# 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(['a', 'b', 'c', 'd', '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

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    0.4691
b   -0.2829
c   -1.5091
dtype: float64

In [15]: s[s > s.median()]
Out[15]:
a    0.4691
e    1.2121
dtype: float64

In [16]: s[[4, 3, 1]]
Out[16]:
e    1.2121
d   -1.1356
b   -0.2829
dtype: float64

In [17]: np.exp(s)
Out[17]:
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 [18]: s['a']
Out[18]: 0.46911229990718628

In [19]: s['e'] = 12.

In [20]: s
Out[20]:
a     0.4691
b    -0.2829
c    -1.5091
d    -1.1356
e    12.0000
dtype: float64

In [21]: 'e' in s
Out[21]: True

In [22]: 'f' in s
Out[22]: 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 [23]: s.get('f')

In [24]: s.get('f', np.nan)
Out[24]: 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 [25]: s + s
Out[25]:
a     0.9382
b    -0.5657
c    -3.0181
d    -2.2713
e    24.0000
dtype: float64

In [26]: s * 2
Out[26]:
a     0.9382
b    -0.5657
c    -3.0181
d    -2.2713
e    24.0000
dtype: float64

In [27]: np.exp(s)
Out[27]:
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 [28]: s[1:] + s[:-1]
Out[28]:
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 [29]: s = pd.Series(np.random.randn(5), name='something')

In [30]: s
Out[30]:
0   -0.4949
1    1.0718
2    0.7216
3   -0.7068
4   -1.0396
Name: something, dtype: float64

In [31]: s.name
Out[31]: '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 [32]: s2 = s.rename("different")

In [33]: s2.name
Out[33]: '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.

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

In [35]: df = pd.DataFrame(d)

In [36]: df
Out[36]:
one  two
a  1.0  1.0
b  2.0  2.0
c  3.0  3.0
d  NaN  4.0

In [37]: pd.DataFrame(d, index=['d', 'b', 'a'])
Out[37]:
one  two
d  NaN  4.0
b  2.0  2.0
a  1.0  1.0

In [38]: pd.DataFrame(d, index=['d', 'b', 'a'], columns=['two', 'three'])
Out[38]:
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 [39]: df.index
Out[39]: Index(['a', 'b', 'c', 'd'], dtype='object')

In [40]: df.columns
Out[40]: 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 [41]: d = {'one' : [1., 2., 3., 4.],
....:      'two' : [4., 3., 2., 1.]}
....:

In [42]: pd.DataFrame(d)
Out[42]:
one  two
0  1.0  4.0
1  2.0  3.0
2  3.0  2.0
3  4.0  1.0

In [43]: pd.DataFrame(d, index=['a', 'b', 'c', 'd'])
Out[43]:
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 [44]: data = np.zeros((2,), dtype=[('A', 'i4'),('B', 'f4'),('C', 'a10')])

In [45]: data[:] = [(1,2.,'Hello'), (2,3.,"World")]

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

In [47]: pd.DataFrame(data, index=['first', 'second'])
Out[47]:
A    B         C
first   1  2.0  b'Hello'
second  2  3.0  b'World'

In [48]: pd.DataFrame(data, columns=['C', 'A', 'B'])
Out[48]:
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 [49]: data2 = [{'a': 1, 'b': 2}, {'a': 5, 'b': 10, 'c': 20}]

In [50]: pd.DataFrame(data2)
Out[50]:
a   b     c
0  1   2   NaN
1  5  10  20.0

In [51]: pd.DataFrame(data2, index=['first', 'second'])
Out[51]:
a   b     c
first   1   2   NaN
second  5  10  20.0

In [52]: pd.DataFrame(data2, columns=['a', 'b'])
Out[52]:
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 [53]: 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[53]:
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 data 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 [54]: pd.DataFrame.from_dict(dict([('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. In this case, you can also pass the desired column names:

