3.3. Series Index

3.3.1. Index Types

• Range Index

• Int64 Index

• Float64 Index

• String Index

• Datetime Index

RangeIndex(start=0, stop=5, step=1)

Int64Index([0, 1, 2, 3, 4], dtype='int64')

Float64Index([0.0, 1.1, 2.2, 3.3, 4.4], dtype='float64')

Index(['a', 'b', 'c', 'd', 'e'], dtype='object')

DatetimeIndex(['2000-01-01', '2000-01-02', '2000-01-03', '2000-01-04', '2000-01-05'],
              dtype='datetime64[ns]', freq='D')

import pandas as pd
import numpy as np


DATA = [11, 22, 33, 44, 55]

# Range Index
r1 = pd.Series(DATA)
r2 = pd.Series(DATA, index=range(5))
r3 = pd.Series(DATA, index=range(len(DATA)))

# Integer Index
i1 = pd.Series(DATA, index=[0, 1, 2, 3, 4])
i2 = pd.Series(DATA, index=np.arange(5))
i3 = pd.Series(DATA, index=np.arange(len(DATA)))
i4 = pd.Series(DATA, index=[99, 3, -5, 0, 77])

# Float Index
f1 = pd.Series(DATA, index=[0.0, 1.1, 2.2, 3.3, 4.4])
f2 = pd.Series(DATA, index=np.arange(0.0, 5.5, 1.1)
f3 = pd.Series(DATA, index=[99.9, 3.14, -5.99, 0.0, 77.1])

# Object Index
o1 = pd.Series(DATA, index=['a', 'b', 'c', 'd', 'e'])
o2 = pd.Series(DATA, index=list('abcde'))
o3 = pd.Series(DATA, index=list('abcdefghijklmnopqrstuvwz')[:len(DATA)])
o4 = pd.Series(DATA, index=['aaa', 'baba', 'cac', 'do or not', 'e,c,h,o'])

# Datetime Index
d1 = pd.Series(DATA, index=pd.date_range('2000-01-01', periods=len(DATA)))
d2 = pd.Series(DATA, index=pd.date_range('2000-01-01', periods=len(DATA), freq='D'))
d3 = pd.Series(DATA, index=[
    pd.Timestamp('2000-01-01'),
    pd.Timestamp('1999-01-28'),
    pd.Timestamp('1961-04-12'),
    pd.Timestamp('1969-07-21'),
    pd.Timestamp('1970-01-01')])

3.3.2. Range Index

• Default

Define Range Index:

import pandas as pd


DATA = [11, 22, 33, 44, 55]

r1 = pd.Series(DATA)
r2 = pd.Series(DATA, index=range(5))

r1.index

import pandas as pd

s = pd.Series([1.0, 2.0, 3.0])

s.index
# RangeIndex(start=0, stop=3, step=1)

s
# 0    1.0
# 1    2.0
# 2    3.0
# dtype: float64

import pandas as pd

s = pd.Series(['a', 'b', 'c'])

s.index
# RangeIndex(start=0, stop=3, step=1)

s
# 0    a
# 1    b
# 2    c
# dtype: object

3.3.3. Int64 Index

• Int64 Index

import pandas as pd

s = pd.Series(
    data = [1.1, 2.2, 3.3, 4.4],
    index = [9, 1337, -2, 0])

s.index
# Int64Index([9, 1337, -2, 0], dtype='int64')

s
#  9      1.1
#  1337   2.2
# -2      3.3
#  0      4.4
# dtype: float64

3.3.4. Float64 Index

• Int64 Index

import pandas as pd

s = pd.Series(
    data = [1.1, 2.2, 3.3, 4.4],
    index = [9.9, 133.7, -2.7, 0.0])

s.index
# Float64Index([9.9, 133.7, -2.7, 0.0], dtype='float64')

s
#  9.9      1.1
#  133.7    2.2
# -2.7      3.3
#  0.0      4.4
# dtype: float64

