pandas 取 DataFrame 的子集

pandas 选择列

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注：引用库的导入和数据的导入只做一次，所有代码是在 jupyter notebook 里完成的。

import pandas as pd 
titanic = pd.read_csv("data/titanic.csv")

只要年龄数据

ages = titanic["Age"]
ages.head()

0    22.0
1    38.0
2    26.0
3    35.0
4    35.0
Name: Age, dtype: float64

取单列的数据，直接用 [] 就可以了，每一列数据都是一个 Series,
type(ages)
pandas.core.series.Series
ages.shape
(891,)
Series 是一维的，并且只返回行

取年龄和性别数据

age_sex = titanic[["Age", "Sex"]]
age_sex.head()

    Age     Sex
0  22.0    male
1  38.0  female
2  26.0  female
3  35.0  female
4  35.0    male

取多列数据，返回的是 DataFrame
type(age_sex)
pandas.core.frame.DataFrame
age_sex.shape
(891, 2)
DataFrame 是二维的，返回行和列

pandas 选择行

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选择大于 35 岁的乘客

above_35 = titanic[titanic["Age"] > 35]
above_35.head()

除了 >, 例如 ==, !=, < 等等，都可以进行数据的过滤选择，
titanic["Age"] > 35 生成的是 布尔值 的 Series。

选择 2舱和3舱的乘客

class_23 = titanic[titanic["Pclass"].isin([2, 3])]
class_23.head()

   PassengerId  Survived  Pclass                            Name     Sex  \
0            1         0       3         Braund, Mr. Owen Harris    male   
2            3         1       3          Heikkinen, Miss. Laina  female   
4            5         0       3        Allen, Mr. William Henry    male   
5            6         0       3                Moran, Mr. James    male   
7            8         0       3  Palsson, Master. Gosta Leonard    male   

    Age  SibSp  Parch            Ticket     Fare Cabin Embarked  
0  22.0      1      0         A/5 21171   7.2500   NaN        S  
2  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  
4  35.0      0      0            373450   8.0500   NaN        S  
5   NaN      0      0            330877   8.4583   NaN        Q  
7   2.0      3      1            349909  21.0750   NaN        S

与条件式相似， isin() 返回的是真，
上面的 titanic[titanic["Pclass"].isin([2, 3])] 与 titanic[(titanic["Pclass"] == 2) | (titanic["Pclass"] == 3)] 的效果是一样的。
结合多个条件时，每个条件应用 () 包起来，并用 | 和 & 连接起来。

选择非空数据

age_no_na = titanic[titanic["Age"].notna()]
age_no_na.head()

notna() 非空数据返回真。

从 DataFrame 里选择特殊的行和列

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选择大于 35 岁乘客的名字

adult_names = titanic.loc[titanic["Age"] > 35, "Name"]
adult_names.head()

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

选择 10~25 行，3~5 列的数据

titanic.iloc[10:25, 3:5]

                                                 Name     Sex
10                    Sandstrom, Miss. Marguerite Rut  female
11                           Bonnell, Miss. Elizabeth  female
12                     Saundercock, Mr. William Henry    male
13                        Andersson, Mr. Anders Johan    male
14               Vestrom, Miss. Hulda Amanda Adolfina  female
15                   Hewlett, Mrs. (Mary D Kingcome)   female
16                               Rice, Master. Eugene    male
17                       Williams, Mr. Charles Eugene    male
18  Vander Planke, Mrs. Julius (Emelia Maria Vande...  female
19                            Masselmani, Mrs. Fatima  female
20                               Fynney, Mr. Joseph J    male
21                              Beesley, Mr. Lawrence    male
22                        McGowan, Miss. Anna "Annie"  female
23                       Sloper, Mr. William Thompson    male
24                      Palsson, Miss. Torborg Danira  female