很多情況下，我們會根據一個dataframe裏面的值來查找而不是根據索引來查找。

首先我們創建一個dataframe：

>>> col = ["id","name","sex","age"]

>>> name = {1:"chen",2:"wang",3:"hu",4:"lee",5:"liu"}
>>> id = range(1,6)
>>> sex = {1:1,2:0,3:1,4:1,5:0}
>>> age = {1:20,2:18,3:21,4:20,5:18}
>>> data = {"
 
id":id,"name":name,"sex":sex,"age":age}

>>> data
{‘sex‘: {1: 1, 2: 0, 3: 1, 4: 1, 5: 0}, ‘age‘: {1: 20, 2: 18, 3: 21, 4: 20, 5: 18}, ‘name‘: {1: ‘chen‘, 2: ‘wang‘, 3: ‘hu‘, 4: ‘lee‘, 5: ‘liu‘}, ‘id‘: range(1, 6)}

>>> df = pd.DataFrame(data,columns=col,index=id)
>>> df
   id  name  sex  age
 
1   1  chen    1   20
2   2  wang    0   18
3   3    hu    1   21
4   4   lee    1   20
5   5   liu    0   18


>>> df = df.set_index("id")

>>> df.set_index("id")
    name  sex  age
id
1   chen    1   20
2   wang    0   18
3     hu    1   21
4    lee    1   20
5    liu    0   18

如果我們要選年齡大於等於20歲的，這個好辦：

>>> df[df["age"]>=20]
    name  sex  age
id
1   chen    1   20
3     hu    1   21
4    lee    1   20

或者選出所有女生（sex=0的），也好辦：

>>> df[df["sex"]==0]
    name  sex  age
id
2   wang    0   18
5    liu    0   18

也可用where，但不太方便：（一般不會這樣用）

>>> df.where(df["sex"]==0)
    name  sex   age
id
1    NaN  NaN   NaN
2   wang  0.0  18.0
3    NaN  NaN   NaN
4    NaN  NaN   NaN
5    liu  0.0  18.0
>>> df.where(df["age"]>=20)
    name  sex   age
id
1   chen  1.0  20.0
2    NaN  NaN   NaN
3     hu  1.0  21.0
4    lee  1.0  20.0
5    NaN  NaN   NaN

但是如果要按名字來選出，就不能這樣了，得用.isin()方法。

>>> select_name = ["chen","lee","liu"]

>>> df[df["name"]==select_name]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "E:\Python3\lib\site-packages\pandas\core\ops.py", line 855, in wrapper
    res = na_op(values, other)
  File "E:\Python3\lib\site-packages\pandas\core\ops.py", line 759, in na_op
    result = _comp_method_OBJECT_ARRAY(op, x, y)
  File "E:\Python3\lib\site-packages\pandas\core\ops.py", line 737, in _comp_method_OBJECT_ARRAY
    result = lib.vec_compare(x, y, op)
  File "pandas\lib.pyx", line 868, in pandas.lib.vec_compare (pandas\lib.c:15418)
ValueError: Arrays were different lengths: 5 vs 3
# 可以看到匹配會出錯


>>> df[df["name"].isin(select_name)]
    name  sex  age
id
1   chen    1   20
4    lee    1   20
5    liu    0   18

如果要選出既是屬於名字裏的又是男生(sex=1)：

>>> df[df["name"].isin(select_name) & df["sex"]==1]
    name  sex  age
id
1   chen    1   20
4    lee    1   20

這裏如果用

>>> df.isin({"name":select_name,"sex":[1]})
     name    sex    age
id
1    True   True  False
2   False  False  False
3   False   True  False
4    True   True  False
5    True  False  False

>>> df[df.isin({"name":select_name,"sex":[1]})] # 這裏得是[1],非1
    name  sex  age
id
1   chen  1.0  NaN
2    NaN  NaN  NaN
3    NaN  1.0  NaN
4    lee  1.0  NaN
5    liu  NaN  NaN

好像並不好。

dataframe按值（非索引）查找多行