如何遍历pandas数据帧，检查条件，执行字符串操作并写入新列？

Question

I have a dataframe like below;

--------------------------------
Col1    Col2                    
--------------------------------
1       AppVer: 1.1.1 | name: A 
0       name:B                  
1       AppVer: 2.3.1 | name: B 

I wanted to create a new column (newCol3) based on the condition 1. If Col1=1 then split the Col2 based on "|" and write to the column newCol3 2. If Col1=0 then write "Not Applicable" to the column newCol3

I tried the below code for loop using iterrows & conditional statements;

for index, row in df1.iterrows():
    if row['Col1']==1:
        df1['newCol3']="NA"
    elif row['Col1']==0:
        a=row['Col2'].split("|")
        df1['newCol3']=a[0]

But i the value in newCol3 is not as expected as shown below. Also, i get a warning like this " main :8: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy "

Obtained Output:

---------------------------------------------------
Col1    Col2                        newCol3
---------------------------------------------------
1       AppVer: 1.1.1 | name: A     1.1.1
0       name:B                      1.1.1
1       AppVer: 2.3.1 | name: B     2.3.1

Expected Output:

---------------------------------------------------
Col1    Col2                        newCol3
---------------------------------------------------
1       AppVer: 1.1.1 | name: A     1.1.1
0       name:B                      Not Applicable
1       AppVer: 2.3.1 | name: B     2.3.1

Provide me any help/suggestions.

Answer 1

In your case I would suggest using loc to create a new column.

Docs: loc

Docs: str expand

Docs for str extract: str.extract

df.loc[df['Col1']==1,'Col3'] = df['Col2'].str.extract(pat='insert the pattern here')
df.loc[df['Col1']==0,'Col3'] = 'Not Applicable'

Just saw the expected output. Read the docs I linked and change the str.extract as required.

Answer 2

I feel like you can do

df['New']=df.Col2.str.extract('(\d*\.?\d+\.?\d+)').fillna('Not Applicable')
df
Out[43]: 
   Col1                      Col2             New
0     1  AppVer: 1.1.1 | name: A            1.1.1
1     0  name:B                    Not Applicable
2     1  AppVer: 2.3.1 | name: B            2.3.1