    2000-01-06  2.4795 -6.4537   3.3154
    2000-01-07 -0.4587 -0.7300  -1.0480
    2000-01-08  0.6837 -0.5737  -1.2098

    In [88]: df ** 4
    Out[88]:
                      A       B           C
    2000-01-01   2.2653  0.3512  2.6948e+00
    2000-01-02   0.2804  0.0002  9.1796e-05
    2000-01-03   0.2344  0.0136  8.4838e-01
    2000-01-04   1.5199  0.1477  5.0286e-04
    2000-01-05   0.2876  0.2237  9.6924e-04
    2000-01-06   0.0265  0.0006  8.2769e-03
    2000-01-07  22.5795  3.5212  8.2903e-01
    2000-01-08   4.5774  9.2332  4.6683e-01

Boolean operators work as well:

    In [89]: df1 = pd.DataFrame({'a' : [1, 0, 1], 'b' : [0, 1, 1] }, dtype=bool)

    In [90]: df2 = pd.DataFrame({'a' : [0, 1, 1], 'b' : [1, 1, 0] }, dtype=bool)

    In [91]: df1 & df2
    Out[91]:
           a      b
    0  False  False
    1  False   True
    2   True  False

    In [92]: df1 | df2
    Out[92]:
          a     b
    0  True  True
    1  True  True
    2  True  True

    In [93]: df1 ^ df2
    Out[93]:
           a      b
    0   True   True
    1   True  False
    2  False   True

    In [94]: -df1
    Out[94]:
           a      b
    0  False   True
    1   True  False
    2  False  False

### Transposing

To transpose, access the `T` attribute (also the `transpose` function), similar to an ndarray:

    # only show the first 5 rows
    In [95]: df[:5].T
    Out[95]:
       2000-01-01  2000-01-02  2000-01-03  2000-01-04  2000-01-05
    A     -1.2268     -0.7277      0.6958     -1.1103     -0.7323
    B      0.7698     -0.1213      0.3417     -0.6200      0.6877
    C     -1.2812     -0.0979      0.9597      0.1497      0.1764

### DataFrame interoperability with NumPy functions

Elementwise NumPy ufuncs (log, exp, sqrt, …) and various other NumPy functions can be used with no issues on DataFrame, assuming the data within are numeric:

    In [96]: np.exp(df)
    Out[96]:
                     A       B       C
    2000-01-01  0.2932  2.1593  0.2777
    2000-01-02  0.4830  0.8858  0.9068
    2000-01-03  2.0053  1.4074  2.6110
    2000-01-04  0.3294  0.5380  1.1615
    2000-01-05  0.4808  1.9892  1.1930
    2000-01-06  1.4968  0.8565  1.3521
    2000-01-07  0.1131  0.2541  0.3851
    2000-01-08  4.3176  0.1750  0.4375

    In [97]: np.asarray(df)
    Out[97]:
    array([[-1.2268,  0.7698, -1.2812],
           [-0.7277, -0.1213, -0.0979],
           [ 0.6958,  0.3417,  0.9597],
           [-1.1103, -0.62  ,  0.1497],
           [-0.7323,  0.6877,  0.1764],
           [ 0.4033, -0.155 ,  0.3016],
           [-2.1799, -1.3698, -0.9542],
           [ 1.4627, -1.7432, -0.8266]])

The dot method on DataFrame implements matrix multiplication:

    In [98]: df.T.dot(df)
    Out[98]:
             A       B       C
    A  11.3419 -0.0598  3.0080
    B  -0.0598  6.5206  2.0833
    C   3.0080  2.0833  4.3105

Similarly, the dot method on Series implements dot product:

    In [99]: s1 = pd.Series(np.arange(5,10))

    In [100]: s1.dot(s1)
    Out[100]: 255

DataFrame is not intended to be a drop-in replacement for ndarray as its indexing semantics are quite different in places from a matrix.

