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使用Pandas選擇數(shù)據(jù)子集的方法示例

更新時間：2024年03月27日 08:46:30 作者：Alex_StarSky

有時數(shù)據(jù)讀入后并不是對整體數(shù)據(jù)進(jìn)行分析,而是數(shù)據(jù)中的部分子集,所以,該如何根據(jù)特定的條件實現(xiàn)數(shù)據(jù)子集的獲取將是本節(jié)的主要內(nèi)容,本文給大家介紹了使用Pandas選擇數(shù)據(jù)子集的方法示例,需要的朋友可以參考下

數(shù)據(jù)分析-Pandas如何選擇數(shù)據(jù)子集

Dataframe的數(shù)據(jù)中，選擇某一列，某一行，或者某個子區(qū)域，該怎么辦呢？

選擇一個屬性列維度

比如，Titanic 數(shù)據(jù)表中，如果僅僅對乘客感興趣，可以這樣操作：

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,)

選擇多個屬性列維度

比如，Titanic 數(shù)據(jù)表中，想選擇多個屬性進(jìn)行組合研究，不僅僅對乘客感興趣，還需要知道性別，可以這樣操作：

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)

篩選屬性值集合

比如，Titanic 數(shù)據(jù)表中，對乘客的年齡大于35歲的集合感興趣

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)

事實上，括號內(nèi)的條件其實是一個真值列表：

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，還對乘客的座艙等級感興趣，篩選等級2,3的，可以這樣操作：

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]

# 等價于：
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，在數(shù)據(jù)清洗中經(jīng)常用到，把NA值或者非NA值篩選出來，另做處理，可以這樣操作：

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)

篩選特定行和列維度集合

比如，Titanic 數(shù)據(jù)表中，對乘客的年齡大于35歲的名字感興趣，

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

如果對第10-25行，3到5列感興趣，可以這樣操作：

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]

以上代碼只是一個簡單示例，示例代碼中的表達(dá)式和變量范圍也可以根據(jù)實際問題進(jìn)行修改。

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