推断熊猫DataFrame小号

DataFrame可能是推断出来的，但是，pandas中没有简单的方法调用，需要另一个库（例如scipy.optimize）。

外推

通常，外推要求人们对要外推的数据做出某些假设。一种方法是将一些通用的参数化方程曲线拟合到数据，以找到最能描述现有数据的参数值，然后将其用于计算超出此数据范围的值。这种方法的困难和局限性问题是对
趋势的 一些假设 __选择参数化方程式时必须进行。
可以通过反复试验找到不同的方程式，以得出所需的结果，或者有时可以从数据源中推断出来。问题中提供的数据实际上不足以获取良好拟合曲线的数据集；但是，它足以说明问题。

下面是外推的一个例子DataFrame用3次多项式

f （ x ）= a x 3 + b x 2 + c x + d
（等式1）

将该通用函数（func()）曲线拟合到每一列上，以获得唯一的列特定参数（即 a ， b ， c ， d
）。然后，将这些参数化的方程式用于使用NaNs推断所有索引的每一列中的数据。

import pandas as pd
from cStringIO import StringIO
from scipy.optimize import curve_fit

df = pd.read_table(StringIO('''
                neg       neu       pos       avg
    0           NaN       NaN       NaN       NaN
    250    0.508475  0.527027  0.641292  0.558931
    500         NaN       NaN       NaN       NaN
    1000   0.650000  0.571429  0.653983  0.625137
    2000        NaN       NaN       NaN       NaN
    3000   0.619718  0.663158  0.665468  0.649448
    4000        NaN       NaN       NaN       NaN
    6000        NaN       NaN       NaN       NaN
    8000        NaN       NaN       NaN       NaN
    10000       NaN       NaN       NaN       NaN
    20000       NaN       NaN       NaN       NaN
    30000       NaN       NaN       NaN       NaN
    50000       NaN       NaN       NaN       NaN'''), sep='\s+')

# Do the original interpolation
df.interpolate(method='nearest', xis=0, inplace=True)

# Display result
print ('Interpolated data:')
print (df)
print ()

# Function to curve fit to the data
def func(x, a, b, c, d):
    return a * (x ** 3) + b * (x ** 2) + c * x + d

# Initial parameter guess, just to kick off the optimization
guess = (0.5, 0.5, 0.5, 0.5)

# Create copy of data to remove NaNs for curve fitting
fit_df = df.dropna()

# Place to store function parameters for each column
col_params = {}

# 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])

# Display result
print ('Extrapolated data:')
print (df)
print ()

print ('Data was extrapolated with these column functions:')
for col in col_params:
    print ('f_{}(x) = {:0.3e} x^3 + {:0.3e} x^2 + {:0.4f} x + {:0.4f}'.format(col, *col_params[col]))

外推结果

Interpolated data:
            neg       neu       pos       avg
0           NaN       NaN       NaN       NaN
250    0.508475  0.527027  0.641292  0.558931
500    0.508475  0.527027  0.641292  0.558931
1000   0.650000  0.571429  0.653983  0.625137
2000   0.650000  0.571429  0.653983  0.625137
3000   0.619718  0.663158  0.665468  0.649448
4000        NaN       NaN       NaN       NaN
6000        NaN       NaN       NaN       NaN
8000        NaN       NaN       NaN       NaN
10000       NaN       NaN       NaN       NaN
20000       NaN       NaN       NaN       NaN
30000       NaN       NaN       NaN       NaN
50000       NaN       NaN       NaN       NaN

Extrapolated data:
               neg          neu         pos          avg
0         0.411206     0.486983    0.631233     0.509807
250       0.508475     0.527027    0.641292     0.558931
500       0.508475     0.527027    0.641292     0.558931
1000      0.650000     0.571429    0.653983     0.625137
2000      0.650000     0.571429    0.653983     0.625137
3000      0.619718     0.663158    0.665468     0.649448
4000      0.621036     0.969232    0.708464     0.766245
6000      1.197762     2.799529    0.991552     1.662954
8000      3.281869     7.191776    1.702860     4.058855
10000     7.767992    15.272849    3.041316     8.694096
20000    97.540944   150.451269   26.103320    91.365599
30000   381.559069   546.881749   94.683310   341.042883
50000  1979.646859  2686.936912  467.861511  1711.489069

Data was extrapolated with these column functions:
f_neg(x) = 1.864e-11 x^3 + -1.471e-07 x^2 + 0.0003 x + 0.4112
f_neu(x) = 2.348e-11 x^3 + -1.023e-07 x^2 + 0.0002 x + 0.4870
f_avg(x) = 1.542e-11 x^3 + -9.016e-08 x^2 + 0.0002 x + 0.5098
f_pos(x) = 4.144e-12 x^3 + -2.107e-08 x^2 + 0.0000 x + 0.6312

对于情节avg列

如果没有更大的数据集或不知道数据源，则此结果可能完全错误，但应举例说明外推a的过程DataFrame。在假设的公式func()很可能需要被 打
与以获得正确的推断。另外，没有尝试使代码高效。