Pandas is an open source library that contains various operations and data structures for manipulating numerical data and time series, it makes analyzing data easier.
The Series Data Structure
In [11]: import pandas as pd
In [12]: animals = ['Tiger', 'Bear', 'Moose']
pd.Series(animals)
In [12]: animals = ['Tiger', 'Bear', 'Moose']
pd.Series(animals)
Out[12]: 0 Tiger
1 Bear
2 Moose
dtype: object
In [13]: numbers = [1, 2, 3]
pd.Series(numbers)
Out[13]: 0 1
1 2
2 3
dtype: int64
In [14]: animals = ['Tiger', 'Bear', None]
pd.Series(animals)
Out[14]: 0 Tiger
1 Bear
2 None
dtype: object
In [15]: numbers = [1, 2, None]
pd.Series(numbers)
Out[15]: 0 1.0
1 2.0
2 NaN
dtype: float64
In [16]: import numpy as np
np.nan == None
1 2.0
2 NaN
dtype: float64
In [16]: import numpy as np
np.nan == None
Out[16]: False
In [17]: np.nan == np.nan
Out[17]: False
In [18]: np.isnan(np.nan)
Out[18]: True
In [19]: sports = {'Archery': 'Bhutan',
'Golf': 'Scotland',
'Sumo': 'Japan',
'Taekwondo': 'South Korea'}
s = pd.Series(sports)
s
Out[19]: Archery Bhutan
Golf Scotland
Sumo Japan
Taekwondo South Korea
dtype: object
In [20]: s.index
Out[20]: Index(['Archery', 'Golf', 'Sumo', 'Taekwondo'], dtype='object')
In [21]: s = pd.Series(['Tiger', 'Bear', 'Moose'], index=['India', 'America', 'Canada'])
s
Out[21]: India Tiger
America Bear
Canada Moose
dtype: object
In [11]: sports = {'Archery': 'Bhutan',
'Golf': 'Scotland',
'Sumo': 'Japan',
'Taekwondo': 'South Korea'}
s = pd.Series(sports, index=['Golf', 'Sumo', 'Hockey'])
s
Out[11]: Golf Scotland
Sumo Japan
Hockey NaN
dtype: object
Querying a Series
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