何时在python中应用（pd.to_numeric）以及何时使用astype（np.float64）？
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When to apply(pd.to_numeric) and when to astype(np.float64) in python?
提问by d8aninja
I have a pandas DataFrame object named xivwhich has a column of int64Volume measurements.
我有一个名为 Pandas DataFrame 的对象xiv，它有一列int64体积测量值。
In[]: xiv['Volume'].head(5)
Out[]:
0 252000
1 484000
2 62000
3 168000
4 232000
Name: Volume, dtype: int64
I have read other posts (like thisand this) that suggest the following solutions. But when I use either approach, it doesn't appear to change the dtypeof the underlying data:
我已经阅读了其他建议以下解决方案的帖子（如this和this）。但是当我使用任何一种方法时，它似乎都不会改变dtype基础数据的：
In[]: xiv['Volume'] = pd.to_numeric(xiv['Volume'])
In[]: xiv['Volume'].dtypes
Out[]:
dtype('int64')
Or...
或者...
In[]: xiv['Volume'] = pd.to_numeric(xiv['Volume'])
Out[]: ###omitted for brevity###
In[]: xiv['Volume'].dtypes
Out[]:
dtype('int64')
In[]: xiv['Volume'] = xiv['Volume'].apply(pd.to_numeric)
In[]: xiv['Volume'].dtypes
Out[]:
dtype('int64')
I've also tried making a separate pandas Seriesand using the methods listed above on that Series and reassigning to the x['Volume']obect, which is a pandas.core.series.Seriesobject.
我还尝试制作一个单独的熊猫Series并使用上面在该系列中列出的方法并重新分配给对象x['Volume']，这是一个pandas.core.series.Series对象。
I have, however, found a solution to this problem using the numpypackage's float64type - this works but I don't know why it's different.
但是，我使用numpy包的float64类型找到了解决此问题的方法-这有效，但我不知道为什么它不同。
In[]: xiv['Volume'] = xiv['Volume'].astype(np.float64)
In[]: xiv['Volume'].dtypes
Out[]:
dtype('float64')
Can someone explain how to accomplish with the pandaslibrary what the numpylibrary seems to do easily with its float64class; that is, convert the column in the xivDataFrame to a float64in place.
有人能解释一下如何用pandas图书馆完成numpy图书馆似乎很容易用它的float64类做的事情吗？也就是说，将xivDataFrame 中的列转换为float64就地。
回答by MaxU
If you already have numeric dtypes (int8|16|32|64,float64,boolean) you can convert it to another "numeric" dtype using Pandas.astype()method.
如果您已经有数字 dtypes ( int8|16|32|64, float64, boolean) 您可以使用Pandas .astype()方法将其转换为另一个“数字” dtype。
Demo:
演示：
In [90]: df = pd.DataFrame(np.random.randint(10**5,10**7,(5,3)),columns=list('abc'), dtype=np.int64)
In [91]: df
Out[91]:
a b c
0 9059440 9590567 2076918
1 5861102 4566089 1947323
2 6636568 162770 2487991
3 6794572 5236903 5628779
4 470121 4044395 4546794
In [92]: df.dtypes
Out[92]:
a int64
b int64
c int64
dtype: object
In [93]: df['a'] = df['a'].astype(float)
In [94]: df.dtypes
Out[94]:
a float64
b int64
c int64
dtype: object
It won't work for object(string) dtypes, that can'tbe converted to numbers:
它不适用于object(string) dtypes，不能转换为数字：
In [95]: df.loc[1, 'b'] = 'XXXXXX'
In [96]: df
Out[96]:
a b c
0 9059440.0 9590567 2076918
1 5861102.0 XXXXXX 1947323
2 6636568.0 162770 2487991
3 6794572.0 5236903 5628779
4 470121.0 4044395 4546794
In [97]: df.dtypes
Out[97]:
a float64
b object
c int64
dtype: object
In [98]: df['b'].astype(float)
...
skipped
...
ValueError: could not convert string to float: 'XXXXXX'
So here we want to use pd.to_numeric()method:
所以这里我们要使用pd.to_numeric()方法：
In [99]: df['b'] = pd.to_numeric(df['b'], errors='coerce')
In [100]: df
Out[100]:
a b c
0 9059440.0 9590567.0 2076918
1 5861102.0 NaN 1947323
2 6636568.0 162770.0 2487991
3 6794572.0 5236903.0 5628779
4 470121.0 4044395.0 4546794
In [101]: df.dtypes
Out[101]:
a float64
b float64
c int64
dtype: object
回答by Shobhit Sharma
I observed that I was able to convert object(str) to float first and then float to Int64.
我观察到我能够先将 object(str) 转换为浮动，然后再浮动为 Int64。
df = pd.DataFrame(np.random.randint(10**5,10**7,(5,3)),columns=list('abc'),
dtype=np.int64)
df['a'] = df['a'].astype('str')
df.dtypes
df['a'] = df['a'].astype('float')
df['a'] = df['a'].astype('int64')
Worked fine.
工作得很好。
回答by reevesnmortimer
I don't have a technical explanation for this but, I have noticed that pd.to_numeric() raises the following error when converting the string 'nan':
我没有对此的技术解释，但是，我注意到 pd.to_numeric() 在转换字符串 'nan' 时会引发以下错误：
In [10]: df = pd.DataFrame({'value': 'nan'}, index=[0])
In [11]: pd.to_numeric(df.value)
Traceback (most recent call last):
File "<ipython-input-11-98729d13e45c>", line 1, in <module>
pd.to_numeric(df.value)
File "C:\Users\joshua.lee\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\tools\numeric.py", line 133, in to_numeric
coerce_numeric=coerce_numeric)
File "pandas/_libs/src\inference.pyx", line 1185, in pandas._libs.lib.maybe_convert_numeric
ValueError: Unable to parse string "nan" at position 0
whereas astype(float) does not:
而 astype(float) 不会：
df.value.astype(float)
Out[12]:
0 NaN
Name: value, dtype: float64
回答by Mohd Waseem
You can use this:
你可以使用这个：
pd.to_numeric(df.valueerrors='coerce').fillna(0, downcast='infer')
It will use zero in place of nan.
它将使用零代替 nan。
