# # 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

Seriesopen in new window 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    0.469112
b   -0.282863
c   -1.509059
d   -1.135632
e    1.212112
dtype: float64

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

In [6]: pd.Series(np.random.randn(5))
Out[6]:
0   -0.173215
1    0.119209
2   -1.044236
3   -0.861849
4   -2.104569
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

Series can be instantiated from dicts:

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

In [8]: pd.Series(d)
Out[8]:
b    1
a    0
c    2
dtype: int64
``````

Note

When the data is a dict, and an index is not passed, the `Series` index will be ordered by the dict’s insertion order, if you’re using Python version >= 3.6 and Pandas version >= 0.23.

If you’re using Python < 3.6 or Pandas < 0.23, and an index is not passed, the `Series` index will be the lexically ordered list of dict keys.

In the example above, if you were on a Python version lower than 3.6 or a Pandas version lower than 0.23, the `Series` would be ordered by the lexical order of the dict keys (i.e. `['a', 'b', 'c']` rather than `['b', 'a', 'c']`).

If an index is passed, the values in data corresponding to the labels in the index will be pulled out.

``````In [9]: d = {'a': 0., 'b': 1., 'c': 2.}

In [10]: pd.Series(d)
Out[10]:
a    0.0
b    1.0
c    2.0
dtype: float64

In [11]: pd.Series(d, index=['b', 'c', 'd', 'a'])
Out[11]:
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 [12]: pd.Series(5., index=['a', 'b', 'c', 'd', 'e'])
Out[12]:
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, operations such as slicing will also slice the index.

``````In [13]: s[0]
Out[13]: 0.46911229990718628

In [14]: s[:3]
Out[14]:
a    0.469112
b   -0.282863
c   -1.509059
dtype: float64

In [15]: s[s > s.median()]
Out[15]:
a    0.469112
e    1.212112
dtype: float64

In [16]: s[[4, 3, 1]]
Out[16]:
e    1.212112
d   -1.135632
b   -0.282863
dtype: float64

In [17]: np.exp(s)
Out[17]:
a    1.598575
b    0.753623
c    0.221118
d    0.321219
e    3.360575
dtype: float64
``````

Note We will address array-based indexing like `s[[4, 3, 1]]` in sectionopen in new window.

Like a NumPy array, a pandas Series has a dtypeopen in new window.

``````In [18]: s.dtype
Out[18]: dtype('float64')
``````

This is often a NumPy dtype. However, pandas and 3rd-party libraries extend NumPy’s type system in a few places, in which case the dtype would be a ExtensionDtypeopen in new window. Some examples within pandas are Categorical Dataopen in new window and Nullable Integer Data Typeopen in new window. See dtypesopen in new window for more.

If you need the actual array backing a `Series`, use Series.arrayopen in new window.

``````In [19]: s.array
Out[19]:
<PandasArray>
[ 0.46911229990718628, -0.28286334432866328,  -1.5090585031735124,
-1.1356323710171934,   1.2121120250208506]
Length: 5, dtype: float64
``````

Accessing the array can be useful when you need to do some operation without the index (to disable automatic alignmentopen in new window, for example).

Series.arrayopen in new window will always be an ExtensionArrayopen in new window. Briefly, an ExtensionArray is a thin wrapper around one or more concrete arrays like a numpy.ndarrayopen in new window. Pandas knows how to take an `ExtensionArray` and store it in a `Series` or a column of a `DataFrame`. See dtypesopen in new window for more.

While Series is ndarray-like, if you need an actual ndarray, then use Series.to_numpy()open in new window.

``````In [20]: s.to_numpy()
Out[20]: array([ 0.4691, -0.2829, -1.5091, -1.1356,  1.2121])
``````

Even if the Series is backed by a ExtensionArrayopen in new window, Series.to_numpy()open in new window will return a NumPy ndarray.

