pandas 用“符号”数字填充数据帧
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Filling a DataFrame with "sign" numbers
提问by vsoler
I have a DataFrame full of floats (positive and negative) and some NaN. I'd like to replace every single float number with its sign:
我有一个充满浮点数（正数和负数）和一些 NaN 的 DataFrame。我想用它的符号替换每个浮点数：
if it's NaN -> it remains Nan
if positive -> replace with 1
if negative -> replace with -1
if zero -> leave it as 0
Any suggestions to make this massive replacement?
有什么建议可以进行这种大规模的更换吗？
Thank you in advance
先感谢您
采纳答案by jezrael
You can use boolean indexing:
您可以使用boolean indexing：
import pandas as pd
import numpy as np
df = pd.DataFrame({'A':[-1,3,0,5],
'B':[4,5,6,5],
'C':[8,-9,np.nan,7]})
print (df)
A B C
0 -1 4 8.0
1 3 5 -9.0
2 0 6 NaN
3 5 5 7.0
print (df > 0)
A B C
0 False True True
1 True True False
2 False True False
3 True True True
print (df < 0)
A B C
0 True False False
1 False False True
2 False False False
3 False False False
df[df > 0] = 1
df[df < 0] = -1
print (df)
A B C
0 -1 1 1.0
1 1 1 -1.0
2 0 1 NaN
3 1 1 1.0
回答by ayhan
You can use np.sign:
您可以使用np.sign：
df
Out[100]:
A
0 -4.0
1 2.0
2 NaN
3 0.0
import numpy as np
np.sign(df["A"])
Out[101]:
0 -1.0
1 1.0
2 NaN
3 0.0
Name: A, dtype: float64
In order to apply to all columns, you can directly pass the dataframe:
为了适用于所有列，您可以直接传递数据框：
df
Out[121]:
0 1 2 3
0 -2.932447 -1.686652 NaN -0.908441
1 1.254436 0.000000 0.072242 0.796944
2 2.626737 0.169639 -1.457195 1.169238
3 0.000000 -1.174251 0.660111 1.115518
4 -1.998091 -0.125095 0.000000 -0.506782
np.sign(df)
Out[122]:
0 1 2 3
0 -1.0 -1.0 NaN -1.0
1 1.0 0.0 1.0 1.0
2 1.0 1.0 -1.0 1.0
3 0.0 -1.0 1.0 1.0
4 -1.0 -1.0 0.0 -1.0
回答by Vedang Mehta
Code -
代码 -
import pandas as pd
df = pd.DataFrame({'x' : [-5.3, 2.5, 0, float('nan')]})
df['x'] = df['x'].apply(func = lambda x : x if not x else x // abs(x))
print(df)
Output -
输出 -
x
0 -1
1 1
2 0
3 NaN
