将 2 df 但列映射到值而不是每个 ID 的值到值

Question

我有一个表，其中包含前 3 个原因（表 1）和另一个表，其中包含每个变量所属的类别（表 2）。 我正在尝试将类别箱匹配到表 3 中的原因表中。

Table 1: top 3 reasons, basically which 3 variable was the most important for that Register number

      RegNo    Reason1  Reason2 Reason3
        1111         v3       v2     v6
        2222         v2       v3     v5
        3333         v3       v2     v6
        4444         v3       v6     v2
        5555         v3       v2     v5


Table 2: The category bin for each variable 

    RegNo           v2              v3        v4          v5                 v6
    1111    (0.44, 0.64]    (0.0, 60.0]     missing    (-inf, 3.0]   (102.0, 175.0]
    2222    (-inf, 0.33]    (0.0, 60.0]     welldone   missing       missing
    3333    (0.44, 0.64]    (0.0, 60.0]     rare       missing       missing
    4444    (0.44, 0.64]    (0.0, 60.0]     missing    missing       (20.0, 102.0]
    5555     (0.64, inf]    (0.0, 60.0]     missing    missing       (-inf, 20.0]


Table 3: For each ID, find category bin label and replace reason1,2 & 3 with the labels

       RegNo            Reason1         Reason2         Reason3
        1111         (0.0, 60.0]    (0.44, 0.64]     (102.0, 175.0]
        2222         (-inf, 0.33]    (0.0, 60.0]       missing
        3333         (0.0, 60.0]    (0.44, 0.64]       missing
        4444         (0.0, 60.0]    (20.0, 102.0]    (0.44, 0.64]
        5555         (0.0, 60.0]     (0.64, inf]        missing

Answer 1

方法

• 以与join() 一起使用的方式索引两个数据帧
• 那么它是连接输出中每个原因的pd.concat()

df1 = pd.read_csv(io.StringIO("""      RegNo    Reason1  Reason2 Reason3
        1111         v3       v2     v6
        2222         v2       v3     v5
        3333         v3       v2     v6
        4444         v3       v6     v2
        5555         v3       v2     v5
"""), sep="\s+")

df2 = pd.read_csv(io.StringIO("""    RegNo           v2              v3        v4          v5                 v6
    1111    (0.44, 0.64]    (0.0, 60.0]     missing    (-inf, 3.0]   (102.0, 175.0]
    2222    (-inf, 0.33]    (0.0, 60.0]     welldone   missing       missing
    3333    (0.44, 0.64]    (0.0, 60.0]     rare       missing       missing
    4444    (0.44, 0.64]    (0.0, 60.0]     missing    missing       (20.0, 102.0]
    5555     (0.64, inf]    (0.0, 60.0]     missing    missing       (-inf, 20.0]
"""),sep="\s\s+", engine="python")

# index main df
df1 = df1.set_index("RegNo")
# reshape and index reasons
dfm = df2.set_index("RegNo").stack()
df3 = pd.concat([
    # add apprpriate column into index,  don't want othert columns
    (df1.set_index(c, append="True").loc[:,[]]
     # now it's a straight forward join
     .join(dfm.rename_axis(["RegNo",c]).to_frame())
     # cleanup index and rename columns of joined DF
     .droplevel(1).rename(columns={0:c}))
    for c in ["Reason1","Reason2","Reason3"]], axis=1)

RegNo	Reason1	Reason2	Reason3
1111	(0.0, 60.0]	(0.44, 0.64]	(102.0, 175.0]
2222	(-inf, 0.33]	(0.0, 60.0]	missing
3333	(0.0, 60.0]	(0.44, 0.64]	missing
4444	(0.0, 60.0]	(20.0, 102.0]	(0.44, 0.64]
5555	(0.0, 60.0]	(0.64, inf]	missing