In [55]: pd.DataFrame.from_dict(dict([('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


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 [56]: data
Out[56]:
array([(1,  2., b'Hello'), (2,  3., b'World')],
dtype=[('A', '<i4'), ('B', '<f4'), ('C', 'S10')])

In [57]: pd.DataFrame.from_records(data, index='C')
Out[57]:
A    B
C
b'Hello'  1  2.0
b'World'  2  3.0


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 [58]: df['one']
Out[58]:
a    1.0
b    2.0
c    3.0
d    NaN
Name: one, dtype: float64

In [59]: df['three'] = df['one'] * df['two']

In [60]: df['flag'] = df['one'] > 2

In [61]: df
Out[61]:
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 [62]: del df['two']

In [63]: three = df.pop('three')

In [64]: df
Out[64]:
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 [65]: df['foo'] = 'bar'

In [66]: df
Out[66]:
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 [67]: df['one_trunc'] = df['one'][:2]

In [68]: df
Out[68]:
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 [69]: df.insert(1, 'bar', df['one'])

In [70]: df
Out[70]:
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 [71]: iris = pd.read_csv('data/iris.data')

Out[72]:
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 [73]: (iris.assign(sepal_ratio = iris['SepalWidth'] / iris['SepalLength'])
....:
Out[73]:
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


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 [74]: iris.assign(sepal_ratio = lambda x: (x['SepalWidth'] /
....:
Out[74]:
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 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 [75]: (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[75]: <matplotlib.axes._subplots.AxesSubplot at 0x1218f5320>


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().

In [76]: dfa = pd.DataFrame({"A": [1, 2, 3],
....:                     "B": [4, 5, 6]})
....:

In [77]: dfa.assign(C=lambda x: x['A'] + x['B'],
....:            D=lambda x: x['A'] + x['C'])
....:
Out[77]:
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 [78]: dependent = pd.DataFrame({"A": [1, 1, 1]})

In [79]: (dependent.assign(A=lambda x: x['A'] + 1)
....:           .assign(B=lambda x: x['A'] + 2))
....:
Out[79]:
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:

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 [80]: df.loc['b']
Out[80]:
one              2
bar              2
flag         False
foo            bar
one_trunc        2
Name: b, dtype: object

In [81]: df.iloc[2]
Out[81]:
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 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 [82]: df = pd.DataFrame(np.random.randn(10, 4), columns=['A', 'B', 'C', 'D'])

In [83]: df2 = pd.DataFrame(np.random.randn(7, 3), columns=['A', 'B', 'C'])

In [84]: df + df2
Out[84]:
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 [85]: df - df.iloc[0]
Out[85]:
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 [86]: index = pd.date_range('1/1/2000', periods=8)

In [87]: df = pd.DataFrame(np.random.randn(8, 3), index=index, columns=list('ABC'))

In [88]: df
Out[88]:
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 [89]: type(df['A'])
Out[89]: pandas.core.series.Series

In [90]: df - df['A']
Out[90]:
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 [91]: df * 5 + 2
Out[91]:
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 [92]: 1 / df
Out[92]:
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 [93]: df ** 4
Out[93]:
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 [94]: df1 = pd.DataFrame({'a' : [1, 0, 1], 'b' : [0, 1, 1] }, dtype=bool)

In [95]: df2 = pd.DataFrame({'a' : [0, 1, 1], 'b' : [1, 1, 0] }, dtype=bool)

In [96]: df1 & df2
Out[96]:
a      b
0  False  False
1  False   True
2   True  False

In [97]: df1 | df2
Out[97]:
a     b
0  True  True
1  True  True
2  True  True

In [98]: df1 ^ df2
Out[98]:
a      b
0   True   True
1   True  False
2  False   True

In [99]: -df1
Out[99]:
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 [100]: df[:5].T
Out[100]:
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 [101]: np.exp(df)
Out[101]:
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 [102]: np.asarray(df)
Out[102]:
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 [103]: df.T.dot(df)
Out[103]:
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 [104]: s1 = pd.Series(np.arange(5,10))