3.3.5. String Index

• Also has RangeIndex

• string.ascii_lowercase

• string.ascii_uppercase

• string.ascii_letters

• string.hexdigits

• string.digits

import string

string.punctuation
# '!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~'

string.whitespace
# ' \t\n\r\x0b\x0c'

string.ascii_lowercase
# 'abcdefghijklmnopqrstuvwxyz'

string.ascii_uppercase
# 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'

string.ascii_letters
# 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'

string.digits
# '0123456789'

string.hexdigits
# '0123456789abcdefABCDEF'

string.octdigits
# '01234567'

string.printable
# '0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~ \t\n\r\x0b\x0c'

import pandas as pd
import string

s = pd.Series(
    data = [1.1, 2.2, 3.3, 4.4]
    index = list(string.ascii_lowercase)[:4])

s.index
# Index(['a', 'b', 'c', 'd'], dtype='object')

s
# a    1.1
# b    2.2
# c    3.3
# d    4.4
# dtype: float64

import pandas as pd

s = pd.Series(
    data = [1.0, 2.0, 3.0, 4.0, 5.0],
    index = ['a', 'b', 'c', 'd', 'e'])

s.index
# Index(['a', 'b', 'c', 'd', 'e'], dtype='object')

s
# a    1.0
# b    2.0
# c    3.0
# d    4.0
# e    5.0
# dtype: float64

import pandas as pd
import numpy as np
np.random.seed(0)

s = pd.Series(
    data = np.random.randn(3),
    index = list('abc'))

s.index
# Index(['a', 'b', 'c'], dtype='object')

s
# a    1.764052
# b    0.400157
# c    0.978738
# dtype: float64

import pandas as pd

s = pd.Series(
    data = [1.0, 2.0, 3.0, 4.0, 5.0],
    index = ['aaa', 'bbb', 'ccc', 'ddd', 'eee'])

s.index
# Index(['aaa', 'bbb', 'ccc', 'ddd', 'eee'], dtype='object')

s
# aaa    1.0
# bbb    2.0
# ccc    3.0
# ddd    4.0
# eee    5.0
# dtype: float64

3.3.6. Date Index

• Also has RangeIndex

• Default is "Daily"

• Works also with ISO time format 1970-01-01T00:00:00

• 00:00:00 is assumed if time is not provided

import pandas as pd

s = pd.Series(
    data = [1.0, 2.0, 3.0, 4.0, 5.0],
    index = pd.date_range('1999-12-30', periods=5))

s.index
# DatetimeIndex(['1999-12-30', '1999-12-31', '2000-01-01',
#                '2000-01-02', '2000-01-03'],
#               dtype='datetime64[ns]', freq='D')

s
# 1999-12-30    1.0
# 1999-12-31    2.0
# 2000-01-01    3.0
# 2000-01-02    4.0
# 2000-01-03    5.0
# Freq: D, dtype: float64

Every year:

import pandas as pd

s = pd.Series(
    data = [1.0, 2.0, 3.0, 4.0, 5.0],
    index = pd.date_range('1999-12-30', periods=5, freq='Y'))

s.index
# DatetimeIndex(['1999-12-31', '2000-12-31', '2001-12-31',
#                '2002-12-31', '2003-12-31'],
#               dtype='datetime64[ns]', freq='A-DEC')

s
# 1999-12-31    1.0
# 2000-12-31    2.0
# 2001-12-31    3.0
# 2002-12-31    4.0
# 2003-12-31    5.0
# Freq: A-DEC, dtype: float64

Every quarter:

import pandas as pd

s = pd.Series(
    data = [1.0, 2.0, 3.0, 4.0, 5.0],
    index = pd.date_range('1999-12-30', periods=5, freq='Q'))

s.index
# DatetimeIndex(['1999-12-31', '2000-03-31', '2000-06-30',
#                '2000-09-30', '2000-12-31'],
#               dtype='datetime64[ns]', freq='Q-DEC')

s
# 1999-12-31    1.0
# 2000-03-31    2.0
# 2000-06-30    3.0
# 2000-09-30    4.0
# 2000-12-31    5.0
# Freq: Q-DEC, dtype: float64

Every month:

import pandas as pd

s = pd.Series(
    data = [1.0, 2.0, 3.0, 4.0, 5.0],
    index = pd.date_range('1999-12-30', periods=5, freq='M'))

s.index
# DatetimeIndex(['1999-12-31', '2000-01-31', '2000-02-29',
#                '2000-03-31', '2000-04-30'],
#               dtype='datetime64[ns]', freq='M')

s
# 1999-12-31    1.0
# 2000-01-31    2.0
# 2000-02-29    3.0
# 2000-03-31    4.0
# 2000-04-30    5.0
# Freq: M, dtype: float64

