pandas使用

import pandas as pd

#读取txt格式的表格文件
pd.read_tables("***.txt",delim_whitespace=True)
#delim_whitespace=True意思每个值用空格分开

#读取csv格式的文件
data = pd.read_csv("***.csv")
data.head(2) #查看前两行
data.columns #查看全有栏目
data.index  #查看所有索引
data.loc[0]  #0是索引可以替换，意思查看索引为0的一行

 在读取log文件，用于生成文件名集合。

>>> import pandas as pd
>>> dates = pd.date_range(pd.to_datetime('2017-01-01'),pd.to_datetime('2018-04-0
4'),freq='M')
>>> type(dates)
<class 'pandas.core.indexes.datetimes.DatetimeIndex'>
>>> dates[:10]
DatetimeIndex(['2017-01-31', '2017-02-28', '2017-03-31', '2017-04-30',
               '2017-05-31', '2017-06-30', '2017-07-31', '2017-08-31',
               '2017-09-30', '2017-10-31'],
              dtype='datetime64[ns]', freq='M')
>>> for i in dates:
...     print(i)
...
2017-01-31 00:00:00
2017-02-28 00:00:00
2017-03-31 00:00:00
2017-04-30 00:00:00
2017-05-31 00:00:00
2017-06-30 00:00:00
2017-07-31 00:00:00
2017-08-31 00:00:00
2017-09-30 00:00:00
2017-10-31 00:00:00
2017-11-30 00:00:00
2017-12-31 00:00:00
2018-01-31 00:00:00
2018-02-28 00:00:00
2018-03-31 00:00:00
>>> dates = pd.date_range(pd.to_datetime('2017-01-01'),pd.to_datetime('2018-04-0
4'))
>>> dates[:10]
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04',
               '2017-01-05', '2017-01-06', '2017-01-07', '2017-01-08',
               '2017-01-09', '2017-01-10'],
              dtype='datetime64[ns]', freq='D')
>>>