### # Series is dict-like

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

``````In [21]: s['a']
Out[21]: 0.46911229990718628

In [22]: s['e'] = 12.

In [23]: s
Out[23]:
a     0.469112
b    -0.282863
c    -1.509059
d    -1.135632
e    12.000000
dtype: float64

In [24]: 'e' in s
Out[24]: True

In [25]: 'f' in s
Out[25]: 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 [26]: s.get('f')

In [27]: s.get('f', np.nan)
Out[27]: nan
``````

### # Vectorized operations and label alignment with Series

When working with raw NumPy arrays, looping through value-by-value is usually not necessary. The same is true when working with Series in pandas. Series can also be passed into most NumPy methods expecting an ndarray.

``````In [28]: s + s
Out[28]:
a     0.938225
b    -0.565727
c    -3.018117
d    -2.271265
e    24.000000
dtype: float64

In [29]: s * 2
Out[29]:
a     0.938225
b    -0.565727
c    -3.018117
d    -2.271265
e    24.000000
dtype: float64

In [30]: np.exp(s)
Out[30]:
a         1.598575
b         0.753623
c         0.221118
d         0.321219
e    162754.791419
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 [31]: s[1:] + s[:-1]
Out[31]:
a         NaN
b   -0.565727
c   -3.018117
d   -2.271265
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 [32]: s = pd.Series(np.random.randn(5), name='something')

In [33]: s
Out[33]:
0   -0.494929
1    1.071804
2    0.721555
3   -0.706771
4   -1.039575
Name: something, dtype: float64

In [34]: s.name
Out[34]: '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()open in new window method.

``````In [35]: s2 = s.rename("different")

In [36]: s2.name
Out[36]: '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:

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.

Note

When the data is a dict, and columns is not specified, the DataFrame columns will be ordered by the dict’s insertion order, if you are using Python version >= 3.6 and Pandas >= 0.23.

If you are using Python < 3.6 or Pandas < 0.23, and columns is not specified, the DataFrame columns will be the lexically ordered list of dict keys.

### # From dict of Series or dicts

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

``````In [37]: d = {'one': pd.Series([1., 2., 3.], index=['a', 'b', 'c']),
....:      'two': pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}
....:

In [38]: df = pd.DataFrame(d)

In [39]: df
Out[39]:
one  two
a  1.0  1.0
b  2.0  2.0
c  3.0  3.0
d  NaN  4.0

In [40]: pd.DataFrame(d, index=['d', 'b', 'a'])
Out[40]:
one  two
d  NaN  4.0
b  2.0  2.0
a  1.0  1.0

In [41]: pd.DataFrame(d, index=['d', 'b', 'a'], columns=['two', 'three'])
Out[41]:
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 [42]: df.index
Out[42]: Index(['a', 'b', 'c', 'd'], dtype='object')

In [43]: df.columns
Out[43]: Index(['one', '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 [44]: d = {'one': [1., 2., 3., 4.],
....:      'two': [4., 3., 2., 1.]}
....:

In [45]: pd.DataFrame(d)
Out[45]:
one  two
0  1.0  4.0
1  2.0  3.0
2  3.0  2.0
3  4.0  1.0

In [46]: pd.DataFrame(d, index=['a', 'b', 'c', 'd'])
Out[46]:
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 [47]: data = np.zeros((2, ), dtype=[('A', 'i4'), ('B', 'f4'), ('C', 'a10')])

In [48]: data[:] = [(1, 2., 'Hello'), (2, 3., "World")]

In [49]: pd.DataFrame(data)
Out[49]:
A    B         C
0  1  2.0  b'Hello'
1  2  3.0  b'World'

In [50]: pd.DataFrame(data, index=['first', 'second'])
Out[50]:
A    B         C
first   1  2.0  b'Hello'
second  2  3.0  b'World'

In [51]: pd.DataFrame(data, columns=['C', 'A', 'B'])
Out[51]:
C  A    B
0  b'Hello'  1  2.0
1  b'World'  2  3.0
``````

Note

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

### # From a list of dicts

``````In [52]: data2 = [{'a': 1, 'b': 2}, {'a': 5, 'b': 10, 'c': 20}]

In [53]: pd.DataFrame(data2)
Out[53]:
a   b     c
0  1   2   NaN
1  5  10  20.0

In [54]: pd.DataFrame(data2, index=['first', 'second'])
Out[54]:
a   b     c
first   1   2   NaN
second  5  10  20.0

In [55]: pd.DataFrame(data2, columns=['a', 'b'])
Out[55]:
a   b
0  1   2
1  5  10
``````

### # From a dict of tuples

You can automatically create a MultiIndexed frame by passing a tuples dictionary.

``````In [56]: 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[56]:
a              b
b    a    c    a     b
A B  1.0  4.0  5.0  8.0  10.0
C  2.0  3.0  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 dataopen in new window section. To construct a DataFrame with missing data, we use `np.nan` to represent missing values. 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.

