1.2. About Types

1.2.1. SetUp

>>> import pandas as pd

1.2.2. Series

  • 1-dimensional data structure similar to ndarray

  • Has index

  • Can have name

>>> pd.Series([1.0, 2.0, 3.0, 4.0])
0    1.0
1    2.0
2    3.0
3    4.0
dtype: float64

1.2.3. DataFrame

  • 2-dimensional object

  • All columns share the same index

  • List of Series

  • Each column must have name

  • Operations can be executed on columns or rows

>>> pd.DataFrame({
...     'A': ['a', 'b', 'c', 'd'],
...     'B': [11, 22, 33, 44],
...     'C': [1.1, 2.2, 3.3, 4.4],
... })
   A   B    C
0  a  11  1.1
1  b  22  2.2
2  c  33  3.3
3  d  44  4.4

1.2.4. SparseArray

  • Data where a single value is repeated many times (e.g. 0 or NaN) may be stored efficiently as a SparseArray

Sparse data with Series:

>>> pd.arrays.SparseArray([1, None, None, None, 3])
[1.0, nan, nan, nan, 3.0]
Fill: nan
IntIndex
Indices: array([0, 4], dtype=int32)

Sparse data with DataFrame:

>>> df = pd.DataFrame({
...     'A': [1, 2, 3],
...     'B': pd.arrays.SparseArray([None, None, None])})
>>>
>>> df
   A    B
0  1  NaN
1  2  NaN
2  3  NaN
>>>
>>> df.dtypes
A                  int64
B    Sparse[object, nan]
dtype: object

1.2.5. Interval

Definition:

>>> pd.Interval(0, 5)
Interval(0, 5, closed='right')

>>> pd.Interval(left=0, right=5)
Interval(0, 5, closed='right')

>>> pd.Interval(left=0, right=5, closed='both')
Interval(0, 5, closed='both')

Contains:

>>> interval = pd.Interval(0, 5, closed='left')
>>>
>>> 2.5 in interval
True
>>>
>>> 5.0 in interval
False

Interval between Timestamps:

>>> year_1970 = pd.Interval(left=pd.Timestamp('1970-01-01 00:00:00'),
...                         right=pd.Timestamp('1971-01-01 00:00:00'),
...                         closed='left')
>>>
>>> apollo11 = pd.Timestamp('1969-07-16')
>>> apollo13 = pd.Timestamp('1970-04-11')
>>>
>>> apollo11 in year_1970
False
>>>
>>> apollo13 in year_1970
True
>>>
>>> year_1970.length
Timedelta('365 days 00:00:00')

>>> fiscalyear2020 = pd.Interval(
...     left=pd.Timestamp('2020-01-01'),
...     right=pd.Timestamp('2021-01-01'),
...     closed='left')
>>>
>>> fiscalyear2021 = pd.Interval(
...     left=pd.Timestamp('2021-01-01'),
...     right=pd.Timestamp('2022-01-01'),
...     closed='left')
>>>
>>>
>>> event1 = pd.Timestamp('2020-04-12')
>>> event2 = pd.Timestamp('2021-07-21')
>>>
>>> event1 in fiscalyear2020
True
>>> event1 in fiscalyear2021
False
>>> event2 in fiscalyear2020
False
>>> event2 in fiscalyear2021
True

1.2.6. Categorical

  • Limited, fixed set of values

>>> iris = pd.Categorical(['setosa', 'virginica', 'versicolor'])
>>>
>>> iris
['setosa', 'virginica', 'versicolor']
Categories (3, object): ['setosa', 'versicolor', 'virginica']
>>>
>>> 'arctica' in iris
False

>>> status = pd.Categorical(['todo', 'done', 'todo', 'done'])
>>>
>>> status
['todo', 'done', 'todo', 'done']
Categories (2, object): ['done', 'todo']
>>>
>>> 'in progress' in status
False
>>>
>>> 'todo' in status
True
>>>
>>> status.categories
Index(['done', 'todo'], dtype='object')

>>> moon_landings = pd.Categorical(['apollo11', 'apollo12', 'apollo14',
...                                 'apollo15', 'apollo16', 'apollo17'])
>>>
>>> moon_landings
['apollo11', 'apollo12', 'apollo14', 'apollo15', 'apollo16', 'apollo17']
Categories (6, object): ['apollo11', 'apollo12', 'apollo14', 'apollo15', 'apollo16', 'apollo17']
>>>
>>> 'apollo11' in moon_landings
True
>>>
>>> 'apollo13' in moon_landings
False
>>>
>>> moon_landings.categories
Index(['apollo11', 'apollo12', 'apollo14', 'apollo15', 'apollo16', 'apollo17'], dtype='object')