In [105]: s1.dot(s1)
Out[105]: 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 [106]: baseball = pd.read_csv('data/baseball.csv')

In [107]: 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 [108]: 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 [109]: 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 [110]: pd.DataFrame(np.random.randn(3, 12))
Out[110]:
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 [111]: pd.set_option('display.width', 40) # default is 80

In [112]: pd.DataFrame(np.random.randn(3, 12))
Out[112]:
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 [113]: datafile={'filename': ['filename_01','filename_02'],
.....:           'path': ["media/user_name/storage/folder_01/filename_01",
.....:                    "media/user_name/storage/folder_02/filename_02"]}
.....:

In [114]: pd.set_option('display.max_colwidth',30)

In [115]: pd.DataFrame(datafile)
Out[115]:
filename                           path
0  filename_01  media/user_name/storage/fo...
1  filename_02  media/user_name/storage/fo...

In [116]: pd.set_option('display.max_colwidth',100)

In [117]: pd.DataFrame(datafile)
Out[117]:
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 [118]: df = pd.DataFrame({'foo1' : np.random.randn(5),
.....:                    'foo2' : np.random.randn(5)})
.....:

In [119]: df
Out[119]:
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 [120]: df.foo1
Out[120]:
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 [121]: 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 [122]: wp
Out[122]:
<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 [123]: data = {'Item1' : pd.DataFrame(np.random.randn(4, 3)),
.....:         'Item2' : pd.DataFrame(np.random.randn(4, 2))}
.....:

In [124]: pd.Panel(data)
Out[124]:
<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 [125]: pd.Panel.from_dict(data, orient='minor')
Out[125]:
<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 [126]: df = pd.DataFrame({'a': ['foo', 'bar', 'baz'],
.....:                    'b': np.random.randn(3)})
.....:

In [127]: df
Out[127]:
a         b
0  foo -0.308853
1  bar -0.681087
2  baz  0.377953

In [128]: data = {'item1': df, 'item2': df}

In [129]: panel = pd.Panel.from_dict(data, orient='minor')

In [130]: panel['a']
Out[130]:
item1 item2
0   foo   foo
1   bar   bar
2   baz   baz

In [131]: panel['b']
Out[131]:
item1     item2
0 -0.308853 -0.308853
1 -0.681087 -0.681087
2  0.377953  0.377953

In [132]: panel['b'].dtypes
Out[132]:
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 [133]: midx = pd.MultiIndex(levels=[['one', 'two'], ['x','y']], labels=[[1,1,0,0],[1,0,1,0]])

In [134]: df = pd.DataFrame({'A' : [1, 2, 3, 4], 'B': [5, 6, 7, 8]}, index=midx)

In [135]: df.to_panel()
Out[135]:
<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 [136]: wp['Item1']
Out[136]:
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 [137]: 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 [138]: wp.transpose(2, 0, 1)
Out[138]:
<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 [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.major_xs(wp.major_axis[2])
Out[140]:
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 [141]: wp.minor_axis
Out[141]: Index(['A', 'B', 'C', 'D'], dtype='object')

In [142]: wp.minor_xs('C')
Out[142]:
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 [143]: wp.reindex(items=['Item1']).squeeze()
Out[143]:
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 [144]: wp.reindex(items=['Item1'], minor=['B']).squeeze()
Out[144]:
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 [145]: 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 [146]: panel.to_frame()
Out[146]:
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 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 usecases. Here is a link to the xarray panel-transition documentation.

In [147]: p = tm.makePanel()

In [148]: p
Out[148]:
<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 [149]: p.to_frame()
Out[149]:
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 [150]: p.to_xarray()
Out[150]:
<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 2000-01-05 ...
* minor_axis  (minor_axis) object 'A' 'B' 'C' 'D'


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

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