Every day:

import pandas as pd

s = pd.Series(
    data = [1.0, 2.0, 3.0, 4.0, 5.0],
    index = pd.date_range('1999-12-30', periods=5, freq='D'))

s.index
# DatetimeIndex(['1999-12-30', '1999-12-31', '2000-01-01',
#                '2000-01-02', '2000-01-03'],
#               dtype='datetime64[ns]', freq='D')

s
# 1999-12-30    1.0
# 1999-12-31    2.0
# 2000-01-01    3.0
# 2000-01-02    4.0
# 2000-01-03    5.0
# Freq: D, dtype: float64

Every two days:

import pandas as pd

s = pd.Series(
    data = [1.0, 2.0, 3.0, 4.0, 5.0],
    index = pd.date_range('1999-12-30', periods=5, freq='2D'))

s.index
# DatetimeIndex(['1999-12-30', '2000-01-01', '2000-01-03',
#                '2000-01-05', '2000-01-07'],
#               dtype='datetime64[ns]', freq='2D')

s
# 1999-12-30    1.0
# 2000-01-01    2.0
# 2000-01-03    3.0
# 2000-01-05    4.0
# 2000-01-07    5.0
# Freq: 2D, dtype: float64

Every hour:

import pandas as pd

s = pd.Series(
    data = [1.0, 2.0, 3.0, 4.0, 5.0],
    index = pd.date_range('1999-12-30', periods=5, freq='H'))

s.index
# DatetimeIndex(['1999-12-30 00:00:00', '1999-12-30 01:00:00',
#                '1999-12-30 02:00:00', '1999-12-30 03:00:00',
#                '1999-12-30 04:00:00'],
#               dtype='datetime64[ns]', freq='H')

s
# 1999-12-30 00:00:00    1.0
# 1999-12-30 01:00:00    2.0
# 1999-12-30 02:00:00    3.0
# 1999-12-30 03:00:00    4.0
# 1999-12-30 04:00:00    5.0
# Freq: H, dtype: float64

Every minute:

import pandas as pd

s = pd.Series(
    data = [1.0, 2.0, 3.0, 4.0, 5.0],
    index = pd.date_range('1999-12-30', periods=5, freq='T'))

s.index
# DatetimeIndex(['1999-12-30 00:00:00', '1999-12-30 00:01:00',
#                '1999-12-30 00:02:00', '1999-12-30 00:03:00',
#                '1999-12-30 00:04:00'],
#               dtype='datetime64[ns]', freq='T')

s
# 1999-12-30 00:00:00    1.0
# 1999-12-30 00:01:00    2.0
# 1999-12-30 00:02:00    3.0
# 1999-12-30 00:03:00    4.0
# 1999-12-30 00:04:00    5.0
# Freq: T, dtype: float64

Every second:

import pandas as pd

s = pd.Series(
    data = [1.0, 2.0, 3.0, 4.0, 5.0],
    index = pd.date_range('1999-12-30', periods=5, freq='S'))

s.index
# DatetimeIndex(['1999-12-30 00:00:00', '1999-12-30 00:00:01',
#                '1999-12-30 00:00:02', '1999-12-30 00:00:03',
#                '1999-12-30 00:00:04'],
#               dtype='datetime64[ns]', freq='S')

s
# 1999-12-30 00:00:00    1.0
# 1999-12-30 00:00:01    2.0
# 1999-12-30 00:00:02    3.0
# 1999-12-30 00:00:03    4.0
# 1999-12-30 00:00:04    5.0
# Freq: S, dtype: float64

Every business day. More information in Date and Time Frequency More information in Date and Time Calendar

import pandas as pd

s = pd.Series(
    data = [1.0, 2.0, 3.0, 4.0, 5.0],
    index = pd.date_range('1999-12-30', periods=5, freq='B'))

s.index
# DatetimeIndex(['1999-12-30', '1999-12-31', '2000-01-03',
#                '2000-01-04', '2000-01-05'],
#               dtype='datetime64[ns]', freq='B')

s
# 1999-12-30    1.0
# 1999-12-31    2.0
# 2000-01-03    3.0
# 2000-01-04    4.0
# 2000-01-05    5.0
# Freq: B, dtype: float64

3.3.7. Assignments

Todo

Create assignments