[英]Merging column values in a data frame in Pandas / Python
I'm trying to merge the values of columns (Columns B and C) within the same dataframe. 我正在尝试合并同一数据框中的列(B和C列)的值。 B and C sometimes have the same values.
B和C有时具有相同的值。 Some values in B are present in C while some values in C are present in B. The final results would show one column that is the combination of the two columns.
B中的某些值存在于C中,而C中的某些值存在于B中。最终结果将显示一列,该列是两列的组合。
A B C D
Apple Canada '' RED
Bananas '' Germany BLUE
Carrot US US GREEN
Dorito '' '' INDIGO
A B C
Apple Canada RED
Bananas Germany BLUE
Carrot US GREEN
Dorito '' INDIGO
IIUC IIUC
df['B']=df[['B','C']].replace("''",np.nan).bfill(1).loc[:,'B']
df=df.drop('C',1).rename(columns={'D':'C'})
df
Out[102]:
A B C
0 Apple Canada RED
1 Bananas Germany BLUE
2 Carrot US GREEN
3 Dorito NaN INDIGO
You can sort strings and take the last one: 您可以对字符串进行排序并采用最后一个字符串:
df['B'] = df[['B', 'C']].apply(lambda x: x.sort_values()[1], axis=1)
df=df.drop('C', 1).rename(columns={'D':'C'})
print(df)
Output: 输出:
A B C
0 Apple Canada RED
1 Bananas Germany BLUE
2 Carrot US GREEN
3 Dorito '' INDIGO
Another way would be to make smart use of list comprehension: 另一种方法是巧妙地使用列表理解:
# Make sets of the column B and C combined to get rid of duplicates
k = [set(b.strip() for b in a) for a in zip(df['B'], df['C'])]
# Flatten sets to strings
k = [''.join(x) for x in k]
# Create desired column
df['B'] = k
df.drop('C', axis=1, inplace=True)
print(df)
A B D
0 Apple Canada RED
1 Bananas Germany BLUE
2 Carrot US GREEN
3 Dorito INDIGO
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