### Console display

Very large DataFrames will be truncated to display them in the console. You can also get a summary using `info()`. (Here I am reading a CSV version of the baseball dataset from the plyr R package):

    In [101]: baseball = pd.read_csv('data/baseball.csv')

    In [102]: print(baseball)
           id     player  year  stint  ...   hbp   sh   sf  gidp
    0   88641  womacto01  2006      2  ...   0.0  3.0  0.0   0.0
    1   88643  schilcu01  2006      1  ...   0.0  0.0  0.0   0.0
    ..    ...        ...   ...    ...  ...   ...  ...  ...   ...
    98  89533   aloumo01  2007      1  ...   2.0  0.0  3.0  13.0
    99  89534  alomasa02  2007      1  ...   0.0  0.0  0.0   0.0

    [100 rows x 23 columns]

    In [103]: baseball.info()
    <class 'pandas.core.frame.DataFrame'>
    RangeIndex: 100 entries, 0 to 99
    Data columns (total 23 columns):
    id        100 non-null int64
    player    100 non-null object
    year      100 non-null int64
    stint     100 non-null int64
    team      100 non-null object
    lg        100 non-null object
    g         100 non-null int64
    ab        100 non-null int64
    r         100 non-null int64
    h         100 non-null int64
    X2b       100 non-null int64
    X3b       100 non-null int64
    hr        100 non-null int64
    rbi       100 non-null float64
    sb        100 non-null float64
    cs        100 non-null float64
    bb        100 non-null int64
    so        100 non-null float64
    ibb       100 non-null float64
    hbp       100 non-null float64
    sh        100 non-null float64
    sf        100 non-null float64
    gidp      100 non-null float64
    dtypes: float64(9), int64(11), object(3)
    memory usage: 18.0+ KB

However, using `to_string` will return a string representation of the DataFrame in tabular form, though it won’t always fit the console width:

    In [104]: print(baseball.iloc[-20:, :12].to_string())
           id     player  year  stint team  lg    g   ab   r    h  X2b  X3b
    80  89474  finlest01  2007      1  COL  NL   43   94   9   17    3    0
    81  89480  embreal01  2007      1  OAK  AL    4    0   0    0    0    0
    82  89481  edmonji01  2007      1  SLN  NL  117  365  39   92   15    2
    83  89482  easleda01  2007      1  NYN  NL   76  193  24   54    6    0
    84  89489  delgaca01  2007      1  NYN  NL  139  538  71  139   30    0
    85  89493  cormirh01  2007      1  CIN  NL    6    0   0    0    0    0
    86  89494  coninje01  2007      2  NYN  NL   21   41   2    8    2    0
    87  89495  coninje01  2007      1  CIN  NL   80  215  23   57   11    1
    88  89497  clemero02  2007      1  NYA  AL    2    2   0    1    0    0
    89  89498  claytro01  2007      2  BOS  AL    8    6   1    0    0    0
    90  89499  claytro01  2007      1  TOR  AL   69  189  23   48   14    0
    91  89501  cirilje01  2007      2  ARI  NL   28   40   6    8    4    0
    92  89502  cirilje01  2007      1  MIN  AL   50  153  18   40    9    2
    93  89521  bondsba01  2007      1  SFN  NL  126  340  75   94   14    0
    94  89523  biggicr01  2007      1  HOU  NL  141  517  68  130   31    3
    95  89525  benitar01  2007      2  FLO  NL   34    0   0    0    0    0
    96  89526  benitar01  2007      1  SFN  NL   19    0   0    0    0    0
    97  89530  ausmubr01  2007      1  HOU  NL  117  349  38   82   16    3
    98  89533   aloumo01  2007      1  NYN  NL   87  328  51  112   19    1
    99  89534  alomasa02  2007      1  NYN  NL    8   22   1    3    1    0

Wide DataFrames will be printed across multiple rows by default:

