Python 使用熊猫删除一列中的非数字行
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Remove non-numeric rows in one column with pandas
提问by HungUnicorn
There is a dataframe like the following, and it has one unclean column 'id' which it sholud be numeric column
有一个如下所示的数据框，它有一个不干净的列“id”，它应该是数字列
id, name
1, A
2, B
3, C
tt, D
4, E
5, F
de, G
Is there a concise way to remove the rows because tt and de are not numeric values
是否有一种简洁的方法来删除行，因为 tt 和 de 不是数值
tt,D
de,G
to make the dataframe clean?
使数据框干净？
id, name
1, A
2, B
3, C
4, E
5, F
采纳答案by Anton Protopopov
You could use standard method of strings isnumericand apply it to each value in your idcolumn:
您可以使用标准的字符串方法isnumeric并将其应用于id列中的每个值：
import pandas as pd
from io import StringIO
data = """
id,name
1,A
2,B
3,C
tt,D
4,E
5,F
de,G
"""
df = pd.read_csv(StringIO(data))
In [55]: df
Out[55]:
id name
0 1 A
1 2 B
2 3 C
3 tt D
4 4 E
5 5 F
6 de G
In [56]: df[df.id.apply(lambda x: x.isnumeric())]
Out[56]:
id name
0 1 A
1 2 B
2 3 C
4 4 E
5 5 F
Or if you want to use idas index you could do:
或者，如果您想id用作索引，您可以这样做：
In [61]: df[df.id.apply(lambda x: x.isnumeric())].set_index('id')
Out[61]:
name
id
1 A
2 B
3 C
4 E
5 F
Edit. Add timings
编辑。添加时间
Although case with pd.to_numericis not using applymethod it is almost two times slower than with applying np.isnumericfor strcolumns. Also I add option with using pandas str.isnumericwhich is less typing and still faster then using pd.to_numeric. But pd.to_numericis more general because it could work with any data types (not only strings).
虽然情况下与pd.to_numeric未使用apply的方法，它比与施加慢几乎两倍np.isnumeric于str列。我还添加了使用熊猫的选项，str.isnumeric它比使用pd.to_numeric. 但pd.to_numeric更通用，因为它可以处理任何数据类型（不仅是字符串）。
df_big = pd.concat([df]*10000)
In [3]: df_big = pd.concat([df]*10000)
In [4]: df_big.shape
Out[4]: (70000, 2)
In [5]: %timeit df_big[df_big.id.apply(lambda x: x.isnumeric())]
15.3 ms ± 2.02 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
In [6]: %timeit df_big[df_big.id.str.isnumeric()]
20.3 ms ± 171 μs per loop (mean ± std. dev. of 7 runs, 10 loops each)
In [7]: %timeit df_big[pd.to_numeric(df_big['id'], errors='coerce').notnull()]
29.9 ms ± 682 μs per loop (mean ± std. dev. of 7 runs, 10 loops each)
回答by DeepSpace
Given that dfis your dataframe,
鉴于这df是您的数据框，
import numpy as np
df[df['id'].apply(lambda x: isinstance(x, (int, np.int64)))]
What it does is passing each value in the idcolumn to the isinstancefunction and checks if it's an int. Then it returns a boolean array, and finally returning only the rows where there is True.
它所做的是将id列中的每个值传递给isinstance函数并检查它是否是int. 然后它返回一个布尔数组，最后只返回有 的行True。
If you also need to account for floatvalues, another option is:
如果您还需要考虑float价值，另一种选择是：
import numpy as np
df[df['id'].apply(lambda x: type(x) in [int, np.int64, float, np.float64])]
Note that either way is not inplace, so you will need to reassign it to your original df, or create a new one:
请注意，这两种方式都没有就位，因此您需要将其重新分配给原始 df，或创建一个新的：
df = df[df['id'].apply(lambda x: type(x) in [int, np.int64, float, np.float64])]
# or
new_df = df[df['id'].apply(lambda x: type(x) in [int, np.int64, float, np.float64])]
回答by Zero
Using pd.to_numeric
使用 pd.to_numeric
In [1079]: df[pd.to_numeric(df['id'], errors='coerce').notnull()]
Out[1079]:
id name
0 1 A
1 2 B
2 3 C
4 4 E
5 5 F
回答by Matphy
x.isnumeric()does not test return Truewhen xis of type float.
x.isnumeric()类型为True时不测试返回。xfloat
One way to filter out values which can be converted to float:
过滤掉可以转换为 的值的一种方法float：
df[df['id'].apply(lambda x: is_float(x))]
df[df['id'].apply(lambda x: is_float(x))]
def is_float(x):
try:
float(x)
except ValueError:
return False
return True
