<jupyter_start><jupyter_text>A Series is a one-dimensional array-like object containing an array of data (of anyNumpy data type) and an associated array of data labels, called its **_index_**.<jupyter_code>s = Series([3,5,-6,4,9])
another_s = Series([3,5,7,9,3], index=['a','d','f','g','e'])
print another_s.values
print another_s.index
print another_s['f']
another_s[['e','g']]
another_s[another_s>5]
another_s>5
another_s*2
np.exp(another_s)<jupyter_output><empty_output><jupyter_text>Another way to think about a Series is as a _fixed-length_, ordered dict, as it is a mappingof index values to data values. It can be substituted into many functions that expect adict<jupyter_code>import numpy as np

from pandas import Series, DataFrame
import pandas as pd 
import matplotlib.pyplot as plt

# if 7 in another_s:
#     print True
sdata = {'Ohio': 35000, 'Texas': 71000, 'Oregon': 16000, 'Utah': 5000}

obj3 = Series(sdata)
obj3
states = ['California', 'Ohio', 'Oregon', 'Texas']

obj4 = Series(sdata, index=states)
obj4
obj4+obj3
obj4.name=u'أي حاجة'
obj4.index.name = 'الولايات الامريكية'
print obj4.index.name
data = {'state': ['بصل','ثوم','كرفس','باذنجان','تقلية', 'd'],
'year': [2000, 2001, 2002, 2001, 2002,2020],
'pop': [1.5, 1.7, 3.6, 2.4, 2.9, 7]}

frame = DataFrame(data, index=['1', '2', '2', '4', '5','f'], 
                 columns=['pop','year','state','kaka'])
frame
import numpy as np

from pandas import Series, DataFrame
import pandas as pd 
import matplotlib.pyplot as plt
frame.ix['2']<jupyter_output><empty_output>
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