python - 如何使用 Pandas 重构简单的数据帧解析代码

Question

我正在使用 Pandas 来解析我创建的数据框:

# Initial DF    
A    B    C
0  -1  qqq  XXX
1  20  www  CCC
2  30  eee  VVV
3  -1  rrr  BBB
4  50  ttt  NNN
5  60  yyy  MMM
6  70  uuu  LLL
7  -1  iii  KKK
8  -1  ooo  JJJ

我的目标是分析 A 列并将以下条件应用于数据框:

1. 调查每一行
2. 确定是否 df['A'].iloc[index]=-1
3. 如果为真且 index=0将第一行标记为要删除
4. 如果为真且 index=N将最后一行标记为要删除
5. 如果 0<index<N和df['A'].iloc[index]=-1并且上一行或下一行包含 -1 ( df['A'].iloc[index+]=-1 或 df['A'].iloc[index-1]=-1 )，将行标记为要删除；否则替换 -1 为前一个值和后一个值的平均值

最终的数据框应如下所示:

# Final DF    
A    B    C
0  20  www  CCC
1  30  eee  VVV
2  40  rrr  BBB
3  50  ttt  NNN
4  60  yyy  MMM
5  70  uuu  LLL

我能够通过编写应用上述条件的简单代码来实现我的目标:

将 pandas 导入为 pd

# create dataframe
data = {'A':[-1,20,30,-1,50,60,70,-1,-1],
        'B':['qqq','www','eee','rrr','ttt','yyy','uuu','iii','ooo'],
        'C':['XXX','CCC','VVV','BBB','NNN','MMM','LLL','KKK','JJJ']}
df = pd.DataFrame(data)

# If df['A'].iloc[index]==-1:
#   - option 1: remove row if first or last row are equal to -1
#   - option 2: remove row if previous or following row contains -1 (df['A'].iloc[index-1]==-1 or df['A'].iloc[index+1]==-1)
#   - option 3: replace df['A'].iloc[index] if: df['A'].iloc[index]==-1 and (df['A'].iloc[index-1]==-1 or df['A'].iloc[index+1]==-1)
N = len(df.index) # number of rows
index_vect = []   # store indexes of rows to be deleated
for index in range(0,N):

    # option 1
    if index==0 and df['A'].iloc[index]==-1:
        index_vect.append(index)
    elif index>1 and index<N and df['A'].iloc[index]==-1:

       # option 2
        if df['A'].iloc[index-1]==-1 or df['A'].iloc[index+1]==-1:
            index_vect.append(index)

        # option 3
        else:
            df['A'].iloc[index] = int((df['A'].iloc[index+1]+df['A'].iloc[index-1])/2)

    # option 1        
    elif index==N and df['A'].iloc[index]==-1:
        index_vect.append(index)

# remove rows to be deleated
df = df.drop(index_vect).reset_index(drop = True)

正如您所看到的，代码相当长，我想知道您是否可以建议一种更智能、更有效的方法来获得相同的结果。 此外，我注意到我的代码返回一条警告消息，原因是 df['A'].iloc[index] = int((df['A'].iloc[index+1]+df['A'].iloc[index-1])/2) 行 你知道我如何优化这一行代码吗？

Answer 1

解决方案如下:

import numpy as np

# Let's replace -1 by Not a Number (NaN)
df.ix[df.A==-1,'A'] = np.nan

# If df.A is NaN and either the previous or next is also NaN, we don't select it
# This takes care of the condition on the first and last row too
df = df[~(df.A.isnull() & (df.A.shift(1).isnull() | df.A.shift(-1).isnull()))]

# Use interpolate to fill with the average of previous and next
df.A = df.A.interpolate(method='linear', limit=1)

这是生成的 df:

    A       B       C
1   20.0    www     CCC
2   30.0    eee     VVV
3   40.0    rrr     BBB
4   50.0    ttt     NNN
5   60.0    yyy     MMM
6   70.0    uuu     LLL

如果需要，您可以重置索引。