    In [105]: pd.DataFrame(np.random.randn(3, 12))
    Out[105]:
             0         1         2         3         4         5         6   \
    0 -0.345352  1.314232  0.690579  0.995761  2.396780  0.014871  3.357427
    1 -2.182937  0.380396  0.084844  0.432390  1.519970 -0.493662  0.600178
    2  0.206053 -0.251905 -2.213588  1.063327  1.266143  0.299368 -0.863838

             7         8         9         10        11
    0 -0.317441 -1.236269  0.896171 -0.487602 -0.082240
    1  0.274230  0.132885 -0.023688  2.410179  1.450520
    2  0.408204 -1.048089 -0.025747 -0.988387  0.094055

You can change how much to print on a single row by setting the `display.width` option:

    In [106]: pd.set_option('display.width', 40) # default is 80

    In [107]: pd.DataFrame(np.random.randn(3, 12))
    Out[107]:
             0         1         2   \
    0  1.262731  1.289997  0.082423
    1  1.126203 -0.977349  1.474071
    2  0.758527  1.729689 -0.964980

             3         4         5   \
    0 -0.055758  0.536580 -0.489682
    1 -0.064034 -1.282782  0.781836
    2 -0.845696 -1.340896  1.846883

             6         7         8   \
    0  0.369374 -0.034571 -2.484478
    1 -1.071357  0.441153  2.353925
    2 -1.328865  1.682706 -1.717693

             9         10        11
    0 -0.281461  0.030711  0.109121
    1  0.583787  0.221471 -0.744471
    2  0.888782  0.228440  0.901805

You can adjust the max width of the individual columns by setting `display.max_colwidth`

    In [108]: datafile={'filename': ['filename_01','filename_02'],
       .....:           'path': ["media/user_name/storage/folder_01/filename_01",
       .....:                    "media/user_name/storage/folder_02/filename_02"]}
       .....:

    In [109]: pd.set_option('display.max_colwidth',30)

    In [110]: pd.DataFrame(datafile)
    Out[110]:
          filename  \
    0  filename_01
    1  filename_02

                                path
    0  media/user_name/storage/fo...
    1  media/user_name/storage/fo...

    In [111]: pd.set_option('display.max_colwidth',100)

    In [112]: pd.DataFrame(datafile)
    Out[112]:
          filename  \
    0  filename_01
    1  filename_02

                                                path
    0  media/user_name/storage/folder_01/filename_01
    1  media/user_name/storage/folder_02/filename_02

You can also disable this feature via the `expand_frame_repr` option. This will print the table in one block.

### DataFrame column attribute access and IPython completion

If a DataFrame column label is a valid Python variable name, the column can be accessed like attributes:

    In [113]: df = pd.DataFrame({'foo1' : np.random.randn(5),
       .....:                    'foo2' : np.random.randn(5)})
       .....:

    In [114]: df
    Out[114]:
           foo1      foo2
    0  1.171216 -0.858447
    1  0.520260  0.306996
    2 -1.197071 -0.028665
    3 -1.066969  0.384316
    4 -0.303421  1.574159

    In [115]: df.foo1
    Out[115]:
    0    1.171216
    1    0.520260
    2   -1.197071
    3   -1.066969
    4   -0.303421
    Name: foo1, dtype: float64

The columns are also connected to the IPython completion mechanism so they can be tab-completed:

    In [5]: df.fo<TAB>
    df.foo1  df.foo2

## Panel

Warning

In 0.20.0, `Panel` is deprecated and will be removed in a future version. See the section Deprecate Panel.

