python - Most efficient way to convert values of column in Pandas DataFrame

Question

I have a a pd.DataFrame that looks like:

I want to create a cutoff on the values to push them into binary digits, my cutoff in this case is 0.85. I want the resulting dataframe to look like:

The script I wrote to do this is easy to understand but for large datasets it is inefficient. I'm sure Pandas has some way of taking care of these types of transformations.

Does anyone know of an efficient way to convert a column of floats to a column of integers using a threshold?

My extremely naive way of doing such a thing:

DF_test = pd.DataFrame(np.array([list("abcde"),list("pqrst"),[0.12,0.23,0.93,0.86,0.33]]).T,columns=["c1","c2","value"])
DF_want = pd.DataFrame(np.array([list("abcde"),list("pqrst"),[0,0,1,1,0]]).T,columns=["c1","c2","value"])


threshold = 0.85

#Empty dataframe to append rows
DF_naive = pd.DataFrame()
for i in range(DF_test.shape[0]):
    #Get first 2 columns
    first2cols = list(DF_test.ix[i][:-1])
    #Check if value is greater than threshold
    binary_value = [int((bool(float(DF_test.ix[i][-1]) > threshold)))]
    #Create series object
    SR_row = pd.Series( first2cols + binary_value,name=i)
    #Add to empty dataframe container
    DF_naive = DF_naive.append(SR_row)
#Relabel columns
DF_naive.columns = DF_test.columns
DF_naive.head()
#the sample DF_want

Answer 1

You can use np.where to set your desired value based on a boolean condition:

In [18]:
DF_test['value'] = np.where(DF_test['value'] > threshold, 1,0)
DF_test

Out[18]:
  c1 c2  value
0  a  p      0
1  b  q      0
2  c  r      1
3  d  s      1
4  e  t      0

Note that because your data is a heterogenous np array the 'value' column contains strings rather than floats:

In [58]:
DF_test.iloc[0]['value']

Out[58]:
'0.12'

So you'll need to convert the dtype to float first: DF_test['value'] = DF_test['value'].astype(float)

You can compare the timings:

In [16]:
%timeit np.where(DF_test['value'] > threshold, 1,0)
1000 loops, best of 3: 297 μs per loop

In [17]:
%%timeit
DF_naive = pd.DataFrame()
for i in range(DF_test.shape[0]):
    #Get first 2 columns
    first2cols = list(DF_test.ix[i][:-1])
    #Check if value is greater than threshold
    binary_value = [int((bool(float(DF_test.ix[i][-1]) > threshold)))]
    #Create series object
    SR_row = pd.Series( first2cols + binary_value,name=i)
    #Add to empty dataframe container
    DF_naive = DF_naive.append(SR_row)
10 loops, best of 3: 39.3 ms per loop

the np.where version is over 100x faster, admittedly your code is doing a lot of unnecessary stuff but you get the point