[Data Analysis & Data Mining] Lagrange polynomial

. 1  from scipy.interpolate Import Lagrange
 2  Import PANDAS AS PD
 . 3  Import numpy AS NP
 . 4  
. 5  # loading data 
. 6 Data = pd.read_excel ( " ./qs.xlsx " )
 . 7  # Print ( "Data: \ n-", Data) 
. 8  Print ( " column index data is: \ n " , data.columns)
 . 9  
10  # set missing values before and after the n to construct Lagrange relationship 
. 11 n. 5 =
 12 is  
13 is  # cycle through the position of missing values 
14  for i inRange (data.shape [0]):
 15      Print (I)
 16      # determines if a value is missing, interpolation proceeds 
. 17      IF np.isnan (data.iloc [I,. 1 ]):
 18 is          Print ( " % d of behavior missing values " % I)
 . 19          iF in < 0:
 20 is              Start = 0
 21 is          the else :
 22 is              Start in
 = 23 is          # acquired before and after the missing values of n data - whether that there is a missing data before and after the value n 
24          mask = data .iloc [Start: n-I + +. 1,. 1 ]
 25          # Get index 
26 is          X =mask.index
 27          Print ( " X: \ n- " , X)
 28          # index of the row containing the missing values removed 
29          X = X [mask.notnull ()]
 30          Print ( " X: \ n- " , X)
 31 is          # or without missing values, wherein the removed portion containing missing values 
32          Y = mask [mask.notnull ()] values.   # containing missing values array 
33 is          Print ( " Y: \ n- " , Y)
 34 is          # Construction pull Grange polynomial, Lagrange polynomial returns the object 
35          La = Lagrange (X = X, W = Y)
 36          # Lagrangian interpolating polynomial
37 [          data.iloc [I,. 1] = La ([I])
 38 is  Print ( " result after interpolation complete: \ n- " , Data)