Panel is a somewhat less-used, but still important container for 3-dimensional data. The term panel data is derived from econometrics and is partially responsible for the name pandas: pan(el)-da(ta)-s. The names for the 3 axes are intended to give some semantic meaning to describing operations involving panel data and, in particular, econometric analysis of panel data. However, for the strict purposes of slicing and dicing a collection of DataFrame objects, you may find the axis names slightly arbitrary:

> - items: axis 0, each item corresponds to a DataFrame contained inside
> - major_axis: axis 1, it is the index (rows) of each of the DataFrames
> - minor_axis: axis 2, it is the columns of each of the DataFrames

Construction of Panels works about like you would expect:

### From 3D ndarray with optional axis labels

    In [116]: wp = pd.Panel(np.random.randn(2, 5, 4), items=['Item1', 'Item2'],
       .....:               major_axis=pd.date_range('1/1/2000', periods=5),
       .....:               minor_axis=['A', 'B', 'C', 'D'])
       .....:

    In [117]: wp
    Out[117]:
    <class 'pandas.core.panel.Panel'>
    Dimensions: 2 (items) x 5 (major_axis) x 4 (minor_axis)
    Items axis: Item1 to Item2
    Major_axis axis: 2000-01-01 00:00:00 to 2000-01-05 00:00:00
    Minor_axis axis: A to D

### From dict of DataFrame objects

    In [118]: data = {'Item1' : pd.DataFrame(np.random.randn(4, 3)),
       .....:         'Item2' : pd.DataFrame(np.random.randn(4, 2))}
       .....:

    In [119]: pd.Panel(data)
    Out[119]:
    <class 'pandas.core.panel.Panel'>
    Dimensions: 2 (items) x 4 (major_axis) x 3 (minor_axis)
    Items axis: Item1 to Item2
    Major_axis axis: 0 to 3
    Minor_axis axis: 0 to 2

Note that the values in the dict need only be convertible to DataFrame. Thus, they can be any of the other valid inputs to DataFrame as per above.

One helpful factory method is `Panel.from_dict`, which takes a dictionary of DataFrames as above, and the following named parameters:

Parameter | Default | Description
--- | --- | ---
intersect | `False` | drops elements whose indices do not align
orient | `items` | use `minor` to use DataFrames’ columns as panel items

For example, compare to the construction above:

    In [120]: pd.Panel.from_dict(data, orient='minor')
    Out[120]:
    <class 'pandas.core.panel.Panel'>
    Dimensions: 3 (items) x 4 (major_axis) x 2 (minor_axis)
    Items axis: 0 to 2
    Major_axis axis: 0 to 3
    Minor_axis axis: Item1 to Item2

Orient is especially useful for mixed-type DataFrames. If you pass a dict of DataFrame objects with mixed-type columns, all of the data will get upcasted to `dtype=object` unless you pass `orient='minor'`:

    In [121]: df = pd.DataFrame({'a': ['foo', 'bar', 'baz'],
       .....:                    'b': np.random.randn(3)})
       .....:

    In [122]: df
    Out[122]:
         a         b
    0  foo -0.308853
    1  bar -0.681087
    2  baz  0.377953

    In [123]: data = {'item1': df, 'item2': df}

    In [124]: panel = pd.Panel.from_dict(data, orient='minor')

    In [125]: panel['a']
    Out[125]:
      item2 item1
    0   foo   foo
    1   bar   bar
    2   baz   baz

    In [126]: panel['b']
    Out[126]:
          item2     item1
    0 -0.308853 -0.308853
    1 -0.681087 -0.681087
    2  0.377953  0.377953

    In [127]: panel['b'].dtypes
    Out[127]:
    item2    float64
    item1    float64
    dtype: object

Note

Panel, being less commonly used than Series and DataFrame, has been slightly neglected feature-wise. A number of methods and options available in DataFrame are not available in Panel.

### From DataFrame using `to_panel` method

`to_panel` converts a DataFrame with a two-level index to a Panel.

