在Pandas.DataFrame使用中插入值很容易Series.interpolate,如何进行外推？
例如,给定一个DataFrame如图所示,我们怎样才能将它推断14个月到2014-12-31？线性外推很好.
X1 = range(10)
X2 = map(lambda x: x**2, X1)
df = pd.DataFrame({'x1': X1, 'x2': X2}, index=pd.date_range('20130101',periods=10,freq='M'))
我认为必须首先创建一个新的DataFrame,DateTimeIndex从2013-11-31开始并延长14个M周期.除此之外,我被困住了.
DataFrame用DatetimeIndex索引外推a这可以通过两个步骤完成:
扩展 DatetimeIndex
推断数据
df使用new 覆盖,DataFrame其中数据根据原始索引的开始,周期和频率重新采样到新的扩展索引上.这允许原始来自任何地方,如示例中的情况.有了这个,方便地填充NaNs!dfcsv
# Fake DataFrame for example (could come from anywhere)
X1 = range(10)
X2 = map(lambda x: x**2, X1)
df = pd.DataFrame({'x1': X1, 'x2': X2}, index=pd.date_range('20130101',periods=10,freq='M'))
# Number of months to extend
extend = 5
# Extrapolate the index first based on original index
df = pd.DataFrame(
data=df,
index=pd.date_range(
start=df.index[0],
periods=len(df.index) + extend,
freq=df.index.freq
)
)
# Display
print df
x1 x2
2013-01-31 0 0
2013-02-28 1 1
2013-03-31 2 4
2013-04-30 3 9
2013-05-31 4 16
2013-06-30 5 25
2013-07-31 6 36
2013-08-31 7 49
2013-09-30 8 64
2013-10-31 9 81
2013-11-30 NaN NaN
2013-12-31 NaN NaN
2014-01-31 NaN NaN
2014-02-28 NaN NaN
2014-03-31 NaN NaN
大多数外推器都要求输入数字而不是日期.这可以通过以下方式完成
# Temporarily remove dates and make index numeric
di = df.index
df = df.reset_index().drop('index', 1)
看到这个答案如何推断的每一列的值DataFrame用3 次多项式.
回答片段
# Curve fit each column for col in fit_df.columns: # Get x & y x = fit_df.index.astype(float).values y = fit_df[col].values # Curve fit column and get curve parameters params = curve_fit(func, x, y, guess) # Store optimized parameters col_params[col] = params[0] # Extrapolate each column for col in df.columns: # Get the index values for NaNs in the column x = df[pd.isnull(df[col])].index.astype(float).values # Extrapolate those points with the fitted function df[col][x] = func(x, *col_params[col])
列外推后,将日期放回去
# Put date index back
df.index = di
# Display
print df
x1 x2
2013-01-31 0 0
2013-02-28 1 1
2013-03-31 2 4
2013-04-30 3 9
2013-05-31 4 16
2013-06-30 5 25
2013-07-31 6 36
2013-08-31 7 49
2013-09-30 8 64
2013-10-31 9 81
2013-11-30 10 100
2013-12-31 11 121
2014-01-31 12 144
2014-02-28 13 169
2014-03-31 14 196
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