[FIXED] How to truncate two columns of decimal points in a python pandas dataframe?

Issue

I need help with truncating columns of decimals within a dataframe, that ties in with other calcuations to output a 1 or 0 binary column called "scenario1". Said dataframe has 20+ columns.

In this case I have two columns (columns A and columns B) of decimals along a time-based index. The columns may have varied amount of decimal points, and have NaN values in earlier rows.

I'm trying to truncate values in ONLY these two columns for use in a calculation and "throw away", WITHOUT changing the original columns or generating new columns.

e.g. ColA can be 4 decimals, ColB can be 6 decimals.

ColA	ColB
NaN	NaN
NaN	NaN
0.9954	0.995642
0.9854	0.997450

If the value of ColA and ColB is close enough, I want to output TRUE. To do that, I have to somehow truncate both ColA and ColB to one decimal place without any rounding up or down. So it becomes the following:

ColA	ColB
NaN	NaN
NaN	NaN
0.9	0.9
0.9	0.9

I need this truncation to happen within a function "scenario1", trying to have the code be efficient as possible. My current failed attempts are those lines with math.trunc(1000 * df.tenkan)/1000. This is from another post here, 3rd solution.:

def scenario1(df):
    df.loc[:, ('scenario1')] = np.where((df.close <= 2) & (df.tenkan > df.tenkan.shift(1)) &
     (df.kijun > df.kijun.shift(1)) & 
    ((math.trunc(1000 * df.tenkan) / 1000) == (math.trunc(1000 * df.kijun) / 1000)) & 
    (df.span_a > df.span_a.shift(1)) & (df.span_b > df.span_b.shift(1)) & 
    ((math.trunc(1000 * df.span_a) / 1000) == (math.trunc(1000 * df.span_b) / 1000)) & 
    (df.volume <= 300000), 1, 0)

    return df

But I got the following error: TypeError: type Series doesn't define trunc method

Any ideas how I can do this?

Screenshot of the actual dataframe below:

Solution

You can use numpy trunc:

df = pd.DataFrame({'ColA ': [0.9954, 0.9854], 'ColB': [0.995642, 0.99745]})

np.trunc(10 * df) / 10

Result:

   ColA   ColB
0    0.9   0.9
1    0.9   0.9

Answered By - Stef

Answer Checked By - David Marino (PHPFixing Volunteer)