更有效的方法是测试大数据帧，并根据不同大小的另一个值添加值/不合并

Question

关于合并和完整列的答案很多，但找不到更有效的方法。我的处境。

当前版本的python、pandas、numpy和文件格式为拼花

简单地说，如果col1==x col10=1，col11=2，col…等等

look1 = 'EMPLOYEE'
look2 = 'CHESTER'
look3 = "TONY'S"
look4 = "VICTOR'S"
tgt1 = 'inv_group'
tgt2 = 'acc_num'
for x in range(len(df['ph_name'])):
df[tgt1][x] = 'MEMORIAL'
df[tgt2][x] = 12345
elif df['ph_name'][x] == look2:
df[tgt1][x] = 'WALMART'
df[tgt2][x] = 45678
elif df['ph_name'][x] == look3:
df[tgt1][x] = 'TONYS'
df[tgt2][x] = 27359
elif df['ph_name'][x] == look4:
df[tgt1][x] = 'VICTOR'
df[tgt2][x] = 45378

basic sample:
unit_name        tgt1        tgt2
0 EMPLOYEE         Nan         Nan
1 EMPLOYEE         Nan         Nan
2 TONY'S           Nan         Nan
3 CHESTER          Nan         Nan
4 VICTOR'S         Nan         Nan
5 EMPLOYEE         Nan         Nan

GOAL:
unit_name        tgt1        tgt2
0 EMPLOYEE         MEMORIAL    12345
1 EMPLOYEE         MEMORIAL    12345
2 TONY'S           TONYS       27359
3 CHESTER          WALMART     45678
4 VICTOR'S         VICTOR      45378
5 EMPLOYEE         MEMORIAL    12345

所以这是有效的。。。我添加了自定义列的值，这不是最快的，但它很有效。

28896行需要6.2429744。我担心当我把它付诸实践时，它会开始拖累我。

另一个缺点是我很烦恼。。。是的，我可以沉默，但我觉得这可能是由于一种糟糕的做法，我应该知道如何减少。

SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

基本上。。。

1. 有办法优化吗
2. 这个警告是因为我的坏习惯，我的无知，还是我只需要沉默

Answer 1

给定：(拥有所有NaN列是愚蠢的(

unit_name
0  EMPLOYEE
1  EMPLOYEE
2    TONY'S
3   CHESTER
4  VICTOR'S
5  EMPLOYEE
df = pd.DataFrame({'unit_name': {0: 'EMPLOYEE', 1: 'EMPLOYEE', 2: "TONY'S", 3: 'CHESTER', 4: "VICTOR'S", 5: 'EMPLOYEE'}})

做：(让我们使用pd.Series.map并创建一个字典，以便将来更容易修改(

looks = ['EMPLOYEE', 'CHESTER', "TONY'S", "VICTOR'S"]
new_cols = {
'inv_group': ["MEMORIAL", "WALMART", "TONYS", "VICTOR"],
'acc_num': [12345, 45678, 27359, 45378]
}
for col, values in new_cols.items():
df[col] = df['unit_name'].map(dict(zip(looks, values)))
print(df)

输出：(我以为你输入的列名是错误的(

unit_name inv_group  acc_num
0  EMPLOYEE  MEMORIAL    12345
1  EMPLOYEE  MEMORIAL    12345
2    TONY'S     TONYS    27359
3   CHESTER   WALMART    45678
4  VICTOR'S    VICTOR    45378
5  EMPLOYEE  MEMORIAL    12345

Answer 2

在这里盲目飞行，因为我看不到你的数据：

cond_list = [df["ph_name"] == look for look in [look1, look2, look3, look4]]
# Rows ph_name outside of the list will keep their original values
df[tgt1] = np.select(cond_list, ["MEMORIAL", "WALMART", "TONY'S", "VICTOR"])
df[tgt2] = np.select(cond_list, [12345, 45678, 27359, 45378])