Python: For循环中的IF语句

Question

我想创建一个新列，然后使用另一个参数作为条件来填充该列。

这是我的代码，它是如何继续到elif的。只应用第一个参数，即使它不符合我设置的参数。

for i in df_csrdata_2mos_Filtered_Done["Agent"]:
    if i == "unez" or i == "rmbua" or i == "destrada" or i == "amateo" or i == "cmabelison":
        df_csrdata_2mos_Filtered_Done["AgentTag"] = "Agent 1"
    elif i == "rverga" or i == "dpcaban" or i == "dgsugui":
        df_csrdata_2mos_Filtered_Done["AgentTag"] = "Agent 2"
    elif i == "gmic" or i == "jdera":
        df_csrdata_2mos_Filtered_Done["AgentTag"] = "Agent 3"
    elif i == "gras" or i == "mcsrra":
        df_csrdata_2mos_Filtered_Done["AgentTag"] = "Agent 4"
    elif i == "jcawan" or i == "rmcola" or i == "mjgamo":
        df_csrdata_2mos_Filtered_Done["AgentTag"] = "Agent 5"
    elif i == "ychaco" or i == "phondra":
        df_csrdata_2mos_Filtered_Done["AgentTag"] = "Agent 6"
    elif i == "mmorang" or i == "vsin":
        df_csrdata_2mos_Filtered_Done["AgentTag"] = "Agent 7"
    elif i == "pbong":
        df_csrdata_2mos_Filtered_Done["AgentTag"] = "Agent 8"
    else:
        print("AgentTag Done!")

Answer 1

你的if/elif运行得很好。

基于df_，我假设您使用的是Pandas数据帧，在这种情况下，df_csrdata_2mos_Filtered_Done["AgentTag"] = "X"只是用一个新值替换了整个序列。

>>> df = pd.DataFrame({"a": [1, 2, 3]})
>>> df
   a
0  1
1  2
2  3
>>> df["b"] = "Agent Perry"
>>> df
   a            b
0  1  Agent Perry
1  2  Agent Perry
2  3  Agent Perry
>>>

如果最后一个Agent是"pbong"，则df中的所有AgentTags都将是代理8。

它看起来像是所有你正在寻找的带有字典的Series.map()：

>>> agent_map = {
...     "unez": "Agent 1",
...     "rverga": "Agent 2",
... }
>>> df = pd.DataFrame({"agent": ["unez", "rverga", "hello"]})
>>> df
    agent
0    unez
1  rverga
2   hello
>>> df["AgentTag"] = df["agent"].map(agent_map)
>>> df
    agent AgentTag
0    unez  Agent 1
1  rverga  Agent 2
2   hello      NaN
>>>

Answer 2

不要使用循环，这里没有必要。

更好的方法是使用Series.map进行映射

d = {
    "Agent 1": ["unez", "rmbua", "destrada", "amateo", "cmabelison"],
    "Agent 2": ["rverga", "dpcaban", "dgsugui"],
    "Agent 3": ["gmic", "jdera"],
    "Agent 4": ["gras", "mcsrra"],
    "Agent 5": ["jcawan", "rmcola", "mjgamo"],
    "Agent 6": ["ychaco", "phondra"],
    "Agent 7": ["mmorang", "vsin"],
    "Agent 8": ["pbong"],
}

# flatten lists
d1 = {k: oldk for oldk, oldv in d.items() for k in oldv}

df_csrdata_2mos_Filtered_Done["AgentTag"] = df_csrdata_2mos_Filtered_Done["Agent"].map(d1)