我要评论数据分析-pandas如何选择数据子集
dataframe的数据中，选择某一列，某一行，或者某个子区域，该怎么办呢？
选择一个属性列维度
比如，titanic 数据表中，如果仅仅对乘客感兴趣，可以这样操作：
in [4]: ages = titanic["age"] in [5]: ages.head() out[5]: 0 22.0 1 38.0 2 26.0 3 35.0 4 35.0 name: age, dtype: float64 in [6]: type(titanic["age"]) out[6]: pandas.core.series.series in [7]: titanic["age"].shape out[7]: (891,)
选择多个属性列维度
比如，titanic 数据表中，想选择多个属性进行组合研究，不仅仅对乘客感兴趣，还需要知道性别，可以这样操作：
in [8]: age_sex = titanic[["age", "sex"]]
in [9]: age_sex.head()
out[9]:
age sex
0 22.0 male
1 38.0 female
2 26.0 female
3 35.0 female
4 35.0 male
in [10]: type(titanic[["age", "sex"]])
out[10]: pandas.core.frame.dataframe
in [11]: titanic[["age", "sex"]].shape
out[11]: (891, 2)
筛选属性值集合
比如，titanic 数据表中，对乘客的年龄大于35岁的集合感兴趣
in [12]: above_35 = titanic[titanic["age"] > 35]
in [13]: above_35.head()
out[13]:
passengerid survived pclass ... fare cabin embarked
1 2 1 1 ... 71.2833 c85 c
6 7 0 1 ... 51.8625 e46 s
11 12 1 1 ... 26.5500 c103 s
13 14 0 3 ... 31.2750 nan s
15 16 1 2 ... 16.0000 nan s
[5 rows x 12 columns]
in [15]: above_35.shape
out[15]: (217, 12)
事实上，括号内的条件其实是一个真值列表：
in [14]: titanic["age"] > 35
out[14]:
0 false
1 true
2 false
3 false
4 false
...
886 false
887 false
888 false
889 false
890 false
name: age, length: 891, dtype: bool
此外，还对乘客的座舱等级感兴趣，筛选等级2,3的，可以这样操作：
in [16]: class_23 = titanic[titanic["pclass"].isin([2, 3])] in [17]: class_23.head() out[17]: passengerid survived pclass ... fare cabin embarked 0 1 0 3 ... 7.2500 nan s 2 3 1 3 ... 7.9250 nan s 4 5 0 3 ... 8.0500 nan s 5 6 0 3 ... 8.4583 nan q 7 8 0 3 ... 21.0750 nan s [5 rows x 12 columns] # 等价于： in [18]: class_23 = titanic[(titanic["pclass"] == 2) | (titanic["pclass"] == 3)] in [19]: class_23.head() out[19]: passengerid survived pclass ... fare cabin embarked 0 1 0 3 ... 7.2500 nan s 2 3 1 3 ... 7.9250 nan s 4 5 0 3 ... 8.0500 nan s 5 6 0 3 ... 8.4583 nan q 7 8 0 3 ... 21.0750 nan s [5 rows x 12 columns]
此外，在数据清洗中经常用到，把na值或者非na值筛选出来，另做处理，可以这样操作：
in [20]: age_no_na = titanic[titanic["age"].notna()] in [21]: age_no_na.head() out[21]: passengerid survived pclass ... fare cabin embarked 0 1 0 3 ... 7.2500 nan s 1 2 1 1 ... 71.2833 c85 c 2 3 1 3 ... 7.9250 nan s 3 4 1 1 ... 53.1000 c123 s 4 5 0 3 ... 8.0500 nan s [5 rows x 12 columns] in [22]: age_no_na.shape out[22]: (714, 12)
筛选特定行和列维度集合
比如，titanic 数据表中，对乘客的年龄大于35岁的名字感兴趣，
in [23]: adult_names = titanic.loc[titanic["age"] > 35, "name"] in [24]: adult_names.head() out[24]: 1 cumings, mrs. john bradley (florence briggs th... 6 mccarthy, mr. timothy j 11 bonnell, miss. elizabeth 13 andersson, mr. anders johan 15 hewlett, mrs. (mary d kingcome) name: name, dtype: object
如果对第10-25行，3到5列感兴趣，可以这样操作：
in [25]: titanic.iloc[9:25, 2:5]
out[25]:
pclass name sex
9 2 nasser, mrs. nicholas (adele achem) female
10 3 sandstrom, miss. marguerite rut female
11 1 bonnell, miss. elizabeth female
12 3 saundercock, mr. william henry male
13 3 andersson, mr. anders johan male
.. ... ... ...
20 2 fynney, mr. joseph j male
21 2 beesley, mr. lawrence male
22 3 mcgowan, miss. anna "annie" female
23 1 sloper, mr. william thompson male
24 3 palsson, miss. torborg danira female
[16 rows x 3 columns]
以上代码只是一个简单示例，示例代码中的表达式和变量范围也可以根据实际问题进行修改。
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