Python 将字符串转换为 DataFrame 中的浮点数
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Converting strings to floats in a DataFrame
提问by Neer
How to covert a DataFrame column containing strings and NaNvalues to floats. And there is another column whose values are strings and floats; how to convert this entire column to floats.
如何将包含字符串和NaN值的 DataFrame 列转换为浮点数。还有另一列的值是字符串和浮点数；如何将整个列转换为浮点数。
回答by root
You can try df.column_name = df.column_name.astype(float). As for the NaNvalues, you need to specify how they should be converted, but you can use the .fillnamethod to do it.
你可以试试df.column_name = df.column_name.astype(float)。至于NaN值，您需要指定它们应该如何转换，但您可以使用.fillna方法来完成。
Example:
例子：
In [12]: df
Out[12]:
a b
0 0.1 0.2
1 NaN 0.3
2 0.4 0.5
In [13]: df.a.values
Out[13]: array(['0.1', nan, '0.4'], dtype=object)
In [14]: df.a = df.a.astype(float).fillna(0.0)
In [15]: df
Out[15]:
a b
0 0.1 0.2
1 0.0 0.3
2 0.4 0.5
In [16]: df.a.values
Out[16]: array([ 0.1, 0. , 0.4])
回答by Jeff
NOTE:
pd.convert_objectshas now been deprecated. You should usepd.Series.astype(float)orpd.to_numericas described in other answers.
注意：
pd.convert_objects现在已被弃用。您应该使用pd.Series.astype(float)或pd.to_numeric如其他答案中所述。
This is available in 0.11. Forces conversion (or set's to nan)
This will work even when astypewill fail; its also series by series
so it won't convert say a complete string column
这在 0.11 中可用。强制转换（或设置为 nan）即使astype失败也能正常工作；它也是一个系列的系列，所以它不会转换成一个完整的字符串列
In [10]: df = DataFrame(dict(A = Series(['1.0','1']), B = Series(['1.0','foo'])))
In [11]: df
Out[11]:
A B
0 1.0 1.0
1 1 foo
In [12]: df.dtypes
Out[12]:
A object
B object
dtype: object
In [13]: df.convert_objects(convert_numeric=True)
Out[13]:
A B
0 1 1
1 1 NaN
In [14]: df.convert_objects(convert_numeric=True).dtypes
Out[14]:
A float64
B float64
dtype: object
回答by Claude COULOMBE
df['MyColumnName'] = df['MyColumnName'].astype('float64')
回答by Salvador Dali
In a newer version of pandas (0.17 and up), you can use to_numericfunction. It allows you to convert the whole dataframe or just individual columns. It also gives you an ability to select how to treat stuff that can't be converted to numeric values:
在较新版本的Pandas（0.17 及更高版本）中，您可以使用to_numeric函数。它允许您转换整个数据框或仅转换单个列。它还使您能够选择如何处理无法转换为数值的内容：
import pandas as pd
s = pd.Series(['1.0', '2', -3])
pd.to_numeric(s)
s = pd.Series(['apple', '1.0', '2', -3])
pd.to_numeric(s, errors='ignore')
pd.to_numeric(s, errors='coerce')
回答by ArmandduPlessis
Here is an example
这是一个例子
GHI Temp Power Day_Type
2016-03-15 06:00:00 -7.99999952505459e-7 18.3 0 NaN
2016-03-15 06:01:00 -7.99999952505459e-7 18.2 0 NaN
2016-03-15 06:02:00 -7.99999952505459e-7 18.3 0 NaN
2016-03-15 06:03:00 -7.99999952505459e-7 18.3 0 NaN
2016-03-15 06:04:00 -7.99999952505459e-7 18.3 0 NaN
but if this is all string values...as was in my case... Convert the desired columns to floats:
但如果这都是字符串值......就像我的情况......将所需的列转换为浮点数：
df_inv_29['GHI'] = df_inv_29.GHI.astype(float)
df_inv_29['Temp'] = df_inv_29.Temp.astype(float)
df_inv_29['Power'] = df_inv_29.Power.astype(float)
Your dataframe will now have float values :-)
您的数据框现在将具有浮点值 :-)
回答by Paul Mwaniki
you have to replace empty strings ('') with np.nan before converting to float. ie:
在转换为浮点数之前，您必须用 np.nan 替换空字符串 ('')。IE：
df['a']=df.a.replace('',np.nan).astype(float)
