Filling a DataFrame with “sign” numbers

Question

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:

if it\'s NaN -> it remai         


        

Answer 1

You can use 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

Answer 2

Code -

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 -

    x
0  -1
1   1
2   0
3 NaN

Answer 3

You can use 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:

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