Python 在 Pandas Dataframe 中为字符串添加前导零
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Add Leading Zeros to Strings in Pandas Dataframe
提问by jgaw
I have a pandas data frame where the first 3 columns are strings:
我有一个熊猫数据框，其中前 3 列是字符串：
ID text1 text 2
0 2345656 blah blah
1 3456 blah blah
2 541304 blah blah
3 201306 hi blah
4 12313201308 hello blah
I want to add leading zeros to the ID:
我想在 ID 中添加前导零：
ID text1 text 2
0 000000002345656 blah blah
1 000000000003456 blah blah
2 000000000541304 blah blah
3 000000000201306 hi blah
4 000012313201308 hello blah
I have tried:
我试过了：
df['ID'] = df.ID.zfill(15)
df['ID'] = '{0:0>15}'.format(df['ID'])
采纳答案by Rohit
Try:
尝试：
df['ID'] = df['ID'].apply(lambda x: '{0:0>15}'.format(x))
or even
甚至
df['ID'] = df['ID'].apply(lambda x: x.zfill(15))
回答by Guangyang Li
strattribute contains most of the methods in string.
str属性包含字符串中的大多数方法。
df['ID'] = df['ID'].str.zfill(15)
See more: http://pandas.pydata.org/pandas-docs/stable/text.html
回答by Daniil Mashkin
It can be achieved with a single line while initialization. Just use convertersargument.
它可以在初始化时用一行来实现。只需使用转换器参数。
df = pd.read_excel('filename.xlsx', converters={'ID': '{:0>15}'.format})
so you'll reduce the code length by half :)
所以你会减少一半的代码长度:)
PS: read_csvhave this argument as well.
PS：read_csv也有这个说法。
回答by jpp
With Python 3.6+, you can also use f-strings:
在 Python 3.6+ 中，您还可以使用 f 字符串：
df['ID'] = df['ID'].map(lambda x: f'{x:0>15}')
Performance is comparable or slightly worse versus df['ID'].map('{:0>15}'.format). On the other hand, f-strings permit more complex output, and you can use them more efficiently via a list comprehension.
性能与df['ID'].map('{:0>15}'.format). 另一方面，f 字符串允许更复杂的输出，您可以通过列表推导更有效地使用它们。
Performance benchmarking
性能基准测试
# Python 3.6.0, Pandas 0.19.2
df = pd.concat([df]*1000)
%timeit df['ID'].map('{:0>15}'.format) # 4.06 ms per loop
%timeit df['ID'].map(lambda x: f'{x:0>15}') # 5.46 ms per loop
%timeit df['ID'].astype(str).str.zfill(15) # 18.6 ms per loop
%timeit list(map('{:0>15}'.format, df['ID'].values)) # 7.91 ms per loop
%timeit ['{:0>15}'.format(x) for x in df['ID'].values] # 7.63 ms per loop
%timeit [f'{x:0>15}' for x in df['ID'].values] # 4.87 ms per loop
%timeit [str(x).zfill(15) for x in df['ID'].values] # 21.2 ms per loop
# check results are the same
x = df['ID'].map('{:0>15}'.format)
y = df['ID'].map(lambda x: f'{x:0>15}')
z = df['ID'].astype(str).str.zfill(15)
assert (x == y).all() and (x == z).all()
回答by Deskjokey
If you are encountering the error:
如果您遇到错误：
Pandas error: Can only use .str accessor with string values, which use np.object_ dtype in pandas
Pandas 错误：只能使用带有字符串值的 .str 访问器，它在 Pandas 中使用 np.object_ dtype
df['ID'] = df['ID'].astype(str).str.zfill(15)
