Python 按每个转换器的浮点值读取数据

我有一个名为“filename”的csv文件，希望将这些数据作为64float读取，但“hour”列除外。我用pd.read_csv-函数和一个转换器来管理它

df = pd.read_csv("../data/filename.csv",
                 delimiter = ';',
                 date_parser = ['hour'],
                 skiprows = 1,
                 converters={'column1': lambda x: float(x.replace   ('.','').replace(',','.'))})

现在，我有两点：

第一:

分隔符与，但如果我在记事本中查看我的数据，就会发现有“，”而不是“；”。但是如果我取“，”我得到：“pandas.parser.CParserError:错误标记化数据。C错误：第13行应为7个字段，锯为9'

第二：

如果我想对所有列使用转换器，我怎么能得到这个？！正确的术语是什么？ 我尝试在readin函数中使用dtype=float，但得到的是'AttributeError:'NoneType'对象没有属性'dtype''，发生了什么？这就是为什么我想用转换器来管理它

df = pd.read_csv("../data/filename.csv",
                 delimiter = ';',
                 date_parser = ['hour'],
                 skiprows = 1,
                 converters={'column1': lambda x: float(x.replace   ('.','').replace(',','.'))})

数据：

---

，小时，PV，陆上风，海上风，PV.1，陆上风。1，风 海上1，PV.2，陆上风2，海上风2 0,1,0.0,"12,985.0","9,614.0",0.0,"32,825.5","9,495.7",0.0,"13,110.3","10,855.5" 1,2,0.0,"12,908.9","9,290.8",0.0,"36,052.3","9,589.1",0.0,"13,670.2","10,828.6" 2,3,0.0,"12,740.9","8,886.9",0.0,"38,540.9","10,087.3",0.0,"14,610.8","10,828.6" 3,4,0.0,"12,485.3","8,644.5",0.0,"40,734.0","10,087.3",0.0,"15,638.3","10,343.7" 4,5,0.0,"11,188.5","8,079.0",0.0,"42,688.0","10,087.3",0.0,"16,809.4","10,343.7" 5,6,0.0,11219.0,7594.2,0.0,43333.5,10025.0,0.0,18266.9,10343.7

这应该起作用：

In [40]:
# text data
temp=''',hour,PV,Wind onshore,Wind offshore,PV.1,Wind onshore.1,Wind offshore.1,PV.2,Wind onshore.2,Wind offshore.2
0,1,0.0,"12,985.0","9,614.0",0.0,"32,825.5","9,495.7",0.0,"13,110.3","10,855.5"
1,2,0.0,"12,908.9","9,290.8",0.0,"36,052.3","9,589.1",0.0,"13,670.2","10,828.6"
2,3,0.0,"12,740.9","8,886.9",0.0,"38,540.9","10,087.3",0.0,"14,610.8","10,828.6"
3,4,0.0,"12,485.3","8,644.5",0.0,"40,734.0","10,087.3",0.0,"15,638.3","10,343.7"
4,5,0.0,"11,188.5","8,079.0",0.0,"42,688.0","10,087.3",0.0,"16,809.4","10,343.7"
5,6,0.0,"11,219.0","7,594.2",0.0,"43,333.5","10,025.0",0.0,"18,266.9","10,343.7"'''
# so read the csv, pass params quotechar and the thousands character
df = pd.read_csv(io.StringIO(temp), quotechar='"', thousands=',')
df
Out[40]:
   Unnamed: 0  hour  PV  Wind onshore  Wind offshore  PV.1  Wind onshore.1  \
0           0     1   0       12985.0         9614.0     0         32825.5   
1           1     2   0       12908.9         9290.8     0         36052.3   
2           2     3   0       12740.9         8886.9     0         38540.9   
3           3     4   0       12485.3         8644.5     0         40734.0   
4           4     5   0       11188.5         8079.0     0         42688.0   
5           5     6   0       11219.0         7594.2     0         43333.5   

   Wind offshore.1  PV.2  Wind onshore.2  Wind offshore.2  
0           9495.7     0         13110.3          10855.5  
1           9589.1     0         13670.2          10828.6  
2          10087.3     0         14610.8          10828.6  
3          10087.3     0         15638.3          10343.7  
4          10087.3     0         16809.4          10343.7  
5          10025.0     0         18266.9          10343.7  
In [41]:
# check the dtypes
df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 6 entries, 0 to 5
Data columns (total 11 columns):
Unnamed: 0         6 non-null int64
hour               6 non-null int64
PV                 6 non-null float64
Wind onshore       6 non-null float64
Wind offshore      6 non-null float64
PV.1               6 non-null float64
Wind onshore.1     6 non-null float64
Wind offshore.1    6 non-null float64
PV.2               6 non-null float64
Wind onshore.2     6 non-null float64
Wind offshore.2    6 non-null float64
dtypes: float64(9), int64(2)
memory usage: 576.0 bytes

---

首先在所有感兴趣的列上替换逗号分隔符，然后像这样调用

convert\u objects

会更快：

df.convert\u objects（convert\u numeric=True）

您能发布示例数据吗，所有行的格式是否相同是另一个问题。如果

read\u csv

无法进行转换，最好在将其作为字符串读入后进行转换。在记事本中，数据如下：，小时，PV，岸上风，海上风，PV.1，岸上风，1，海上风，PV.2，岸上风，2，海上风，20,1,0.0，“12985.0”，“9614.0”，0.0，“32825.5”，“9495.7”，0.0，“13110.3”，“10855.5”1,2,0.0，“12908.9”，“9290.8”，0.0，“36052.3”，“9589.1”，0.0，“13670.2”，“10828.6”2,3,0.0.0，“9495.7”，0.0，“10828.6”13110.3,0.3,0.0，”12908.9，“389”，389“，"10,087.3",0.0,"14,610.8","10,828.6" 3,4,0.0,"12,485.3","8,644.5",0.0,"40,734.0","10,087.3",0.0,"15,638.3","10,343.7" 4,5,0.0,"11,188.5","8,079.0",0.0,"42,688.0","10,087.3",0.0,"16,809.4","10,343.7" 5,6,0.0,"11,219.0","7,594.2",0.0,"43,333.5","10,025.0",0.0,"18,266.9","10,343.7“好的。如果我这样做，我得到的名称“io”是没有定义的。此外：我怎样才能改变所有列的数据类型后，读与读_csv？。。。或特定列的？忽略仅适用于我的io位，对您来说，行应该是：

df=pd.read_csv（../data/filename.csv），delimiter='；'，date_parser=['hour']，skiprows=1，quotechar='，'）

您可以接受（并向上投票）如果答案回答了你的问题，那么答案就是正确的