Python 在 Pandas 中将浮点数转换为字符串
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Convert Float to String in Pandas
提问by Rohit
I am a little confused with datatype "object" in Pandas. What exactly is "object"?
我对 Pandas 中的数据类型“对象”有点困惑。“对象”究竟是什么？
I would like to change the variable "SpT" (see below) from object to String.
我想将变量“SpT”（见下文）从对象更改为字符串。
> df_cleaned.dtypes
Vmag float64
RA float64
DE float64
Plx float64
pmRA float64
pmDE float64
B-V float64
SpT object
M_V float64
distance float64
dtype: object
For this I do the following:
为此，我执行以下操作：
df_cleaned['SpT'] = df_cleaned['SpT'].astype(str)
But that has no effect on the dtype of SpT.
但这对 SpT 的 dtype 没有影响。
The reason for doing is when I do the following:
这样做的原因是当我执行以下操作时：
f = lambda s: (len(s) >= 2) and (s[0].isalpha()) and (s[1].isdigit())
i = df_cleaned['SpT'].apply(f)
df_cleaned = df_cleaned[i]
I get:
我得到：
TypeError: object of type 'float' has no len()
Hence, I believe if I convert "object" to "String", I will get to do what I want.
因此，我相信如果我将“对象”转换为“字符串”，我就会做我想做的事。
More info: This is how SpT looks like:
更多信息：这就是 SpT 的样子：
HIP
1 F5
2 K3V
3 B9
4 F0V
5 G8III
6 M0V:
7 G0
8 M6e-M8.5e Tc
9 G5
10 F6V
11 A2
12 K4III
13 K0III
14 K0
15 K2
...
118307 M2III:
118308 K:
118309 A2
118310 K5
118312 G5
118313 F0
118314 K0
118315 K0III
118316 F2
118317 F8
118318 K2
118319 G2V
118320 K0
118321 G5V
118322 B9IV
Name: SpT, Length: 114472, dtype: object
采纳答案by YS-L
If a column contains string or is treated as string, it will have a dtypeof object(but not necessarily true backward -- more below). Here is a simple example:
如果一列包含字符串或被视为字符串，则它将有一个dtypeof object（但不一定是向后的 - 更多信息）。这是一个简单的例子：
import pandas as pd
df = pd.DataFrame({'SpT': ['string1', 'string2', 'string3'],
'num': ['0.1', '0.2', '0.3'],
'strange': ['0.1', '0.2', 0.3]})
print df.dtypes
#SpT object
#num object
#strange object
#dtype: object
If a column contains only strings, we can apply lenon it like what you did should work fine:
如果一列只包含字符串，我们可以len像你所做的那样应用它，应该可以正常工作：
print df['num'].apply(lambda x: len(x))
#0 3
#1 3
#2 3
However, a dtypeof object does not means it only contains strings. For example, the column strangecontains objects with mixed types -- and some strand a float. Applying the function lenwill raise an error similar to what you have seen:
但是，dtype对象的a并不意味着它只包含字符串。例如，该列strange包含具有混合类型的对象 - 以及 somestr和 a float。应用该函数len将引发类似于您所看到的错误：
print df['strange'].apply(lambda x: len(x))
# TypeError: object of type 'float' has no len()
Thus, the problem could be that you have not properly converted the column to string, and the column still contains mixed object types.
因此，问题可能是您没有正确地将列转换为字符串，并且该列仍然包含混合对象类型。
Continuing the above example, let us convert strangeto strings and check if applyworks:
继续上面的例子，让我们转换strange为字符串并检查是否apply有效：
df['strange'] = df['strange'].astype(str)
print df['strange'].apply(lambda x: len(x))
#0 3
#1 3
#2 3
(There is a suspicious discrepancy between df_cleanedand df_cleanthere in your question, is it a typo or a mistake in the code that causes the problem?)
（在您的问题中df_cleaned和df_clean那里之间存在可疑的差异，是代码中的错字还是错误导致了问题？）