``````In [57]: pd.DataFrame.from_dict(dict([('A', [1, 2, 3]), ('B', [4, 5, 6])]))
Out[57]:
A  B
0  1  4
1  2  5
2  3  6
``````

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

``````In [58]: pd.DataFrame.from_dict(dict([('A', [1, 2, 3]), ('B', [4, 5, 6])]),
....:                        orient='index', columns=['one', 'two', 'three'])
....:
Out[58]:
one  two  three
A    1    2      3
B    4    5      6
``````

DataFrame.from_records

`DataFrame.from_records` takes a list of tuples or an ndarray with structured dtype. It works analogously to the normal DataFrame constructor, except that the resulting DataFrame index may be a specific field of the structured dtype. For example:

``````In [59]: data
Out[59]:
array([(1,  2., b'Hello'), (2,  3., b'World')],
dtype=[('A', '<i4'), ('B', '<f4'), ('C', 'S10')])

In [60]: pd.DataFrame.from_records(data, index='C')
Out[60]:
A    B
C
b'Hello'  1  2.0
b'World'  2  3.0
``````

### # 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 [61]: df['one']
Out[61]:
a    1.0
b    2.0
c    3.0
d    NaN
Name: one, dtype: float64

In [62]: df['three'] = df['one'] * df['two']

In [63]: df['flag'] = df['one'] > 2

In [64]: df
Out[64]:
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 [65]: del df['two']

In [66]: three = df.pop('three')

In [67]: df
Out[67]:
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 [68]: df['foo'] = 'bar'

In [69]: df
Out[69]:
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 [70]: df['one_trunc'] = df['one'][:2]

In [71]: df
Out[71]:
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 [72]: df.insert(1, 'bar', df['one'])

In [73]: df
Out[73]:
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’sopen in new window `mutate` verb, DataFrame has an assign()open in new window method that allows you to easily create new columns that are potentially derived from existing columns.

``````In [74]: iris = pd.read_csv('data/iris.data')

Out[75]:
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 [76]: (iris.assign(sepal_ratio=iris['SepalWidth'] / iris['SepalLength'])
....:
Out[76]:
SepalLength  SepalWidth  PetalLength  PetalWidth         Name  sepal_ratio
0          5.1         3.5          1.4         0.2  Iris-setosa     0.686275
1          4.9         3.0          1.4         0.2  Iris-setosa     0.612245
2          4.7         3.2          1.3         0.2  Iris-setosa     0.680851
3          4.6         3.1          1.5         0.2  Iris-setosa     0.673913
4          5.0         3.6          1.4         0.2  Iris-setosa     0.720000
``````

In the example above, we inserted a precomputed value. We can also pass in a function of one argument to be evaluated on the DataFrame being assigned to.

``````In [77]: iris.assign(sepal_ratio=lambda x: (x['SepalWidth'] / x['SepalLength'])).head()
Out[77]:
SepalLength  SepalWidth  PetalLength  PetalWidth         Name  sepal_ratio
0          5.1         3.5          1.4         0.2  Iris-setosa     0.686275
1          4.9         3.0          1.4         0.2  Iris-setosa     0.612245
2          4.7         3.2          1.3         0.2  Iris-setosa     0.680851
3          4.6         3.1          1.5         0.2  Iris-setosa     0.673913
4          5.0         3.6          1.4         0.2  Iris-setosa     0.720000
``````

`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 a chain 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 [78]: (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[78]: <matplotlib.axes._subplots.AxesSubplot at 0x7f2b527b1a58>
``````

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.

Changed in version 0.23.0.

Starting with Python 3.6 the order of `**kwargs` is preserved. This allows for dependent assignment, where an expression later in `**kwargs` can refer to a column created earlier in the same assign()open in new window.

``````In [79]: dfa = pd.DataFrame({"A": [1, 2, 3],
....:                     "B": [4, 5, 6]})
....:

In [80]: dfa.assign(C=lambda x: x['A'] + x['B'],
....:            D=lambda x: x['A'] + x['C'])
....:
Out[80]:
A  B  C   D
0  1  4  5   6
1  2  5  7   9
2  3  6  9  12
``````

In the second expression, `x['C']` will refer to the newly created column, that’s equal to `dfa['A'] + dfa['B']`.

