Question

数据框不工作的多维插值

import pandas as pd
import numpy as np
raw_data = {'CCY_CODE': ['SGD','USD','USD','USD','USD','USD','USD','EUR','EUR','EUR','EUR','EUR','EUR','USD'],
            'END_DATE': ['16/03/2018','17/03/2018','17/03/2018','17/03/2018','17/03/2018','17/03/2018','17/03/2018',
                        '17/03/2018','17/03/2018','17/03/2018','17/03/2018','17/03/2018','17/03/2018','17/03/2018'],
            'STRIKE':[0.005,0.01,0.015,0.02,0.025,0.03,0.035,0.04,0.045,0.05,0.55,0.06,0.065,0.07],
            'VOLATILITY':[np.nan,np.nan,0.3424,np.nan,0.2617,0.2414,np.nan,np.nan,0.215,0.212,0.2103,np.nan,0.2092,np.nan]
           }
df_volsurface = pd.DataFrame(raw_data,columns = ['CCY_CODE','END_DATE','STRIKE','VOLATILITY'])
df_volsurface['END_DATE'] = pd.to_datetime(df_volsurface['END_DATE'])
df_volsurface.interpolate(method='akima',limit_direction='both')

输出：

<table><tbody><tr><th> </th><th>CCY_CODE</th><th>END_DATE</th><th>STRIKE</th><th>VOLATILITY</th></tr><tr><td>0</td><td>SGD</td><td>3/16/2018</td><td>0.005</td><td>NaN</td></tr><tr><td>1</td><td>USD</td><td>3/17/2018</td><td>0.01</td><td>NaN</td></tr><tr><td>2</td><td>USD</td><td>3/17/2018</td><td>0.015</td><td>0.3424</td></tr><tr><td>3</td><td>USD</td><td>3/17/2018</td><td>0.02</td><td>0.296358</td></tr><tr><td>4</td><td>USD</td><td>3/17/2018</td><td>0.025</td><td>0.2617</td></tr><tr><td>5</td><td>USD</td><td>3/17/2018</td><td>0.03</td><td>0.2414</td></tr><tr><td>6</td><td>USD</td><td>3/17/2018</td><td>0.035</td><td>0.230295</td></tr><tr><td>7</td><td>EUR</td><td>3/17/2018</td><td>0.04</td><td>0.220911</td></tr><tr><td>8</td><td>EUR</td><td>3/17/2018</td><td>0.045</td><td>0.215</td></tr><tr><td>9</td><td>EUR</td><td>3/17/2018</td><td>0.05</td><td>0.212</td></tr><tr><td>10</td><td>EUR</td><td>3/17/2018</td><td>0.55</td><td>0.2103</td></tr><tr><td>11</td><td>EUR</td><td>3/17/2018</td><td>0.06</td><td>0.209471</td></tr><tr><td>12</td><td>EUR</td><td>3/17/2018</td><td>0.065</td><td>0.2092</td></tr><tr><td>13</td><td>USD</td><td>3/17/2018</td><td>0.07</td><td>NaN</td></tr></tbody></table>

预期结果：

<table><tbody><tr><th> </th><th>CCY_CODE</th><th>END_DATE</th><th>STRIKE</th><th>VOLATILITY</th></tr><tr><td>0</td><td>SGD</td><td>3/16/2018</td><td>0.005</td><td>NaN</td></tr><tr><td>1</td><td>USD</td><td>3/17/2018</td><td>0.01</td><td>Expected some logical value</td></tr><tr><td>2</td><td>USD</td><td>3/17/2018</td><td>0.015</td><td>0.3424</td></tr><tr><td>3</td><td>USD</td><td>3/17/2018</td><td>0.02</td><td>0.296358</td></tr><tr><td>4</td><td>USD</td><td>3/17/2018</td><td>0.025</td><td>0.2617</td></tr><tr><td>5</td><td>USD</td><td>3/17/2018</td><td>0.03</td><td>0.2414</td></tr><tr><td>6</td><td>USD</td><td>3/17/2018</td><td>0.035</td><td>0.230295</td></tr><tr><td>7</td><td>EUR</td><td>3/17/2018</td><td>0.04</td><td>0.220911</td></tr><tr><td>8</td><td>EUR</td><td>3/17/2018</td><td>0.045</td><td>0.215</td></tr><tr><td>9</td><td>EUR</td><td>3/17/2018</td><td>0.05</td><td>0.212</td></tr><tr><td>10</td><td>EUR</td><td>3/17/2018</td><td>0.55</td><td>0.2103</td></tr><tr><td>11</td><td>EUR</td><td>3/17/2018</td><td>0.06</td><td>0.209471</td></tr><tr><td>12</td><td>EUR</td><td>3/17/2018</td><td>0.065</td><td>0.2092</td></tr><tr><td>13</td><td>USD</td><td>3/17/2018</td><td>0.07</td><td>Expected some logical value</td></tr></tbody></table>

