使用Pandas数据分析之Series

import pandas as pd import numpy as np users=['张三','李四','王老五'] series1=pd.Series(users) print(series1)

0 张三 1 李四 2 王老五 dtype: object

users={'张三':20,'李四':25,'王五':21} series2=pd.Series(users) print(series2)

张三 20 李四 25 王五 21 dtype: int64

print(series2.index)

Index(['张三', '李四', '王五'], dtype='object')

print(series2.values)

[20 25 21]

print(series2.values) print(series2[1]) print(series2['王五'])

25 21

pd.date_range('2022-10-01',periods=4,freq='M')

pd.TimedeltaIndex([10,12,14,16],unit="D")

TimedeltaIndex(['10 days', '12 days', '14 days', '16 days'], dtype='timedelta64[ns]', freq=None)

import numpy as np import pandas as pd pd=pd.DataFrame(np.random.randint(1,100,(4,5)),index=['A','B','C','D']) # pd['A':'C']#通过索引名称取值，结果包含最后一个 pd[0:3]#通过索引下标取值，结果不包含最后一个

conditon=series>50 series[conditon] 或 series[series>50]

0 1 2 3 4 A 84.0 63.0 76.0 72.0 77.0 B NaN 96.0 NaN 65.0 NaN C NaN NaN NaN 81.0 NaN D 74.0 89.0 NaN NaN 53.0