To write code compatible with all versions of Python, split the assignment in two.

``````In [81]: dependent = pd.DataFrame({"A": [1, 1, 1]})

In [82]: (dependent.assign(A=lambda x: x['A'] + 1)
....:           .assign(B=lambda x: x['A'] + 2))
....:
Out[82]:
A  B
0  2  4
1  2  4
2  2  4
``````

Warning

Dependent assignment maybe subtly change the behavior of your code between Python 3.6 and older versions of Python.

If you wish write code that supports versions of python before and after 3.6, you’ll need to take care when passing `assign` expressions that

• Updating an existing column
• Referring to the newly updated column in the same `assign`

For example, we’ll update column “A” and then refer to it when creating “B”.

``````>>> dependent = pd.DataFrame({"A": [1, 1, 1]})
>>> dependent.assign(A=lambda x: x["A"] + 1, B=lambda x: x["A"] + 2)
``````

For Python 3.5 and earlier the expression creating `B` refers to the “old” value of `A, [1, 1, 1]`. The output is then

``````   A  B
0  2  3
1  2  3
2  2  3
``````

For Python 3.6 and later, the expression creating A refers to the “new” value of `A, [2, 2, 2]`, which results in

``````   A  B
0  2  4
1  2  4
2  2  4
``````

### # Indexing / Selection

The basics of indexing are as follows:

OperationSyntaxResult
Select columndf[col]Series
Select row by labeldf.loc[label]Series
Select row by integer locationdf.iloc[loc]Series
Slice rowsdf[5:10]DataFrame
Select rows by boolean vectordf[bool_vec]DataFrame

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

``````In [83]: df.loc['b']
Out[83]:
one              2
bar              2
flag         False
foo            bar
one_trunc        2
Name: b, dtype: object

In [84]: df.iloc[2]
Out[84]:
one             3
bar             3
flag         True
foo           bar
one_trunc     NaN
Name: c, dtype: object
``````

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

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

In [86]: df2 = pd.DataFrame(np.random.randn(7, 3), columns=['A', 'B', 'C'])

In [87]: df + df2
Out[87]:
A         B         C   D
0  0.045691 -0.014138  1.380871 NaN
1 -0.955398 -1.501007  0.037181 NaN
2 -0.662690  1.534833 -0.859691 NaN
3 -2.452949  1.237274 -0.133712 NaN
4  1.414490  1.951676 -2.320422 NaN
5 -0.494922 -1.649727 -1.084601 NaN
6 -1.047551 -0.748572 -0.805479 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 broadcastingopen in new window row-wise. For example:

``````In [88]: df - df.iloc[0]
Out[88]:
A         B         C         D
0  0.000000  0.000000  0.000000  0.000000
1 -1.359261 -0.248717 -0.453372 -1.754659
2  0.253128  0.829678  0.010026 -1.991234
3 -1.311128  0.054325 -1.724913 -1.620544
4  0.573025  1.500742 -0.676070  1.367331
5 -1.741248  0.781993 -1.241620 -2.053136
6 -1.240774 -0.869551 -0.153282  0.000430
7 -0.743894  0.411013 -0.929563 -0.282386
8 -1.194921  1.320690  0.238224 -1.482644
9  2.293786  1.856228  0.773289 -1.446531
``````

