在使用 pandas 的 python 中，我希望我的程序在 csv 文件中的 A 列和 B 列中搜索字符串并将结果写入 Z0D650F837D4F243B11 列

Question

import pandas as pd
path = "C:\\Users\\Desktop\\Python\\"

filename = 'file.csv'

df = pd.read_csv(path+filename)
arr = []
for i in range(len(df['Column_A'])):


    if df['Column_A'][i] == pd.np.nan:
        continue

    if df['Column_A'][i] is not pd.np.nan:
        if 'ABC' in df['Column_A'][i]:
            arr.append('X')
        elif 'DEF' in df['Column_A'][i]:
            arr.append('Y')
        elif 'GHI' in df['Column_A'][i]:
            arr.append('Z')

        else:
            arr.append('')
    else:
        arr.append(' ')
        continue

df['Column_C'] = arr

filename = 'output.csv'

df.to_csv(path+filename)

In the above code I want to add column_B to search strings ("ABC", "DEF","GHI") along with column_A to write results in column_C as desired if match is found.

Answer 1

It is not clear to me in your question if:

• you want to add 'X' if both Column_A and Column_B include 'ABC', or
• add 'X' if Column_A includes 'ABC' and 'Y' if Column_B includes 'DEF'.

In the first situation Column_C will be 'X', 'Y', 'Z', '', or ' '. But what would happen if Column_A has 'ABC' and Column_B has 'DEF'?

import pandas as pd

path = "C:\\Users\\Desktop\\Python\\"

filename = 'file.csv'

df = pd.read_csv(path+filename)
arr = []

lookup = {'ABC': 'X', 'DEF': 'Y', 'GHI': 'Z'}

for col_A, col_B in zip(df['Column_A'], df['Column_B']):
    to_append = ''
    if col_A is not pd.np.nan and col_B is not pd.np.nan:
        for key in lookup.keys():
            if key in col_A and key in col_B:
                to_append = to_append + lookup[key]
    arr.append(to_append)
df['Column_C'] = arr

filename = 'output.csv'

df.to_csv(path+filename, index=False)

In the second situation Column_C will be 'XX', 'XY', ..., 'YZ','ZZ', '', ' '.

import pandas as pd
path = "C:\\Users\\Desktop\\Python\\"

filename = 'file.csv'

df = pd.read_csv(path+filename)
arr = []

lookup = {'ABC': 'X', 'DEF': 'Y', 'GHI': 'Z'}

for col_A, col_B in zip(df['Column_A'], df['Column_B']):
    to_append = ''
    if col_A is not pd.np.nan and col_B is not pd.np.nan:
        for key in lookup.keys():
            if key in col_A:
                to_append = to_append + lookup[key]
            elif key in col_B:
                to_append = to_append + lookup[key]
    arr.append(to_append)

df['Column_C'] = arr

filename = 'output.csv'

df.to_csv(path+filename, index=False)

In both situations I also added index=False when writing the csv file, because I assumed you wanted to have a file exactly as your input file, but with an extra column, Column C.