pandas 熊猫将科学记数法中的浮点数转换为字符串
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Pandas convert float in scientific notation to string
提问by Cheng
I used read_csv()to load a dataset that looks like this
我曾经read_csv()加载一个看起来像这样的数据集
userid
NaN
1.091178e+11
1.137856e+11
I want to convert the user ids to string. One solution is to add keep_default_na=Falseto read_csv(), which is suggested by this SO: Converting long integers to strings in pandas (to avoid scientific notation)
我想将用户 ID 转换为字符串。一种解决方案是添加keep_default_na=False到read_csv()，这是 SO 建议的：将长整数转换为Pandas中的字符串（以避免科学记数法）
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.
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。
I tried df.userid.astype(str)and I got 1.091178e+11back. I was expecting the result in the expanded form not scientific form.
我试过了df.userid.astype(str)，我1.091178e+11回来了。我期待的是扩展形式而不是科学形式的结果。
What should I do？
我该怎么办？
采纳答案by jezrael
You can use mapor apply, as mentioned in this comment:
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
I wondered whether mapwould be faster, but it is the same:
我想知道是否map会更快，但它是一样的：
#[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
Another solution is to_string, but it is slow:
另一个解决方案是to_string，但速度很慢：
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
回答by Douglas Navarro
I just stumbled upon this problem after reading a dataframe from a json file using the read_jsonmethod and unfortunately it does not have a keep_default_naparameter.
在使用该read_json方法从 json 文件读取数据帧后，我偶然发现了这个问题，不幸的是它没有keep_default_na参数。
The solution was to convert the long floats to np.int64before converting them to str.
解决方案是np.int64先将长浮点数转换为str.
In [53]: tweet_id_sample = tweets.iloc[0]['id']
tweet_id_sample
Out[53]: 8.924206435553362e+17
In [54]: tweet_id_sample.astype(str)
Out[54]: '8.924206435553362e+17'
In [55]: tweet_id_sample.astype(np.int64).astype(str)
Out[55]: '892420643555336192'
In [56]: # This overflows
tweet_id_sample.astype(int)
Out[56]: -2147483648