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

``````In [89]: index = pd.date_range('1/1/2000', periods=8)

In [90]: df = pd.DataFrame(np.random.randn(8, 3), index=index, columns=list('ABC'))

In [91]: df
Out[91]:
A         B         C
2000-01-01 -1.226825  0.769804 -1.281247
2000-01-02 -0.727707 -0.121306 -0.097883
2000-01-03  0.695775  0.341734  0.959726
2000-01-04 -1.110336 -0.619976  0.149748
2000-01-05 -0.732339  0.687738  0.176444
2000-01-06  0.403310 -0.154951  0.301624
2000-01-07 -2.179861 -1.369849 -0.954208
2000-01-08  1.462696 -1.743161 -0.826591

In [92]: type(df['A'])
Out[92]: pandas.core.series.Series

In [93]: df - df['A']
Out[93]:
2000-01-01 00:00:00  2000-01-02 00:00:00  2000-01-03 00:00:00  2000-01-04 00:00:00  2000-01-05 00:00:00  ...  2000-01-07 00:00:00  2000-01-08 00:00:00   A   B   C
2000-01-01                  NaN                  NaN                  NaN                  NaN                  NaN  ...                  NaN                  NaN NaN NaN NaN
2000-01-02                  NaN                  NaN                  NaN                  NaN                  NaN  ...                  NaN                  NaN NaN NaN NaN
2000-01-03                  NaN                  NaN                  NaN                  NaN                  NaN  ...                  NaN                  NaN NaN NaN NaN
2000-01-04                  NaN                  NaN                  NaN                  NaN                  NaN  ...                  NaN                  NaN NaN NaN NaN
2000-01-05                  NaN                  NaN                  NaN                  NaN                  NaN  ...                  NaN                  NaN NaN NaN NaN
2000-01-06                  NaN                  NaN                  NaN                  NaN                  NaN  ...                  NaN                  NaN NaN NaN NaN
2000-01-07                  NaN                  NaN                  NaN                  NaN                  NaN  ...                  NaN                  NaN NaN NaN NaN
2000-01-08                  NaN                  NaN                  NaN                  NaN                  NaN  ...                  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 operationsopen in new window.

Operations with scalars are just as you would expect:

``````In [94]: df * 5 + 2
Out[94]:
A         B         C
2000-01-01 -4.134126  5.849018 -4.406237
2000-01-02 -1.638535  1.393469  1.510587
2000-01-03  5.478873  3.708672  6.798628
2000-01-04 -3.551681 -1.099880  2.748742
2000-01-05 -1.661697  5.438692  2.882222
2000-01-06  4.016548  1.225246  3.508122
2000-01-07 -8.899303 -4.849247 -2.771039
2000-01-08  9.313480 -6.715805 -2.132955

In [95]: 1 / df
Out[95]:
A         B          C
2000-01-01 -0.815112  1.299033  -0.780489
2000-01-02 -1.374179 -8.243600 -10.216313
2000-01-03  1.437247  2.926250   1.041965
2000-01-04 -0.900628 -1.612966   6.677871
2000-01-05 -1.365487  1.454041   5.667510
2000-01-06  2.479485 -6.453662   3.315381
2000-01-07 -0.458745 -0.730007  -1.047990
2000-01-08  0.683669 -0.573671  -1.209788

In [96]: df ** 4
Out[96]:
A         B         C
2000-01-01   2.265327  0.351172  2.694833
2000-01-02   0.280431  0.000217  0.000092
2000-01-03   0.234355  0.013638  0.848376
2000-01-04   1.519910  0.147740  0.000503
2000-01-05   0.287640  0.223714  0.000969
2000-01-06   0.026458  0.000576  0.008277
2000-01-07  22.579530  3.521204  0.829033
2000-01-08   4.577374  9.233151  0.466834
``````

Boolean operators work as well:

``````In [97]: df1 = pd.DataFrame({'a': [1, 0, 1], 'b': [0, 1, 1]}, dtype=bool)

In [98]: df2 = pd.DataFrame({'a': [0, 1, 1], 'b': [1, 1, 0]}, dtype=bool)

In [99]: df1 & df2
Out[99]:
a      b
0  False  False
1  False   True
2   True  False

In [100]: df1 | df2
Out[100]:
a     b
0  True  True
1  True  True
2  True  True

In [101]: df1 ^ df2
Out[101]:
a      b
0   True   True
1   True  False
2  False   True

In [102]: -df1
Out[102]:
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 [103]: df[:5].T
Out[103]:
2000-01-01  2000-01-02  2000-01-03  2000-01-04  2000-01-05
A   -1.226825   -0.727707    0.695775   -1.110336   -0.732339
B    0.769804   -0.121306    0.341734   -0.619976    0.687738
C   -1.281247   -0.097883    0.959726    0.149748    0.176444
``````

### # 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 [104]: np.exp(df)
Out[104]:
A         B         C
2000-01-01  0.293222  2.159342  0.277691
2000-01-02  0.483015  0.885763  0.906755
2000-01-03  2.005262  1.407386  2.610980
2000-01-04  0.329448  0.537957  1.161542
2000-01-05  0.480783  1.989212  1.192968
2000-01-06  1.496770  0.856457  1.352053
2000-01-07  0.113057  0.254145  0.385117
2000-01-08  4.317584  0.174966  0.437538

In [105]: np.asarray(df)
Out[105]:
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 [106]: df.T.dot(df)
Out[106]:
A         B         C
A  11.341858 -0.059772  3.007998
B  -0.059772  6.520556  2.083308
C   3.007998  2.083308  4.310549
``````