线性插值方法将所有后向和前向缺失值的最后可用值复制，而不考虑ccy_code

df_volsurface.interpolate(method='linear',limit_direction='both')

输出：

<table><tbody><tr><th>CCY_CODE</th><th>END_DATE</th><th>STRIKE</th><th>VOLATILITY</th><th> </th></tr><tr><td>0</td><td>SGD</td><td>3/16/2018</td><td>0.005</td><td>0.3424</td></tr><tr><td>1</td><td>USD</td><td>3/17/2018</td><td>0.01</td><td>0.3424</td></tr><tr><td>2</td><td>USD</td><td>3/17/2018</td><td>0.015</td><td>0.3424</td></tr><tr><td>3</td><td>USD</td><td>3/17/2018</td><td>0.02</td><td>0.30205</td></tr><tr><td>4</td><td>USD</td><td>3/17/2018</td><td>0.025</td><td>0.2617</td></tr><tr><td>5</td><td>USD</td><td>3/17/2018</td><td>0.03</td><td>0.2414</td></tr><tr><td>6</td><td>USD</td><td>3/17/2018</td><td>0.035</td><td>0.2326</td></tr><tr><td>7</td><td>EUR</td><td>3/17/2018</td><td>0.04</td><td>0.2238</td></tr><tr><td>8</td><td>EUR</td><td>3/17/2018</td><td>0.045</td><td>0.215</td></tr><tr><td>9</td><td>EUR</td><td>3/17/2018</td><td>0.05</td><td>0.212</td></tr><tr><td>10</td><td>EUR</td><td>3/17/2018</td><td>0.55</td><td>0.2103</td></tr><tr><td>11</td><td>EUR</td><td>3/17/2018</td><td>0.06</td><td>0.20975</td></tr><tr><td>12</td><td>EUR</td><td>3/17/2018</td><td>0.065</td><td>0.2092</td></tr><tr><td>13</td><td>USD</td><td>3/17/2018</td><td>0.07</td><td>0.2092</td></tr></tbody></table>

任何帮助表示赞赏！谢谢！

Answer 1

我想指出这仍然是一维插值。我们有一个自变量（'STRIKE'）和一个因变量（＆＃39; VOLATILITY'）。对不同的条件进行插值，例如对于每一天，每种货币，每种方案等。以下是基于'END_DATE'和＆＃39; CCY_CODE'如何进行插值的示例。

# set all the conditions as index
df_volsurface.set_index(['END_DATE', 'CCY_CODE', 'STRIKE'], inplace=True)
df_volsurface.sort_index(level=['END_DATE', 'CCY_CODE', 'STRIKE'], inplace=True)
# create separate columns for all criteria except the independent variable
df_volsurface = df_volsurface.unstack(level=['END_DATE', 'CCY_CODE'])
for ccy in df_volsurface:
    indices = df_volsurface[ccy].notna()
    if not any(indices):
        continue  # we are not interested in a column with only NaN
    x = df_volsurface.index.get_level_values(level='STRIKE')  # independent var
    y = df_volsurface[ccy]  # dependent var
    # create interpolation function
    f_interp = scipy.interpolate.interp1d(x[indices], y[indices], kind='linear', 
                            bounds_error=False, fill_value='extrapolate')
    df_volsurface['VOL_INTERP', ccy[1], ccy[2]] = f_interp(x)
print(df_volsurface)

其他条件的插值应该类似。这是生成的DataFrame：

         VOLATILITY                    VOL_INTERP         
END_DATE 2018-03-16 2018-03-17         2018-03-17         
CCY_CODE        SGD        EUR     USD        EUR      USD
STRIKE                                                    
0.005           NaN        NaN     NaN    0.23900  0.42310
0.010           NaN        NaN     NaN    0.23600  0.38275
0.015           NaN        NaN  0.3424    0.23300  0.34240
0.020           NaN        NaN     NaN    0.23000  0.30205
0.025           NaN        NaN  0.2617    0.22700  0.26170
0.030           NaN        NaN  0.2414    0.22400  0.24140
0.035           NaN        NaN     NaN    0.22100  0.22110
0.040           NaN        NaN     NaN    0.21800  0.20080
0.045           NaN     0.2150     NaN    0.21500  0.18050
0.050           NaN     0.2120     NaN    0.21200  0.16020
0.055           NaN     0.2103     NaN    0.21030  0.13990
0.060           NaN        NaN     NaN    0.20975  0.11960
0.065           NaN     0.2092     NaN    0.20920  0.09930
0.070           NaN        NaN     NaN    0.20865  0.07900

使用df_volsurface.stack()返回您选择的多索引。还有几种pandas插值方法可供选择。但是，我没有找到一个令人满意的问题解决方案使用method='akima'，因为它只在给定的数据点之间进行插值，但似乎没有推断出来。

日期帧插值不适用于熊猫+多维插值

1 个答案: