pandas中read_csv、rolling、expanding用法详解

import pandas as pd
from pandas import DataFrame

series = pd.read_csv('daily-min-temperatures.csv',header=0, index_col=0,
      parse_dates=True,squeeze=True)
temps = DataFrame(series.values)
width = 3
shifted = temps.shift(width-1)
print(shifted)
window = shifted.rolling(window=width)
dataframe = DataFrame()
dataframe = pd.concat([window.min(),window.mean(),window.max(),temps],axis=1)
dataframe.columns=['min','mean','max','t+1']
print(dataframe.head(5))

 def rolling(self, *args, **kwargs):
  """
  Return a rolling grouper, providing rolling functionality per group.
  """
  from pandas.core.window import RollingGroupby

  return RollingGroupby(self, *args, **kwargs)

 @Substitution(name="groupby")
 @Appender(_common_see_also)

import matplotlib.pylab as plt
import numpy as np
import pandas as pd
index=pd.date_range('20190116','20190130')
data=[4,8,6,5,9,1,4,5,2,4,6,7,9,13,6]
ser_data=pd.Series(data,index=index)
print(ser_data)

import pandas as pd
from pandas import DataFrame

series = pd.read_csv('daily-min-temperatures.csv',header=0, index_col=0,
      parse_dates=True,squeeze=True)
temps = DataFrame(series.values)
window = temps.expanding()
dataframe = DataFrame()
dataframe = pd.concat([window.min(),window.mean(),window.max(),temps.shift(-1)],axis=1)
dataframe.columns=['min','mean','max','t+1']
print(dataframe.head(5))