Similarly, the dot method on Series implements dot product:

``````In [107]: s1 = pd.Series(np.arange(5, 10))

In [108]: s1.dot(s1)
Out[108]: 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()open in new window. (Here I am reading a CSV version of the baseball dataset from the plyr R package):

``````In [109]: baseball = pd.read_csv('data/baseball.csv')

In [110]: print(baseball)
id     player  year  stint team  lg   g   ab   r    h  X2b  X3b  hr   rbi   sb   cs  bb    so  ibb  hbp   sh   sf  gidp
0   88641  womacto01  2006      2  CHN  NL  19   50   6   14    1    0   1   2.0  1.0  1.0   4   4.0  0.0  0.0  3.0  0.0   0.0
1   88643  schilcu01  2006      1  BOS  AL  31    2   0    1    0    0   0   0.0  0.0  0.0   0   1.0  0.0  0.0  0.0  0.0   0.0
..    ...        ...   ...    ...  ...  ..  ..  ...  ..  ...  ...  ...  ..   ...  ...  ...  ..   ...  ...  ...  ...  ...   ...
98  89533   aloumo01  2007      1  NYN  NL  87  328  51  112   19    1  13  49.0  3.0  0.0  27  30.0  5.0  2.0  0.0  3.0  13.0
99  89534  alomasa02  2007      1  NYN  NL   8   22   1    3    1    0   0   0.0  0.0  0.0   0   3.0  0.0  0.0  0.0  0.0   0.0

[100 rows x 23 columns]

In [111]: 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 [112]: 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 [113]: pd.DataFrame(np.random.randn(3, 12))
Out[113]:
0         1         2         3         4         5         6         7         8         9         10        11
0 -0.345352  1.314232  0.690579  0.995761  2.396780  0.014871  3.357427 -0.317441 -1.236269  0.896171 -0.487602 -0.082240
1 -2.182937  0.380396  0.084844  0.432390  1.519970 -0.493662  0.600178  0.274230  0.132885 -0.023688  2.410179  1.450520
2  0.206053 -0.251905 -2.213588  1.063327  1.266143  0.299368 -0.863838  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 [114]: pd.set_option('display.width', 40)  # default is 80

In [115]: pd.DataFrame(np.random.randn(3, 12))
Out[115]:
0         1         2         3         4         5         6         7         8         9         10        11
0  1.262731  1.289997  0.082423 -0.055758  0.536580 -0.489682  0.369374 -0.034571 -2.484478 -0.281461  0.030711  0.109121
1  1.126203 -0.977349  1.474071 -0.064034 -1.282782  0.781836 -1.071357  0.441153  2.353925  0.583787  0.221471 -0.744471
2  0.758527  1.729689 -0.964980 -0.845696 -1.340896  1.846883 -1.328865  1.682706 -1.717693  0.888782  0.228440  0.901805
``````

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

``````In [116]: datafile = {'filename': ['filename_01', 'filename_02'],
.....:             'path': ["media/user_name/storage/folder_01/filename_01",
.....:                      "media/user_name/storage/folder_02/filename_02"]}
.....:

In [117]: pd.set_option('display.max_colwidth', 30)

In [118]: pd.DataFrame(datafile)
Out[118]:
filename                           path
0  filename_01  media/user_name/storage/fo...
1  filename_02  media/user_name/storage/fo...

In [119]: pd.set_option('display.max_colwidth', 100)

In [120]: pd.DataFrame(datafile)
Out[120]:
filename                                           path
0  filename_01  media/user_name/storage/folder_01/filename_01
1  filename_02  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 an attribute:

``````In [121]: df = pd.DataFrame({'foo1': np.random.randn(5),
.....:                    'foo2': np.random.randn(5)})
.....:

In [122]: df
Out[122]:
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 [123]: df.foo1
Out[123]:
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 IPythonopen in new window completion mechanism so they can be tab-completed:

``````In [5]: df.fo<TAB>  # noqa: E225, E999
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 Panelopen in new window.