    In [128]: midx = pd.MultiIndex(levels=[['one', 'two'], ['x','y']], labels=[[1,1,0,0],[1,0,1,0]])

    In [129]: df = pd.DataFrame({'A' : [1, 2, 3, 4], 'B': [5, 6, 7, 8]}, index=midx)

    In [130]: df.to_panel()
    Out[130]:
    <class 'pandas.core.panel.Panel'>
    Dimensions: 2 (items) x 2 (major_axis) x 2 (minor_axis)
    Items axis: A to B
    Major_axis axis: one to two
    Minor_axis axis: x to y

### Item selection / addition / deletion

Similar to DataFrame functioning as a dict of Series, Panel is like a dict of DataFrames:

    In [131]: wp['Item1']
    Out[131]:
                       A         B         C         D
    2000-01-01  1.588931  0.476720  0.473424 -0.242861
    2000-01-02 -0.014805 -0.284319  0.650776 -1.461665
    2000-01-03 -1.137707 -0.891060 -0.693921  1.613616
    2000-01-04  0.464000  0.227371 -0.496922  0.306389
    2000-01-05 -2.290613 -1.134623 -1.561819 -0.260838

    In [132]: wp['Item3'] = wp['Item1'] / wp['Item2']

The API for insertion and deletion is the same as for DataFrame. And as with DataFrame, if the item is a valid python identifier, you can access it as an attribute and tab-complete it in IPython.

### Transposing

A Panel can be rearranged using its `transpose` method (which does not make a copy by default unless the data are heterogeneous):

    In [133]: wp.transpose(2, 0, 1)
    Out[133]:
    <class 'pandas.core.panel.Panel'>
    Dimensions: 4 (items) x 3 (major_axis) x 5 (minor_axis)
    Items axis: A to D
    Major_axis axis: Item1 to Item3
    Minor_axis axis: 2000-01-01 00:00:00 to 2000-01-05 00:00:00

### Indexing / Selection

Operation | Syntax | Result
--- | --- | ---
Select item | `wp[item]` | DataFrame
Get slice at major_axis label | `wp.major_xs(val)` | DataFrame
Get slice at minor_axis label | `wp.minor_xs(val)` | DataFrame

For example, using the earlier example data, we could do:

    In [134]: wp['Item1']
    Out[134]:
                       A         B         C         D
    2000-01-01  1.588931  0.476720  0.473424 -0.242861
    2000-01-02 -0.014805 -0.284319  0.650776 -1.461665
    2000-01-03 -1.137707 -0.891060 -0.693921  1.613616
    2000-01-04  0.464000  0.227371 -0.496922  0.306389
    2000-01-05 -2.290613 -1.134623 -1.561819 -0.260838

    In [135]: wp.major_xs(wp.major_axis[2])
    Out[135]:
          Item1     Item2     Item3
    A -1.137707  0.800193 -1.421791
    B -0.891060  0.782098 -1.139320
    C -0.693921 -1.069094  0.649074
    D  1.613616 -1.099248 -1.467927

    In [136]: wp.minor_axis
    Out[136]: Index([u'A', u'B', u'C', u'D'], dtype='object')

    In [137]: wp.minor_xs('C')
    Out[137]:
                   Item1     Item2     Item3
    2000-01-01  0.473424 -0.902937 -0.524316
    2000-01-02  0.650776 -1.144073 -0.568824
    2000-01-03 -0.693921 -1.069094  0.649074
    2000-01-04 -0.496922  0.661084 -0.751678
    2000-01-05 -1.561819 -1.056652  1.478083

### Squeezing

Another way to change the dimensionality of an object is to `squeeze` a 1-len object, similar to `wp['Item1']`

    In [138]: wp.reindex(items=['Item1']).squeeze()
    Out[138]:
                       A         B         C         D
    2000-01-01  1.588931  0.476720  0.473424 -0.242861
    2000-01-02 -0.014805 -0.284319  0.650776 -1.461665
    2000-01-03 -1.137707 -0.891060 -0.693921  1.613616
    2000-01-04  0.464000  0.227371 -0.496922  0.306389
    2000-01-05 -2.290613 -1.134623 -1.561819 -0.260838

