pandas 将浮点数转换为字符串 [英] Pandas convert float in scientific notation to string

问题描述

我用read_csv()加载了一个看起来像这样的数据集

I used read_csv() to load a dataset that looks like this

userid
NaN
1.091178e+11
1.137856e+11

我想将用户ID转换为字符串.一种解决方案是将keep_default_na=False添加到read_csv()，此SO建议这样做:

I want to convert the user ids to string. One solution is to add keep_default_na=False to read_csv(), which is suggested by this SO: Converting long integers to strings in pandas (to avoid scientific notation)

假设我不想使用keep_default_na=False.有什么方法可以将用户ID列转换为str.

Let's say I don't want to use keep_default_na=False. Is there any way to convert the user id column to str.

我尝试了df.userid.astype(str)，然后又得到了1.091178e+11.我期望扩展形式的结果而不是科学形式.

I tried df.userid.astype(str) and I got 1.091178e+11 back. I was expecting the result in the expanded form not scientific form.

我该怎么办?

推荐答案

您可以使用评论:

print (df.userid.map(lambda x: '{:.0f}'.format(x)))
0             nan
1    109117800000
2    113785600000
Name: userid, dtype: object

---

df.userid = df.userid.map(lambda x: '{:.0f}'.format(x))
print (df)
         userid
0           nan
1  109117800000
2  113785600000

我想知道map是否会更快，但这是相同的:

I wondered whether map would be faster, but it is the same:

#[300000 rows x 1 columns]
df = pd.concat([df]*100000).reset_index(drop=True)
#print (df)

In [40]: %timeit (df.userid.map(lambda x: '{:.0f}'.format(x)))
1 loop, best of 3: 211 ms per loop

In [41]: %timeit (df.userid.apply(lambda x: '{:.0f}'.format(x)))
1 loop, best of 3: 210 ms per loop

另一种解决方案是 to_string ，但速度很慢:

Another solution is to_string, but it is slow:

print(df.userid.to_string(float_format='{:.0f}'.format))
0            nan
1   109117800000
2   113785600000

In [41]: (df.userid.to_string(float_format='{:.0f}'.format))
1 loop, best of 3: 2.52 s per loop

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