Panel is a somewhat less-used, but still important container for 3-dimensional data. The term panel dataopen in new window 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 [124]: 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 [125]: wp
Out[125]:
<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 [126]: data = {'Item1': pd.DataFrame(np.random.randn(4, 3)),
.....:         'Item2': pd.DataFrame(np.random.randn(4, 2))}
.....:

In [127]: pd.Panel(data)
Out[127]:
<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:

ParameterDefaultDescription
intersectFalsedrops elements whose indices do not align
orientitemsuse minor to use DataFrames’ columns as panel items

For example, compare to the construction above:

``````In [128]: pd.Panel.from_dict(data, orient='minor')
Out[128]:
<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 [129]: df = pd.DataFrame({'a': ['foo', 'bar', 'baz'],
.....:                    'b': np.random.randn(3)})
.....:

In [130]: df
Out[130]:
a         b
0  foo -0.308853
1  bar -0.681087
2  baz  0.377953

In [131]: data = {'item1': df, 'item2': df}

In [132]: panel = pd.Panel.from_dict(data, orient='minor')

In [133]: panel['a']
Out[133]:
item1 item2
0   foo   foo
1   bar   bar
2   baz   baz

In [134]: panel['b']
Out[134]:
item1     item2
0 -0.308853 -0.308853
1 -0.681087 -0.681087
2  0.377953  0.377953

In [135]: panel['b'].dtypes
Out[135]:
item1    float64
item2    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 [136]: midx = pd.MultiIndex(levels=[['one', 'two'], ['x', 'y']],
.....:                      codes=[[1, 1, 0, 0], [1, 0, 1, 0]])
.....:

In [137]: df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]}, index=midx)

In [138]: df.to_panel()
Out[138]:
<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 [139]: wp['Item1']
Out[139]:
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 [140]: 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 [141]: wp.transpose(2, 0, 1)
Out[141]:
<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

OperationSyntaxResult
Select itemwp[item]DataFrame
Get slice at major_axis labelwp.major_xs(val)DataFrame
Get slice at minor_axis labelwp.minor_xs(val)DataFrame

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

``````In [142]: wp['Item1']
Out[142]:
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 [143]: wp.major_xs(wp.major_axis[2])
Out[143]:
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 [144]: wp.minor_axis
Out[144]: Index(['A', 'B', 'C', 'D'], dtype='object')

In [145]: wp.minor_xs('C')
Out[145]:
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 [146]: wp.reindex(items=['Item1']).squeeze()
Out[146]:
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 [147]: wp.reindex(items=['Item1'], minor=['B']).squeeze()
Out[147]:
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 indexingopen in new window for more on this. To convert a Panel to a DataFrame, use the `to_frame` method:

``````In [148]: 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 [149]: panel.to_frame()
Out[149]:
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 code base.

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 addition, 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 use cases. Here is a link to the xarray panel-transition documentationopen in new window.

``````In [150]: import pandas.util.testing as tm

In [151]: p = tm.makePanel()

In [152]: p
Out[152]:
<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 [153]: p.to_frame()
Out[153]:
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 [154]: p.to_xarray()
Out[154]:
<xarray.DataArray (items: 3, major_axis: 30, minor_axis: 4)>
array([[[-0.390201,  1.562443, -1.085663,  0.136235],
[ 1.207122,  0.763264, -1.114738,  0.886313],
...,
[ 1.592673, -0.571329,  1.998044,  0.303638],
[ 1.559318, -0.026671, -0.244548, -0.917368]],

[[-1.624062,  0.483103,  0.768159, -0.021763],
[-0.758514,  0.061495,  0.225441, -0.047152],
...,
[ 2.137827, -1.761442,  0.292058,  0.388254],
[ 0.452429, -0.899454, -2.01961 ,  0.47963 ]],

[[-0.605044,  0.583129, -0.273458, -0.700648],
[ 0.878404, -0.87669 , -0.335117, -1.166607],
...,
[-1.82874 , -0.826439, -0.280343, -0.500569],
[-1.716981,  0.124808,  0.931536,  0.87069 ]]])
Coordinates:
* items       (items) object 'ItemA' 'ItemB' 'ItemC'
* major_axis  (major_axis) datetime64[ns] 2000-01-03 2000-01-04 ... 2000-02-11
* minor_axis  (minor_axis) object 'A' 'B' 'C' 'D'
``````

You can see the full-documentation for the xarray packageopen in new window.