    In [139]: wp.reindex(items=['Item1'], minor=['B']).squeeze()
    Out[139]:
    2000-01-01    0.476720
    2000-01-02   -0.284319
    2000-01-03   -0.891060
    2000-01-04    0.227371
    2000-01-05   -1.134623
    Freq: D, Name: B, dtype: float64

### Conversion to DataFrame

A Panel can be represented in 2D form as a hierarchically indexed DataFrame. See the section hierarchical indexing for more on this. To convert a Panel to a DataFrame, use the `to_frame` method:

    In [140]: panel = pd.Panel(np.random.randn(3, 5, 4), items=['one', 'two', 'three'],
       .....:                  major_axis=pd.date_range('1/1/2000', periods=5),
       .....:                  minor_axis=['a', 'b', 'c', 'd'])
       .....:

    In [141]: panel.to_frame()
    Out[141]:
                           one       two     three
    major      minor
    2000-01-01 a      0.493672  1.219492 -1.290493
               b     -2.461467  0.062297  0.787872
               c     -1.553902 -0.110388  1.515707
               d      2.015523 -1.184357 -0.276487
    2000-01-02 a     -1.833722 -0.558081 -0.223762
               b      1.771740  0.077849  1.397431
               c     -0.670027  0.629498  1.503874
               d      0.049307 -1.035260 -0.478905
    2000-01-03 a     -0.521493 -0.438229 -0.135950
               b     -3.201750  0.503703 -0.730327
               c      0.792716  0.413086 -0.033277
               d      0.146111 -1.139050  0.281151
    2000-01-04 a      1.903247  0.660342 -1.298915
               b     -0.747169  0.464794 -2.819487
               c     -0.309038 -0.309337 -0.851985
               d      0.393876 -0.649593 -1.106952
    2000-01-05 a      1.861468  0.683758 -0.937731
               b      0.936527 -0.643834 -1.537770
               c      1.255746  0.421287  0.555759
               d     -2.655452  1.032814 -2.277282

## Deprecate Panel

Over the last few years, pandas has increased in both breadth and depth, with new features, datatype support, and manipulation routines. As a result, supporting efficient indexing and functional routines for `Series`, `DataFrame` and `Panel` has contributed to an increasingly fragmented and difficult-to-understand codebase.

The 3-D structure of a `Panel` is much less common for many types of data analysis, than the 1-D of the `Series` or the 2-D of the `DataFrame`. Going forward it makes sense for pandas to focus on these areas exclusively.

Oftentimes, one can simply use a MultiIndex `DataFrame` for easily working with higher dimensional data.

In additon, the `xarray` package was built from the ground up, specifically in order to support the multi-dimensional analysis that is one of `Panel` s main usecases. Here is a link to the xarray panel-transition documentation.

    In [142]: p = tm.makePanel()

    In [143]: p
    Out[143]:
    <class 'pandas.core.panel.Panel'>
    Dimensions: 3 (items) x 30 (major_axis) x 4 (minor_axis)
    Items axis: ItemA to ItemC
    Major_axis axis: 2000-01-03 00:00:00 to 2000-02-11 00:00:00
    Minor_axis axis: A to D

