pandas 过滤数据框列值大于零？
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Filter dataframe columns values greater than zero?
提问by Dheeraj
I have a csv file which i am reading as pd.read_csv(File) and i am trying to get only those rows which have values greater than zero.
我有一个 csv 文件，我将其读取为 pd.read_csv(File) 并且我试图只获取那些值大于零的行。
The dataframe hase some empty cells and some negative values and some exp numbers like -1.72E+10.
数据帧有一些空单元格和一些负值以及一些 exp 数字，如 -1.72E+10。
Time A B C D E F G
9/8/2017 8:40 1.29 0.27 1.78 0.23 0.33 0.05 -13.72
9/8/2017 9:00 1.28 0.26 1.78 0.22 0.35 0.02 -13.59
9/8/2017 9:20 1.43
9/8/2017 9:40 1.44 0.29 1.93 0.25 0.28 0.01 -13.92
9/8/2017 10:00 1.36 0.27 1.84 0.23 0.31 0.02 -13.77
9/8/2017 10:20 1.38 0.27 1.89 0.23 0.31 0.01 -13.83
9/8/2017 10:40 -1.72E+10 -1.72E+10 -1.72E+10 -1.72E+10 -1.72E+10 -1.72E+10
9/8/2017 11:00 1.4 0.28 1.88 0.24 0.28 0.02 -13.92
9/8/2017 11:20 1.43 0.28 1.92 0.24 0.29 0.02 -13.83
Whenever i run the code it doesn't filter those data.
每当我运行代码时，它都不会过滤这些数据。
df = df[df > 0]
type of the column is str instead of numpy.float64
列的类型是 str 而不是 numpy.float64
Can someone tell me the problem?
有人可以告诉我问题吗？
I want to filter the whole dataframe rows whose values are graeter than 0.
我想过滤值大于 0 的整个数据帧行。
回答by jezrael
I think you need anyfor check at least one True:
我认为您any至少需要检查一个True：
df = df[(df > 0).any(axis=1)]
Or allfor check if all Trues:
或者all检查是否全部为True：
df = df[(df > 0).all(axis=1)]
#last row and first numeric column was modify for no negative values
print (df)
Time A B C D \
0 9/8/2017 8:40 1.290000e+00 2.700000e-01 1.780000e+00 2.300000e-01
1 9/8/2017 9:00 1.280000e+00 2.600000e-01 1.780000e+00 2.200000e-01
2 9/8/2017 9:20 1.430000e+00 NaN NaN NaN
3 9/8/2017 9:40 1.440000e+00 2.900000e-01 1.930000e+00 2.500000e-01
4 9/8/2017 10:00 1.360000e+00 2.700000e-01 1.840000e+00 2.300000e-01
5 9/8/2017 10:20 1.380000e+00 2.700000e-01 1.890000e+00 2.300000e-01
6 9/8/2017 10:40 1.720000e+10 -1.720000e+10 -1.720000e+10 -1.720000e+10
7 9/8/2017 11:00 1.400000e+00 2.800000e-01 1.880000e+00 2.400000e-01
8 9/8/2017 11:20 1.430000e+00 2.800000e-01 1.920000e+00 2.400000e-01
E F G
0 3.300000e-01 5.000000e-02 -13.72
1 3.500000e-01 2.000000e-02 -13.59
2 NaN NaN NaN
3 2.800000e-01 1.000000e-02 -13.92
4 3.100000e-01 2.000000e-02 -13.77
5 3.100000e-01 1.000000e-02 -13.83
6 -1.720000e+10 -1.720000e+10 NaN
7 2.800000e-01 2.000000e-02 -13.92
8 2.900000e-01 2.000000e-02 13.83
df1 = df[(df > 0).all(axis=1)]
print (df1)
Time A B C D E F G
8 9/8/2017 11:20 1.43 0.28 1.92 0.24 0.29 0.02 13.83
df1 = df.loc[:, (df > 0).all()]
print (df1)
Time A
0 9/8/2017 8:40 1.290000e+00
1 9/8/2017 9:00 1.280000e+00
2 9/8/2017 9:20 1.430000e+00
3 9/8/2017 9:40 1.440000e+00
4 9/8/2017 10:00 1.360000e+00
5 9/8/2017 10:20 1.380000e+00
6 9/8/2017 10:40 1.720000e+10
7 9/8/2017 11:00 1.400000e+00
8 9/8/2017 11:20 1.430000e+00
EDIT1:
编辑1：
For convert to floats all columns without Time:
对于转换为floats 的所有列，没有Time：
cols = df.columns.difference(['Time'])
df[cols] = df[cols].astype(float)
print (df.dtypes)
Time object
A float64
B float64
C float64
D float64
E float64
F float64
G float64
dtype: object
df1 = df.loc[:, (df > 0).all()]
print (df1)
Time A
0 9/8/2017 8:40 1.290000e+00
1 9/8/2017 9:00 1.280000e+00
2 9/8/2017 9:20 1.430000e+00
3 9/8/2017 9:40 1.440000e+00
4 9/8/2017 10:00 1.360000e+00
5 9/8/2017 10:20 1.380000e+00
6 9/8/2017 10:40 1.720000e+10
7 9/8/2017 11:00 1.400000e+00
8 9/8/2017 11:20 1.430000e+00
