在python数据框中转换数据类型

Question

	House Number	Street	First Name	Surname	Age	Relationship to Head of House	Marital Status	Gender	Occupation	Infirmity	Religion
0	1	Smith Radial	Grace	Patel	46	Head	Widowed	Female	Petroleum engineer	None	Catholic
1	1	Smith Radial	Ian	Nixon	24	Lodger	Single	Male	Publishing rights manager	None	Christian
2	2	Smith Radial	Frederick	Read	87	Head	Divorced	Male	Retired TEFL teacher	None	Catholic
3	3	Smith Radial	Daniel	Adams	58	Head	Divorced	Male	Therapist, music	None	Catholic
4	3	Smith Radial	Matthew	Hall	13	Grandson	NaN	Male	Student	None	NaN
5	3	Smith Radial	Steven	Fletcher	9	Grandson	NaN	Male	Student	None	NaN
6	4	Smith Radial	Alison	Jenkins	38	Head	Single	Female	Physiotherapist	None	Catholic
7	4	Smith Radial	Kelly	Jenkins	12	Daughter	NaN	Female	Student	None	NaN
8	5	Smith Radial	Kim	Browne	69	Head	Married	Female	Retired Estate manager/land agent	None	Christian
9	5	Smith Radial	Oliver	Browne	69	Husband	Married	Male	Retired Merchandiser, retail	None	None

我有一个数据集，您可以从问题的侧面看到它。我想将所有这些数据集从对象转换为整数和字符串。

df = pd.read_csv('user-data.csv')
df[['Street','Relationship to Head of House','Marital Status','Gender','Occupation','Infirmity','Religion']] = df[['Street','Relationship to Head of House','Marital Status','Gender','Occupation','Infirmity','Religion']].astype('str') 
df[['House Number','Age']] = df[['House Number','Age']].astype('int') 

我尝试了两种不同的方法，但在该操作之后所有数据集都消失了。

df = df['Street'].astype(str)
df = df['Relationship to Head of House'].astype(str)
df = df['Marital Status'].astype(str)
df = df['Gender'].astype(str)
df = df['Occupation'].astype(str)
df = df['Infirmity'].astype(str)
df = df['Religion'].astype(str)
df = df['Gender'].astype(str)

您能帮我转换列吗？ 谢谢

我仍然得到与以下相同的类型：

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10610 entries, 0 to 10609
Data columns (total 11 columns):
 #   Column                         Non-Null Count  Dtype 
---  ------                         --------------  ----- 
 0   House Number                   10610 non-null  int64 
 1   Street                         10610 non-null  object
 2   First Name                     10610 non-null  object
 3   Surname                        10610 non-null  object
 4   Age                            10610 non-null  object
 5   Relationship to Head of House  10610 non-null  object
 6   Marital Status                 7995 non-null   object
 7   Gender                         10610 non-null  object
 8   Occupation                     10610 non-null  object
 9   Infirmity                      10610 non-null  object
 10  Religion                       7928 non-null   object
dtypes: int64(1), object(10)
memory usage: 911.9+ KB

Object 而不是 int 或 string，你能帮我解决这个问题吗？

Answer 1

你需要赋值左侧的 df['Street']= df['Street'].astype(str)

df['Street']= df['Street'].astype(str)
df['Relationship to Head of House'] = df['Relationship to Head of House'].astype(str)
df['Marital Status'] = df['Marital Status'].astype(str)
df['Gender'] = df['Gender'].astype(str)
df['Occupation'] = df['Occupation'].astype(str)
df['Infirmity'] = df['Infirmity'].astype(str)
df['Religion'] = df['Religion'].astype(str)
df['Gender'] = df['Gender'].astype(str)

或

columns=df.columns
for column in columns:
    df[column]=df[column].astype(str)

或

in the pd.read_csv you can set the dtypes=[str,str,...] for each column

或

 numeric_df=df.select_dtypes(exclude='object')

 returns the a dataframe with non-numeric columns

 columns=numeric_df.columns