Series

构造Series

obj=Series([4,5,-7,7]) obj Out[140]: 0 4 1 5 2 -7 3 7 dtype: int64 obj.index Out[141]: RangeIndex(start=0, stop=4, step=1) obj.values Out[142]: array([ 4, 5, -7, 7], dtype=int64)

构造同时指定索引

obj2=Series([4,5,-7,7],index=['a','b','c','d']) obj2 Out[144]: a 4 b 5 c -7 d 7 dtype: int64

读取

obj2.a Out[148]: 4 obj2['a'] Out[149]: 4

数学运算

obj2[obj2>0] Out[150]: a 4 b 5 d 7 dtype: int64 obj2*2 Out[151]: a 8 b 10 c -14 d 14 dtype: int64 np.exp(obj2) Out[152]: a 54.598150 b 148.413159 c 0.000912 d 1096.633158 dtype: float64 'b' in obj2 Out[153]: True 't' in obj2 Out[154]: False

由字典变成Series

sdata={'ohio':3500,'texas':71000,'oregon':16000,'utah':500} obj3=Series(sdata) obj3 Out[159]: ohio 3500 oregon 16000 texas 71000 utah 500 dtype: int64

替换字典的index,与California 对应的sdata 没有，显示是NaN

states=['california','ohio','oregon','texas'] sdata={'ohio':3500,'texas':71000,'oregon':16000,'utah':500} obj4=Series(sdata,index=states) obj4 Out[170]: california NaN ohio 3500.0 oregon 16000.0 texas 71000.0 dtype: float64

判断数据是否缺失

pd.isnull(obj4) Out[171]: california True ohio False oregon False texas False dtype: bool pd.notnull(obj4) Out[172]: california False ohio True oregon True texas True dtype: bool

判断缺失的另一种表达

obj4.notnull() Out[176]: california False ohio True oregon True texas True dtype: bool obj4.isnull() Out[177]: california True ohio False oregon False texas False dtype: bool obj3 Out[18]: ohio 3500 oregon 16000 texas 71000 utah 500 dtype: int64 obj4 Out[19]: california NaN ohio 3500.0 oregon 16000.0 texas 71000.0 dtype: float64 obj3+obj4 Out[20]: california NaN ohio 7000.0 oregon 32000.0 texas 142000.0 utah NaN dtype: float64 obj4.name='population' obj4.index.name='state' obj4 Out[30]: state california NaN ohio 3500.0 oregon 16000.0 texas 71000.0 Name: population, dtype: float64 obj=Series([4,7,-5,3]) obj Out[43]: 0 4 1 7 2 -5 3 3 dtype: int64 obj.index=['bob','steve','jeff','ryan'] obj Out[45]: bob 4 steve 7 jeff -5 ryan 3 dtype: int64