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Python: numpy / pandas change values ​​provided

I would like to know if there is a faster and more “pythonic” way to do the following, for example. using some built-in methods. Given a pandas DataFrame or numpy array for float, if the value is equal to or less than 0.5, I need to calculate the inverse value and multiply by -1 and replace the old value with the newly calculated one. "Transformation" is probably a poor choice of words, please tell me if you have a better / more accurate description.

Thanks for the help and support!

Data:

import numpy as np
import pandas as pd
dicti = {"A" : np.arange(0.0, 3, 0.1), 
         "B" : np.arange(0, 30, 1),
         "C" : list("ELVISLIVES")*3}
df = pd.DataFrame(dicti)

my function:

def transform_colname(df, colname):
    series = df[colname]    
    newval_list = []
    for val in series:
        if val <= 0.5:
            newval = (1/val)*-1
            newval_list.append(newval)
        else:
            newval_list.append(val)
    df[colname] = newval_list
    return df

function call:

transform_colname(df, colname="A")

** → I am summarizing the results here, since the comments do not allow sending the code (or I do not know how to do this). **

Thank you all for your quick and excellent answers!

ipython "% timeit" "" :

: 10, 3: 24,1

jojo:

def transform_colname_v2(df, colname):
    series = df[colname]        
    df[colname] = np.where(series <= 0.5, 1/series*-1, series)
    return df

100 , 3: 2,76

FooBar:

def transform_colname_v3(df, colname):
    df.loc[df[colname] <= 0.5, colname]  = - 1 / df[colname][df[colname] <= 0.5]
    return df

100 , 3: 3,32

dmvianna:

def transform_colname_v4(df, colname):
    df[colname] = df[colname].where(df[colname] <= 0.5, (1/df[colname])*-1)
    return df

100 , 3: 3,7

/ , -!

: () "FooBar" "dmvianna" ""? , ( ). , ! - > jojo, ".loc" - , df [colname]. "". ( " > " "< =" )

!

+4
3

:

import numpy as np
a = np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6], dtype=np.float)
print 1 / a[a <= 0.5] * (-1)

, , 0.5.

np.where:

import numpy as np
a = np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6], dtype=np.float)
print np.where(a < 0.5, 1 / a * (-1), a)

pandas DataFrame:

@dmvianna ( ;)), pd.DataFrame:

df.a = df.a.where(df.a > 0.5, (1 / df.a) * (-1))
+5

, , , :

df.loc[df.A < 0.5, 'A']  = - 1 / df.A[df.A < 0.5] 

In[13]: df
Out[13]: 
            A   B  C
0        -inf   0  E
1  -10.000000   1  L
2   -5.000000   2  V
3   -3.333333   3  I
4   -2.500000   4  S
5    0.500000   5  L
6    0.600000   6  I
7    0.700000   7  V
8    0.800000   8  E
9    0.900000   9  S
10   1.000000  10  E
11   1.100000  11  L
12   1.200000  12  V
13   1.300000  13  I
14   1.400000  14  S
15   1.500000  15  L
16   1.600000  16  I
17   1.700000  17  V
18   1.800000  18  E
19   1.900000  19  S
20   2.000000  20  E
21   2.100000  21  L
22   2.200000  22  V
23   2.300000  23  I
24   2.400000  24  S
25   2.500000  25  L
26   2.600000  26  I
27   2.700000  27  V
28   2.800000  28  E
29   2.900000  29  S
+5

@jojo, pandas:

df.A = df.A.where(df.A > 0.5, (1/df.A)*-1)


df.A.where(df.A > 0.5, (1/df.A)*-1, inplace=True) # this should be faster

. where docstring:

: df.A.where(self, cond, other = nan, inplace = False, axis = None, level = None, try_cast = False, raise_on_error = True)

: , , , cond - True, - .

+2

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