Convert to a MultiIndex DataFrame

    In [144]: p.to_frame()
    Out[144]:
                         ItemA     ItemB     ItemC
    major      minor
    2000-01-03 A     -0.390201 -1.624062 -0.605044
               B      1.562443  0.483103  0.583129
               C     -1.085663  0.768159 -0.273458
               D      0.136235 -0.021763 -0.700648
    2000-01-04 A      1.207122 -0.758514  0.878404
               B      0.763264  0.061495 -0.876690
               C     -1.114738  0.225441 -0.335117
               D      0.886313 -0.047152 -1.166607
    2000-01-05 A      0.178690 -0.560859 -0.921485
               B      0.162027  0.240767 -1.919354
               C     -0.058216  0.543294 -0.476268
               D     -1.350722  0.088472 -0.367236
    2000-01-06 A     -1.004168 -0.589005 -0.200312
               B     -0.902704  0.782413 -0.572707
               C     -0.486768  0.771931 -1.765602
               D     -0.886348 -0.857435  1.296674
    2000-01-07 A     -1.377627 -1.070678  0.522423
               B      1.106010  0.628462 -1.736484
               C      1.685148 -0.968145  0.578223
               D     -1.013316 -2.503786  0.641385
    2000-01-10 A      0.499281 -1.681101  0.722511
               B     -0.199234 -0.880627 -1.335113
               C      0.112572 -1.176383  0.242697
               D      1.920906 -1.058041 -0.779432
    2000-01-11 A     -1.405256  0.403776 -1.702486
               B      0.458265  0.777575 -1.244471
               C     -1.495309 -3.192716  0.208129
               D     -0.388231 -0.657981  0.602456
    2000-01-12 A      0.162565  0.609862 -0.709535
               B      0.491048 -0.779367  0.347339
    ...                    ...       ...       ...
    2000-02-02 C     -0.303961 -0.463752 -0.288962
               D      0.104050  1.116086  0.506445
    2000-02-03 A     -2.338595 -0.581967 -0.801820
               B     -0.557697 -0.033731 -0.176382
               C      0.625555 -0.055289  0.875359
               D      0.174068 -0.443915  1.626369
    2000-02-04 A     -0.374279 -1.233862 -0.915751
               B      0.381353 -1.108761 -1.970108
               C     -0.059268 -0.360853 -0.614618
               D     -0.439461 -0.200491  0.429518
    2000-02-07 A     -2.359958 -3.520876 -0.288156
               B      1.337122 -0.314399 -1.044208
               C      0.249698  0.728197  0.565375
               D     -0.741343  1.092633  0.013910
    2000-02-08 A     -1.157886  0.516870 -1.199945
               B     -1.531095 -0.860626 -0.821179
               C      1.103949  1.326768  0.068184
               D     -0.079673 -1.675194 -0.458272
    2000-02-09 A     -0.551865  0.343125 -0.072869
               B      1.331458  0.370397 -1.914267
               C     -1.087532  0.208927  0.788871
               D     -0.922875  0.437234 -1.531004
    2000-02-10 A      1.592673  2.137827 -1.828740
               B     -0.571329 -1.761442 -0.826439
               C      1.998044  0.292058 -0.280343
               D      0.303638  0.388254 -0.500569
    2000-02-11 A      1.559318  0.452429 -1.716981
               B     -0.026671 -0.899454  0.124808
               C     -0.244548 -2.019610  0.931536
               D     -0.917368  0.479630  0.870690

    [120 rows x 3 columns]

Alternatively, one can convert to an xarray `DataArray`.

    In [145]: p.to_xarray()

    ImportErrorTraceback (most recent call last)
    <ipython-input-145-21989fb4243c> in <module>()
    ----> 1 p.to_xarray()

    /build/pandas-68JanS/pandas-0.22.0/debian/python-pandas/usr/lib/python2.7/dist-packages/pandas/core/generic.pyc in to_xarray(self)
       1679         except ImportError:
       1680             # Give a nice error message
    -> 1681             raise ImportError("the xarray library is not installed\n"
       1682                               "you can install via conda\n"
       1683                               "conda install xarray\n"

    ImportError: the xarray library is not installed
    you can install via conda
    conda install xarray
    or via pip
    pip install xarray

You can see the full-documentation for the xarray package.

## Panel4D and PanelND (Deprecated)

Warning

In 0.19.0 `Panel4D` and `PanelND` are deprecated and will be removed in a future version. The recommended way to represent these types of n-dimensional data are with the xarray package. Pandas provides a `to_xarray()` method to automate this conversion.

See the docs of a previous version for documentation on these objects.

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