在 Pandas 中使用 iterrows 的有效方法（另一种方式）

McsLk 发表于 Dev

麦克斯

这是我在论坛上的第一个问题。谢谢你的帮助！

我基于 df.iterrows () (sic.) 编写了嵌套的 for 循环，它需要花费大量的时间来执行。我需要通过检查描述条件下的所有单元格来将一个数据帧的值分配给另一个数据帧。你能帮我让它有效吗？（多处理、应用方法、矢量化或其他任何东西？）将不胜感激！:)

样本数据：

import pandas as pd
import numpy as np

d1 = {'geno_start' : [60, 1120, 1660], 'geno_end' : [90, 1150, 1690], 'original_subseq' : ['AAATGCCTGAACCTTGGAATTGGA',  'AAATGCCTGAACCTTGGAATTGGA', 'AAATGCCTGAACCTTGGAATTGGA']}

d2 = {'most_left_coordinate_genome' : [56, 1120, 1655], 'most_right_coordinate_genome' : [88, 1150, 1690], 'protein_ID' : ['XYZ_1', 'XYZ_2', 'XYZ_3']}

df_1 = pd.DataFrame(data=d1)
df_2 = pd.DataFrame(data=d2)

df_1['protein_ID'] = np.nan


def match_ranges(df1: pd.DataFrame, df2: pd.DataFrame):

for index, row_2 in df2.iterrows():
    for index_1, row_1 in df1.iterrows():
        if (row_1['geno_start'] >= row_2['most_left_coordinate_genome']) & (row_1['geno_end'] <= row_2['most_right_coordinate_genome']):
            df1['protein_ID'].iloc[index_1] = row_2['protein_ID']
                
        elif (abs(row_1['geno_start'] - row_2['most_left_coordinate_genome']) < 30) & (row_1['geno_end'] <= row_2['most_right_coordinate_genome']):
            df1['protein_ID'].iloc[index_1] = row_2['protein_ID']
                
        elif (row_1['geno_start'] >= row_2['most_left_coordinate_genome']) & (abs(row_1['geno_end'] - row_2['most_right_coordinate_genome']) < 30):
            df1['protein_ID'].iloc[index_1] = row_2['protein_ID']

match_ranges(df_1, df_2)

期望的输出：

知乎

这是一种从 2 个 for 循环到 1 个循环的方法。我重命名了几列以减少线宽。

首先，创建数据框：

import pandas as pd

d1 = {'geno_start' : [60, 1120, 1660], 
      'geno_end' : [90, 1150, 1690], 
      'original_subseq' : ['AAATGCCTGAACCTTGGAATTGGA',  
                           'AAATGCCTGAACCTTGGAATTGGA', 
                           'AAATGCCTGAACCTTGGAATTGGA'],}
d2 = {'left' : [56, 1120, 1655], 
      'right' : [88, 1150, 1690], 
      'protein_ID' : ['XYZ_1', 'XYZ_2', 'XYZ_3']}
df_1 = pd.DataFrame(data=d1)
df_1['protein_ID'] = '?'
df_1['rule'] = '?'
df_2 = pd.DataFrame(data=d2)

其次，填充protein_ID第一个数据框中的列（即，基因组开始，基因组结束）：

for g in df_1.itertuples():
    
    # Rule A: left most <= geno start < geno end <= right-most
    # LM-----------------------RM   left- and right-most
    #     GS-----------GE           genome start, end
    if ((df_2['left'] <= g.geno_start) & (g.geno_end <= df_2['right'])).any():
        mask = (df_2['left'] <= g.geno_start) & (g.geno_end <= df_2['right'])
        df_1.at[g.Index, 'protein_ID'] = df_2.loc[mask, 'protein_ID'].values[0]
        df_1.at[g.Index, 'rule'] = 'Rule A'
        
        
    # Rule B: geno start before left-most
    #      LM-----------------RM
    #  GS-----------------GE
    elif ((df_2['left'] - g.geno_start < 30) & (g.geno_end <= df_2['right'])).any():
        mask = (df_2['left'] - g.geno_start < 30) & (g.geno_end <= df_2['right'])
        df_1.at[g.Index, 'protein_ID'] = df_2.loc[mask, 'protein_ID'].values[0]
        df_1.at[g.Index, 'rule'] = 'Rule B'

    # Rule C: geno end after right-most 
    #      LM-----------------RM
    #          GS-----------------GE

    elif ((df_2['left'] <= g.geno_start) & (g.geno_end - df_2['right'] < 30)).any():
        mask = (df_2['left'] <= g.geno_start) & (g.geno_end - df_2['right'] < 30)
        df_1.at[g.Index, 'protein_ID'] = df_2.loc[mask, 'protein_ID'].values[0]
        df_1.at[g.Index, 'rule'] = 'Rule C'
        
    else:
        pass

print(df_1)
   geno_start  geno_end           original_subseq protein_ID    rule
0          60        90  AAATGCCTGAACCTTGGAATTGGA      XYZ_1  Rule C
1        1120      1150  AAATGCCTGAACCTTGGAATTGGA      XYZ_2  Rule A
2        1660      1690  AAATGCCTGAACCTTGGAATTGGA      XYZ_3  Rule A

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